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'''simple docstring'''
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: "DiagonalGaussianDistribution"
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = True
@register_to_config
def __init__( self : Dict , A : int = 3 , A : int = 3 , A : Tuple[str] = ("DownEncoderBlock2D",) , A : Tuple[str] = ("UpDecoderBlock2D",) , A : Tuple[int] = (64,) , A : int = 1 , A : str = "silu" , A : int = 4 , A : int = 32 , A : int = 32 , A : float = 0.18_215 , ):
super().__init__()
# pass init params to Encoder
_UpperCAmelCase : Union[str, Any] = Encoder(
in_channels=A , out_channels=A , down_block_types=A , block_out_channels=A , layers_per_block=A , act_fn=A , norm_num_groups=A , double_z=A , )
# pass init params to Decoder
_UpperCAmelCase : List[str] = Decoder(
in_channels=A , out_channels=A , up_block_types=A , block_out_channels=A , layers_per_block=A , norm_num_groups=A , act_fn=A , )
_UpperCAmelCase : Union[str, Any] = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
_UpperCAmelCase : Optional[Any] = nn.Convad(A , A , 1 )
_UpperCAmelCase : List[Any] = False
_UpperCAmelCase : List[Any] = False
# only relevant if vae tiling is enabled
_UpperCAmelCase : Union[str, Any] = self.config.sample_size
_UpperCAmelCase : Any = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
_UpperCAmelCase : Dict = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
_UpperCAmelCase : Any = 0.25
def _A ( self : Optional[Any] , A : str , A : Union[str, Any]=False ):
if isinstance(A , (Encoder, Decoder) ):
_UpperCAmelCase : Union[str, Any] = value
def _A ( self : int , A : bool = True ):
_UpperCAmelCase : Any = use_tiling
def _A ( self : Dict ):
self.enable_tiling(A )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = True
def _A ( self : Optional[int] ):
_UpperCAmelCase : Tuple = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[str] = {}
def fn_recursive_add_processors(A : str , A : torch.nn.Module , A : Dict[str, AttentionProcessor] ):
if hasattr(A , "set_processor" ):
_UpperCAmelCase : Any = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(F"""{name}.{sub_name}""" , A , A )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(A , A , A )
return processors
def _A ( self : Optional[int] , A : Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
_UpperCAmelCase : str = len(self.attn_processors.keys() )
if isinstance(A , A ) and len(A ) != count:
raise ValueError(
F"""A dict of processors was passed, but the number of processors {len(A )} does not match the"""
F""" number of attention layers: {count}. Please make sure to pass {count} processor classes.""" )
def fn_recursive_attn_processor(A : str , A : torch.nn.Module , A : str ):
if hasattr(A , "set_processor" ):
if not isinstance(A , A ):
module.set_processor(A )
else:
module.set_processor(processor.pop(F"""{name}.processor""" ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(F"""{name}.{sub_name}""" , A , A )
for name, module in self.named_children():
fn_recursive_attn_processor(A , A , A )
def _A ( self : Optional[int] ):
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def _A ( self : Optional[Any] , A : torch.FloatTensor , A : bool = True ):
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(A , return_dict=A )
if self.use_slicing and x.shape[0] > 1:
_UpperCAmelCase : Dict = [self.encoder(A ) for x_slice in x.split(1 )]
_UpperCAmelCase : str = torch.cat(A )
else:
_UpperCAmelCase : str = self.encoder(A )
_UpperCAmelCase : Union[str, Any] = self.quant_conv(A )
_UpperCAmelCase : str = DiagonalGaussianDistribution(A )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=A )
def _A ( self : List[str] , A : torch.FloatTensor , A : bool = True ):
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(A , return_dict=A )
_UpperCAmelCase : Dict = self.post_quant_conv(A )
_UpperCAmelCase : Any = self.decoder(A )
if not return_dict:
return (dec,)
return DecoderOutput(sample=A )
@apply_forward_hook
def _A ( self : List[str] , A : torch.FloatTensor , A : bool = True ):
if self.use_slicing and z.shape[0] > 1:
_UpperCAmelCase : List[Any] = [self._decode(A ).sample for z_slice in z.split(1 )]
_UpperCAmelCase : str = torch.cat(A )
else:
_UpperCAmelCase : Optional[int] = self._decode(A ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=A )
def _A ( self : Tuple , A : Any , A : Tuple , A : Optional[int] ):
_UpperCAmelCase : int = min(a.shape[2] , b.shape[2] , A )
for y in range(A ):
_UpperCAmelCase : Tuple = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def _A ( self : Optional[Any] , A : Dict , A : Union[str, Any] , A : List[str] ):
_UpperCAmelCase : Optional[int] = min(a.shape[3] , b.shape[3] , A )
for x in range(A ):
_UpperCAmelCase : Optional[int] = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def _A ( self : Any , A : torch.FloatTensor , A : bool = True ):
_UpperCAmelCase : str = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
_UpperCAmelCase : Optional[Any] = int(self.tile_latent_min_size * self.tile_overlap_factor )
_UpperCAmelCase : List[Any] = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
_UpperCAmelCase : List[Any] = []
for i in range(0 , x.shape[2] , A ):
_UpperCAmelCase : Any = []
for j in range(0 , x.shape[3] , A ):
_UpperCAmelCase : str = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
_UpperCAmelCase : int = self.encoder(A )
_UpperCAmelCase : Optional[int] = self.quant_conv(A )
row.append(A )
rows.append(A )
_UpperCAmelCase : Dict = []
for i, row in enumerate(A ):
_UpperCAmelCase : Union[str, Any] = []
for j, tile in enumerate(A ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_UpperCAmelCase : Union[str, Any] = self.blend_v(rows[i - 1][j] , A , A )
if j > 0:
_UpperCAmelCase : Tuple = self.blend_h(row[j - 1] , A , A )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(A , dim=3 ) )
_UpperCAmelCase : Optional[int] = torch.cat(A , dim=2 )
_UpperCAmelCase : List[Any] = DiagonalGaussianDistribution(A )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=A )
def _A ( self : str , A : torch.FloatTensor , A : bool = True ):
_UpperCAmelCase : Any = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
_UpperCAmelCase : List[Any] = int(self.tile_sample_min_size * self.tile_overlap_factor )
_UpperCAmelCase : Optional[Any] = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
_UpperCAmelCase : str = []
for i in range(0 , z.shape[2] , A ):
_UpperCAmelCase : List[str] = []
for j in range(0 , z.shape[3] , A ):
_UpperCAmelCase : Tuple = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
_UpperCAmelCase : List[str] = self.post_quant_conv(A )
_UpperCAmelCase : Dict = self.decoder(A )
row.append(A )
rows.append(A )
_UpperCAmelCase : List[Any] = []
for i, row in enumerate(A ):
_UpperCAmelCase : List[str] = []
for j, tile in enumerate(A ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
_UpperCAmelCase : List[Any] = self.blend_v(rows[i - 1][j] , A , A )
if j > 0:
_UpperCAmelCase : Optional[int] = self.blend_h(row[j - 1] , A , A )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(A , dim=3 ) )
_UpperCAmelCase : Any = torch.cat(A , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=A )
def _A ( self : List[Any] , A : torch.FloatTensor , A : bool = False , A : bool = True , A : Optional[torch.Generator] = None , ):
_UpperCAmelCase : Union[str, Any] = sample
_UpperCAmelCase : Tuple = self.encode(A ).latent_dist
if sample_posterior:
_UpperCAmelCase : Any = posterior.sample(generator=A )
else:
_UpperCAmelCase : Tuple = posterior.mode()
_UpperCAmelCase : List[Any] = self.decode(A ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=A )
| 31
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "encodec"
def __init__( self : Optional[int] , A : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , A : List[Any]=24000 , A : Union[str, Any]=1 , A : List[Any]=False , A : Optional[int]=None , A : int=None , A : str=128 , A : List[Any]=32 , A : List[Any]=1 , A : int=[8, 5, 4, 2] , A : Optional[int]="weight_norm" , A : List[Any]=7 , A : Any=7 , A : Dict=3 , A : Optional[int]=2 , A : Dict=True , A : Dict="reflect" , A : Any=2 , A : Dict=2 , A : str=1.0 , A : Optional[int]=1024 , A : Any=None , A : Any=True , **A : str , ):
_UpperCAmelCase : Optional[int] = target_bandwidths
_UpperCAmelCase : List[str] = sampling_rate
_UpperCAmelCase : Optional[int] = audio_channels
_UpperCAmelCase : str = normalize
_UpperCAmelCase : int = chunk_length_s
_UpperCAmelCase : str = overlap
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : int = num_filters
_UpperCAmelCase : Optional[Any] = num_residual_layers
_UpperCAmelCase : Optional[int] = upsampling_ratios
_UpperCAmelCase : int = norm_type
_UpperCAmelCase : List[Any] = kernel_size
_UpperCAmelCase : List[Any] = last_kernel_size
_UpperCAmelCase : List[Any] = residual_kernel_size
_UpperCAmelCase : List[str] = dilation_growth_rate
_UpperCAmelCase : Dict = use_causal_conv
_UpperCAmelCase : Tuple = pad_mode
_UpperCAmelCase : Tuple = compress
_UpperCAmelCase : List[str] = num_lstm_layers
_UpperCAmelCase : List[Any] = trim_right_ratio
_UpperCAmelCase : int = codebook_size
_UpperCAmelCase : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size
_UpperCAmelCase : Optional[int] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**A )
@property
def _A ( self : Any ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A ( self : Union[str, Any] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A ( self : str ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 31
| 1
|
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(_UpperCAmelCase ):
return ext
raise Exception(
F"""Unable to determine file format from file extension {path}. """
F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
_UpperCAmelCase : Optional[Any] = try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
_UpperCAmelCase : List[Any] = PipelineDataFormat.from_str(
format=_UpperCAmelCase , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(_UpperCAmelCase , _UpperCAmelCase )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : str , A : Pipeline , A : PipelineDataFormat ):
_UpperCAmelCase : Any = nlp
_UpperCAmelCase : List[Any] = reader
@staticmethod
def _A ( A : ArgumentParser ):
_UpperCAmelCase : List[str] = parser.add_parser("run" , help="Run a pipeline through the CLI" )
run_parser.add_argument("--task" , choices=get_supported_tasks() , help="Task to run" )
run_parser.add_argument("--input" , type=A , help="Path to the file to use for inference" )
run_parser.add_argument("--output" , type=A , help="Path to the file that will be used post to write results." )
run_parser.add_argument("--model" , type=A , help="Name or path to the model to instantiate." )
run_parser.add_argument("--config" , type=A , help="Name or path to the model's config to instantiate." )
run_parser.add_argument(
"--tokenizer" , type=A , help="Name of the tokenizer to use. (default: same as the model name)" )
run_parser.add_argument(
"--column" , type=A , help="Name of the column to use as input. (For multi columns input as QA use column1,columns2)" , )
run_parser.add_argument(
"--format" , type=A , default="infer" , choices=PipelineDataFormat.SUPPORTED_FORMATS , help="Input format to read from" , )
run_parser.add_argument(
"--device" , type=A , default=-1 , help="Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)" , )
run_parser.add_argument("--overwrite" , action="store_true" , help="Allow overwriting the output file." )
run_parser.set_defaults(func=A )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Dict = self._nlp, []
for entry in self._reader:
_UpperCAmelCase : Optional[int] = nlp(**A ) if self._reader.is_multi_columns else nlp(A )
if isinstance(A , A ):
outputs.append(A )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
_UpperCAmelCase : Dict = self._reader.save_binary(A )
logger.warning(F"""Current pipeline requires output to be in binary format, saving at {binary_path}""" )
else:
self._reader.save(A )
| 31
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Any , A : Optional[int]=None , A : Tuple=None , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
if config is None:
assert isinstance(self.model , A ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
_UpperCAmelCase : str = self.model.config
else:
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : List[Any] = data_args
_UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , A ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
_UpperCAmelCase : Optional[Any] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase : Dict = label_smoothed_nll_loss
def _A ( self : Tuple , A : int ):
if self.optimizer is None:
_UpperCAmelCase : Tuple = ["bias", "LayerNorm.weight"]
_UpperCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
_UpperCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase : List[str] = Adafactor
_UpperCAmelCase : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
_UpperCAmelCase : List[str] = AdamW
_UpperCAmelCase : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
_UpperCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase : List[Any] = OSS(
params=A , optim=A , **A , )
else:
_UpperCAmelCase : Union[str, Any] = optimizer_cls(A , **A )
if self.lr_scheduler is None:
_UpperCAmelCase : List[str] = self._get_lr_scheduler(A )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : List[str] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A )
return scheduler
def _A ( self : Tuple ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _A ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : List[Any] ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase : List[str] = model(**A , use_cache=A )[0]
_UpperCAmelCase : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase : Any = model(**A , labels=A , use_cache=A )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase : Optional[int] = model(**A , use_cache=A )[0]
_UpperCAmelCase : List[str] = torch.nn.functional.log_softmax(A , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.loss_fn(A , A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _A ( self : List[str] , A : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Union[str, Any] = inputs.pop("labels" )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self._compute_loss(A , A , A )
return loss
def _A ( self : List[str] , A : nn.Module , A : Dict[str, Union[torch.Tensor, Any]] , A : bool , A : Optional[List[str]] = None , ):
_UpperCAmelCase : List[str] = self._prepare_inputs(A )
_UpperCAmelCase : Dict = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : int = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
_UpperCAmelCase : Any = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase : str = self._compute_loss(A , A , A )
_UpperCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
return (loss, logits, labels)
def _A ( self : Dict , A : int , A : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
_UpperCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase : Tuple = tensor
return padded_tensor
| 31
| 1
|
'''simple docstring'''
import argparse
import json
import subprocess
def UpperCamelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : Any = (
F"""curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""""
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_UpperCAmelCase : Tuple = subprocess.run(_UpperCAmelCase , shell=_UpperCAmelCase , stdout=subprocess.PIPE )
_UpperCAmelCase : Optional[Any] = output.stdout.decode("utf-8" )
_UpperCAmelCase : Dict = json.loads(_UpperCAmelCase )
_UpperCAmelCase : Tuple = status["runners"]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(_UpperCAmelCase )
# save the result so we can report them on Slack
with open("offline_runners.txt" , "w" ) as fp:
fp.write(json.dumps(_UpperCAmelCase ) )
if len(_UpperCAmelCase ) > 0:
_UpperCAmelCase : List[Any] = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(F"""The following runners are offline:\n{failed}""" )
if __name__ == "__main__":
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> int:
"""simple docstring"""
return values.split("," )
__SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--target_runners""",
default=None,
type=list_str,
required=True,
help="""Comma-separated list of runners to check status.""",
)
parser.add_argument(
"""--token""", default=None, type=str, required=True, help="""A token that has actions:read permission."""
)
__SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 31
|
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["input_features", "is_longer"]
def __init__( self : str , A : int=64 , A : Dict=48000 , A : str=480 , A : List[Any]=10 , A : Optional[Any]=1024 , A : Tuple=0.0 , A : List[Any]=False , A : float = 0 , A : float = 14000 , A : int = None , A : str = "fusion" , A : str = "repeatpad" , **A : Dict , ):
super().__init__(
feature_size=A , sampling_rate=A , padding_value=A , return_attention_mask=A , **A , )
_UpperCAmelCase : Optional[Any] = top_db
_UpperCAmelCase : Dict = truncation
_UpperCAmelCase : List[Any] = padding
_UpperCAmelCase : Optional[Any] = fft_window_size
_UpperCAmelCase : Dict = (fft_window_size >> 1) + 1
_UpperCAmelCase : Any = hop_length
_UpperCAmelCase : Tuple = max_length_s
_UpperCAmelCase : str = max_length_s * sampling_rate
_UpperCAmelCase : Any = sampling_rate
_UpperCAmelCase : Optional[int] = frequency_min
_UpperCAmelCase : str = frequency_max
_UpperCAmelCase : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm=A , mel_scale="htk" , )
_UpperCAmelCase : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm="slaney" , mel_scale="slaney" , )
def _A ( self : List[str] ):
_UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : Dict = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _A ( self : Optional[Any] , A : np.array , A : Optional[np.array] = None ):
_UpperCAmelCase : Dict = spectrogram(
A , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=A , log_mel="dB" , )
return log_mel_spectrogram.T
def _A ( self : str , A : str , A : List[str] , A : List[Any] ):
_UpperCAmelCase : List[str] = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Optional[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Tuple = [0]
# randomly choose index for each part
_UpperCAmelCase : Dict = np.random.choice(ranges[0] )
_UpperCAmelCase : str = np.random.choice(ranges[1] )
_UpperCAmelCase : Tuple = np.random.choice(ranges[2] )
_UpperCAmelCase : str = mel[idx_front : idx_front + chunk_frames, :]
_UpperCAmelCase : str = mel[idx_middle : idx_middle + chunk_frames, :]
_UpperCAmelCase : List[Any] = mel[idx_back : idx_back + chunk_frames, :]
_UpperCAmelCase : Dict = torch.tensor(mel[None, None, :] )
_UpperCAmelCase : Optional[Any] = torch.nn.functional.interpolate(
A , size=[chunk_frames, 64] , mode="bilinear" , align_corners=A )
_UpperCAmelCase : List[str] = mel_shrink[0][0].numpy()
_UpperCAmelCase : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _A ( self : List[Any] , A : np.array , A : List[str] , A : Any , A : Optional[int] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
_UpperCAmelCase : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
_UpperCAmelCase : str = len(A ) - max_length
_UpperCAmelCase : str = np.random.randint(0 , overflow + 1 )
_UpperCAmelCase : int = waveform[idx : idx + max_length]
_UpperCAmelCase : Any = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
_UpperCAmelCase : Tuple = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : List[str] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
_UpperCAmelCase : Optional[Any] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
_UpperCAmelCase : Any = np.stack([mel, mel, mel, mel] , axis=0 )
_UpperCAmelCase : int = False
else:
_UpperCAmelCase : Tuple = self._random_mel_fusion(A , A , A )
_UpperCAmelCase : Any = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
_UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
_UpperCAmelCase : str = int(max_length / len(A ) )
_UpperCAmelCase : Dict = np.stack(np.tile(A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
_UpperCAmelCase : Dict = int(max_length / len(A ) )
_UpperCAmelCase : List[str] = np.stack(np.tile(A , A ) )
_UpperCAmelCase : Optional[Any] = np.pad(A , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
_UpperCAmelCase : str = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : Optional[int] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
_UpperCAmelCase : List[str] = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : Union[str, Any] , A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A : str = None , A : Optional[str] = None , A : Optional[int] = None , A : Optional[int] = None , A : Optional[Union[str, TensorType]] = None , **A : List[str] , ):
_UpperCAmelCase : int = truncation if truncation is not None else self.truncation
_UpperCAmelCase : Optional[int] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : Optional[Any] = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : int = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
_UpperCAmelCase : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[str] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
_UpperCAmelCase : Dict = [
self._get_input_mel(A , max_length if max_length else self.nb_max_samples , A , A )
for waveform in raw_speech
]
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[Any] = []
for mel, longer in padded_inputs:
input_mel.append(A )
is_longer.append(A )
if truncation == "fusion" and sum(A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
_UpperCAmelCase : Union[str, Any] = np.random.randint(0 , len(A ) )
_UpperCAmelCase : Optional[Any] = True
if isinstance(input_mel[0] , A ):
_UpperCAmelCase : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
_UpperCAmelCase : Tuple = [[longer] for longer in is_longer]
_UpperCAmelCase : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
_UpperCAmelCase : Tuple = BatchFeature(A )
if return_tensors is not None:
_UpperCAmelCase : List[Any] = input_features.convert_to_tensors(A )
return input_features
| 31
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[int] = {
"""configuration_altclip""": [
"""ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AltCLIPConfig""",
"""AltCLIPTextConfig""",
"""AltCLIPVisionConfig""",
],
"""processing_altclip""": ["""AltCLIPProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = [
"""ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AltCLIPPreTrainedModel""",
"""AltCLIPModel""",
"""AltCLIPTextModel""",
"""AltCLIPVisionModel""",
]
if TYPE_CHECKING:
from .configuration_altclip import (
ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
AltCLIPConfig,
AltCLIPTextConfig,
AltCLIPVisionConfig,
)
from .processing_altclip import AltCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_altclip import (
ALTCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
AltCLIPModel,
AltCLIPPreTrainedModel,
AltCLIPTextModel,
AltCLIPVisionModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""GPTNeoXTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXForCausalLM""",
"""GPTNeoXForQuestionAnswering""",
"""GPTNeoXForSequenceClassification""",
"""GPTNeoXForTokenClassification""",
"""GPTNeoXLayer""",
"""GPTNeoXModel""",
"""GPTNeoXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
| 1
|
'''simple docstring'''
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Tuple = "M-CLIP"
def __init__( self : Optional[Any] , A : List[Any]=1024 , A : Any=768 , **A : Tuple ):
_UpperCAmelCase : str = transformerDimSize
_UpperCAmelCase : Optional[int] = imageDimSize
super().__init__(**A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = MCLIPConfig
def __init__( self : Optional[Any] , A : Any , *A : Any , **A : Optional[int] ):
super().__init__(A , *A , **A )
_UpperCAmelCase : Union[str, Any] = XLMRobertaModel(A )
_UpperCAmelCase : Optional[int] = torch.nn.Linear(
in_features=config.transformerDimensions , out_features=config.numDims )
def _A ( self : Union[str, Any] , A : int , A : Any ):
_UpperCAmelCase : Tuple = self.transformer(input_ids=A , attention_mask=A )[0]
_UpperCAmelCase : List[str] = (embs * attention_mask.unsqueeze(2 )).sum(dim=1 ) / attention_mask.sum(dim=1 )[:, None]
return self.LinearTransformation(A ), embs
| 31
|
'''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , A : Any , A : str , A : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = graph
self._normalize_graph(A , A )
_UpperCAmelCase : List[str] = len(A )
_UpperCAmelCase : Tuple = None
def _A ( self : Any , A : List[Any] , A : str ):
if sources is int:
_UpperCAmelCase : List[Any] = [sources]
if sinks is int:
_UpperCAmelCase : List[Any] = [sinks]
if len(A ) == 0 or len(A ) == 0:
return
_UpperCAmelCase : str = sources[0]
_UpperCAmelCase : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A ) > 1 or len(A ) > 1:
_UpperCAmelCase : Dict = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCAmelCase : Optional[Any] = max_input_flow
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCAmelCase : Dict = max_input_flow
_UpperCAmelCase : List[Any] = size - 1
def _A ( self : Union[str, Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _A ( self : Tuple , A : Dict ):
_UpperCAmelCase : str = algorithm(self )
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : str ):
_UpperCAmelCase : Optional[int] = flow_network
_UpperCAmelCase : Any = flow_network.verticesCount
_UpperCAmelCase : List[str] = flow_network.sourceIndex
_UpperCAmelCase : Union[str, Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCAmelCase : Any = flow_network.graph
_UpperCAmelCase : Union[str, Any] = False
def _A ( self : List[str] ):
if not self.executed:
self._algorithm()
_UpperCAmelCase : int = True
def _A ( self : List[Any] ):
pass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[str, Any] ):
super().__init__(A )
# use this to save your result
_UpperCAmelCase : Any = -1
def _A ( self : Union[str, Any] ):
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : int ):
super().__init__(A )
_UpperCAmelCase : List[str] = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCAmelCase : Union[str, Any] = [0] * self.verticies_count
_UpperCAmelCase : int = [0] * self.verticies_count
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCAmelCase : Optional[int] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCAmelCase : Any = 0
while i < len(A ):
_UpperCAmelCase : int = vertices_list[i]
_UpperCAmelCase : int = self.heights[vertex_index]
self.process_vertex(A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A ) )
_UpperCAmelCase : Union[str, Any] = 0
else:
i += 1
_UpperCAmelCase : List[Any] = sum(self.preflow[self.source_index] )
def _A ( self : Union[str, Any] , A : str ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A , A )
self.relabel(A )
def _A ( self : int , A : Dict , A : List[str] ):
_UpperCAmelCase : int = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _A ( self : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : str = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCAmelCase : Tuple = self.heights[to_index]
if min_height is not None:
_UpperCAmelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__SCREAMING_SNAKE_CASE : List[Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__SCREAMING_SNAKE_CASE : Optional[Any] = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
TFDebertaVaModel,
)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str , A : List[Any] , A : Optional[int]=13 , A : Optional[Any]=7 , A : Optional[Any]=True , A : List[str]=True , A : Optional[Any]=True , A : str=True , A : Optional[int]=99 , A : int=32 , A : Union[str, Any]=2 , A : List[Any]=4 , A : Dict=37 , A : Union[str, Any]="gelu" , A : Optional[Any]=0.1 , A : Optional[int]=0.1 , A : List[str]=512 , A : Optional[int]=16 , A : int=2 , A : Optional[Any]=0.02 , A : List[str]=False , A : Dict=True , A : Any="None" , A : List[str]=3 , A : str=4 , A : List[Any]=None , ):
_UpperCAmelCase : Union[str, Any] = parent
_UpperCAmelCase : str = batch_size
_UpperCAmelCase : Any = seq_length
_UpperCAmelCase : Optional[Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Union[str, Any] = use_token_type_ids
_UpperCAmelCase : Optional[Any] = use_labels
_UpperCAmelCase : Optional[Any] = vocab_size
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Optional[int] = num_hidden_layers
_UpperCAmelCase : List[Any] = num_attention_heads
_UpperCAmelCase : Tuple = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Tuple = hidden_dropout_prob
_UpperCAmelCase : List[str] = attention_probs_dropout_prob
_UpperCAmelCase : int = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = type_vocab_size
_UpperCAmelCase : int = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : List[str] = num_labels
_UpperCAmelCase : Union[str, Any] = num_choices
_UpperCAmelCase : List[str] = relative_attention
_UpperCAmelCase : List[Any] = position_biased_input
_UpperCAmelCase : List[str] = pos_att_type
_UpperCAmelCase : Dict = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Optional[int] = None
if self.use_input_mask:
_UpperCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : int = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : int = DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , initializer_range=self.initializer_range , return_dict=A , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Union[str, Any] , A : Tuple , A : List[str] , A : Tuple , A : List[Any] , A : Any , A : Any , A : Tuple ):
_UpperCAmelCase : str = TFDebertaVaModel(config=A )
_UpperCAmelCase : List[Any] = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCAmelCase : int = [input_ids, input_mask]
_UpperCAmelCase : List[Any] = model(A )
_UpperCAmelCase : Tuple = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : Optional[Any] , A : str , A : Union[str, Any] , A : str , A : List[str] , A : Any , A : List[Any] , A : Optional[Any] ):
_UpperCAmelCase : Optional[int] = TFDebertaVaForMaskedLM(config=A )
_UpperCAmelCase : int = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_UpperCAmelCase : List[str] = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : Optional[int] , A : List[Any] , A : List[str] , A : str , A : Dict , A : Union[str, Any] , A : str , A : Tuple ):
_UpperCAmelCase : Optional[int] = self.num_labels
_UpperCAmelCase : Optional[Any] = TFDebertaVaForSequenceClassification(config=A )
_UpperCAmelCase : Union[str, Any] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self : Tuple , A : List[Any] , A : List[Any] , A : List[Any] , A : Union[str, Any] , A : Any , A : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Optional[Any] = TFDebertaVaForTokenClassification(config=A )
_UpperCAmelCase : Optional[int] = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_UpperCAmelCase : Any = model(A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : Any , A : Any , A : Tuple , A : Dict , A : Any , A : int , A : Optional[int] , A : Any ):
_UpperCAmelCase : Tuple = TFDebertaVaForQuestionAnswering(config=A )
_UpperCAmelCase : Tuple = {
"input_ids": input_ids,
"attention_mask": input_mask,
"token_type_ids": token_type_ids,
}
_UpperCAmelCase : Optional[int] = model(A )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _A ( self : Dict ):
_UpperCAmelCase : Optional[int] = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : Union[str, Any] = config_and_inputs
_UpperCAmelCase : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = (
(
TFDebertaVaModel,
TFDebertaVaForMaskedLM,
TFDebertaVaForQuestionAnswering,
TFDebertaVaForSequenceClassification,
TFDebertaVaForTokenClassification,
)
if is_tf_available()
else ()
)
__UpperCamelCase: int = (
{
"feature-extraction": TFDebertaVaModel,
"fill-mask": TFDebertaVaForMaskedLM,
"question-answering": TFDebertaVaForQuestionAnswering,
"text-classification": TFDebertaVaForSequenceClassification,
"token-classification": TFDebertaVaForTokenClassification,
"zero-shot": TFDebertaVaForSequenceClassification,
}
if is_tf_available()
else {}
)
__UpperCamelCase: str = False
__UpperCamelCase: int = False
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = TFDebertaVaModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Optional[int] ):
self.config_tester.run_common_tests()
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*A )
def _A ( self : Dict ):
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*A )
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*A )
@slow
def _A ( self : str ):
_UpperCAmelCase : int = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
self.assertIsNotNone(A )
@require_tf
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@unittest.skip(reason="Model not available yet" )
def _A ( self : Dict ):
pass
@slow
def _A ( self : Any ):
_UpperCAmelCase : Tuple = TFDebertaVaModel.from_pretrained("kamalkraj/deberta-v2-xlarge" )
_UpperCAmelCase : List[Any] = tf.constant([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]] )
_UpperCAmelCase : Optional[int] = tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )[0]
_UpperCAmelCase : Any = tf.constant(
[[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]] )
tf.debugging.assert_near(output[:, 1:4, 1:4] , A , atol=1E-4 )
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> float:
"""simple docstring"""
def get_matched_characters(_UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_UpperCAmelCase : int = int(max(0 , i - limit ) )
_UpperCAmelCase : Any = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = F"""{_stra[0:_stra.index(_UpperCAmelCase )]} {_stra[_stra.index(_UpperCAmelCase ) + 1:]}"""
return "".join(_UpperCAmelCase )
# matching characters
_UpperCAmelCase : Union[str, Any] = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = len(_UpperCAmelCase )
# transposition
_UpperCAmelCase : Optional[Any] = (
len([(ca, ca) for ca, ca in zip(_UpperCAmelCase , _UpperCAmelCase ) if ca != ca] ) // 2
)
if not match_count:
_UpperCAmelCase : Dict = 0.0
else:
_UpperCAmelCase : Optional[int] = (
1
/ 3
* (
match_count / len(_UpperCAmelCase )
+ match_count / len(_UpperCAmelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_UpperCAmelCase : str = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 31
| 1
|
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = os.path.join(args.tf_model_dir , "parameters.json" )
_UpperCAmelCase : Any = json.loads(open(_UpperCAmelCase ).read() )
if not params:
raise ValueError(
F"""It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file.""" )
if not args.output.endswith(".pt" ):
_UpperCAmelCase : Optional[Any] = args.output + ".pt"
_UpperCAmelCase : Union[str, Any] = OrderedDict()
with tf.device("/CPU:0" ):
_UpperCAmelCase : List[Any] = tf.train.load_checkpoint(args.tf_model_dir )
_UpperCAmelCase : Dict = reader.get_variable_to_shape_map()
for key_name in shapes.keys():
_UpperCAmelCase : int = reader.get_tensor(_UpperCAmelCase ).astype(np.floataa )
if key_name.endswith("/adam_m" ) or key_name.endswith("/adam_v" ):
continue
if key_name.startswith("pasts/" ):
if key_name.startswith("pasts/mlp" ):
_UpperCAmelCase : Union[str, Any] = int(key_name[9] )
elif key_name.startswith("pasts/out" ):
_UpperCAmelCase : List[str] = 8
_UpperCAmelCase : Union[str, Any] = "model.sqout.%d.weight" % (player * 2) # enter to nn.Sequencial with Tanh, so 2 at a time
_UpperCAmelCase : Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Optional[Any] = torch.tensor(_UpperCAmelCase )
elif key_name.startswith("model/moe" ):
_UpperCAmelCase : Optional[Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/switch_gating/kernel" ):
_UpperCAmelCase : str = "model.blocks.%d.feed_forward.mlp.router.classifier.weight" % player
_UpperCAmelCase : Union[str, Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Any = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/softmlp/kernel" ):
_UpperCAmelCase : Optional[Any] = "model.blocks.%d.feed_forward.soft_bypass_mlp.weight" % player
_UpperCAmelCase : Tuple = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Optional[int] = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/wo/kernel" ) or key_name.endswith("/wi/kernel" ):
_UpperCAmelCase : Any = key_name[-9:-7]
for i in range(16 ):
_UpperCAmelCase : int = "model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight" % (player, i, nlayer)
_UpperCAmelCase : Union[str, Any] = (
vnp[i].transpose([1, 0] ).copy()
) # In Mesh-Tensorflow, it is one array, so it is divided
_UpperCAmelCase : List[str] = torch.tensor(_UpperCAmelCase )
elif key_name.startswith("model/mlp" ):
_UpperCAmelCase : List[Any] = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/p1/kernel" ):
_UpperCAmelCase : Dict = "model.blocks.%d.feed_forward.mlp.wi.weight" % player
_UpperCAmelCase : Optional[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Optional[Any] = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/p1/bias" ):
_UpperCAmelCase : List[str] = "model.blocks.%d.feed_forward.mlp.wi.bias" % player
_UpperCAmelCase : Any = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : int = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/p2/kernel" ):
_UpperCAmelCase : Tuple = "model.blocks.%d.feed_forward.mlp.wo.weight" % player
_UpperCAmelCase : Dict = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/p2/bias" ):
_UpperCAmelCase : Optional[int] = "model.blocks.%d.feed_forward.mlp.wo.bias" % player
_UpperCAmelCase : Union[str, Any] = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : int = torch.tensor(_UpperCAmelCase )
elif key_name.startswith("model/ln" ):
_UpperCAmelCase : Any = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_UpperCAmelCase : int = "model.blocks.%d.feed_forward.norm.bias" % player
_UpperCAmelCase : Tuple = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/g" ):
_UpperCAmelCase : int = "model.blocks.%d.feed_forward.norm.weight" % player
_UpperCAmelCase : List[Any] = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Any = torch.tensor(_UpperCAmelCase )
elif key_name.startswith("model/att" ):
_UpperCAmelCase : int = int(key_name[9:].split("/" )[0] )
if key_name.endswith("/qkv/kernel" ):
_UpperCAmelCase : List[Any] = vnp.copy() # Compute same dimension as Mesh-tensorflow using einsum
_UpperCAmelCase : Dict = state[:, 0, :, :]
_UpperCAmelCase : Tuple = state[:, 1, :, :]
_UpperCAmelCase : Dict = state[:, 2, :, :]
_UpperCAmelCase : str = (
state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Union[str, Any] = (
state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Dict = (
state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]] )
.transpose([1, 0] )
.copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Tuple = "model.blocks.%d.self_attn.self_attn.q_proj.weight" % player
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = "model.blocks.%d.self_attn.self_attn.k_proj.weight" % player
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCAmelCase )
_UpperCAmelCase : int = "model.blocks.%d.self_attn.self_attn.v_proj.weight" % player
_UpperCAmelCase : Optional[Any] = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/o/kernel" ):
_UpperCAmelCase : Dict = "model.blocks.%d.self_attn.self_attn.out_proj.weight" % player
_UpperCAmelCase : Tuple = (
vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]] ).transpose([1, 0] ).copy()
) # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Dict = torch.tensor(_UpperCAmelCase )
elif key_name.startswith("model/an" ):
_UpperCAmelCase : int = int(key_name[8:].split("/" )[0] )
if key_name.endswith("/b" ):
_UpperCAmelCase : List[Any] = "model.blocks.%d.self_attn.norm.bias" % player
_UpperCAmelCase : Dict = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Dict = torch.tensor(_UpperCAmelCase )
elif key_name.endswith("/g" ):
_UpperCAmelCase : Union[str, Any] = "model.blocks.%d.self_attn.norm.weight" % player
_UpperCAmelCase : Tuple = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : List[Any] = torch.tensor(_UpperCAmelCase )
elif (
key_name.startswith("model/wte" )
or key_name.startswith("model/wpe" )
or key_name.startswith("model/ete" )
):
_UpperCAmelCase : Union[str, Any] = {"wte": "embed_tokens", "wpe": "position_embeddings", "ete": "extra_position_embeddings"}[
key_name[-3:]
]
_UpperCAmelCase : str = "model.%s.weight" % nlayer
_UpperCAmelCase : int = vnp.copy() # same in embedded
_UpperCAmelCase : List[str] = torch.tensor(_UpperCAmelCase )
if key_name.startswith("model/wte" ):
_UpperCAmelCase : Any = "lm_head.weight"
_UpperCAmelCase : List[Any] = vnp.copy() # same in embedded
_UpperCAmelCase : Optional[Any] = torch.tensor(_UpperCAmelCase )
elif key_name.startswith("model/wob" ):
_UpperCAmelCase : Union[str, Any] = "final_logits_bias"
_UpperCAmelCase : List[str] = vnp.copy() # same in embedded
_UpperCAmelCase : List[str] = state.reshape((1, -1) )
_UpperCAmelCase : Union[str, Any] = torch.tensor(_UpperCAmelCase )
elif key_name == "model/dense/kernel":
_UpperCAmelCase : int = "model.last_project.weight"
_UpperCAmelCase : Optional[Any] = vnp.transpose([1, 0] ).copy() # Mesh-Tensorflow is a diagonal matrix
_UpperCAmelCase : Optional[int] = torch.tensor(_UpperCAmelCase )
elif key_name == "model/dense_1/bias":
_UpperCAmelCase : Dict = "model.last_project.bias"
_UpperCAmelCase : Optional[int] = vnp.copy() # same because it is one dimensional
_UpperCAmelCase : Optional[int] = torch.tensor(_UpperCAmelCase )
torch.save(_UpperCAmelCase , args.output )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser(
description="""model converter.""", formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument("""--tf_model_dir""", metavar="""PATH""", type=str, required=True, help="""import model""")
parser.add_argument("""--output""", metavar="""PATH""", type=str, required=True, help="""output model""")
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
convert_tf_gptsan_to_pt(args)
| 31
|
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = 1
@register_to_config
def __init__( self : Optional[int] , A : int = 1000 , A : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(A )
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_UpperCAmelCase : int = 4
# running values
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : int , A : Union[str, torch.device] = None ):
_UpperCAmelCase : int = num_inference_steps
_UpperCAmelCase : Union[str, Any] = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_UpperCAmelCase : Any = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_UpperCAmelCase : str = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_UpperCAmelCase : Dict = torch.sin(steps * math.pi / 2 ) ** 2
_UpperCAmelCase : List[Any] = (1.0 - self.betas**2) ** 0.5
_UpperCAmelCase : List[str] = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_UpperCAmelCase : Dict = timesteps.to(A )
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : torch.FloatTensor , A : int , A : torch.FloatTensor , A : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )
_UpperCAmelCase : Tuple = (self.timesteps == timestep).nonzero().item()
_UpperCAmelCase : Optional[Any] = timestep_index + 1
_UpperCAmelCase : int = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(A )
if len(self.ets ) == 1:
_UpperCAmelCase : List[Any] = self.ets[-1]
elif len(self.ets ) == 2:
_UpperCAmelCase : str = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_UpperCAmelCase : Tuple = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_UpperCAmelCase : Union[str, Any] = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_UpperCAmelCase : Union[str, Any] = self._get_prev_sample(A , A , A , A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def _A ( self : Union[str, Any] , A : torch.FloatTensor , *A : Union[str, Any] , **A : Dict ):
return sample
def _A ( self : Optional[Any] , A : Optional[int] , A : int , A : Optional[Any] , A : List[str] ):
_UpperCAmelCase : List[str] = self.alphas[timestep_index]
_UpperCAmelCase : List[Any] = self.betas[timestep_index]
_UpperCAmelCase : Optional[Any] = self.alphas[prev_timestep_index]
_UpperCAmelCase : Dict = self.betas[prev_timestep_index]
_UpperCAmelCase : Tuple = (sample - sigma * ets) / max(A , 1E-8 )
_UpperCAmelCase : List[str] = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Union[str, Any] ):
return self.config.num_train_timesteps
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
__SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
__SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def UpperCamelCase_ ( _UpperCAmelCase : list[int] , _UpperCAmelCase : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_UpperCAmelCase : str = ""
_UpperCAmelCase : int
_UpperCAmelCase : int
_UpperCAmelCase : int
for keychar, cipherchar in zip(cycle(_UpperCAmelCase ) , _UpperCAmelCase ):
_UpperCAmelCase : str = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(_UpperCAmelCase )
return decoded
def UpperCamelCase_ ( _UpperCAmelCase : list[int] ) -> list[str]:
"""simple docstring"""
_UpperCAmelCase : list[str] = []
for key in product(_UpperCAmelCase , repeat=3 ):
_UpperCAmelCase : Tuple = try_key(_UpperCAmelCase , _UpperCAmelCase )
if encoded is not None:
possibles.append(_UpperCAmelCase )
return possibles
def UpperCamelCase_ ( _UpperCAmelCase : list[str] , _UpperCAmelCase : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def UpperCamelCase_ ( _UpperCAmelCase : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_UpperCAmelCase : list[int]
_UpperCAmelCase : list[str]
_UpperCAmelCase : str
_UpperCAmelCase : str
_UpperCAmelCase : str = Path(_UpperCAmelCase ).parent.joinpath(_UpperCAmelCase ).read_text(encoding="utf-8" )
_UpperCAmelCase : Optional[int] = [int(_UpperCAmelCase ) for number in data.strip().split("," )]
_UpperCAmelCase : Tuple = filter_valid_chars(_UpperCAmelCase )
for common_word in COMMON_WORDS:
_UpperCAmelCase : str = filter_common_word(_UpperCAmelCase , _UpperCAmelCase )
if len(_UpperCAmelCase ) == 1:
break
_UpperCAmelCase : Tuple = possibles[0]
return sum(ord(_UpperCAmelCase ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 31
|
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase_ ( _UpperCAmelCase : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
_UpperCAmelCase : Any = XGBClassifier()
classifier.fit(_UpperCAmelCase , _UpperCAmelCase )
return classifier
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : List[str] = load_iris()
_UpperCAmelCase , _UpperCAmelCase : Dict = data_handling(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = train_test_split(
_UpperCAmelCase , _UpperCAmelCase , test_size=0.2_5 )
_UpperCAmelCase : Optional[Any] = iris["target_names"]
# Create an XGBoost Classifier from the training data
_UpperCAmelCase : Tuple = xgboost(_UpperCAmelCase , _UpperCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , display_labels=_UpperCAmelCase , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 31
| 1
|
'''simple docstring'''
import math
import os
import sys
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[str] = ""
try:
with open(_UpperCAmelCase , "rb" ) as binary_file:
_UpperCAmelCase : Tuple = binary_file.read()
for dat in data:
_UpperCAmelCase : int = F"""{dat:08b}"""
result += curr_byte
return result
except OSError:
print("File not accessible" )
sys.exit()
def UpperCamelCase_ ( _UpperCAmelCase : dict[str, str] , _UpperCAmelCase : str , _UpperCAmelCase : int , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
lexicon.pop(_UpperCAmelCase )
_UpperCAmelCase : int = last_match_id
if math.loga(_UpperCAmelCase ).is_integer():
for curr_key in lexicon:
_UpperCAmelCase : Optional[int] = "0" + lexicon[curr_key]
_UpperCAmelCase : Union[str, Any] = bin(_UpperCAmelCase )[2:]
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[Any] = {"0": "0", "1": "1"}
_UpperCAmelCase , _UpperCAmelCase : str = "", ""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for i in range(len(_UpperCAmelCase ) ):
curr_string += data_bits[i]
if curr_string not in lexicon:
continue
_UpperCAmelCase : Union[str, Any] = lexicon[curr_string]
result += last_match_id
add_key_to_lexicon(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
index += 1
_UpperCAmelCase : int = ""
while curr_string != "" and curr_string not in lexicon:
curr_string += "0"
if curr_string != "":
_UpperCAmelCase : Any = lexicon[curr_string]
result += last_match_id
return result
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
"""simple docstring"""
_UpperCAmelCase : List[Any] = os.path.getsize(_UpperCAmelCase )
_UpperCAmelCase : Tuple = bin(_UpperCAmelCase )[2:]
_UpperCAmelCase : int = len(_UpperCAmelCase )
return "0" * (length_length - 1) + file_length_binary + compressed
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_UpperCAmelCase : List[str] = 8
try:
with open(_UpperCAmelCase , "wb" ) as opened_file:
_UpperCAmelCase : Optional[int] = [
to_write[i : i + byte_length]
for i in range(0 , len(_UpperCAmelCase ) , _UpperCAmelCase )
]
if len(result_byte_array[-1] ) % byte_length == 0:
result_byte_array.append("10000000" )
else:
result_byte_array[-1] += "1" + "0" * (
byte_length - len(result_byte_array[-1] ) - 1
)
for elem in result_byte_array:
opened_file.write(int(_UpperCAmelCase , 2 ).to_bytes(1 , byteorder="big" ) )
except OSError:
print("File not accessible" )
sys.exit()
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_UpperCAmelCase : int = read_file_binary(_UpperCAmelCase )
_UpperCAmelCase : Tuple = compress_data(_UpperCAmelCase )
_UpperCAmelCase : Optional[Any] = add_file_length(_UpperCAmelCase , _UpperCAmelCase )
write_file_binary(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
compress(sys.argv[1], sys.argv[2])
| 31
|
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : Optional[Any]=13 , A : Optional[Any]=7 , A : Union[str, Any]=True , A : Optional[Any]=True , A : int=False , A : str=True , A : Optional[Any]=99 , A : Union[str, Any]=32 , A : int=5 , A : Tuple=4 , A : Union[str, Any]=37 , A : Dict="gelu" , A : Union[str, Any]=0.1 , A : str=0.1 , A : Union[str, Any]=512 , A : int=16 , A : List[str]=2 , A : Tuple=0.02 , A : int=3 , A : List[str]=4 , A : str=None , ):
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : str = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : List[str] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Dict ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _A ( self : int , A : List[Any] , A : Any , A : int , A : Union[str, Any] , A : Dict , A : List[Any] , A : Dict ):
_UpperCAmelCase : List[str] = BioGptModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : List[Any] , A : str , A : List[Any] , A : Dict , A : List[Any] , A : List[str] , A : Union[str, Any] , A : int , A : List[str] , A : Dict , ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : List[Any] , A : str , A : str , A : str , A : Any , A : List[str] , *A : Optional[int] ):
_UpperCAmelCase : str = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
_UpperCAmelCase : Optional[int] = self.seq_length // 2
_UpperCAmelCase : List[Any] = 0
# first forward pass
_UpperCAmelCase , _UpperCAmelCase : List[str] = model(A , attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_UpperCAmelCase : List[str] = ids_tensor((1,) , A ).item() + 1
_UpperCAmelCase : str = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_UpperCAmelCase : Any = random_other_next_tokens
# append to next input_ids and attn_mask
_UpperCAmelCase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=A )] , dim=1 , )
# get two different outputs
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Optional[Any] = model(A , past_key_values=A , attention_mask=A )["last_hidden_state"]
# select random slice
_UpperCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : int , A : Dict , A : str , A : Dict , A : Union[str, Any] , A : Any , *A : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = BioGptModel(config=A ).to(A ).eval()
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase , _UpperCAmelCase : Dict = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase : Any = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Dict = model(A , attention_mask=A , past_key_values=A )[
"last_hidden_state"
]
# select random slice
_UpperCAmelCase : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : Optional[Any] , A : Tuple , A : List[str] , A : Tuple , A : Dict , A : List[Any] , *A : Tuple , A : List[str]=False ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_UpperCAmelCase : Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _A ( self : Optional[Any] , A : Any , *A : Optional[Any] ):
_UpperCAmelCase : Tuple = BioGptModel(A )
_UpperCAmelCase : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def _A ( self : Optional[int] , A : Dict , A : Tuple , A : Optional[int] , A : int , A : List[str] , *A : Dict ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Any = BioGptForTokenClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
__UpperCamelCase: List[str] = (BioGptForCausalLM,) if is_torch_available() else ()
__UpperCamelCase: str = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase: Union[str, Any] = False
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = BioGptModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def _A ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : Tuple = type
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A , gradient_checkpointing=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
_UpperCAmelCase : Tuple = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : str = "left"
# Define PAD Token = EOS Token = 50256
_UpperCAmelCase : Any = tokenizer.eos_token
_UpperCAmelCase : int = model.config.eos_token_id
# use different length sentences to test batching
_UpperCAmelCase : Any = [
"Hello, my dog is a little",
"Today, I",
]
_UpperCAmelCase : Tuple = tokenizer(A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Optional[Any] = inputs["input_ids"].to(A )
_UpperCAmelCase : Any = model.generate(
input_ids=A , attention_mask=inputs["attention_mask"].to(A ) , )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : List[Any] = model.generate(input_ids=A )
_UpperCAmelCase : List[Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
_UpperCAmelCase : int = tokenizer(sentences[1] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : int = model.generate(input_ids=A , max_length=model.config.max_length - num_paddings )
_UpperCAmelCase : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
_UpperCAmelCase : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : str = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(A , A )
self.assertListEqual(A , [non_padded_sentence, padded_sentence] )
@slow
def _A ( self : str ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _A ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : List[str] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : List[str] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = 3
_UpperCAmelCase : Dict = "multi_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Optional[int] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : Optional[Any] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
_UpperCAmelCase : List[Any] = model(A )[0]
_UpperCAmelCase : int = 42384
_UpperCAmelCase : int = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@slow
def _A ( self : Any ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Tuple = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = tokenizer("COVID-19 is" , return_tensors="pt" ).to(A )
_UpperCAmelCase : Dict = model.generate(
**A , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=A , )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=A )
_UpperCAmelCase : List[str] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(A , A )
| 31
| 1
|
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : Union[str, Any]=False ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = "backbone." if is_semantic else ""
_UpperCAmelCase : Tuple = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""{prefix}blocks.{i}.norm1.weight""", F"""beit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.norm1.bias""", F"""beit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""{prefix}blocks.{i}.attn.proj.weight""", F"""beit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(F"""{prefix}blocks.{i}.attn.proj.bias""", F"""beit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""{prefix}blocks.{i}.norm2.weight""", F"""beit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.norm2.bias""", F"""beit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.weight""", F"""beit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc1.bias""", F"""beit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.weight""", F"""beit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""{prefix}blocks.{i}.mlp.fc2.bias""", F"""beit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
(F"""{prefix}cls_token""", "beit.embeddings.cls_token"),
(F"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
(F"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
(F"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("mask_token", "beit.embeddings.mask_token"),
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("fc_norm.weight", "beit.pooler.layernorm.weight"),
("fc_norm.bias", "beit.pooler.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
for i in range(config.num_hidden_layers ):
_UpperCAmelCase : List[str] = "backbone." if is_semantic else ""
# queries, keys and values
_UpperCAmelCase : int = state_dict.pop(F"""{prefix}blocks.{i}.attn.qkv.weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""{prefix}blocks.{i}.attn.q_bias""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""{prefix}blocks.{i}.attn.v_bias""" )
_UpperCAmelCase : Dict = in_proj_weight[
: config.hidden_size, :
]
_UpperCAmelCase : Optional[int] = q_bias
_UpperCAmelCase : Any = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_UpperCAmelCase : Any = in_proj_weight[
-config.hidden_size :, :
]
_UpperCAmelCase : Dict = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
_UpperCAmelCase : Any = state_dict.pop(F"""{prefix}blocks.{i}.gamma_1""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""{prefix}blocks.{i}.gamma_2""" )
_UpperCAmelCase : List[Any] = gamma_a
_UpperCAmelCase : Any = gamma_a
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : Any , _UpperCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : int = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = val
def UpperCamelCase_ ( ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Dict = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : List[str] = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : str , _UpperCAmelCase : List[Any]=False ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[str] = False if "rvlcdip" in checkpoint_url else True
_UpperCAmelCase : Tuple = BeitConfig(use_absolute_position_embeddings=_UpperCAmelCase , use_mask_token=_UpperCAmelCase )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
_UpperCAmelCase : Optional[int] = 1_024
_UpperCAmelCase : Union[str, Any] = 4_096
_UpperCAmelCase : Tuple = 24
_UpperCAmelCase : int = 16
# labels
if "rvlcdip" in checkpoint_url:
_UpperCAmelCase : int = 16
_UpperCAmelCase : Optional[int] = "huggingface/label-files"
_UpperCAmelCase : Dict = "rvlcdip-id2label.json"
_UpperCAmelCase : Any = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : int = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : Optional[Any] = idalabel
_UpperCAmelCase : int = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
_UpperCAmelCase : Optional[int] = torch.hub.load_state_dict_from_url(_UpperCAmelCase , map_location="cpu" )["model"]
_UpperCAmelCase : Tuple = create_rename_keys(_UpperCAmelCase , has_lm_head=_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_q_k_v(_UpperCAmelCase , _UpperCAmelCase , has_lm_head=_UpperCAmelCase )
# load HuggingFace model
_UpperCAmelCase : Union[str, Any] = BeitForMaskedImageModeling(_UpperCAmelCase ) if has_lm_head else BeitForImageClassification(_UpperCAmelCase )
model.eval()
model.load_state_dict(_UpperCAmelCase )
# Check outputs on an image
_UpperCAmelCase : int = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=_UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = prepare_img()
_UpperCAmelCase : Any = image_processor(images=_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : Optional[int] = encoding["pixel_values"]
_UpperCAmelCase : List[str] = model(_UpperCAmelCase )
_UpperCAmelCase : str = outputs.logits
# verify logits
_UpperCAmelCase : Any = [1, 16] if "rvlcdip" in checkpoint_url else [1, 196, 8_192]
assert logits.shape == torch.Size(_UpperCAmelCase ), "Shape of logits not as expected"
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(_UpperCAmelCase )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
if has_lm_head:
_UpperCAmelCase : Optional[int] = "dit-base" if "base" in checkpoint_url else "dit-large"
else:
_UpperCAmelCase : List[str] = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
image_processor.push_to_hub(
repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=_UpperCAmelCase , )
model.push_to_hub(
repo_path_or_name=Path(_UpperCAmelCase , _UpperCAmelCase ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=_UpperCAmelCase , )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth""",
type=str,
help="""URL to the original PyTorch checkpoint (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 31
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : Tuple = {
"""configuration_clip""": [
"""CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""CLIPConfig""",
"""CLIPOnnxConfig""",
"""CLIPTextConfig""",
"""CLIPVisionConfig""",
],
"""processing_clip""": ["""CLIPProcessor"""],
"""tokenization_clip""": ["""CLIPTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Any = ["""CLIPTokenizerFast"""]
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[str] = ["""CLIPFeatureExtractor"""]
__SCREAMING_SNAKE_CASE : Dict = ["""CLIPImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""CLIPModel""",
"""CLIPPreTrainedModel""",
"""CLIPTextModel""",
"""CLIPTextModelWithProjection""",
"""CLIPVisionModel""",
"""CLIPVisionModelWithProjection""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Any = [
"""TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFCLIPModel""",
"""TFCLIPPreTrainedModel""",
"""TFCLIPTextModel""",
"""TFCLIPVisionModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""FlaxCLIPModel""",
"""FlaxCLIPPreTrainedModel""",
"""FlaxCLIPTextModel""",
"""FlaxCLIPTextPreTrainedModel""",
"""FlaxCLIPVisionModel""",
"""FlaxCLIPVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = """▁"""
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : int = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
__SCREAMING_SNAKE_CASE : str = {
"""google/pegasus-xsum""": 512,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase: Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Optional[int] = PegasusTokenizer
__UpperCamelCase: Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : Dict , A : List[str]=None , A : Union[str, Any]=None , A : Optional[int]="<pad>" , A : Tuple="</s>" , A : Union[str, Any]="<unk>" , A : Union[str, Any]="<mask_2>" , A : Dict="<mask_1>" , A : Union[str, Any]=None , A : int=103 , **A : Optional[Any] , ):
_UpperCAmelCase : Dict = offset
if additional_special_tokens is not None:
if not isinstance(A , A ):
raise TypeError(
F"""additional_special_tokens should be of type {type(A )}, but is"""
F""" {type(A )}""" )
_UpperCAmelCase : Optional[int] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(A ) , self.offset - 1 )
]
if len(set(A ) ) != len(A ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
_UpperCAmelCase : Any = additional_special_tokens_extended
else:
_UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
A , tokenizer_file=A , pad_token=A , eos_token=A , unk_token=A , mask_token=A , mask_token_sent=A , offset=A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[Any] = vocab_file
_UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
def _A ( self : List[str] , A : Optional[Any] ):
_UpperCAmelCase : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : str , A : List , A : Optional[List] = None , A : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(A )
elif token_ids_a is None:
return self._special_token_mask(A ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : Optional[int] , A : Union[str, Any] , A : int=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : Union[str, Any] , A : str , A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 31
| 1
|
'''simple docstring'''
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCamelCase_ :
'''simple docstring'''
@staticmethod
def _A ( *A : int , **A : Tuple ):
pass
@is_pipeline_test
@require_vision
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@require_torch
def _A ( self : str ):
_UpperCAmelCase : Tuple = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , )
_UpperCAmelCase : str = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : Optional[Any] = image_classifier(A , candidate_labels=["a", "b", "c"] )
# The floating scores are so close, we enter floating error approximation and the order is not guaranteed across
# python and torch versions.
self.assertIn(
nested_simplify(A ) , [
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}],
[{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "c"}, {"score": 0.333, "label": "b"}],
] , )
_UpperCAmelCase : Tuple = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
] , )
@require_tf
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Tuple = pipeline(
model="hf-internal-testing/tiny-random-clip-zero-shot-image-classification" , framework="tf" )
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : List[str] = image_classifier(A , candidate_labels=["a", "b", "c"] )
self.assertEqual(
nested_simplify(A ) , [{"score": 0.333, "label": "a"}, {"score": 0.333, "label": "b"}, {"score": 0.333, "label": "c"}] , )
_UpperCAmelCase : Any = image_classifier([image] * 5 , candidate_labels=["A", "B", "C"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
[
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
{"score": 0.333, "label": ANY(A )},
],
] , )
@slow
@require_torch
def _A ( self : int ):
_UpperCAmelCase : Tuple = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , )
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : List[str] = image_classifier(A , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(A ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
_UpperCAmelCase : Optional[Any] = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
@slow
@require_tf
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Tuple = pipeline(
task="zero-shot-image-classification" , model="openai/clip-vit-base-patch32" , framework="tf" )
# This is an image of 2 cats with remotes and no planes
_UpperCAmelCase : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
_UpperCAmelCase : Optional[int] = image_classifier(A , candidate_labels=["cat", "plane", "remote"] )
self.assertEqual(
nested_simplify(A ) , [
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
] , )
_UpperCAmelCase : Dict = image_classifier([image] * 5 , candidate_labels=["cat", "plane", "remote"] , batch_size=2 )
self.assertEqual(
nested_simplify(A ) , [
[
{"score": 0.511, "label": "remote"},
{"score": 0.485, "label": "cat"},
{"score": 0.004, "label": "plane"},
],
]
* 5 , )
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__SCREAMING_SNAKE_CASE : Optional[int] = 256_047
__SCREAMING_SNAKE_CASE : Optional[int] = 256_145
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = NllbTokenizer
__UpperCamelCase: Tuple = NllbTokenizerFast
__UpperCamelCase: Union[str, Any] = True
__UpperCamelCase: Dict = True
__UpperCamelCase: Optional[Any] = {}
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
_UpperCAmelCase : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase : List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Dict = tokenizer_r.save_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
_UpperCAmelCase : Optional[int] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : List[str] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
_UpperCAmelCase : str = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : Optional[int] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
def _A ( self : Tuple ):
if not self.test_seqaseq:
return
_UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_UpperCAmelCase : Optional[Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
_UpperCAmelCase : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=A , tgt_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_UpperCAmelCase : Tuple = tokenizer.prepare_seqaseq_batch(
A , tgt_texts=A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_UpperCAmelCase : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def _A ( self : List[Any] ):
pass
def _A ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Any = [AddedToken("<special>" , lstrip=A )]
_UpperCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Dict = tokenizer_r.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_r.encode("<special>" , add_special_tokens=A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = "facebook/nllb-200-distilled-600M"
__UpperCamelCase: Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__UpperCamelCase: str = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__UpperCamelCase: str = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _A ( cls : int ):
_UpperCAmelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
_UpperCAmelCase : Union[str, Any] = 1
return cls
def _A ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 256057 )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def _A ( self : Tuple ):
self.assertIn(A , self.tokenizer.all_special_ids )
# fmt: off
_UpperCAmelCase : List[Any] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_UpperCAmelCase : Tuple = self.tokenizer.decode(A , skip_special_tokens=A )
_UpperCAmelCase : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , A )
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Tuple = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , A )
self.assertEqual(len(A ) , A )
def _A ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [256203, 3] )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
_UpperCAmelCase : Tuple = NllbTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def _A ( self : Dict ):
_UpperCAmelCase : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Tuple = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(A , A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_UpperCAmelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : Dict = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=10 , return_tensors="pt" )
_UpperCAmelCase : List[Any] = targets["input_ids"]
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(
A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
} , )
@require_torch
def _A ( self : Any ):
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Any = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : str = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 31
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__SCREAMING_SNAKE_CASE : Dict = {
"""configuration_vivit""": ["""VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VivitConfig"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = ["""VivitImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
"""VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""VivitModel""",
"""VivitPreTrainedModel""",
"""VivitForVideoClassification""",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_UpperCAmelCase , _UpperCAmelCase : int = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = list(range(10, 0, -1))
print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')
| 31
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...utils import logging
if TYPE_CHECKING:
from ...feature_extraction_utils import FeatureExtractionMixin
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType
__SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""openai/whisper-base""": """https://huggingface.co/openai/whisper-base/resolve/main/config.json""",
}
# fmt: off
__SCREAMING_SNAKE_CASE : Tuple = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 357, 366, 438, 532, 685,
705, 796, 930, 1_058, 1_220, 1_267, 1_279, 1_303, 1_343, 1_377,
1_391, 1_635, 1_782, 1_875, 2_162, 2_361, 2_488, 3_467, 4_008, 4_211,
4_600, 4_808, 5_299, 5_855, 6_329, 7_203, 9_609, 9_959, 10_563, 10_786,
11_420, 11_709, 11_907, 13_163, 13_697, 13_700, 14_808, 15_306, 16_410, 16_791,
17_992, 19_203, 19_510, 20_724, 22_305, 22_935, 27_007, 30_109, 30_420, 33_409,
34_949, 40_283, 40_493, 40_549, 47_282, 49_146, 50_257, 50_359, 50_360, 50_361
]
__SCREAMING_SNAKE_CASE : List[Any] = [
1, 2, 7, 8, 9, 10, 14, 25,
26, 27, 28, 29, 31, 58, 59, 60, 61, 62,
63, 90, 91, 92, 93, 359, 503, 522, 542, 873,
893, 902, 918, 922, 931, 1_350, 1_853, 1_982, 2_460, 2_627,
3_246, 3_253, 3_268, 3_536, 3_846, 3_961, 4_183, 4_667, 6_585, 6_647,
7_273, 9_061, 9_383, 10_428, 10_929, 11_938, 12_033, 12_331, 12_562, 13_793,
14_157, 14_635, 15_265, 15_618, 16_553, 16_604, 18_362, 18_956, 20_075, 21_675,
22_520, 26_130, 26_161, 26_435, 28_279, 29_464, 31_650, 32_302, 32_470, 36_865,
42_863, 47_425, 49_870, 50_254, 50_258, 50_360, 50_361, 50_362
]
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = "whisper"
__UpperCamelCase: int = ["past_key_values"]
__UpperCamelCase: Optional[int] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Any , A : List[str]=51865 , A : Dict=80 , A : Any=6 , A : Any=4 , A : List[str]=6 , A : Union[str, Any]=4 , A : Optional[Any]=1536 , A : Optional[int]=1536 , A : Tuple=0.0 , A : str=0.0 , A : str=50257 , A : Optional[int]=True , A : Union[str, Any]=True , A : Dict="gelu" , A : Optional[Any]=256 , A : Tuple=0.0 , A : List[str]=0.0 , A : str=0.0 , A : Optional[Any]=0.02 , A : Tuple=False , A : Optional[Any]=1500 , A : Any=448 , A : Dict=50256 , A : int=50256 , A : str=50256 , A : str=None , A : Dict=[220, 50256] , A : str=False , A : int=256 , A : int=False , A : Optional[Any]=0.05 , A : Optional[Any]=10 , A : str=2 , A : str=0.0 , A : Any=10 , A : Any=0 , A : List[str]=7 , **A : Union[str, Any] , ):
_UpperCAmelCase : str = vocab_size
_UpperCAmelCase : Union[str, Any] = num_mel_bins
_UpperCAmelCase : int = d_model
_UpperCAmelCase : Any = encoder_layers
_UpperCAmelCase : Union[str, Any] = encoder_attention_heads
_UpperCAmelCase : List[Any] = decoder_layers
_UpperCAmelCase : Dict = decoder_attention_heads
_UpperCAmelCase : List[Any] = decoder_ffn_dim
_UpperCAmelCase : Tuple = encoder_ffn_dim
_UpperCAmelCase : Tuple = dropout
_UpperCAmelCase : List[str] = attention_dropout
_UpperCAmelCase : Tuple = activation_dropout
_UpperCAmelCase : Dict = activation_function
_UpperCAmelCase : Any = init_std
_UpperCAmelCase : Tuple = encoder_layerdrop
_UpperCAmelCase : Any = decoder_layerdrop
_UpperCAmelCase : Any = use_cache
_UpperCAmelCase : Tuple = encoder_layers
_UpperCAmelCase : List[str] = scale_embedding # scale factor will be sqrt(d_model) if True
_UpperCAmelCase : Optional[Any] = max_source_positions
_UpperCAmelCase : Optional[Any] = max_target_positions
# Audio Classification-specific parameters. Feel free to ignore for other classes.
_UpperCAmelCase : Optional[int] = classifier_proj_size
_UpperCAmelCase : List[Any] = use_weighted_layer_sum
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_UpperCAmelCase : Union[str, Any] = apply_spec_augment
_UpperCAmelCase : List[str] = mask_time_prob
_UpperCAmelCase : List[Any] = mask_time_length
_UpperCAmelCase : Optional[Any] = mask_time_min_masks
_UpperCAmelCase : Optional[Any] = mask_feature_prob
_UpperCAmelCase : List[str] = mask_feature_length
_UpperCAmelCase : str = mask_feature_min_masks
_UpperCAmelCase : Optional[Any] = median_filter_width
super().__init__(
pad_token_id=A , bos_token_id=A , eos_token_id=A , is_encoder_decoder=A , decoder_start_token_id=A , suppress_tokens=A , begin_suppress_tokens=A , **A , )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@property
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Optional[int] = OrderedDict(
[
("input_features", {0: "batch", 1: "feature_size", 2: "encoder_sequence"}),
] )
if self.use_past:
_UpperCAmelCase : Any = {0: "batch"}
else:
_UpperCAmelCase : Tuple = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(A , direction="inputs" )
return common_inputs
def _A ( self : str , A : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , A : int = -1 , A : int = -1 , A : bool = False , A : Optional["TensorType"] = None , A : int = 22050 , A : float = 5.0 , A : int = 220 , ):
_UpperCAmelCase : int = OrderedDict()
_UpperCAmelCase : Optional[int] = OnnxConfig.generate_dummy_inputs(
self , preprocessor=preprocessor.feature_extractor , batch_size=A , framework=A , sampling_rate=A , time_duration=A , frequency=A , )
_UpperCAmelCase : List[str] = encoder_inputs["input_features"].shape[2]
_UpperCAmelCase : Optional[int] = encoder_sequence_length // 2 if self.use_past else seq_length
_UpperCAmelCase : Any = super().generate_dummy_inputs(
preprocessor.tokenizer , A , A , A , A )
_UpperCAmelCase : int = encoder_inputs.pop("input_features" )
_UpperCAmelCase : Any = decoder_inputs.pop("decoder_input_ids" )
if "past_key_values" in decoder_inputs:
_UpperCAmelCase : str = decoder_inputs.pop("past_key_values" )
return dummy_inputs
@property
def _A ( self : Dict ):
return 1E-3
| 31
|
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
_UpperCAmelCase : Optional[int] = nn.ModuleList(A )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : torch.Tensor , A : List[torch.tensor] , A : List[float] , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[Dict[str, Any]] = None , A : bool = False , A : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(A , A , self.nets ) ):
_UpperCAmelCase , _UpperCAmelCase : str = controlnet(
A , A , A , A , A , A , A , A , A , A , A , )
# merge samples
if i == 0:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = down_samples, mid_sample
else:
_UpperCAmelCase : Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A , A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _A ( self : List[str] , A : Union[str, os.PathLike] , A : bool = True , A : Callable = None , A : bool = False , A : Optional[str] = None , ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A , is_main_process=A , save_function=A , safe_serialization=A , variant=A , )
idx += 1
_UpperCAmelCase : Tuple = model_path_to_save + F"""_{idx}"""
@classmethod
def _A ( cls : int , A : Optional[Union[str, os.PathLike]] , **A : Tuple ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_UpperCAmelCase : int = pretrained_model_path
while os.path.isdir(A ):
_UpperCAmelCase : List[str] = ControlNetModel.from_pretrained(A , **A )
controlnets.append(A )
idx += 1
_UpperCAmelCase : Dict = pretrained_model_path + F"""_{idx}"""
logger.info(F"""{len(A )} controlnets loaded from {pretrained_model_path}.""" )
if len(A ) == 0:
raise ValueError(
F"""No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(A )
| 31
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : str = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = ["""NllbTokenizerFast"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__SCREAMING_SNAKE_CASE : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_UpperCAmelCase : List[Any] = MaskFormerConfig(backbone_config=_UpperCAmelCase )
_UpperCAmelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_UpperCAmelCase : Dict = 847
_UpperCAmelCase : Any = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_UpperCAmelCase : Any = 150
_UpperCAmelCase : Any = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_UpperCAmelCase : Tuple = 171
_UpperCAmelCase : Union[str, Any] = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_UpperCAmelCase : Any = 133
_UpperCAmelCase : int = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 19
_UpperCAmelCase : str = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 65
_UpperCAmelCase : Tuple = "mapillary-vistas-id2label.json"
_UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : List[str] = val
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase : Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase : Any = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_UpperCAmelCase : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[:dim, :]
_UpperCAmelCase : Tuple = in_proj_bias[: dim]
_UpperCAmelCase : List[Any] = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase : List[str] = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase : Optional[Any] = in_proj_weight[
-dim :, :
]
_UpperCAmelCase : Dict = in_proj_bias[-dim :]
# fmt: on
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : int = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : int = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Optional[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Any = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Tuple = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : Optional[int] = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[-hidden_size :]
# fmt: on
def UpperCamelCase_ ( ) -> torch.Tensor:
"""simple docstring"""
_UpperCAmelCase : int = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : Any = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = get_maskformer_config(_UpperCAmelCase )
# load original state_dict
with open(_UpperCAmelCase , "rb" ) as f:
_UpperCAmelCase : Optional[int] = pickle.load(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_UpperCAmelCase : Any = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_swin_q_k_v(_UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_UpperCAmelCase , _UpperCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_UpperCAmelCase : Tuple = torch.from_numpy(_UpperCAmelCase )
# load 🤗 model
_UpperCAmelCase : Union[str, Any] = MaskFormerForInstanceSegmentation(_UpperCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_UpperCAmelCase , param.shape )
_UpperCAmelCase , _UpperCAmelCase : Any = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_UpperCAmelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_UpperCAmelCase : Optional[int] = prepare_img()
if "vistas" in model_name:
_UpperCAmelCase : int = 65
elif "cityscapes" in model_name:
_UpperCAmelCase : Tuple = 65_535
else:
_UpperCAmelCase : Any = 255
_UpperCAmelCase : Optional[Any] = True if "ade" in model_name else False
_UpperCAmelCase : Optional[int] = MaskFormerImageProcessor(ignore_index=_UpperCAmelCase , reduce_labels=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = image_processor(_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : List[Any] = model(**_UpperCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_UpperCAmelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 31
| 1
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : Dict = {
"""vocab_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-german-cased""": (
"""https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
),
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
),
},
}
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""distilbert-base-uncased""": 512,
"""distilbert-base-uncased-distilled-squad""": 512,
"""distilbert-base-cased""": 512,
"""distilbert-base-cased-distilled-squad""": 512,
"""distilbert-base-german-cased""": 512,
"""distilbert-base-multilingual-cased""": 512,
}
__SCREAMING_SNAKE_CASE : List[Any] = {
"""distilbert-base-uncased""": {"""do_lower_case""": True},
"""distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
"""distilbert-base-cased""": {"""do_lower_case""": False},
"""distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
"""distilbert-base-german-cased""": {"""do_lower_case""": False},
"""distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase: str = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Any = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase: str = ["input_ids", "attention_mask"]
__UpperCamelCase: List[str] = DistilBertTokenizer
def __init__( self : str , A : int=None , A : Tuple=None , A : Tuple=True , A : Dict="[UNK]" , A : List[Any]="[SEP]" , A : Optional[Any]="[PAD]" , A : Dict="[CLS]" , A : Tuple="[MASK]" , A : str=True , A : Dict=None , **A : List[Any] , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Dict = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : int = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : str = tokenize_chinese_chars
_UpperCAmelCase : List[Any] = normalizer_class(**A )
_UpperCAmelCase : Dict = do_lower_case
def _A ( self : List[Any] , A : Tuple , A : Any=None ):
_UpperCAmelCase : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _A ( self : int , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _A ( self : Dict , A : str , A : Optional[str] = None ):
_UpperCAmelCase : Any = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 31
|
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : Optional[str] = None ):
_UpperCAmelCase : Dict = (
os.path.join(A , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_UpperCAmelCase : Union[str, Any] = Extractor
def _A ( self : Tuple , A : str ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_UpperCAmelCase : Dict = os.path.abspath(A )
return os.path.join(self.extract_dir , hash_url_to_filename(A ) )
def _A ( self : int , A : str , A : bool ):
return force_extract or (
not os.path.isfile(A ) and not (os.path.isdir(A ) and os.listdir(A ))
)
def _A ( self : Optional[int] , A : str , A : bool = False ):
_UpperCAmelCase : Union[str, Any] = self.extractor.infer_extractor_format(A )
if not extractor_format:
return input_path
_UpperCAmelCase : Optional[Any] = self._get_output_path(A )
if self._do_extract(A , A ):
self.extractor.extract(A , A , A )
return output_path
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
@abstractmethod
def _A ( cls : str , A : Union[Path, str] , **A : Dict ):
...
@staticmethod
@abstractmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
...
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[bytes] = []
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
with open(A , "rb" ) as f:
return f.read(A )
@classmethod
def _A ( cls : Any , A : Union[Path, str] , A : bytes = b"" ):
if not magic_number:
_UpperCAmelCase : Any = max(len(A ) for cls_magic_number in cls.magic_numbers )
try:
_UpperCAmelCase : int = cls.read_magic_number(A , A )
except OSError:
return False
return any(magic_number.startswith(A ) for cls_magic_number in cls.magic_numbers )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
def _A ( cls : str , A : Union[Path, str] , **A : List[Any] ):
return tarfile.is_tarfile(A )
@staticmethod
def _A ( A : Union[str, Any] , A : str ):
def resolved(A : str ) -> str:
return os.path.realpath(os.path.abspath(A ) )
def badpath(A : str , A : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(A , A ) ).startswith(A )
def badlink(A : str , A : str ) -> bool:
# Links are interpreted relative to the directory containing the link
_UpperCAmelCase : List[str] = resolved(os.path.join(A , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=A )
_UpperCAmelCase : Optional[int] = resolved(A )
for finfo in members:
if badpath(finfo.name , A ):
logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : int = tarfile.open(A )
tar_file.extractall(A , members=TarExtractor.safemembers(A , A ) )
tar_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = [b"\x1F\x8B"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with gzip.open(A , "rb" ) as gzip_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [
b"PK\x03\x04",
b"PK\x05\x06", # empty archive
b"PK\x07\x08", # spanned archive
]
@classmethod
def _A ( cls : Dict , A : Union[Path, str] , A : bytes = b"" ):
if super().is_extractable(A , magic_number=A ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(A , "rb" ) as fp:
_UpperCAmelCase : Tuple = _EndRecData(A )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_UpperCAmelCase : Dict = fp.read(A ) # CD is where we expect it to be
if len(A ) == sizeCentralDir:
_UpperCAmelCase : Any = struct.unpack(A , A ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
with zipfile.ZipFile(A , "r" ) as zip_file:
zip_file.extractall(A )
zip_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [b"\xFD\x37\x7A\x58\x5A\x00"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with lzma.open(A ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[str] = [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : List[str] = rarfile.RarFile(A )
rf.extractall(A )
rf.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x28\xb5\x2F\xFD"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
_UpperCAmelCase : Optional[Any] = zstd.ZstdDecompressor()
with open(A , "rb" ) as ifh, open(A , "wb" ) as ofh:
dctx.copy_stream(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x42\x5A\x68"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with bza.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = [b"\x37\x7A\xBC\xAF\x27\x1C"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr" )
import pyazr
os.makedirs(A , exist_ok=A )
with pyazr.SevenZipFile(A , "r" ) as archive:
archive.extractall(A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = [b"\x04\x22\x4D\x18"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4" )
import lza.frame
with lza.frame.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _A ( cls : List[Any] ):
return max(
len(A )
for extractor in cls.extractors.values()
if issubclass(A , A )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
try:
return MagicNumberBaseExtractor.read_magic_number(A , magic_number_length=A )
except OSError:
return b""
@classmethod
def _A ( cls : Optional[Any] , A : Union[Path, str] , A : bool = False ):
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead." , category=A , )
_UpperCAmelCase : Union[str, Any] = cls.infer_extractor_format(A )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _A ( cls : Dict , A : Union[Path, str] ): # <Added version="2.4.0"/>
_UpperCAmelCase : Optional[int] = cls._get_magic_number_max_length()
_UpperCAmelCase : str = cls._read_magic_number(A , A )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(A , magic_number=A ):
return extractor_format
@classmethod
def _A ( cls : List[str] , A : Union[Path, str] , A : Union[Path, str] , A : Optional[str] = None , A : Optional[BaseExtractor] = "deprecated" , ):
os.makedirs(os.path.dirname(A ) , exist_ok=A )
# Prevent parallel extractions
_UpperCAmelCase : Tuple = str(Path(A ).with_suffix(".lock" ) )
with FileLock(A ):
shutil.rmtree(A , ignore_errors=A )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(A , A ): # passed as positional arg
warnings.warn(
"Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'extractor_format' instead." , category=A , )
_UpperCAmelCase : Tuple = extractor if extractor != "deprecated" else extractor_format
else:
_UpperCAmelCase : Tuple = cls.extractors[extractor_format]
return extractor.extract(A , A )
else:
warnings.warn(
"Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
"exception in 3.0.0." , category=A , )
for extractor in cls.extractors.values():
if extractor.is_extractable(A ):
return extractor.extract(A , A )
| 31
| 1
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
def _A ( self : Any , A : Union[str, Any] , A : Optional[int] , A : List[str] , **A : Tuple ):
self.events.append("on_init_end" )
def _A ( self : Any , A : str , A : List[Any] , A : List[Any] , **A : Tuple ):
self.events.append("on_train_begin" )
def _A ( self : Tuple , A : List[str] , A : Tuple , A : int , **A : List[str] ):
self.events.append("on_train_end" )
def _A ( self : Optional[Any] , A : Dict , A : Any , A : Optional[Any] , **A : List[Any] ):
self.events.append("on_epoch_begin" )
def _A ( self : Optional[Any] , A : List[Any] , A : List[str] , A : Optional[int] , **A : Optional[int] ):
self.events.append("on_epoch_end" )
def _A ( self : List[str] , A : Optional[int] , A : List[Any] , A : Union[str, Any] , **A : Any ):
self.events.append("on_step_begin" )
def _A ( self : Tuple , A : Union[str, Any] , A : int , A : Optional[int] , **A : int ):
self.events.append("on_step_end" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Union[str, Any] , A : str , **A : Union[str, Any] ):
self.events.append("on_evaluate" )
def _A ( self : Optional[Any] , A : Optional[int] , A : Dict , A : List[Any] , **A : Dict ):
self.events.append("on_predict" )
def _A ( self : Dict , A : Dict , A : List[Any] , A : Dict , **A : str ):
self.events.append("on_save" )
def _A ( self : Tuple , A : Optional[Any] , A : Union[str, Any] , A : Optional[int] , **A : Dict ):
self.events.append("on_log" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Tuple , A : Tuple , **A : List[str] ):
self.events.append("on_prediction_step" )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
def _A ( self : List[Any] ):
shutil.rmtree(self.output_dir )
def _A ( self : Union[str, Any] , A : Optional[int]=0 , A : Optional[Any]=0 , A : Optional[Any]=64 , A : Dict=64 , A : Any=None , A : Tuple=False , **A : Optional[int] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
_UpperCAmelCase : str = RegressionDataset(length=A )
_UpperCAmelCase : Union[str, Any] = RegressionDataset(length=A )
_UpperCAmelCase : Any = RegressionModelConfig(a=A , b=A )
_UpperCAmelCase : List[Any] = RegressionPreTrainedModel(A )
_UpperCAmelCase : Dict = TrainingArguments(self.output_dir , disable_tqdm=A , report_to=[] , **A )
return Trainer(
A , A , train_dataset=A , eval_dataset=A , callbacks=A , )
def _A ( self : str , A : List[str] , A : List[str] ):
self.assertEqual(len(A ) , len(A ) )
# Order doesn't matter
_UpperCAmelCase : Tuple = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
_UpperCAmelCase : Any = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
for cba, cba in zip(A , A ):
if isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(A , A )
elif isinstance(A , A ) and not isinstance(A , A ):
self.assertEqual(A , cba.__class__ )
elif not isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(cba.__class__ , A )
else:
self.assertEqual(A , A )
def _A ( self : int , A : List[str] ):
_UpperCAmelCase : List[str] = ["on_init_end", "on_train_begin"]
_UpperCAmelCase : str = 0
_UpperCAmelCase : Optional[Any] = len(trainer.get_eval_dataloader() )
_UpperCAmelCase : Optional[int] = ["on_prediction_step"] * len(trainer.get_eval_dataloader() ) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("on_epoch_begin" )
for _ in range(A ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save" )
expected_events.append("on_epoch_end" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _A ( self : str ):
_UpperCAmelCase : Any = self.get_trainer()
_UpperCAmelCase : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# Callbacks passed at init are added to the default callbacks
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_UpperCAmelCase : List[Any] = self.get_trainer(disable_tqdm=A )
_UpperCAmelCase : Tuple = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_UpperCAmelCase : Dict = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : Optional[Any] = self.get_trainer()
_UpperCAmelCase : Any = trainer.pop_callback(A )
self.assertEqual(cb.__class__ , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# We can also add, pop, or remove by instance
_UpperCAmelCase : Union[str, Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : List[Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
_UpperCAmelCase : Union[str, Any] = trainer.pop_callback(A )
self.assertEqual(A , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" , category=A )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_UpperCAmelCase : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# Independent log/save/eval
_UpperCAmelCase : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_UpperCAmelCase : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="steps" )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="epoch" )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# A bit of everything
_UpperCAmelCase : int = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="steps" , )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning" ) as warn_mock:
_UpperCAmelCase : Optional[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(A ) in warn_mock.call_args[0][0]
| 31
|
'''simple docstring'''
from typing import Any
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : dict , _UpperCAmelCase : dict , _UpperCAmelCase : dict , ) -> list:
"""simple docstring"""
_validation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# Creates data structures and fill initial step
_UpperCAmelCase : dict = {}
_UpperCAmelCase : dict = {}
for state in states_space:
_UpperCAmelCase : Union[str, Any] = observations_space[0]
_UpperCAmelCase : Tuple = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
_UpperCAmelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase : Optional[Any] = observations_space[o]
_UpperCAmelCase : int = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_UpperCAmelCase : str = ""
_UpperCAmelCase : Tuple = -1
for k_state in states_space:
_UpperCAmelCase : Any = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_UpperCAmelCase : Union[str, Any] = probability
_UpperCAmelCase : str = k_state
# Update probabilities and pointers dicts
_UpperCAmelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_UpperCAmelCase : Tuple = arg_max
# The final observation
_UpperCAmelCase : Optional[Any] = observations_space[len(_UpperCAmelCase ) - 1]
# argmax for given final observation
_UpperCAmelCase : List[str] = ""
_UpperCAmelCase : Any = -1
for k_state in states_space:
_UpperCAmelCase : Optional[int] = probabilities[(k_state, final_observation)]
if probability > max_probability:
_UpperCAmelCase : int = probability
_UpperCAmelCase : Dict = k_state
_UpperCAmelCase : Dict = arg_max
# Process pointers backwards
_UpperCAmelCase : List[Any] = last_state
_UpperCAmelCase : str = []
for o in range(len(_UpperCAmelCase ) - 1 , -1 , -1 ):
result.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_not_empty(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
_validate_lists(_UpperCAmelCase , _UpperCAmelCase )
_validate_dicts(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any ) -> None:
"""simple docstring"""
_validate_list(_UpperCAmelCase , "observations_space" )
_validate_list(_UpperCAmelCase , "states_space" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list"""
raise ValueError(_UpperCAmelCase )
else:
for x in _object:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list of strings"""
raise ValueError(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_dict(_UpperCAmelCase , "initial_probabilities" , _UpperCAmelCase )
_validate_nested_dict(_UpperCAmelCase , "transition_probabilities" )
_validate_nested_dict(_UpperCAmelCase , "emission_probabilities" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_validate_dict(_object , _UpperCAmelCase , _UpperCAmelCase )
for x in _object.values():
_validate_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : type , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Any = F"""{var_name} must be a dict"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object ):
_UpperCAmelCase : Tuple = F"""{var_name} all keys must be strings"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object.values() ):
_UpperCAmelCase : List[str] = "nested dictionary " if nested else ""
_UpperCAmelCase : List[str] = F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(_UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING
__SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""salesforce/blip2-opt-2.7b""": """https://huggingface.co/salesforce/blip2-opt-2.7b/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = "blip_2_vision_model"
def __init__( self : Optional[Any] , A : Union[str, Any]=1408 , A : int=6144 , A : Union[str, Any]=39 , A : List[str]=16 , A : List[str]=224 , A : List[Any]=14 , A : int="gelu" , A : Optional[Any]=0.00_001 , A : str=0.0 , A : List[str]=1E-10 , A : Dict=True , **A : Optional[Any] , ):
super().__init__(**A )
_UpperCAmelCase : int = hidden_size
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : str = num_hidden_layers
_UpperCAmelCase : Union[str, Any] = num_attention_heads
_UpperCAmelCase : int = patch_size
_UpperCAmelCase : str = image_size
_UpperCAmelCase : Tuple = initializer_range
_UpperCAmelCase : Union[str, Any] = attention_dropout
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : Any = qkv_bias
@classmethod
def _A ( cls : Tuple , A : Union[str, os.PathLike] , **A : List[str] ):
cls._set_token_in_kwargs(A )
_UpperCAmelCase , _UpperCAmelCase : List[Any] = cls.get_config_dict(A , **A )
# get the vision config dict if we are loading from Blip2Config
if config_dict.get("model_type" ) == "blip-2":
_UpperCAmelCase : Dict = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(A , **A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = "blip_2_qformer"
def __init__( self : List[str] , A : Dict=30522 , A : Optional[Any]=768 , A : List[str]=12 , A : int=12 , A : List[Any]=3072 , A : List[Any]="gelu" , A : List[str]=0.1 , A : int=0.1 , A : Any=512 , A : List[Any]=0.02 , A : List[Any]=1E-12 , A : Tuple=0 , A : int="absolute" , A : str=2 , A : List[Any]=1408 , **A : Optional[int] , ):
super().__init__(pad_token_id=A , **A )
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : Any = num_hidden_layers
_UpperCAmelCase : str = num_attention_heads
_UpperCAmelCase : Tuple = hidden_act
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Optional[int] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Dict = max_position_embeddings
_UpperCAmelCase : List[Any] = initializer_range
_UpperCAmelCase : Any = layer_norm_eps
_UpperCAmelCase : Optional[int] = position_embedding_type
_UpperCAmelCase : Optional[Any] = cross_attention_frequency
_UpperCAmelCase : int = encoder_hidden_size
@classmethod
def _A ( cls : Optional[Any] , A : Union[str, os.PathLike] , **A : Optional[int] ):
cls._set_token_in_kwargs(A )
_UpperCAmelCase , _UpperCAmelCase : str = cls.get_config_dict(A , **A )
# get the qformer config dict if we are loading from Blip2Config
if config_dict.get("model_type" ) == "blip-2":
_UpperCAmelCase : List[str] = config_dict["qformer_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(A , **A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = "blip-2"
__UpperCamelCase: List[Any] = True
def __init__( self : str , A : Optional[Any]=None , A : List[Any]=None , A : str=None , A : Optional[int]=32 , **A : Union[str, Any] ):
super().__init__(**A )
if vision_config is None:
_UpperCAmelCase : Dict = {}
logger.info("vision_config is None. initializing the Blip2VisionConfig with default values." )
if qformer_config is None:
_UpperCAmelCase : int = {}
logger.info("qformer_config is None. Initializing the Blip2QFormerConfig with default values." )
if text_config is None:
_UpperCAmelCase : int = {}
logger.info("text_config is None. Initializing the text config with default values (`OPTConfig`)." )
_UpperCAmelCase : Dict = BlipaVisionConfig(**A )
_UpperCAmelCase : Optional[int] = BlipaQFormerConfig(**A )
_UpperCAmelCase : str = text_config["model_type"] if "model_type" in text_config else "opt"
_UpperCAmelCase : int = CONFIG_MAPPING[text_model_type](**A )
_UpperCAmelCase : Any = self.text_config.tie_word_embeddings
_UpperCAmelCase : Any = self.text_config.is_encoder_decoder
_UpperCAmelCase : List[str] = num_query_tokens
_UpperCAmelCase : Optional[int] = self.vision_config.hidden_size
_UpperCAmelCase : List[Any] = self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
_UpperCAmelCase : int = 1.0
_UpperCAmelCase : Optional[Any] = 0.02
@classmethod
def _A ( cls : List[Any] , A : BlipaVisionConfig , A : BlipaQFormerConfig , A : PretrainedConfig , **A : Any , ):
return cls(
vision_config=vision_config.to_dict() , qformer_config=qformer_config.to_dict() , text_config=text_config.to_dict() , **A , )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Optional[Any] = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : List[Any] = self.vision_config.to_dict()
_UpperCAmelCase : Tuple = self.qformer_config.to_dict()
_UpperCAmelCase : Dict = self.text_config.to_dict()
_UpperCAmelCase : Tuple = self.__class__.model_type
return output
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , A : Dict , A : Dict=7 , A : Optional[int]=3 , A : Optional[int]=18 , A : Dict=30 , A : List[Any]=400 , A : Union[str, Any]=True , A : Tuple=None , A : List[Any]=True , A : int=None , A : Optional[int]=True , ):
_UpperCAmelCase : Optional[int] = size if size is not None else {"shortest_edge": 20}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : Optional[Any] = num_channels
_UpperCAmelCase : Union[str, Any] = image_size
_UpperCAmelCase : int = min_resolution
_UpperCAmelCase : Optional[int] = max_resolution
_UpperCAmelCase : List[str] = do_resize
_UpperCAmelCase : Optional[Any] = size
_UpperCAmelCase : Tuple = do_center_crop
_UpperCAmelCase : Optional[int] = crop_size
_UpperCAmelCase : Optional[Any] = do_flip_channel_order
def _A ( self : Dict ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Tuple = MobileViTImageProcessor if is_vision_available() else None
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = MobileViTImageProcessingTester(self )
@property
def _A ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Tuple ):
_UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "size" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "center_crop" ) )
self.assertTrue(hasattr(A , "do_flip_channel_order" ) )
def _A ( self : Any ):
_UpperCAmelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Any ):
pass
def _A ( self : Dict ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_UpperCAmelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Union[str, Any] ):
# Initialize image_processing
_UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_UpperCAmelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Any ):
# Initialize image_processing
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Any = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[int] , A : int , A : List[str]=13 , A : Dict=32 , A : Any=3 , A : Union[str, Any]=4 , A : Optional[Any]=[10, 20, 30, 40] , A : List[str]=[2, 2, 3, 2] , A : str=True , A : Dict=True , A : Tuple=37 , A : Optional[int]="gelu" , A : Tuple=10 , A : int=0.02 , A : List[Any]=["stage2", "stage3", "stage4"] , A : List[Any]=[2, 3, 4] , A : List[Any]=None , ):
_UpperCAmelCase : Dict = parent
_UpperCAmelCase : Dict = batch_size
_UpperCAmelCase : List[Any] = image_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : List[str] = num_stages
_UpperCAmelCase : Any = hidden_sizes
_UpperCAmelCase : Any = depths
_UpperCAmelCase : Dict = is_training
_UpperCAmelCase : int = use_labels
_UpperCAmelCase : Dict = intermediate_size
_UpperCAmelCase : int = hidden_act
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : Optional[int] = initializer_range
_UpperCAmelCase : Tuple = out_features
_UpperCAmelCase : List[Any] = out_indices
_UpperCAmelCase : Optional[Any] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Any = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : str = None
if self.use_labels:
_UpperCAmelCase : Any = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase : Optional[Any] = self.get_config()
return config, pixel_values, labels
def _A ( self : Optional[Any] ):
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=A , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _A ( self : Dict , A : Any , A : List[str] , A : Optional[Any] ):
_UpperCAmelCase : Optional[int] = ConvNextModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _A ( self : List[Any] , A : Tuple , A : int , A : int ):
_UpperCAmelCase : int = ConvNextForImageClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self : Union[str, Any] , A : Optional[int] , A : List[Any] , A : str ):
_UpperCAmelCase : List[str] = ConvNextBackbone(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Union[str, Any] = model(A )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
_UpperCAmelCase : List[str] = None
_UpperCAmelCase : Dict = ConvNextBackbone(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : int = config_and_inputs
_UpperCAmelCase : Optional[int] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__UpperCamelCase: List[Any] = (
{"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
if is_torch_available()
else {}
)
__UpperCamelCase: str = True
__UpperCamelCase: List[Any] = False
__UpperCamelCase: str = False
__UpperCamelCase: Optional[int] = False
__UpperCamelCase: List[Any] = False
def _A ( self : Optional[int] ):
_UpperCAmelCase : int = ConvNextModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , has_text_modality=A , hidden_size=37 )
def _A ( self : List[str] ):
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _A ( self : Tuple ):
return
@unittest.skip(reason="ConvNext does not use inputs_embeds" )
def _A ( self : str ):
pass
@unittest.skip(reason="ConvNext does not support input and output embeddings" )
def _A ( self : Dict ):
pass
@unittest.skip(reason="ConvNext does not use feedforward chunking" )
def _A ( self : List[Any] ):
pass
def _A ( self : Any ):
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : int = model_class(A )
_UpperCAmelCase : List[str] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Dict = [*signature.parameters.keys()]
_UpperCAmelCase : Tuple = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*A )
def _A ( self : int ):
def check_hidden_states_output(A : Dict , A : Optional[Any] , A : str ):
_UpperCAmelCase : List[Any] = model_class(A )
model.to(A )
model.eval()
with torch.no_grad():
_UpperCAmelCase : Any = model(**self._prepare_for_class(A , A ) )
_UpperCAmelCase : List[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : List[str] = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
_UpperCAmelCase , _UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : List[str] = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : int = True
check_hidden_states_output(A , A , A )
def _A ( self : Dict ):
_UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def _A ( self : str ):
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : str = ConvNextModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def _A ( self : Optional[Any] ):
return AutoImageProcessor.from_pretrained("facebook/convnext-tiny-224" ) if is_vision_available() else None
@slow
def _A ( self : int ):
_UpperCAmelCase : int = ConvNextForImageClassification.from_pretrained("facebook/convnext-tiny-224" ).to(A )
_UpperCAmelCase : List[str] = self.default_image_processor
_UpperCAmelCase : Tuple = prepare_img()
_UpperCAmelCase : List[Any] = image_processor(images=A , return_tensors="pt" ).to(A )
# forward pass
with torch.no_grad():
_UpperCAmelCase : List[str] = model(**A )
# verify the logits
_UpperCAmelCase : Union[str, Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , A )
_UpperCAmelCase : Any = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(A )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , A , atol=1E-4 ) )
@require_torch
class lowerCamelCase_ (unittest.TestCase , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = (ConvNextBackbone,) if is_torch_available() else ()
__UpperCamelCase: int = ConvNextConfig
__UpperCamelCase: Any = False
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = ConvNextModelTester(self )
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Any = n - k
# Calculate C(n,k)
for i in range(_UpperCAmelCase ):
result *= n - i
result //= i + 1
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , _UpperCAmelCase ) // (node_count + 1)
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
_UpperCAmelCase : List[str] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return catalan_number(_UpperCAmelCase ) * factorial(_UpperCAmelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
F'binary trees and {catalan_number(node_count)} binary search trees.'
)
| 31
| 1
|
'''simple docstring'''
import torch
from torch import nn
class lowerCamelCase_ (nn.Module ):
'''simple docstring'''
def __init__( self : Optional[Any] , A : Dict , A : Tuple , A : Optional[Any] , A : Tuple , A : Union[str, Any]=1 , A : str=False ):
super().__init__()
_UpperCAmelCase : Union[str, Any] = n_token
_UpperCAmelCase : List[Any] = d_embed
_UpperCAmelCase : List[str] = d_proj
_UpperCAmelCase : Union[str, Any] = cutoffs + [n_token]
_UpperCAmelCase : str = [0] + self.cutoffs
_UpperCAmelCase : Dict = div_val
_UpperCAmelCase : Tuple = self.cutoffs[0]
_UpperCAmelCase : Tuple = len(self.cutoffs ) - 1
_UpperCAmelCase : Tuple = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
_UpperCAmelCase : Any = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
_UpperCAmelCase : Optional[int] = nn.Parameter(torch.zeros(self.n_clusters ) )
_UpperCAmelCase : str = nn.ModuleList()
_UpperCAmelCase : Dict = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(A , A ) ) )
else:
self.out_projs.append(A )
self.out_layers.append(nn.Linear(A , A ) )
else:
for i in range(len(self.cutoffs ) ):
_UpperCAmelCase , _UpperCAmelCase : Dict = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase : Tuple = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(A , A ) ) )
self.out_layers.append(nn.Linear(A , r_idx - l_idx ) )
_UpperCAmelCase : Dict = keep_order
def _A ( self : Optional[int] , A : Optional[int] , A : List[str] , A : Optional[Any] , A : List[str] ):
if proj is None:
_UpperCAmelCase : Optional[int] = nn.functional.linear(A , A , bias=A )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
_UpperCAmelCase : Optional[int] = nn.functional.linear(A , proj.t().contiguous() )
_UpperCAmelCase : str = nn.functional.linear(A , A , bias=A )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def _A ( self : Optional[int] , A : Tuple , A : Union[str, Any]=None , A : Any=False ):
if labels is not None:
# Shift so that tokens < n predict n
_UpperCAmelCase : Union[str, Any] = hidden[..., :-1, :].contiguous()
_UpperCAmelCase : str = labels[..., 1:].contiguous()
_UpperCAmelCase : Any = hidden.view(-1 , hidden.size(-1 ) )
_UpperCAmelCase : str = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("Input and labels should have the same size in the batch dimension." )
else:
_UpperCAmelCase : Union[str, Any] = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
_UpperCAmelCase : Any = self._compute_logit(A , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
_UpperCAmelCase : Optional[int] = labels != -100
_UpperCAmelCase : List[str] = torch.zeros_like(A , dtype=hidden.dtype , device=hidden.device )
_UpperCAmelCase : List[str] = (
-nn.functional.log_softmax(A , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
_UpperCAmelCase : str = nn.functional.log_softmax(A , dim=-1 )
else:
# construct weights and biases
_UpperCAmelCase , _UpperCAmelCase : Dict = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase : Any = self.out_layers[0].weight[l_idx:r_idx]
_UpperCAmelCase : Union[str, Any] = self.out_layers[0].bias[l_idx:r_idx]
else:
_UpperCAmelCase : Optional[int] = self.out_layers[i].weight
_UpperCAmelCase : Dict = self.out_layers[i].bias
if i == 0:
_UpperCAmelCase : int = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_UpperCAmelCase : Union[str, Any] = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(A )
biases.append(A )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = weights[0], biases[0], self.out_projs[0]
_UpperCAmelCase : Dict = self._compute_logit(A , A , A , A )
_UpperCAmelCase : List[str] = nn.functional.log_softmax(A , dim=1 )
if labels is None:
_UpperCAmelCase : int = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
_UpperCAmelCase : int = torch.zeros_like(A , dtype=hidden.dtype , device=hidden.device )
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Any = [0] + self.cutoffs
for i in range(len(A ) - 1 ):
_UpperCAmelCase , _UpperCAmelCase : str = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
_UpperCAmelCase : List[Any] = (labels >= l_idx) & (labels < r_idx)
_UpperCAmelCase : Any = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
_UpperCAmelCase : Union[str, Any] = labels.index_select(0 , A ) - l_idx
_UpperCAmelCase : Any = head_logprob.index_select(0 , A )
_UpperCAmelCase : Dict = hidden.index_select(0 , A )
else:
_UpperCAmelCase : List[Any] = hidden
if i == 0:
if labels is not None:
_UpperCAmelCase : str = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
_UpperCAmelCase : Optional[Any] = head_logprob[:, : self.cutoffs[0]]
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Dict = weights[i], biases[i], self.out_projs[i]
_UpperCAmelCase : Optional[Any] = self._compute_logit(A , A , A , A )
_UpperCAmelCase : Optional[int] = nn.functional.log_softmax(A , dim=1 )
_UpperCAmelCase : Tuple = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
_UpperCAmelCase : Union[str, Any] = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
_UpperCAmelCase : str = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
_UpperCAmelCase : Optional[Any] = logprob_i
if labels is not None:
if (hasattr(self , "keep_order" ) and self.keep_order) or keep_order:
out.index_copy_(0 , A , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def _A ( self : Optional[int] , A : str ):
if self.n_clusters == 0:
_UpperCAmelCase : List[str] = self._compute_logit(A , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(A , dim=-1 )
else:
# construct weights and biases
_UpperCAmelCase , _UpperCAmelCase : List[Any] = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_UpperCAmelCase : Union[str, Any] = self.out_layers[0].weight[l_idx:r_idx]
_UpperCAmelCase : List[Any] = self.out_layers[0].bias[l_idx:r_idx]
else:
_UpperCAmelCase : int = self.out_layers[i].weight
_UpperCAmelCase : List[str] = self.out_layers[i].bias
if i == 0:
_UpperCAmelCase : Tuple = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_UpperCAmelCase : Any = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(A )
biases.append(A )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = weights[0], biases[0], self.out_projs[0]
_UpperCAmelCase : Optional[Any] = self._compute_logit(A , A , A , A )
_UpperCAmelCase : Union[str, Any] = hidden.new_empty((head_logit.size(0 ), self.n_token) )
_UpperCAmelCase : Any = nn.functional.log_softmax(A , dim=1 )
_UpperCAmelCase : Optional[Any] = [0] + self.cutoffs
for i in range(len(A ) - 1 ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
_UpperCAmelCase : str = head_logprob[:, : self.cutoffs[0]]
else:
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Tuple = weights[i], biases[i], self.out_projs[i]
_UpperCAmelCase : int = self._compute_logit(A , A , A , A )
_UpperCAmelCase : List[str] = nn.functional.log_softmax(A , dim=1 )
_UpperCAmelCase : Optional[Any] = head_logprob[:, -i] + tail_logprob_i
_UpperCAmelCase : Any = logprob_i
return out
| 31
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : Dict = {
"""vocab_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-german-cased""": (
"""https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
),
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
),
},
}
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""distilbert-base-uncased""": 512,
"""distilbert-base-uncased-distilled-squad""": 512,
"""distilbert-base-cased""": 512,
"""distilbert-base-cased-distilled-squad""": 512,
"""distilbert-base-german-cased""": 512,
"""distilbert-base-multilingual-cased""": 512,
}
__SCREAMING_SNAKE_CASE : List[Any] = {
"""distilbert-base-uncased""": {"""do_lower_case""": True},
"""distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
"""distilbert-base-cased""": {"""do_lower_case""": False},
"""distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
"""distilbert-base-german-cased""": {"""do_lower_case""": False},
"""distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase: str = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Any = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase: str = ["input_ids", "attention_mask"]
__UpperCamelCase: List[str] = DistilBertTokenizer
def __init__( self : str , A : int=None , A : Tuple=None , A : Tuple=True , A : Dict="[UNK]" , A : List[Any]="[SEP]" , A : Optional[Any]="[PAD]" , A : Dict="[CLS]" , A : Tuple="[MASK]" , A : str=True , A : Dict=None , **A : List[Any] , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Dict = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : int = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : str = tokenize_chinese_chars
_UpperCAmelCase : List[Any] = normalizer_class(**A )
_UpperCAmelCase : Dict = do_lower_case
def _A ( self : List[Any] , A : Tuple , A : Any=None ):
_UpperCAmelCase : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _A ( self : int , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _A ( self : Dict , A : str , A : Optional[str] = None ):
_UpperCAmelCase : Any = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> float:
"""simple docstring"""
_UpperCAmelCase : Dict = 0
while len(_UpperCAmelCase ) > 1:
_UpperCAmelCase : List[str] = 0
# Consider two files with minimum cost to be merged
for _ in range(2 ):
_UpperCAmelCase : List[Any] = files.index(min(_UpperCAmelCase ) )
temp += files[min_index]
files.pop(_UpperCAmelCase )
files.append(_UpperCAmelCase )
optimal_merge_cost += temp
return optimal_merge_cost
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
def _A ( self : Any , A : Union[str, Any] , A : Optional[int] , A : List[str] , **A : Tuple ):
self.events.append("on_init_end" )
def _A ( self : Any , A : str , A : List[Any] , A : List[Any] , **A : Tuple ):
self.events.append("on_train_begin" )
def _A ( self : Tuple , A : List[str] , A : Tuple , A : int , **A : List[str] ):
self.events.append("on_train_end" )
def _A ( self : Optional[Any] , A : Dict , A : Any , A : Optional[Any] , **A : List[Any] ):
self.events.append("on_epoch_begin" )
def _A ( self : Optional[Any] , A : List[Any] , A : List[str] , A : Optional[int] , **A : Optional[int] ):
self.events.append("on_epoch_end" )
def _A ( self : List[str] , A : Optional[int] , A : List[Any] , A : Union[str, Any] , **A : Any ):
self.events.append("on_step_begin" )
def _A ( self : Tuple , A : Union[str, Any] , A : int , A : Optional[int] , **A : int ):
self.events.append("on_step_end" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Union[str, Any] , A : str , **A : Union[str, Any] ):
self.events.append("on_evaluate" )
def _A ( self : Optional[Any] , A : Optional[int] , A : Dict , A : List[Any] , **A : Dict ):
self.events.append("on_predict" )
def _A ( self : Dict , A : Dict , A : List[Any] , A : Dict , **A : str ):
self.events.append("on_save" )
def _A ( self : Tuple , A : Optional[Any] , A : Union[str, Any] , A : Optional[int] , **A : Dict ):
self.events.append("on_log" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Tuple , A : Tuple , **A : List[str] ):
self.events.append("on_prediction_step" )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
def _A ( self : List[Any] ):
shutil.rmtree(self.output_dir )
def _A ( self : Union[str, Any] , A : Optional[int]=0 , A : Optional[Any]=0 , A : Optional[Any]=64 , A : Dict=64 , A : Any=None , A : Tuple=False , **A : Optional[int] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
_UpperCAmelCase : str = RegressionDataset(length=A )
_UpperCAmelCase : Union[str, Any] = RegressionDataset(length=A )
_UpperCAmelCase : Any = RegressionModelConfig(a=A , b=A )
_UpperCAmelCase : List[Any] = RegressionPreTrainedModel(A )
_UpperCAmelCase : Dict = TrainingArguments(self.output_dir , disable_tqdm=A , report_to=[] , **A )
return Trainer(
A , A , train_dataset=A , eval_dataset=A , callbacks=A , )
def _A ( self : str , A : List[str] , A : List[str] ):
self.assertEqual(len(A ) , len(A ) )
# Order doesn't matter
_UpperCAmelCase : Tuple = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
_UpperCAmelCase : Any = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
for cba, cba in zip(A , A ):
if isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(A , A )
elif isinstance(A , A ) and not isinstance(A , A ):
self.assertEqual(A , cba.__class__ )
elif not isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(cba.__class__ , A )
else:
self.assertEqual(A , A )
def _A ( self : int , A : List[str] ):
_UpperCAmelCase : List[str] = ["on_init_end", "on_train_begin"]
_UpperCAmelCase : str = 0
_UpperCAmelCase : Optional[Any] = len(trainer.get_eval_dataloader() )
_UpperCAmelCase : Optional[int] = ["on_prediction_step"] * len(trainer.get_eval_dataloader() ) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("on_epoch_begin" )
for _ in range(A ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save" )
expected_events.append("on_epoch_end" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _A ( self : str ):
_UpperCAmelCase : Any = self.get_trainer()
_UpperCAmelCase : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# Callbacks passed at init are added to the default callbacks
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_UpperCAmelCase : List[Any] = self.get_trainer(disable_tqdm=A )
_UpperCAmelCase : Tuple = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_UpperCAmelCase : Dict = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : Optional[Any] = self.get_trainer()
_UpperCAmelCase : Any = trainer.pop_callback(A )
self.assertEqual(cb.__class__ , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# We can also add, pop, or remove by instance
_UpperCAmelCase : Union[str, Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : List[Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
_UpperCAmelCase : Union[str, Any] = trainer.pop_callback(A )
self.assertEqual(A , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" , category=A )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_UpperCAmelCase : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# Independent log/save/eval
_UpperCAmelCase : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_UpperCAmelCase : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="steps" )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="epoch" )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# A bit of everything
_UpperCAmelCase : int = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="steps" , )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning" ) as warn_mock:
_UpperCAmelCase : Optional[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(A ) in warn_mock.call_args[0][0]
| 31
| 1
|
'''simple docstring'''
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
__SCREAMING_SNAKE_CASE : Optional[Any] = """Run commands across TPU VMs for initial setup before running `accelerate launch`."""
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any]=None ) -> Tuple:
"""simple docstring"""
if subparsers is not None:
_UpperCAmelCase : List[Any] = subparsers.add_parser("tpu-config" , description=_description )
else:
_UpperCAmelCase : Any = argparse.ArgumentParser("Accelerate tpu-config command" , description=_description )
# Core arguments
_UpperCAmelCase : Optional[Any] = parser.add_argument_group(
"Config Arguments" , "Arguments that can be configured through `accelerate config`." )
config_args.add_argument(
"--config_file" , type=_UpperCAmelCase , default=_UpperCAmelCase , help="Path to the config file to use for accelerate." , )
config_args.add_argument(
"--tpu_name" , default=_UpperCAmelCase , help="The name of the TPU to use. If not specified, will use the TPU specified in the config file." , )
config_args.add_argument(
"--tpu_zone" , default=_UpperCAmelCase , help="The zone of the TPU to use. If not specified, will use the zone specified in the config file." , )
_UpperCAmelCase : Tuple = parser.add_argument_group("TPU Arguments" , "Arguments for options ran inside the TPU." )
pod_args.add_argument(
"--use_alpha" , action="store_true" , help="Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`." , )
pod_args.add_argument(
"--command_file" , default=_UpperCAmelCase , help="The path to the file containing the commands to run on the pod on startup." , )
pod_args.add_argument(
"--command" , action="append" , nargs="+" , help="A command to run on the pod. Can be passed multiple times." , )
pod_args.add_argument(
"--install_accelerate" , action="store_true" , help="Whether to install accelerate on the pod. Defaults to False." , )
pod_args.add_argument(
"--accelerate_version" , default="latest" , help="The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub." , )
pod_args.add_argument(
"--debug" , action="store_true" , help="If set, will print the command that would be run instead of running it." )
if subparsers is not None:
parser.set_defaults(func=_UpperCAmelCase )
return parser
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> str:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(_UpperCAmelCase ):
_UpperCAmelCase : Optional[Any] = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
_UpperCAmelCase : List[Any] = defaults.command_file
if not args.command and defaults.commands is not None:
_UpperCAmelCase : Tuple = defaults.commands
if not args.tpu_name:
_UpperCAmelCase : Union[str, Any] = defaults.tpu_name
if not args.tpu_zone:
_UpperCAmelCase : List[str] = defaults.tpu_zone
if args.accelerate_version == "dev":
_UpperCAmelCase : int = "git+https://github.com/huggingface/accelerate.git"
elif args.accelerate_version == "latest":
_UpperCAmelCase : List[Any] = "accelerate -U"
elif isinstance(parse(args.accelerate_version ) , _UpperCAmelCase ):
_UpperCAmelCase : int = F"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError("You must specify either a command file or a command to run on the pod." )
if args.command_file:
with open(args.command_file , "r" ) as f:
_UpperCAmelCase : Tuple = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , _UpperCAmelCase ):
_UpperCAmelCase : Tuple = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
_UpperCAmelCase : Any = ["cd /usr/share"]
if args.install_accelerate:
new_cmd += [F"""pip install {args.accelerate_version}"""]
new_cmd += args.command
_UpperCAmelCase : int = "; ".join(_UpperCAmelCase )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
_UpperCAmelCase : Tuple = ["gcloud"]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"""Running {' '.join(_UpperCAmelCase )}""" )
return
subprocess.run(_UpperCAmelCase )
print("Successfully setup pod." )
def UpperCamelCase_ ( ) -> Any:
"""simple docstring"""
_UpperCAmelCase : Any = tpu_command_parser()
_UpperCAmelCase : Tuple = parser.parse_args()
tpu_command_launcher(_UpperCAmelCase )
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : Dict , A : Optional[int]=7 , A : Tuple=3 , A : Optional[Any]=10 , A : int=18 , A : Dict=30 , A : List[str]=400 , A : int=True , A : Optional[Any]=None , A : Optional[Any]=True , A : List[Any]=[0.5, 0.5, 0.5] , A : List[str]=[0.5, 0.5, 0.5] , A : Optional[int]=None , ):
_UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 18}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : Optional[Any] = num_frames
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : Any = max_resolution
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : List[Any] = do_normalize
_UpperCAmelCase : Any = image_mean
_UpperCAmelCase : Tuple = image_std
_UpperCAmelCase : Any = crop_size
def _A ( self : List[Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = VivitImageProcessor if is_vision_available() else None
def _A ( self : int ):
_UpperCAmelCase : Tuple = VivitImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "size" ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Tuple ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_UpperCAmelCase : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_UpperCAmelCase : str = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_UpperCAmelCase : Tuple = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
from ... import PretrainedConfig
__SCREAMING_SNAKE_CASE : str = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__UpperCamelCase: str = "nezha"
def __init__( self : Optional[int] , A : Optional[Any]=21128 , A : Any=768 , A : Optional[int]=12 , A : Dict=12 , A : List[str]=3072 , A : Dict="gelu" , A : List[str]=0.1 , A : Optional[int]=0.1 , A : str=512 , A : int=64 , A : Optional[int]=2 , A : str=0.02 , A : List[str]=1E-12 , A : List[Any]=0.1 , A : Dict=0 , A : Any=2 , A : Union[str, Any]=3 , A : str=True , **A : Any , ):
super().__init__(pad_token_id=A , bos_token_id=A , eos_token_id=A , **A )
_UpperCAmelCase : List[str] = vocab_size
_UpperCAmelCase : str = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : Dict = num_attention_heads
_UpperCAmelCase : Dict = hidden_act
_UpperCAmelCase : Optional[int] = intermediate_size
_UpperCAmelCase : int = hidden_dropout_prob
_UpperCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_UpperCAmelCase : int = max_position_embeddings
_UpperCAmelCase : Union[str, Any] = max_relative_position
_UpperCAmelCase : Union[str, Any] = type_vocab_size
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : str = layer_norm_eps
_UpperCAmelCase : Dict = classifier_dropout
_UpperCAmelCase : List[Any] = use_cache
| 31
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "encodec"
def __init__( self : Optional[int] , A : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , A : List[Any]=24000 , A : Union[str, Any]=1 , A : List[Any]=False , A : Optional[int]=None , A : int=None , A : str=128 , A : List[Any]=32 , A : List[Any]=1 , A : int=[8, 5, 4, 2] , A : Optional[int]="weight_norm" , A : List[Any]=7 , A : Any=7 , A : Dict=3 , A : Optional[int]=2 , A : Dict=True , A : Dict="reflect" , A : Any=2 , A : Dict=2 , A : str=1.0 , A : Optional[int]=1024 , A : Any=None , A : Any=True , **A : str , ):
_UpperCAmelCase : Optional[int] = target_bandwidths
_UpperCAmelCase : List[str] = sampling_rate
_UpperCAmelCase : Optional[int] = audio_channels
_UpperCAmelCase : str = normalize
_UpperCAmelCase : int = chunk_length_s
_UpperCAmelCase : str = overlap
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : int = num_filters
_UpperCAmelCase : Optional[Any] = num_residual_layers
_UpperCAmelCase : Optional[int] = upsampling_ratios
_UpperCAmelCase : int = norm_type
_UpperCAmelCase : List[Any] = kernel_size
_UpperCAmelCase : List[Any] = last_kernel_size
_UpperCAmelCase : List[Any] = residual_kernel_size
_UpperCAmelCase : List[str] = dilation_growth_rate
_UpperCAmelCase : Dict = use_causal_conv
_UpperCAmelCase : Tuple = pad_mode
_UpperCAmelCase : Tuple = compress
_UpperCAmelCase : List[str] = num_lstm_layers
_UpperCAmelCase : List[Any] = trim_right_ratio
_UpperCAmelCase : int = codebook_size
_UpperCAmelCase : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size
_UpperCAmelCase : Optional[int] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**A )
@property
def _A ( self : Any ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A ( self : Union[str, Any] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A ( self : str ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 31
| 1
|
'''simple docstring'''
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import AcceleratorState, PartialState
from ..utils import (
gather,
is_bnb_available,
is_comet_ml_available,
is_datasets_available,
is_deepspeed_available,
is_mps_available,
is_safetensors_available,
is_tensorboard_available,
is_torch_version,
is_tpu_available,
is_transformers_available,
is_wandb_available,
is_xpu_available,
)
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
try:
_UpperCAmelCase : str = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
_UpperCAmelCase : Optional[Any] = default
else:
# KEY is set, convert it to True or False.
try:
_UpperCAmelCase : List[str] = strtobool(_UpperCAmelCase )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F"""If set, {key} must be yes or no.""" )
return _value
__SCREAMING_SNAKE_CASE : Optional[Any] = parse_flag_from_env("""RUN_SLOW""", default=False)
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skip("Test was skipped" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(_run_slow_tests , "test is slow" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(not torch.cuda.is_available() , "test requires only a CPU" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.is_available() , "test requires a GPU" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] ) -> List[str]:
"""simple docstring"""
return unittest.skipUnless(is_xpu_available() , "test requires a XPU" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : List[str] ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_mps_available() , "test requires a `mps` backend support in `torch`" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> Dict:
"""simple docstring"""
return unittest.skipUnless(
is_transformers_available() and is_datasets_available() , "test requires the Hugging Face suite" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return unittest.skipUnless(is_bnb_available() , "test requires the bitsandbytes library" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Tuple ) -> str:
"""simple docstring"""
return unittest.skipUnless(is_tpu_available() , "test requires TPU" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() == 1 , "test requires a GPU" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Dict ) -> str:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() == 1 , "test requires a XPU" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(torch.cuda.device_count() > 1 , "test requires multiple GPUs" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> Optional[int]:
"""simple docstring"""
return unittest.skipUnless(torch.xpu.device_count() > 1 , "test requires multiple XPUs" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_safetensors_available() , "test requires safetensors" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
return unittest.skipUnless(is_deepspeed_available() , "test requires DeepSpeed" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> Dict:
"""simple docstring"""
return unittest.skipUnless(is_torch_version(">=" , "1.12.0" ) , "test requires torch version >= 1.12.0" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : Optional[int]=None ) -> Dict:
"""simple docstring"""
if test_case is None:
return partial(_UpperCAmelCase , version=_UpperCAmelCase )
return unittest.skipUnless(is_torch_version(">=" , _UpperCAmelCase ) , F"""test requires torch version >= {version}""" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> List[Any]:
"""simple docstring"""
return unittest.skipUnless(is_tensorboard_available() , "test requires Tensorboard" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> Optional[int]:
"""simple docstring"""
return unittest.skipUnless(is_wandb_available() , "test requires wandb" )(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
return unittest.skipUnless(is_comet_ml_available() , "test requires comet_ml" )(_UpperCAmelCase )
__SCREAMING_SNAKE_CASE : str = (
any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> str:
"""simple docstring"""
return unittest.skipUnless(
_atleast_one_tracker_available , "test requires at least one tracker to be available and for `comet_ml` to not be installed" , )(_UpperCAmelCase )
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = True
@classmethod
def _A ( cls : Any ):
_UpperCAmelCase : Union[str, Any] = tempfile.mkdtemp()
@classmethod
def _A ( cls : Union[str, Any] ):
if os.path.exists(cls.tmpdir ):
shutil.rmtree(cls.tmpdir )
def _A ( self : Tuple ):
if self.clear_on_setup:
for path in Path(self.tmpdir ).glob("**/*" ):
if path.is_file():
path.unlink()
elif path.is_dir():
shutil.rmtree(A )
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Dict ):
super().tearDown()
# Reset the state of the AcceleratorState singleton.
AcceleratorState._reset_state()
PartialState._reset_state()
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Tuple , A : Union[mock.Mock, List[mock.Mock]] ):
_UpperCAmelCase : Optional[int] = mocks if isinstance(A , (tuple, list) ) else [mocks]
for m in self.mocks:
m.start()
self.addCleanup(m.stop )
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = AcceleratorState()
_UpperCAmelCase : Dict = tensor[None].clone().to(state.device )
_UpperCAmelCase : Union[str, Any] = gather(_UpperCAmelCase ).cpu()
_UpperCAmelCase : str = tensor[0].cpu()
for i in range(tensors.shape[0] ):
if not torch.equal(tensors[i] , _UpperCAmelCase ):
return False
return True
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : int , A : str , A : Optional[int] ):
_UpperCAmelCase : Dict = returncode
_UpperCAmelCase : Optional[int] = stdout
_UpperCAmelCase : str = stderr
async def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any ) -> Optional[int]:
"""simple docstring"""
while True:
_UpperCAmelCase : Union[str, Any] = await stream.readline()
if line:
callback(_UpperCAmelCase )
else:
break
async def UpperCamelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Optional[int]=None , _UpperCAmelCase : Any=None , _UpperCAmelCase : List[Any]=None , _UpperCAmelCase : Any=False , _UpperCAmelCase : Optional[Any]=False ) -> _RunOutput:
"""simple docstring"""
if echo:
print("\nRunning: " , " ".join(_UpperCAmelCase ) )
_UpperCAmelCase : Optional[int] = await asyncio.create_subprocess_exec(
cmd[0] , *cmd[1:] , stdin=_UpperCAmelCase , stdout=asyncio.subprocess.PIPE , stderr=asyncio.subprocess.PIPE , env=_UpperCAmelCase , )
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Union[str, Any] = []
def tee(_UpperCAmelCase : List[Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[int]="" ):
_UpperCAmelCase : Optional[int] = line.decode("utf-8" ).rstrip()
sink.append(_UpperCAmelCase )
if not quiet:
print(_UpperCAmelCase , _UpperCAmelCase , file=_UpperCAmelCase )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
asyncio.create_task(_read_stream(p.stdout , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stdout , label="stdout:" ) ) ),
asyncio.create_task(_read_stream(p.stderr , lambda _UpperCAmelCase : tee(_UpperCAmelCase , _UpperCAmelCase , sys.stderr , label="stderr:" ) ) ),
] , timeout=_UpperCAmelCase , )
return _RunOutput(await p.wait() , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Union[str, Any]=None , _UpperCAmelCase : str=None , _UpperCAmelCase : List[Any]=180 , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : List[Any]=True ) -> _RunOutput:
"""simple docstring"""
_UpperCAmelCase : str = asyncio.get_event_loop()
_UpperCAmelCase : Dict = loop.run_until_complete(
_stream_subprocess(_UpperCAmelCase , env=_UpperCAmelCase , stdin=_UpperCAmelCase , timeout=_UpperCAmelCase , quiet=_UpperCAmelCase , echo=_UpperCAmelCase ) )
_UpperCAmelCase : Any = " ".join(_UpperCAmelCase )
if result.returncode > 0:
_UpperCAmelCase : Union[str, Any] = "\n".join(result.stderr )
raise RuntimeError(
F"""'{cmd_str}' failed with returncode {result.returncode}\n\n"""
F"""The combined stderr from workers follows:\n{stderr}""" )
return result
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
pass
def UpperCamelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : List[Any]=False ) -> Optional[Any]:
"""simple docstring"""
try:
_UpperCAmelCase : Union[str, Any] = subprocess.check_output(_UpperCAmelCase , stderr=subprocess.STDOUT )
if return_stdout:
if hasattr(_UpperCAmelCase , "decode" ):
_UpperCAmelCase : Optional[Any] = output.decode("utf-8" )
return output
except subprocess.CalledProcessError as e:
raise SubprocessCallException(
F"""Command `{' '.join(_UpperCAmelCase )}` failed with the following error:\n\n{e.output.decode()}""" ) from e
| 31
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Any , A : Optional[int]=None , A : Tuple=None , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
if config is None:
assert isinstance(self.model , A ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
_UpperCAmelCase : str = self.model.config
else:
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : List[Any] = data_args
_UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , A ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
_UpperCAmelCase : Optional[Any] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase : Dict = label_smoothed_nll_loss
def _A ( self : Tuple , A : int ):
if self.optimizer is None:
_UpperCAmelCase : Tuple = ["bias", "LayerNorm.weight"]
_UpperCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
_UpperCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase : List[str] = Adafactor
_UpperCAmelCase : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
_UpperCAmelCase : List[str] = AdamW
_UpperCAmelCase : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
_UpperCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase : List[Any] = OSS(
params=A , optim=A , **A , )
else:
_UpperCAmelCase : Union[str, Any] = optimizer_cls(A , **A )
if self.lr_scheduler is None:
_UpperCAmelCase : List[str] = self._get_lr_scheduler(A )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : List[str] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A )
return scheduler
def _A ( self : Tuple ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _A ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : List[Any] ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase : List[str] = model(**A , use_cache=A )[0]
_UpperCAmelCase : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase : Any = model(**A , labels=A , use_cache=A )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase : Optional[int] = model(**A , use_cache=A )[0]
_UpperCAmelCase : List[str] = torch.nn.functional.log_softmax(A , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.loss_fn(A , A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _A ( self : List[str] , A : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Union[str, Any] = inputs.pop("labels" )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self._compute_loss(A , A , A )
return loss
def _A ( self : List[str] , A : nn.Module , A : Dict[str, Union[torch.Tensor, Any]] , A : bool , A : Optional[List[str]] = None , ):
_UpperCAmelCase : List[str] = self._prepare_inputs(A )
_UpperCAmelCase : Dict = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : int = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
_UpperCAmelCase : Any = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase : str = self._compute_loss(A , A , A )
_UpperCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
return (loss, logits, labels)
def _A ( self : Dict , A : int , A : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
_UpperCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase : Tuple = tensor
return padded_tensor
| 31
| 1
|
'''simple docstring'''
import math
from collections.abc import Callable
def UpperCamelCase_ ( _UpperCAmelCase : Callable[[float], float] , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
_UpperCAmelCase : float = xa
_UpperCAmelCase : float = xa
while True:
if x_n == x_na or function(_UpperCAmelCase ) == function(_UpperCAmelCase ):
raise ZeroDivisionError("float division by zero, could not find root" )
_UpperCAmelCase : float = x_na - (
function(_UpperCAmelCase ) / ((function(_UpperCAmelCase ) - function(_UpperCAmelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
_UpperCAmelCase : Optional[int] = x_na
_UpperCAmelCase : Union[str, Any] = x_na
def UpperCamelCase_ ( _UpperCAmelCase : float ) -> float:
"""simple docstring"""
return math.pow(_UpperCAmelCase , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 31
|
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["input_features", "is_longer"]
def __init__( self : str , A : int=64 , A : Dict=48000 , A : str=480 , A : List[Any]=10 , A : Optional[Any]=1024 , A : Tuple=0.0 , A : List[Any]=False , A : float = 0 , A : float = 14000 , A : int = None , A : str = "fusion" , A : str = "repeatpad" , **A : Dict , ):
super().__init__(
feature_size=A , sampling_rate=A , padding_value=A , return_attention_mask=A , **A , )
_UpperCAmelCase : Optional[Any] = top_db
_UpperCAmelCase : Dict = truncation
_UpperCAmelCase : List[Any] = padding
_UpperCAmelCase : Optional[Any] = fft_window_size
_UpperCAmelCase : Dict = (fft_window_size >> 1) + 1
_UpperCAmelCase : Any = hop_length
_UpperCAmelCase : Tuple = max_length_s
_UpperCAmelCase : str = max_length_s * sampling_rate
_UpperCAmelCase : Any = sampling_rate
_UpperCAmelCase : Optional[int] = frequency_min
_UpperCAmelCase : str = frequency_max
_UpperCAmelCase : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm=A , mel_scale="htk" , )
_UpperCAmelCase : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm="slaney" , mel_scale="slaney" , )
def _A ( self : List[str] ):
_UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : Dict = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _A ( self : Optional[Any] , A : np.array , A : Optional[np.array] = None ):
_UpperCAmelCase : Dict = spectrogram(
A , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=A , log_mel="dB" , )
return log_mel_spectrogram.T
def _A ( self : str , A : str , A : List[str] , A : List[Any] ):
_UpperCAmelCase : List[str] = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Optional[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Tuple = [0]
# randomly choose index for each part
_UpperCAmelCase : Dict = np.random.choice(ranges[0] )
_UpperCAmelCase : str = np.random.choice(ranges[1] )
_UpperCAmelCase : Tuple = np.random.choice(ranges[2] )
_UpperCAmelCase : str = mel[idx_front : idx_front + chunk_frames, :]
_UpperCAmelCase : str = mel[idx_middle : idx_middle + chunk_frames, :]
_UpperCAmelCase : List[Any] = mel[idx_back : idx_back + chunk_frames, :]
_UpperCAmelCase : Dict = torch.tensor(mel[None, None, :] )
_UpperCAmelCase : Optional[Any] = torch.nn.functional.interpolate(
A , size=[chunk_frames, 64] , mode="bilinear" , align_corners=A )
_UpperCAmelCase : List[str] = mel_shrink[0][0].numpy()
_UpperCAmelCase : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _A ( self : List[Any] , A : np.array , A : List[str] , A : Any , A : Optional[int] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
_UpperCAmelCase : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
_UpperCAmelCase : str = len(A ) - max_length
_UpperCAmelCase : str = np.random.randint(0 , overflow + 1 )
_UpperCAmelCase : int = waveform[idx : idx + max_length]
_UpperCAmelCase : Any = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
_UpperCAmelCase : Tuple = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : List[str] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
_UpperCAmelCase : Optional[Any] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
_UpperCAmelCase : Any = np.stack([mel, mel, mel, mel] , axis=0 )
_UpperCAmelCase : int = False
else:
_UpperCAmelCase : Tuple = self._random_mel_fusion(A , A , A )
_UpperCAmelCase : Any = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
_UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
_UpperCAmelCase : str = int(max_length / len(A ) )
_UpperCAmelCase : Dict = np.stack(np.tile(A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
_UpperCAmelCase : Dict = int(max_length / len(A ) )
_UpperCAmelCase : List[str] = np.stack(np.tile(A , A ) )
_UpperCAmelCase : Optional[Any] = np.pad(A , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
_UpperCAmelCase : str = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : Optional[int] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
_UpperCAmelCase : List[str] = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : Union[str, Any] , A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A : str = None , A : Optional[str] = None , A : Optional[int] = None , A : Optional[int] = None , A : Optional[Union[str, TensorType]] = None , **A : List[str] , ):
_UpperCAmelCase : int = truncation if truncation is not None else self.truncation
_UpperCAmelCase : Optional[int] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : Optional[Any] = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : int = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
_UpperCAmelCase : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[str] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
_UpperCAmelCase : Dict = [
self._get_input_mel(A , max_length if max_length else self.nb_max_samples , A , A )
for waveform in raw_speech
]
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[Any] = []
for mel, longer in padded_inputs:
input_mel.append(A )
is_longer.append(A )
if truncation == "fusion" and sum(A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
_UpperCAmelCase : Union[str, Any] = np.random.randint(0 , len(A ) )
_UpperCAmelCase : Optional[Any] = True
if isinstance(input_mel[0] , A ):
_UpperCAmelCase : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
_UpperCAmelCase : Tuple = [[longer] for longer in is_longer]
_UpperCAmelCase : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
_UpperCAmelCase : Tuple = BatchFeature(A )
if return_tensors is not None:
_UpperCAmelCase : List[Any] = input_features.convert_to_tensors(A )
return input_features
| 31
| 1
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ (metaclass=snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = ["speech"]
def __init__( self : str , *A : Any , **A : Any ):
requires_backends(self , ["speech"] )
class lowerCamelCase_ (metaclass=snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = ["speech"]
def __init__( self : str , *A : Union[str, Any] , **A : str ):
requires_backends(self , ["speech"] )
| 31
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""GPTNeoXTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXForCausalLM""",
"""GPTNeoXForQuestionAnswering""",
"""GPTNeoXForSequenceClassification""",
"""GPTNeoXForTokenClassification""",
"""GPTNeoXLayer""",
"""GPTNeoXModel""",
"""GPTNeoXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if density <= 0:
raise ValueError("Impossible fluid density" )
if bulk_modulus <= 0:
raise ValueError("Impossible bulk modulus" )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , A : Any , A : str , A : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = graph
self._normalize_graph(A , A )
_UpperCAmelCase : List[str] = len(A )
_UpperCAmelCase : Tuple = None
def _A ( self : Any , A : List[Any] , A : str ):
if sources is int:
_UpperCAmelCase : List[Any] = [sources]
if sinks is int:
_UpperCAmelCase : List[Any] = [sinks]
if len(A ) == 0 or len(A ) == 0:
return
_UpperCAmelCase : str = sources[0]
_UpperCAmelCase : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A ) > 1 or len(A ) > 1:
_UpperCAmelCase : Dict = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCAmelCase : Optional[Any] = max_input_flow
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCAmelCase : Dict = max_input_flow
_UpperCAmelCase : List[Any] = size - 1
def _A ( self : Union[str, Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _A ( self : Tuple , A : Dict ):
_UpperCAmelCase : str = algorithm(self )
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : str ):
_UpperCAmelCase : Optional[int] = flow_network
_UpperCAmelCase : Any = flow_network.verticesCount
_UpperCAmelCase : List[str] = flow_network.sourceIndex
_UpperCAmelCase : Union[str, Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCAmelCase : Any = flow_network.graph
_UpperCAmelCase : Union[str, Any] = False
def _A ( self : List[str] ):
if not self.executed:
self._algorithm()
_UpperCAmelCase : int = True
def _A ( self : List[Any] ):
pass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[str, Any] ):
super().__init__(A )
# use this to save your result
_UpperCAmelCase : Any = -1
def _A ( self : Union[str, Any] ):
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : int ):
super().__init__(A )
_UpperCAmelCase : List[str] = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCAmelCase : Union[str, Any] = [0] * self.verticies_count
_UpperCAmelCase : int = [0] * self.verticies_count
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCAmelCase : Optional[int] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCAmelCase : Any = 0
while i < len(A ):
_UpperCAmelCase : int = vertices_list[i]
_UpperCAmelCase : int = self.heights[vertex_index]
self.process_vertex(A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A ) )
_UpperCAmelCase : Union[str, Any] = 0
else:
i += 1
_UpperCAmelCase : List[Any] = sum(self.preflow[self.source_index] )
def _A ( self : Union[str, Any] , A : str ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A , A )
self.relabel(A )
def _A ( self : int , A : Dict , A : List[str] ):
_UpperCAmelCase : int = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _A ( self : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : str = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCAmelCase : Tuple = self.heights[to_index]
if min_height is not None:
_UpperCAmelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__SCREAMING_SNAKE_CASE : List[Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__SCREAMING_SNAKE_CASE : Optional[Any] = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 31
| 1
|
'''simple docstring'''
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[str] = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}
# See all BART models at https://huggingface.co/models?filter=bart
__SCREAMING_SNAKE_CASE : Dict = {
"""vocab_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
},
"""merges_file""": {
"""facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
"""facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
"""facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
"""facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
"""facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
"""yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
},
}
__SCREAMING_SNAKE_CASE : List[str] = {
"""facebook/bart-base""": 1_024,
"""facebook/bart-large""": 1_024,
"""facebook/bart-large-mnli""": 1_024,
"""facebook/bart-large-cnn""": 1_024,
"""facebook/bart-large-xsum""": 1_024,
"""yjernite/bart_eli5""": 1_024,
}
@lru_cache()
def UpperCamelCase_ ( ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
_UpperCAmelCase : Union[str, Any] = bs[:]
_UpperCAmelCase : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_UpperCAmelCase )
cs.append(2**8 + n )
n += 1
_UpperCAmelCase : Dict = [chr(_UpperCAmelCase ) for n in cs]
return dict(zip(_UpperCAmelCase , _UpperCAmelCase ) )
def UpperCamelCase_ ( _UpperCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = set()
_UpperCAmelCase : Tuple = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_UpperCAmelCase : str = char
return pairs
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = VOCAB_FILES_NAMES
__UpperCamelCase: Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: List[str] = ["input_ids", "attention_mask"]
def __init__( self : List[str] , A : Union[str, Any] , A : int , A : Union[str, Any]="replace" , A : Any="<s>" , A : Any="</s>" , A : int="</s>" , A : int="<s>" , A : Tuple="<unk>" , A : Optional[Any]="<pad>" , A : List[str]="<mask>" , A : Any=False , **A : List[str] , ):
_UpperCAmelCase : Dict = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else bos_token
_UpperCAmelCase : Optional[Any] = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else eos_token
_UpperCAmelCase : Any = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else sep_token
_UpperCAmelCase : str = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else cls_token
_UpperCAmelCase : List[Any] = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else unk_token
_UpperCAmelCase : List[Any] = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
_UpperCAmelCase : Dict = AddedToken(A , lstrip=A , rstrip=A ) if isinstance(A , A ) else mask_token
super().__init__(
errors=A , bos_token=A , eos_token=A , unk_token=A , sep_token=A , cls_token=A , pad_token=A , mask_token=A , add_prefix_space=A , **A , )
with open(A , encoding="utf-8" ) as vocab_handle:
_UpperCAmelCase : Any = json.load(A )
_UpperCAmelCase : Dict = {v: k for k, v in self.encoder.items()}
_UpperCAmelCase : List[str] = errors # how to handle errors in decoding
_UpperCAmelCase : List[str] = bytes_to_unicode()
_UpperCAmelCase : Optional[int] = {v: k for k, v in self.byte_encoder.items()}
with open(A , encoding="utf-8" ) as merges_handle:
_UpperCAmelCase : Dict = merges_handle.read().split("\n" )[1:-1]
_UpperCAmelCase : int = [tuple(merge.split() ) for merge in bpe_merges]
_UpperCAmelCase : Dict = dict(zip(A , range(len(A ) ) ) )
_UpperCAmelCase : Tuple = {}
_UpperCAmelCase : Any = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_UpperCAmelCase : List[str] = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+" )
@property
def _A ( self : int ):
return len(self.encoder )
def _A ( self : Dict ):
return dict(self.encoder , **self.added_tokens_encoder )
def _A ( self : Dict , A : Dict ):
if token in self.cache:
return self.cache[token]
_UpperCAmelCase : Optional[Any] = tuple(A )
_UpperCAmelCase : List[Any] = get_pairs(A )
if not pairs:
return token
while True:
_UpperCAmelCase : Tuple = min(A , key=lambda A : self.bpe_ranks.get(A , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = bigram
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[int] = 0
while i < len(A ):
try:
_UpperCAmelCase : List[Any] = word.index(A , A )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
_UpperCAmelCase : List[Any] = j
if word[i] == first and i < len(A ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
_UpperCAmelCase : Optional[int] = tuple(A )
_UpperCAmelCase : List[Any] = new_word
if len(A ) == 1:
break
else:
_UpperCAmelCase : Union[str, Any] = get_pairs(A )
_UpperCAmelCase : Optional[Any] = " ".join(A )
_UpperCAmelCase : Any = word
return word
def _A ( self : List[Any] , A : Any ):
_UpperCAmelCase : List[str] = []
for token in re.findall(self.pat , A ):
_UpperCAmelCase : Dict = "".join(
self.byte_encoder[b] for b in token.encode("utf-8" ) ) # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
bpe_tokens.extend(bpe_token for bpe_token in self.bpe(A ).split(" " ) )
return bpe_tokens
def _A ( self : Union[str, Any] , A : Union[str, Any] ):
return self.encoder.get(A , self.encoder.get(self.unk_token ) )
def _A ( self : int , A : Any ):
return self.decoder.get(A )
def _A ( self : Dict , A : Union[str, Any] ):
_UpperCAmelCase : Dict = "".join(A )
_UpperCAmelCase : Optional[int] = bytearray([self.byte_decoder[c] for c in text] ).decode("utf-8" , errors=self.errors )
return text
def _A ( self : Tuple , A : str , A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : Tuple = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase : List[str] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
with open(A , "w" , encoding="utf-8" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=A , ensure_ascii=A ) + "\n" )
_UpperCAmelCase : Union[str, Any] = 0
with open(A , "w" , encoding="utf-8" ) as writer:
writer.write("#version: 0.2\n" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda A : kv[1] ):
if index != token_index:
logger.warning(
F"""Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."""
" Please check that the tokenizer is not corrupted!" )
_UpperCAmelCase : Union[str, Any] = token_index
writer.write(" ".join(A ) + "\n" )
index += 1
return vocab_file, merge_file
def _A ( self : Tuple , A : List[int] , A : Optional[List[int]] = None ):
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_UpperCAmelCase : List[Any] = [self.cls_token_id]
_UpperCAmelCase : List[str] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _A ( self : List[str] , A : List[int] , A : Optional[List[int]] = None , A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
if token_ids_a is None:
return [1] + ([0] * len(A )) + [1]
return [1] + ([0] * len(A )) + [1, 1] + ([0] * len(A )) + [1]
def _A ( self : Dict , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : List[Any] = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def _A ( self : Union[str, Any] , A : Dict , A : List[str]=False , **A : int ):
_UpperCAmelCase : Dict = kwargs.pop("add_prefix_space" , self.add_prefix_space )
if (is_split_into_words or add_prefix_space) and (len(A ) > 0 and not text[0].isspace()):
_UpperCAmelCase : List[Any] = " " + text
return (text, kwargs)
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> float:
"""simple docstring"""
def get_matched_characters(_UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_UpperCAmelCase : int = int(max(0 , i - limit ) )
_UpperCAmelCase : Any = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = F"""{_stra[0:_stra.index(_UpperCAmelCase )]} {_stra[_stra.index(_UpperCAmelCase ) + 1:]}"""
return "".join(_UpperCAmelCase )
# matching characters
_UpperCAmelCase : Union[str, Any] = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = len(_UpperCAmelCase )
# transposition
_UpperCAmelCase : Optional[Any] = (
len([(ca, ca) for ca, ca in zip(_UpperCAmelCase , _UpperCAmelCase ) if ca != ca] ) // 2
)
if not match_count:
_UpperCAmelCase : Dict = 0.0
else:
_UpperCAmelCase : Optional[int] = (
1
/ 3
* (
match_count / len(_UpperCAmelCase )
+ match_count / len(_UpperCAmelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_UpperCAmelCase : str = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 31
| 1
|
'''simple docstring'''
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__SCREAMING_SNAKE_CASE : Optional[int] = logging.getLogger(__name__)
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser(
description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
)
parser.add_argument(
"""--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
)
parser.add_argument(
"""--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
)
parser.add_argument("""--vocab_size""", default=30_522, type=int)
__SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
logger.info(F'Loading data from {args.data_file}')
with open(args.data_file, """rb""") as fp:
__SCREAMING_SNAKE_CASE : Optional[Any] = pickle.load(fp)
logger.info("""Counting occurrences for MLM.""")
__SCREAMING_SNAKE_CASE : int = Counter()
for tk_ids in data:
counter.update(tk_ids)
__SCREAMING_SNAKE_CASE : Any = [0] * args.vocab_size
for k, v in counter.items():
__SCREAMING_SNAKE_CASE : Tuple = v
logger.info(F'Dump to {args.token_counts_dump}')
with open(args.token_counts_dump, """wb""") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 31
|
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = 1
@register_to_config
def __init__( self : Optional[int] , A : int = 1000 , A : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(A )
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_UpperCAmelCase : int = 4
# running values
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : int , A : Union[str, torch.device] = None ):
_UpperCAmelCase : int = num_inference_steps
_UpperCAmelCase : Union[str, Any] = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_UpperCAmelCase : Any = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_UpperCAmelCase : str = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_UpperCAmelCase : Dict = torch.sin(steps * math.pi / 2 ) ** 2
_UpperCAmelCase : List[Any] = (1.0 - self.betas**2) ** 0.5
_UpperCAmelCase : List[str] = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_UpperCAmelCase : Dict = timesteps.to(A )
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : torch.FloatTensor , A : int , A : torch.FloatTensor , A : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )
_UpperCAmelCase : Tuple = (self.timesteps == timestep).nonzero().item()
_UpperCAmelCase : Optional[Any] = timestep_index + 1
_UpperCAmelCase : int = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(A )
if len(self.ets ) == 1:
_UpperCAmelCase : List[Any] = self.ets[-1]
elif len(self.ets ) == 2:
_UpperCAmelCase : str = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_UpperCAmelCase : Tuple = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_UpperCAmelCase : Union[str, Any] = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_UpperCAmelCase : Union[str, Any] = self._get_prev_sample(A , A , A , A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def _A ( self : Union[str, Any] , A : torch.FloatTensor , *A : Union[str, Any] , **A : Dict ):
return sample
def _A ( self : Optional[Any] , A : Optional[int] , A : int , A : Optional[Any] , A : List[str] ):
_UpperCAmelCase : List[str] = self.alphas[timestep_index]
_UpperCAmelCase : List[Any] = self.betas[timestep_index]
_UpperCAmelCase : Optional[Any] = self.alphas[prev_timestep_index]
_UpperCAmelCase : Dict = self.betas[prev_timestep_index]
_UpperCAmelCase : Tuple = (sample - sigma * ets) / max(A , 1E-8 )
_UpperCAmelCase : List[str] = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Union[str, Any] ):
return self.config.num_train_timesteps
| 31
| 1
|
'''simple docstring'''
import os
import unittest
from huggingface_hub.utils import are_progress_bars_disabled
import transformers.models.bart.tokenization_bart
from transformers import logging
from transformers.testing_utils import CaptureLogger, mockenv, mockenv_context
from transformers.utils.logging import disable_progress_bar, enable_progress_bar
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : List[Any] ):
_UpperCAmelCase : List[Any] = logging.get_logger()
# the current default level is logging.WARNING
_UpperCAmelCase : int = logging.get_verbosity()
logging.set_verbosity_error()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_warning()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_info()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
logging.set_verbosity_debug()
self.assertEqual(logger.getEffectiveLevel() , logging.get_verbosity() )
# restore to the original level
logging.set_verbosity(A )
def _A ( self : int ):
_UpperCAmelCase : int = logging.get_verbosity()
_UpperCAmelCase : int = logging.get_logger("transformers.models.bart.tokenization_bart" )
_UpperCAmelCase : List[str] = "Testing 1, 2, 3"
# should be able to log warnings (if default settings weren't overridden by `pytest --log-level-all`)
if level_origin <= logging.WARNING:
with CaptureLogger(A ) as cl:
logger.warning(A )
self.assertEqual(cl.out , msg + "\n" )
# this is setting the level for all of `transformers.*` loggers
logging.set_verbosity_error()
# should not be able to log warnings
with CaptureLogger(A ) as cl:
logger.warning(A )
self.assertEqual(cl.out , "" )
# should be able to log warnings again
logging.set_verbosity_warning()
with CaptureLogger(A ) as cl:
logger.warning(A )
self.assertEqual(cl.out , msg + "\n" )
# restore to the original level
logging.set_verbosity(A )
@mockenv(TRANSFORMERS_VERBOSITY="error" )
def _A ( self : Dict ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
# this action activates the env var
_UpperCAmelCase : str = logging.get_logger("transformers.models.bart.tokenization_bart" )
_UpperCAmelCase : Optional[int] = os.getenv("TRANSFORMERS_VERBOSITY" , A )
_UpperCAmelCase : Any = logging.log_levels[env_level_str]
_UpperCAmelCase : Tuple = logging.get_verbosity()
self.assertEqual(
A , A , F"""TRANSFORMERS_VERBOSITY={env_level_str}/{env_level}, but internal verbosity is {current_level}""" , )
# restore to the original level
_UpperCAmelCase : int = ""
transformers.utils.logging._reset_library_root_logger()
@mockenv(TRANSFORMERS_VERBOSITY="super-error" )
def _A ( self : Union[str, Any] ):
# reset for the env var to take effect, next time some logger call is made
transformers.utils.logging._reset_library_root_logger()
_UpperCAmelCase : List[Any] = logging.logging.getLogger()
with CaptureLogger(A ) as cl:
# this action activates the env var
logging.get_logger("transformers.models.bart.tokenization_bart" )
self.assertIn("Unknown option TRANSFORMERS_VERBOSITY=super-error" , cl.out )
# no need to restore as nothing was changed
def _A ( self : List[Any] ):
# testing `logger.warning_advice()`
transformers.utils.logging._reset_library_root_logger()
_UpperCAmelCase : Optional[Any] = logging.get_logger("transformers.models.bart.tokenization_bart" )
_UpperCAmelCase : List[Any] = "Testing 1, 2, 3"
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="1" ):
# nothing should be logged as env var disables this method
with CaptureLogger(A ) as cl:
logger.warning_advice(A )
self.assertEqual(cl.out , "" )
with mockenv_context(TRANSFORMERS_NO_ADVISORY_WARNINGS="" ):
# should log normally as TRANSFORMERS_NO_ADVISORY_WARNINGS is unset
with CaptureLogger(A ) as cl:
logger.warning_advice(A )
self.assertEqual(cl.out , msg + "\n" )
def UpperCamelCase_ ( ) -> List[Any]:
"""simple docstring"""
disable_progress_bar()
assert are_progress_bars_disabled()
enable_progress_bar()
assert not are_progress_bars_disabled()
| 31
|
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase_ ( _UpperCAmelCase : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
_UpperCAmelCase : Any = XGBClassifier()
classifier.fit(_UpperCAmelCase , _UpperCAmelCase )
return classifier
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : List[str] = load_iris()
_UpperCAmelCase , _UpperCAmelCase : Dict = data_handling(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = train_test_split(
_UpperCAmelCase , _UpperCAmelCase , test_size=0.2_5 )
_UpperCAmelCase : Optional[Any] = iris["target_names"]
# Create an XGBoost Classifier from the training data
_UpperCAmelCase : Tuple = xgboost(_UpperCAmelCase , _UpperCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , display_labels=_UpperCAmelCase , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 31
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""configuration_vision_encoder_decoder""": ["""VisionEncoderDecoderConfig""", """VisionEncoderDecoderOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""VisionEncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = ["""TFVisionEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""FlaxVisionEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_vision_encoder_decoder import VisionEncoderDecoderConfig, VisionEncoderDecoderOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_encoder_decoder import VisionEncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_encoder_decoder import TFVisionEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_encoder_decoder import FlaxVisionEncoderDecoderModel
else:
import sys
__SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : Optional[Any]=13 , A : Optional[Any]=7 , A : Union[str, Any]=True , A : Optional[Any]=True , A : int=False , A : str=True , A : Optional[Any]=99 , A : Union[str, Any]=32 , A : int=5 , A : Tuple=4 , A : Union[str, Any]=37 , A : Dict="gelu" , A : Union[str, Any]=0.1 , A : str=0.1 , A : Union[str, Any]=512 , A : int=16 , A : List[str]=2 , A : Tuple=0.02 , A : int=3 , A : List[str]=4 , A : str=None , ):
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : str = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : List[str] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Dict ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _A ( self : int , A : List[Any] , A : Any , A : int , A : Union[str, Any] , A : Dict , A : List[Any] , A : Dict ):
_UpperCAmelCase : List[str] = BioGptModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : List[Any] , A : str , A : List[Any] , A : Dict , A : List[Any] , A : List[str] , A : Union[str, Any] , A : int , A : List[str] , A : Dict , ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : List[Any] , A : str , A : str , A : str , A : Any , A : List[str] , *A : Optional[int] ):
_UpperCAmelCase : str = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
_UpperCAmelCase : Optional[int] = self.seq_length // 2
_UpperCAmelCase : List[Any] = 0
# first forward pass
_UpperCAmelCase , _UpperCAmelCase : List[str] = model(A , attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_UpperCAmelCase : List[str] = ids_tensor((1,) , A ).item() + 1
_UpperCAmelCase : str = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_UpperCAmelCase : Any = random_other_next_tokens
# append to next input_ids and attn_mask
_UpperCAmelCase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=A )] , dim=1 , )
# get two different outputs
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Optional[Any] = model(A , past_key_values=A , attention_mask=A )["last_hidden_state"]
# select random slice
_UpperCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : int , A : Dict , A : str , A : Dict , A : Union[str, Any] , A : Any , *A : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = BioGptModel(config=A ).to(A ).eval()
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase , _UpperCAmelCase : Dict = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase : Any = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Dict = model(A , attention_mask=A , past_key_values=A )[
"last_hidden_state"
]
# select random slice
_UpperCAmelCase : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : Optional[Any] , A : Tuple , A : List[str] , A : Tuple , A : Dict , A : List[Any] , *A : Tuple , A : List[str]=False ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_UpperCAmelCase : Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _A ( self : Optional[Any] , A : Any , *A : Optional[Any] ):
_UpperCAmelCase : Tuple = BioGptModel(A )
_UpperCAmelCase : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def _A ( self : Optional[int] , A : Dict , A : Tuple , A : Optional[int] , A : int , A : List[str] , *A : Dict ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Any = BioGptForTokenClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
__UpperCamelCase: List[str] = (BioGptForCausalLM,) if is_torch_available() else ()
__UpperCamelCase: str = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase: Union[str, Any] = False
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = BioGptModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def _A ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : Tuple = type
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A , gradient_checkpointing=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
_UpperCAmelCase : Tuple = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : str = "left"
# Define PAD Token = EOS Token = 50256
_UpperCAmelCase : Any = tokenizer.eos_token
_UpperCAmelCase : int = model.config.eos_token_id
# use different length sentences to test batching
_UpperCAmelCase : Any = [
"Hello, my dog is a little",
"Today, I",
]
_UpperCAmelCase : Tuple = tokenizer(A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Optional[Any] = inputs["input_ids"].to(A )
_UpperCAmelCase : Any = model.generate(
input_ids=A , attention_mask=inputs["attention_mask"].to(A ) , )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : List[Any] = model.generate(input_ids=A )
_UpperCAmelCase : List[Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
_UpperCAmelCase : int = tokenizer(sentences[1] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : int = model.generate(input_ids=A , max_length=model.config.max_length - num_paddings )
_UpperCAmelCase : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
_UpperCAmelCase : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : str = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(A , A )
self.assertListEqual(A , [non_padded_sentence, padded_sentence] )
@slow
def _A ( self : str ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _A ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : List[str] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : List[str] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = 3
_UpperCAmelCase : Dict = "multi_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Optional[int] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : Optional[Any] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
_UpperCAmelCase : List[Any] = model(A )[0]
_UpperCAmelCase : int = 42384
_UpperCAmelCase : int = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@slow
def _A ( self : Any ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Tuple = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = tokenizer("COVID-19 is" , return_tensors="pt" ).to(A )
_UpperCAmelCase : Dict = model.generate(
**A , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=A , )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=A )
_UpperCAmelCase : List[str] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(A , A )
| 31
| 1
|
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def UpperCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = ArgumentParser("Transformers CLI tool" , usage="transformers-cli <command> [<args>]" )
_UpperCAmelCase : List[str] = parser.add_subparsers(help="transformers-cli command helpers" )
# Register commands
ConvertCommand.register_subcommand(_UpperCAmelCase )
DownloadCommand.register_subcommand(_UpperCAmelCase )
EnvironmentCommand.register_subcommand(_UpperCAmelCase )
RunCommand.register_subcommand(_UpperCAmelCase )
ServeCommand.register_subcommand(_UpperCAmelCase )
UserCommands.register_subcommand(_UpperCAmelCase )
AddNewModelCommand.register_subcommand(_UpperCAmelCase )
AddNewModelLikeCommand.register_subcommand(_UpperCAmelCase )
LfsCommands.register_subcommand(_UpperCAmelCase )
PTtoTFCommand.register_subcommand(_UpperCAmelCase )
# Let's go
_UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not hasattr(_UpperCAmelCase , "func" ):
parser.print_help()
exit(1 )
# Run
_UpperCAmelCase : List[Any] = args.func(_UpperCAmelCase )
service.run()
if __name__ == "__main__":
main()
| 31
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger()
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: nn.Module
__UpperCamelCase: List[nn.Module] = field(default_factory=snake_case__ )
__UpperCamelCase: list = field(default_factory=snake_case__ )
def _A ( self : List[str] , A : Optional[int] , A : Tensor , A : Tensor ):
_UpperCAmelCase : Union[str, Any] = len(list(m.modules() ) ) == 1 or isinstance(A , nn.Convad ) or isinstance(A , nn.BatchNormad )
if has_not_submodules:
self.traced.append(A )
def __call__( self : Any , A : Tensor ):
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(A )
[x.remove() for x in self.handles]
return self
@property
def _A ( self : List[Any] ):
# check the len of the state_dict keys to see if we have learnable params
return list(filter(lambda A : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: nn.Module
__UpperCamelCase: nn.Module
__UpperCamelCase: int = 0
__UpperCamelCase: List = field(default_factory=snake_case__ )
__UpperCamelCase: List = field(default_factory=snake_case__ )
def __call__( self : Optional[Any] , A : Tensor ):
_UpperCAmelCase : Optional[Any] = Tracker(self.dest )(A ).parametrized
_UpperCAmelCase : List[Any] = Tracker(self.src )(A ).parametrized
_UpperCAmelCase : str = list(filter(lambda A : type(A ) not in self.src_skip , A ) )
_UpperCAmelCase : int = list(filter(lambda A : type(A ) not in self.dest_skip , A ) )
if len(A ) != len(A ):
raise Exception(
F"""Numbers of operations are different. Source module has {len(A )} operations while"""
F""" destination module has {len(A )}.""" )
for dest_m, src_m in zip(A , A ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"""Transfered from={src_m} to={dest_m}""" )
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : ResNetConfig , _UpperCAmelCase : Path , _UpperCAmelCase : bool = True ) -> str:
"""simple docstring"""
print(F"""Converting {name}...""" )
with torch.no_grad():
_UpperCAmelCase : Any = timm.create_model(_UpperCAmelCase , pretrained=_UpperCAmelCase ).eval()
_UpperCAmelCase : Union[str, Any] = ResNetForImageClassification(_UpperCAmelCase ).eval()
_UpperCAmelCase : Optional[int] = ModuleTransfer(src=_UpperCAmelCase , dest=_UpperCAmelCase )
_UpperCAmelCase : List[Any] = torch.randn((1, 3, 224, 224) )
module_transfer(_UpperCAmelCase )
assert torch.allclose(from_model(_UpperCAmelCase ) , our_model(_UpperCAmelCase ).logits ), "The model logits don't match the original one."
_UpperCAmelCase : Tuple = F"""resnet{'-'.join(name.split('resnet' ) )}"""
print(_UpperCAmelCase )
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add model" , use_temp_dir=_UpperCAmelCase , )
# we can use the convnext one
_UpperCAmelCase : Tuple = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
image_processor.push_to_hub(
repo_path_or_name=save_directory / checkpoint_name , commit_message="Add image processor" , use_temp_dir=_UpperCAmelCase , )
print(F"""Pushed {checkpoint_name}""" )
def UpperCamelCase_ ( _UpperCAmelCase : Path , _UpperCAmelCase : str = None , _UpperCAmelCase : bool = True ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Dict = "imagenet-1k-id2label.json"
_UpperCAmelCase : Optional[int] = 1_000
_UpperCAmelCase : Optional[int] = (1, num_labels)
_UpperCAmelCase : Union[str, Any] = "huggingface/label-files"
_UpperCAmelCase : int = num_labels
_UpperCAmelCase : Optional[int] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : Optional[int] = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
_UpperCAmelCase : str = idalabel
_UpperCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_UpperCAmelCase : Union[str, Any] = partial(_UpperCAmelCase , num_labels=_UpperCAmelCase , idalabel=_UpperCAmelCase , labelaid=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = {
"resnet18": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ),
"resnet26": ImageNetPreTrainedConfig(
depths=[2, 2, 2, 2] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
"resnet34": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[64, 128, 256, 512] , layer_type="basic" ),
"resnet50": ImageNetPreTrainedConfig(
depths=[3, 4, 6, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
"resnet101": ImageNetPreTrainedConfig(
depths=[3, 4, 23, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
"resnet152": ImageNetPreTrainedConfig(
depths=[3, 8, 36, 3] , hidden_sizes=[256, 512, 1_024, 2_048] , layer_type="bottleneck" ),
}
if model_name:
convert_weight_and_push(_UpperCAmelCase , names_to_config[model_name] , _UpperCAmelCase , _UpperCAmelCase )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
return config, expected_shape
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported resnet* architecture,"""
""" currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
__SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
__SCREAMING_SNAKE_CASE : Path = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 31
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = """▁"""
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : int = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
__SCREAMING_SNAKE_CASE : str = {
"""google/pegasus-xsum""": 512,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase: Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Optional[int] = PegasusTokenizer
__UpperCamelCase: Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : Dict , A : List[str]=None , A : Union[str, Any]=None , A : Optional[int]="<pad>" , A : Tuple="</s>" , A : Union[str, Any]="<unk>" , A : Union[str, Any]="<mask_2>" , A : Dict="<mask_1>" , A : Union[str, Any]=None , A : int=103 , **A : Optional[Any] , ):
_UpperCAmelCase : Dict = offset
if additional_special_tokens is not None:
if not isinstance(A , A ):
raise TypeError(
F"""additional_special_tokens should be of type {type(A )}, but is"""
F""" {type(A )}""" )
_UpperCAmelCase : Optional[int] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(A ) , self.offset - 1 )
]
if len(set(A ) ) != len(A ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
_UpperCAmelCase : Any = additional_special_tokens_extended
else:
_UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
A , tokenizer_file=A , pad_token=A , eos_token=A , unk_token=A , mask_token=A , mask_token_sent=A , offset=A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[Any] = vocab_file
_UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
def _A ( self : List[str] , A : Optional[Any] ):
_UpperCAmelCase : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : str , A : List , A : Optional[List] = None , A : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(A )
elif token_ids_a is None:
return self._special_token_mask(A ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : Optional[int] , A : Union[str, Any] , A : int=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : Union[str, Any] , A : str , A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> str:
"""simple docstring"""
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : Union[str, Any] = F"""Expected string as input, found {type(_UpperCAmelCase )}"""
raise ValueError(_UpperCAmelCase )
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : str = F"""Expected boolean as use_pascal parameter, found {type(_UpperCAmelCase )}"""
raise ValueError(_UpperCAmelCase )
_UpperCAmelCase : Union[str, Any] = input_str.split("_" )
_UpperCAmelCase : Any = 0 if use_pascal else 1
_UpperCAmelCase : Dict = words[start_index:]
_UpperCAmelCase : Tuple = [word[0].upper() + word[1:] for word in words_to_capitalize]
_UpperCAmelCase : int = "" if use_pascal else words[0]
return "".join([initial_word, *capitalized_words] )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__SCREAMING_SNAKE_CASE : Optional[int] = 256_047
__SCREAMING_SNAKE_CASE : Optional[int] = 256_145
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = NllbTokenizer
__UpperCamelCase: Tuple = NllbTokenizerFast
__UpperCamelCase: Union[str, Any] = True
__UpperCamelCase: Dict = True
__UpperCamelCase: Optional[Any] = {}
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
_UpperCAmelCase : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase : List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Dict = tokenizer_r.save_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
_UpperCAmelCase : Optional[int] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : List[str] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
_UpperCAmelCase : str = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : Optional[int] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
def _A ( self : Tuple ):
if not self.test_seqaseq:
return
_UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_UpperCAmelCase : Optional[Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
_UpperCAmelCase : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=A , tgt_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_UpperCAmelCase : Tuple = tokenizer.prepare_seqaseq_batch(
A , tgt_texts=A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_UpperCAmelCase : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def _A ( self : List[Any] ):
pass
def _A ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Any = [AddedToken("<special>" , lstrip=A )]
_UpperCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Dict = tokenizer_r.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_r.encode("<special>" , add_special_tokens=A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = "facebook/nllb-200-distilled-600M"
__UpperCamelCase: Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__UpperCamelCase: str = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__UpperCamelCase: str = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _A ( cls : int ):
_UpperCAmelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
_UpperCAmelCase : Union[str, Any] = 1
return cls
def _A ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 256057 )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def _A ( self : Tuple ):
self.assertIn(A , self.tokenizer.all_special_ids )
# fmt: off
_UpperCAmelCase : List[Any] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_UpperCAmelCase : Tuple = self.tokenizer.decode(A , skip_special_tokens=A )
_UpperCAmelCase : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , A )
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Tuple = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , A )
self.assertEqual(len(A ) , A )
def _A ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [256203, 3] )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
_UpperCAmelCase : Tuple = NllbTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def _A ( self : Dict ):
_UpperCAmelCase : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Tuple = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(A , A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_UpperCAmelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : Dict = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=10 , return_tensors="pt" )
_UpperCAmelCase : List[Any] = targets["input_ids"]
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(
A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
} , )
@require_torch
def _A ( self : Any ):
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Any = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : str = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
from typing import Any
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
pass
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : Any ):
_UpperCAmelCase : Any = data
_UpperCAmelCase : Node | None = None
def __iter__( self : Dict ):
_UpperCAmelCase : List[str] = self
_UpperCAmelCase : Any = []
while node:
if node in visited:
raise ContainsLoopError
visited.append(A )
yield node.data
_UpperCAmelCase : Union[str, Any] = node.next_node
@property
def _A ( self : int ):
try:
list(self )
return False
except ContainsLoopError:
return True
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = Node(1)
__SCREAMING_SNAKE_CASE : List[str] = Node(2)
__SCREAMING_SNAKE_CASE : Optional[int] = Node(3)
__SCREAMING_SNAKE_CASE : Tuple = Node(4)
print(root_node.has_loop) # False
__SCREAMING_SNAKE_CASE : Tuple = root_node.next_node
print(root_node.has_loop) # True
__SCREAMING_SNAKE_CASE : str = Node(5)
__SCREAMING_SNAKE_CASE : Union[str, Any] = Node(6)
__SCREAMING_SNAKE_CASE : Dict = Node(5)
__SCREAMING_SNAKE_CASE : Dict = Node(6)
print(root_node.has_loop) # False
__SCREAMING_SNAKE_CASE : int = Node(1)
print(root_node.has_loop) # False
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_UpperCAmelCase , _UpperCAmelCase : int = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = list(range(10, 0, -1))
print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> bool:
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
_UpperCAmelCase : Optional[int] = nn.ModuleList(A )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : torch.Tensor , A : List[torch.tensor] , A : List[float] , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[Dict[str, Any]] = None , A : bool = False , A : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(A , A , self.nets ) ):
_UpperCAmelCase , _UpperCAmelCase : str = controlnet(
A , A , A , A , A , A , A , A , A , A , A , )
# merge samples
if i == 0:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = down_samples, mid_sample
else:
_UpperCAmelCase : Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A , A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _A ( self : List[str] , A : Union[str, os.PathLike] , A : bool = True , A : Callable = None , A : bool = False , A : Optional[str] = None , ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A , is_main_process=A , save_function=A , safe_serialization=A , variant=A , )
idx += 1
_UpperCAmelCase : Tuple = model_path_to_save + F"""_{idx}"""
@classmethod
def _A ( cls : int , A : Optional[Union[str, os.PathLike]] , **A : Tuple ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_UpperCAmelCase : int = pretrained_model_path
while os.path.isdir(A ):
_UpperCAmelCase : List[str] = ControlNetModel.from_pretrained(A , **A )
controlnets.append(A )
idx += 1
_UpperCAmelCase : Dict = pretrained_model_path + F"""_{idx}"""
logger.info(F"""{len(A )} controlnets loaded from {pretrained_model_path}.""" )
if len(A ) == 0:
raise ValueError(
F"""No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(A )
| 31
| 1
|
'''simple docstring'''
import torch
from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = (KDPMaDiscreteScheduler,)
__UpperCamelCase: Any = 1_0
def _A ( self : Dict , **A : Optional[Any] ):
_UpperCAmelCase : Tuple = {
"num_train_timesteps": 1100,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
}
config.update(**A )
return config
def _A ( self : Optional[int] ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _A ( self : Optional[Any] ):
for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] , [0.0_002, 0.002, 0.02] ):
self.check_over_configs(beta_start=A , beta_end=A )
def _A ( self : Union[str, Any] ):
for schedule in ["linear", "scaled_linear"]:
self.check_over_configs(beta_schedule=A )
def _A ( self : int ):
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def _A ( self : int ):
_UpperCAmelCase : Tuple = self.scheduler_classes[0]
_UpperCAmelCase : Tuple = self.get_scheduler_config(prediction_type="v_prediction" )
_UpperCAmelCase : Dict = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Union[str, Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Optional[Any] = sample.to(A )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Tuple = scheduler.scale_model_input(A , A )
_UpperCAmelCase : List[str] = model(A , A )
_UpperCAmelCase : Any = scheduler.step(A , A , A )
_UpperCAmelCase : Dict = output.prev_sample
_UpperCAmelCase : Union[str, Any] = torch.sum(torch.abs(A ) )
_UpperCAmelCase : int = torch.mean(torch.abs(A ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 4.69_34E-07 ) < 1E-2
assert abs(result_mean.item() - 6.11_12E-10 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 4.6_93_42_86_50_17_09_72E-07 ) < 1E-2
assert abs(result_mean.item() - 0.0_002 ) < 1E-3
def _A ( self : Any ):
if torch_device == "mps":
return
_UpperCAmelCase : Any = self.scheduler_classes[0]
_UpperCAmelCase : str = self.get_scheduler_config()
_UpperCAmelCase : Tuple = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps )
_UpperCAmelCase : List[Any] = self.dummy_model()
_UpperCAmelCase : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
_UpperCAmelCase : Dict = sample.to(A )
for i, t in enumerate(scheduler.timesteps ):
_UpperCAmelCase : Tuple = scheduler.scale_model_input(A , A )
_UpperCAmelCase : List[str] = model(A , A )
_UpperCAmelCase : Optional[int] = scheduler.step(A , A , A )
_UpperCAmelCase : Any = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(A ) )
_UpperCAmelCase : List[str] = torch.mean(torch.abs(A ) )
if torch_device in ["cpu", "mps"]:
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
def _A ( self : Tuple ):
if torch_device == "mps":
return
_UpperCAmelCase : List[str] = self.scheduler_classes[0]
_UpperCAmelCase : List[str] = self.get_scheduler_config()
_UpperCAmelCase : str = scheduler_class(**A )
scheduler.set_timesteps(self.num_inference_steps , device=A )
_UpperCAmelCase : Dict = self.dummy_model()
_UpperCAmelCase : List[str] = self.dummy_sample_deter.to(A ) * scheduler.init_noise_sigma
for t in scheduler.timesteps:
_UpperCAmelCase : Optional[int] = scheduler.scale_model_input(A , A )
_UpperCAmelCase : Dict = model(A , A )
_UpperCAmelCase : int = scheduler.step(A , A , A )
_UpperCAmelCase : Tuple = output.prev_sample
_UpperCAmelCase : Optional[Any] = torch.sum(torch.abs(A ) )
_UpperCAmelCase : Any = torch.mean(torch.abs(A ) )
if str(A ).startswith("cpu" ):
# The following sum varies between 148 and 156 on mps. Why?
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
else:
# CUDA
assert abs(result_sum.item() - 20.4_125 ) < 1E-2
assert abs(result_mean.item() - 0.0_266 ) < 1E-3
| 31
|
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_UpperCAmelCase : List[Any] = MaskFormerConfig(backbone_config=_UpperCAmelCase )
_UpperCAmelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_UpperCAmelCase : Dict = 847
_UpperCAmelCase : Any = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_UpperCAmelCase : Any = 150
_UpperCAmelCase : Any = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_UpperCAmelCase : Tuple = 171
_UpperCAmelCase : Union[str, Any] = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_UpperCAmelCase : Any = 133
_UpperCAmelCase : int = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 19
_UpperCAmelCase : str = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 65
_UpperCAmelCase : Tuple = "mapillary-vistas-id2label.json"
_UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : List[str] = val
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase : Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase : Any = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_UpperCAmelCase : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[:dim, :]
_UpperCAmelCase : Tuple = in_proj_bias[: dim]
_UpperCAmelCase : List[Any] = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase : List[str] = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase : Optional[Any] = in_proj_weight[
-dim :, :
]
_UpperCAmelCase : Dict = in_proj_bias[-dim :]
# fmt: on
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : int = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : int = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Optional[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Any = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Tuple = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : Optional[int] = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[-hidden_size :]
# fmt: on
def UpperCamelCase_ ( ) -> torch.Tensor:
"""simple docstring"""
_UpperCAmelCase : int = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : Any = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = get_maskformer_config(_UpperCAmelCase )
# load original state_dict
with open(_UpperCAmelCase , "rb" ) as f:
_UpperCAmelCase : Optional[int] = pickle.load(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_UpperCAmelCase : Any = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_swin_q_k_v(_UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_UpperCAmelCase , _UpperCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_UpperCAmelCase : Tuple = torch.from_numpy(_UpperCAmelCase )
# load 🤗 model
_UpperCAmelCase : Union[str, Any] = MaskFormerForInstanceSegmentation(_UpperCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_UpperCAmelCase , param.shape )
_UpperCAmelCase , _UpperCAmelCase : Any = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_UpperCAmelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_UpperCAmelCase : Optional[int] = prepare_img()
if "vistas" in model_name:
_UpperCAmelCase : int = 65
elif "cityscapes" in model_name:
_UpperCAmelCase : Tuple = 65_535
else:
_UpperCAmelCase : Any = 255
_UpperCAmelCase : Optional[Any] = True if "ade" in model_name else False
_UpperCAmelCase : Optional[int] = MaskFormerImageProcessor(ignore_index=_UpperCAmelCase , reduce_labels=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = image_processor(_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : List[Any] = model(**_UpperCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_UpperCAmelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 31
| 1
|
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[Any] = BioGptTokenizer
__UpperCamelCase: str = False
def _A ( self : Dict ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
_UpperCAmelCase : Optional[int] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
_UpperCAmelCase : Optional[Any] = dict(zip(A , range(len(A ) ) ) )
_UpperCAmelCase : List[Any] = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
_UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(A ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(A ) )
def _A ( self : Any , A : Optional[int] ):
_UpperCAmelCase : Optional[int] = "lower newer"
_UpperCAmelCase : str = "lower newer"
return input_text, output_text
def _A ( self : str ):
_UpperCAmelCase : str = BioGptTokenizer(self.vocab_file , self.merges_file )
_UpperCAmelCase : List[str] = "lower"
_UpperCAmelCase : Union[str, Any] = ["low", "er</w>"]
_UpperCAmelCase : Union[str, Any] = tokenizer.tokenize(A )
self.assertListEqual(A , A )
_UpperCAmelCase : List[str] = tokens + ["<unk>"]
_UpperCAmelCase : List[Any] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(A ) , A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Optional[Any] = tokenizer.encode("sequence builders" , add_special_tokens=A )
_UpperCAmelCase : Union[str, Any] = tokenizer.encode("multi-sequence build" , add_special_tokens=A )
_UpperCAmelCase : Optional[Any] = tokenizer.build_inputs_with_special_tokens(A )
_UpperCAmelCase : List[str] = tokenizer.build_inputs_with_special_tokens(A , A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 31
|
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : Optional[str] = None ):
_UpperCAmelCase : Dict = (
os.path.join(A , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_UpperCAmelCase : Union[str, Any] = Extractor
def _A ( self : Tuple , A : str ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_UpperCAmelCase : Dict = os.path.abspath(A )
return os.path.join(self.extract_dir , hash_url_to_filename(A ) )
def _A ( self : int , A : str , A : bool ):
return force_extract or (
not os.path.isfile(A ) and not (os.path.isdir(A ) and os.listdir(A ))
)
def _A ( self : Optional[int] , A : str , A : bool = False ):
_UpperCAmelCase : Union[str, Any] = self.extractor.infer_extractor_format(A )
if not extractor_format:
return input_path
_UpperCAmelCase : Optional[Any] = self._get_output_path(A )
if self._do_extract(A , A ):
self.extractor.extract(A , A , A )
return output_path
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
@abstractmethod
def _A ( cls : str , A : Union[Path, str] , **A : Dict ):
...
@staticmethod
@abstractmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
...
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[bytes] = []
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
with open(A , "rb" ) as f:
return f.read(A )
@classmethod
def _A ( cls : Any , A : Union[Path, str] , A : bytes = b"" ):
if not magic_number:
_UpperCAmelCase : Any = max(len(A ) for cls_magic_number in cls.magic_numbers )
try:
_UpperCAmelCase : int = cls.read_magic_number(A , A )
except OSError:
return False
return any(magic_number.startswith(A ) for cls_magic_number in cls.magic_numbers )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
def _A ( cls : str , A : Union[Path, str] , **A : List[Any] ):
return tarfile.is_tarfile(A )
@staticmethod
def _A ( A : Union[str, Any] , A : str ):
def resolved(A : str ) -> str:
return os.path.realpath(os.path.abspath(A ) )
def badpath(A : str , A : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(A , A ) ).startswith(A )
def badlink(A : str , A : str ) -> bool:
# Links are interpreted relative to the directory containing the link
_UpperCAmelCase : List[str] = resolved(os.path.join(A , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=A )
_UpperCAmelCase : Optional[int] = resolved(A )
for finfo in members:
if badpath(finfo.name , A ):
logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : int = tarfile.open(A )
tar_file.extractall(A , members=TarExtractor.safemembers(A , A ) )
tar_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = [b"\x1F\x8B"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with gzip.open(A , "rb" ) as gzip_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [
b"PK\x03\x04",
b"PK\x05\x06", # empty archive
b"PK\x07\x08", # spanned archive
]
@classmethod
def _A ( cls : Dict , A : Union[Path, str] , A : bytes = b"" ):
if super().is_extractable(A , magic_number=A ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(A , "rb" ) as fp:
_UpperCAmelCase : Tuple = _EndRecData(A )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_UpperCAmelCase : Dict = fp.read(A ) # CD is where we expect it to be
if len(A ) == sizeCentralDir:
_UpperCAmelCase : Any = struct.unpack(A , A ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
with zipfile.ZipFile(A , "r" ) as zip_file:
zip_file.extractall(A )
zip_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [b"\xFD\x37\x7A\x58\x5A\x00"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with lzma.open(A ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[str] = [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : List[str] = rarfile.RarFile(A )
rf.extractall(A )
rf.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x28\xb5\x2F\xFD"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
_UpperCAmelCase : Optional[Any] = zstd.ZstdDecompressor()
with open(A , "rb" ) as ifh, open(A , "wb" ) as ofh:
dctx.copy_stream(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x42\x5A\x68"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with bza.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = [b"\x37\x7A\xBC\xAF\x27\x1C"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr" )
import pyazr
os.makedirs(A , exist_ok=A )
with pyazr.SevenZipFile(A , "r" ) as archive:
archive.extractall(A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = [b"\x04\x22\x4D\x18"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4" )
import lza.frame
with lza.frame.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _A ( cls : List[Any] ):
return max(
len(A )
for extractor in cls.extractors.values()
if issubclass(A , A )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
try:
return MagicNumberBaseExtractor.read_magic_number(A , magic_number_length=A )
except OSError:
return b""
@classmethod
def _A ( cls : Optional[Any] , A : Union[Path, str] , A : bool = False ):
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead." , category=A , )
_UpperCAmelCase : Union[str, Any] = cls.infer_extractor_format(A )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _A ( cls : Dict , A : Union[Path, str] ): # <Added version="2.4.0"/>
_UpperCAmelCase : Optional[int] = cls._get_magic_number_max_length()
_UpperCAmelCase : str = cls._read_magic_number(A , A )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(A , magic_number=A ):
return extractor_format
@classmethod
def _A ( cls : List[str] , A : Union[Path, str] , A : Union[Path, str] , A : Optional[str] = None , A : Optional[BaseExtractor] = "deprecated" , ):
os.makedirs(os.path.dirname(A ) , exist_ok=A )
# Prevent parallel extractions
_UpperCAmelCase : Tuple = str(Path(A ).with_suffix(".lock" ) )
with FileLock(A ):
shutil.rmtree(A , ignore_errors=A )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(A , A ): # passed as positional arg
warnings.warn(
"Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'extractor_format' instead." , category=A , )
_UpperCAmelCase : Tuple = extractor if extractor != "deprecated" else extractor_format
else:
_UpperCAmelCase : Tuple = cls.extractors[extractor_format]
return extractor.extract(A , A )
else:
warnings.warn(
"Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
"exception in 3.0.0." , category=A , )
for extractor in cls.extractors.values():
if extractor.is_extractable(A ):
return extractor.extract(A , A )
| 31
| 1
|
'''simple docstring'''
from typing import Any
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : dict , _UpperCAmelCase : dict , _UpperCAmelCase : dict , ) -> list:
"""simple docstring"""
_validation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# Creates data structures and fill initial step
_UpperCAmelCase : dict = {}
_UpperCAmelCase : dict = {}
for state in states_space:
_UpperCAmelCase : Union[str, Any] = observations_space[0]
_UpperCAmelCase : Tuple = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
_UpperCAmelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase : Optional[Any] = observations_space[o]
_UpperCAmelCase : int = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_UpperCAmelCase : str = ""
_UpperCAmelCase : Tuple = -1
for k_state in states_space:
_UpperCAmelCase : Any = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_UpperCAmelCase : Union[str, Any] = probability
_UpperCAmelCase : str = k_state
# Update probabilities and pointers dicts
_UpperCAmelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_UpperCAmelCase : Tuple = arg_max
# The final observation
_UpperCAmelCase : Optional[Any] = observations_space[len(_UpperCAmelCase ) - 1]
# argmax for given final observation
_UpperCAmelCase : List[str] = ""
_UpperCAmelCase : Any = -1
for k_state in states_space:
_UpperCAmelCase : Optional[int] = probabilities[(k_state, final_observation)]
if probability > max_probability:
_UpperCAmelCase : int = probability
_UpperCAmelCase : Dict = k_state
_UpperCAmelCase : Dict = arg_max
# Process pointers backwards
_UpperCAmelCase : List[Any] = last_state
_UpperCAmelCase : str = []
for o in range(len(_UpperCAmelCase ) - 1 , -1 , -1 ):
result.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_not_empty(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
_validate_lists(_UpperCAmelCase , _UpperCAmelCase )
_validate_dicts(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any ) -> None:
"""simple docstring"""
_validate_list(_UpperCAmelCase , "observations_space" )
_validate_list(_UpperCAmelCase , "states_space" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list"""
raise ValueError(_UpperCAmelCase )
else:
for x in _object:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list of strings"""
raise ValueError(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_dict(_UpperCAmelCase , "initial_probabilities" , _UpperCAmelCase )
_validate_nested_dict(_UpperCAmelCase , "transition_probabilities" )
_validate_nested_dict(_UpperCAmelCase , "emission_probabilities" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_validate_dict(_object , _UpperCAmelCase , _UpperCAmelCase )
for x in _object.values():
_validate_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : type , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Any = F"""{var_name} must be a dict"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object ):
_UpperCAmelCase : Tuple = F"""{var_name} all keys must be strings"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object.values() ):
_UpperCAmelCase : List[str] = "nested dictionary " if nested else ""
_UpperCAmelCase : List[str] = F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(_UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
|
'''simple docstring'''
from typing import Any
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : dict , _UpperCAmelCase : dict , _UpperCAmelCase : dict , ) -> list:
"""simple docstring"""
_validation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# Creates data structures and fill initial step
_UpperCAmelCase : dict = {}
_UpperCAmelCase : dict = {}
for state in states_space:
_UpperCAmelCase : Union[str, Any] = observations_space[0]
_UpperCAmelCase : Tuple = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
_UpperCAmelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase : Optional[Any] = observations_space[o]
_UpperCAmelCase : int = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_UpperCAmelCase : str = ""
_UpperCAmelCase : Tuple = -1
for k_state in states_space:
_UpperCAmelCase : Any = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_UpperCAmelCase : Union[str, Any] = probability
_UpperCAmelCase : str = k_state
# Update probabilities and pointers dicts
_UpperCAmelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_UpperCAmelCase : Tuple = arg_max
# The final observation
_UpperCAmelCase : Optional[Any] = observations_space[len(_UpperCAmelCase ) - 1]
# argmax for given final observation
_UpperCAmelCase : List[str] = ""
_UpperCAmelCase : Any = -1
for k_state in states_space:
_UpperCAmelCase : Optional[int] = probabilities[(k_state, final_observation)]
if probability > max_probability:
_UpperCAmelCase : int = probability
_UpperCAmelCase : Dict = k_state
_UpperCAmelCase : Dict = arg_max
# Process pointers backwards
_UpperCAmelCase : List[Any] = last_state
_UpperCAmelCase : str = []
for o in range(len(_UpperCAmelCase ) - 1 , -1 , -1 ):
result.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_not_empty(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
_validate_lists(_UpperCAmelCase , _UpperCAmelCase )
_validate_dicts(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any ) -> None:
"""simple docstring"""
_validate_list(_UpperCAmelCase , "observations_space" )
_validate_list(_UpperCAmelCase , "states_space" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list"""
raise ValueError(_UpperCAmelCase )
else:
for x in _object:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list of strings"""
raise ValueError(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_dict(_UpperCAmelCase , "initial_probabilities" , _UpperCAmelCase )
_validate_nested_dict(_UpperCAmelCase , "transition_probabilities" )
_validate_nested_dict(_UpperCAmelCase , "emission_probabilities" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_validate_dict(_object , _UpperCAmelCase , _UpperCAmelCase )
for x in _object.values():
_validate_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : type , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Any = F"""{var_name} must be a dict"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object ):
_UpperCAmelCase : Tuple = F"""{var_name} all keys must be strings"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object.values() ):
_UpperCAmelCase : List[str] = "nested dictionary " if nested else ""
_UpperCAmelCase : List[str] = F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(_UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = ["pixel_values"]
def __init__( self : Optional[int] , A : bool = True , A : Dict[str, int] = None , A : float = None , A : PILImageResampling = PILImageResampling.BILINEAR , A : bool = True , A : Union[int, float] = 1 / 255 , A : bool = True , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , **A : int , ):
super().__init__(**A )
_UpperCAmelCase : Optional[int] = size if size is not None else {"shortest_edge": 384}
_UpperCAmelCase : Union[str, Any] = get_size_dict(A , default_to_square=A )
_UpperCAmelCase : int = do_resize
_UpperCAmelCase : Optional[int] = size
# Default value set here for backwards compatibility where the value in config is None
_UpperCAmelCase : str = crop_pct if crop_pct is not None else 224 / 256
_UpperCAmelCase : Tuple = resample
_UpperCAmelCase : Union[str, Any] = do_rescale
_UpperCAmelCase : List[str] = rescale_factor
_UpperCAmelCase : Tuple = do_normalize
_UpperCAmelCase : Optional[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _A ( self : List[Any] , A : np.ndarray , A : Dict[str, int] , A : float , A : PILImageResampling = PILImageResampling.BICUBIC , A : Optional[Union[str, ChannelDimension]] = None , **A : Any , ):
_UpperCAmelCase : Optional[int] = get_size_dict(A , default_to_square=A )
if "shortest_edge" not in size:
raise ValueError(F"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}""" )
_UpperCAmelCase : Optional[Any] = size["shortest_edge"]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
_UpperCAmelCase : Dict = int(shortest_edge / crop_pct )
_UpperCAmelCase : Optional[Any] = get_resize_output_image_size(A , size=A , default_to_square=A )
_UpperCAmelCase : int = resize(image=A , size=A , resample=A , data_format=A , **A )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=A , size=(shortest_edge, shortest_edge) , data_format=A , **A )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
A , size=(shortest_edge, shortest_edge) , resample=A , data_format=A , **A )
def _A ( self : Dict , A : np.ndarray , A : Union[int, float] , A : Optional[Union[str, ChannelDimension]] = None , **A : int , ):
return rescale(A , scale=A , data_format=A , **A )
def _A ( self : List[Any] , A : np.ndarray , A : Union[float, List[float]] , A : Union[float, List[float]] , A : Optional[Union[str, ChannelDimension]] = None , **A : Optional[int] , ):
return normalize(A , mean=A , std=A , data_format=A , **A )
def _A ( self : str , A : ImageInput , A : bool = None , A : Dict[str, int] = None , A : float = None , A : PILImageResampling = None , A : bool = None , A : float = None , A : bool = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[str, TensorType]] = None , A : ChannelDimension = ChannelDimension.FIRST , **A : List[Any] , ):
_UpperCAmelCase : Optional[int] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : str = crop_pct if crop_pct is not None else self.crop_pct
_UpperCAmelCase : Dict = resample if resample is not None else self.resample
_UpperCAmelCase : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase : Optional[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase : str = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase : Dict = image_std if image_std is not None else self.image_std
_UpperCAmelCase : Tuple = size if size is not None else self.size
_UpperCAmelCase : List[str] = get_size_dict(A , default_to_square=A )
_UpperCAmelCase : Any = make_list_of_images(A )
if not valid_images(A ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError("crop_pct must be specified if size < 384." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
_UpperCAmelCase : List[Any] = [to_numpy_array(A ) for image in images]
if do_resize:
_UpperCAmelCase : List[Any] = [self.resize(image=A , size=A , crop_pct=A , resample=A ) for image in images]
if do_rescale:
_UpperCAmelCase : Any = [self.rescale(image=A , scale=A ) for image in images]
if do_normalize:
_UpperCAmelCase : Any = [self.normalize(image=A , mean=A , std=A ) for image in images]
_UpperCAmelCase : Any = [to_channel_dimension_format(A , A ) for image in images]
_UpperCAmelCase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=A , tensor_type=A )
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , A : Dict , A : Dict=7 , A : Optional[int]=3 , A : Optional[int]=18 , A : Dict=30 , A : List[Any]=400 , A : Union[str, Any]=True , A : Tuple=None , A : List[Any]=True , A : int=None , A : Optional[int]=True , ):
_UpperCAmelCase : Optional[int] = size if size is not None else {"shortest_edge": 20}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : Optional[Any] = num_channels
_UpperCAmelCase : Union[str, Any] = image_size
_UpperCAmelCase : int = min_resolution
_UpperCAmelCase : Optional[int] = max_resolution
_UpperCAmelCase : List[str] = do_resize
_UpperCAmelCase : Optional[Any] = size
_UpperCAmelCase : Tuple = do_center_crop
_UpperCAmelCase : Optional[int] = crop_size
_UpperCAmelCase : Optional[Any] = do_flip_channel_order
def _A ( self : Dict ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Tuple = MobileViTImageProcessor if is_vision_available() else None
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = MobileViTImageProcessingTester(self )
@property
def _A ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Tuple ):
_UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "size" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "center_crop" ) )
self.assertTrue(hasattr(A , "do_flip_channel_order" ) )
def _A ( self : Any ):
_UpperCAmelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Any ):
pass
def _A ( self : Dict ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_UpperCAmelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Union[str, Any] ):
# Initialize image_processing
_UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_UpperCAmelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Any ):
# Initialize image_processing
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Any = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Dict , A : Dict , A : Union[str, Any]=None ):
_UpperCAmelCase : str = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , A , getattr(A , A ) )
_UpperCAmelCase : str = module._original_module if isinstance(A , _PatchedModuleObj ) else module
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: Tuple = []
def __init__( self : int , A : int , A : str , A : Union[str, Any] , A : Dict=None ):
_UpperCAmelCase : Tuple = obj
_UpperCAmelCase : List[Any] = target
_UpperCAmelCase : Any = new
_UpperCAmelCase : str = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : str = attrs or []
def __enter__( self : List[str] ):
*_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(A ) ):
try:
_UpperCAmelCase : str = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : Optional[Any] = getattr(self.obj , A )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(A , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Optional[Any] = obj_attr
# patch at top level
setattr(self.obj , A , _PatchedModuleObj(A , attrs=self.attrs ) )
_UpperCAmelCase : int = getattr(self.obj , A )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(A , A , _PatchedModuleObj(getattr(A , A , A ) , attrs=self.attrs ) )
_UpperCAmelCase : str = getattr(A , A )
# finally set the target attribute
setattr(A , A , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : int = getattr(import_module(".".join(A ) ) , A )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , A ) is attr_value:
_UpperCAmelCase : List[Any] = getattr(self.obj , A )
setattr(self.obj , A , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Union[str, Any] = globals()["__builtins__"][target_attr]
setattr(self.obj , A , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Union[str, Any] , *A : str ):
for attr in list(self.original ):
setattr(self.obj , A , self.original.pop(A ) )
def _A ( self : Dict ):
self.__enter__()
self._active_patches.append(self )
def _A ( self : List[str] ):
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Any = n - k
# Calculate C(n,k)
for i in range(_UpperCAmelCase ):
result *= n - i
result //= i + 1
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , _UpperCAmelCase ) // (node_count + 1)
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
_UpperCAmelCase : List[str] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return catalan_number(_UpperCAmelCase ) * factorial(_UpperCAmelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
F'binary trees and {catalan_number(node_count)} binary search trees.'
)
| 31
| 1
|
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
_UpperCAmelCase : int = "ZinengTang/tvlt-base"
_UpperCAmelCase : List[Any] = tempfile.mkdtemp()
def _A ( self : str , **A : Optional[int] ):
return TvltImageProcessor.from_pretrained(self.checkpoint , **A )
def _A ( self : Any , **A : Dict ):
return TvltFeatureExtractor.from_pretrained(self.checkpoint , **A )
def _A ( self : Union[str, Any] ):
shutil.rmtree(self.tmpdirname )
def _A ( self : Tuple ):
_UpperCAmelCase : Union[str, Any] = self.get_image_processor()
_UpperCAmelCase : Tuple = self.get_feature_extractor()
_UpperCAmelCase : int = TvltProcessor(image_processor=A , feature_extractor=A )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase : Union[str, Any] = TvltProcessor.from_pretrained(self.tmpdirname )
self.assertIsInstance(processor.feature_extractor , A )
self.assertIsInstance(processor.image_processor , A )
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[int] = self.get_image_processor()
_UpperCAmelCase : Tuple = self.get_feature_extractor()
_UpperCAmelCase : Tuple = TvltProcessor(image_processor=A , feature_extractor=A )
_UpperCAmelCase : Tuple = np.ones([12000] )
_UpperCAmelCase : Dict = feature_extractor(A , return_tensors="np" )
_UpperCAmelCase : int = processor(audio=A , return_tensors="np" )
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = self.get_image_processor()
_UpperCAmelCase : str = self.get_feature_extractor()
_UpperCAmelCase : Any = TvltProcessor(image_processor=A , feature_extractor=A )
_UpperCAmelCase : Any = np.ones([3, 224, 224] )
_UpperCAmelCase : str = image_processor(A , return_tensors="np" )
_UpperCAmelCase : Tuple = processor(images=A , return_tensors="np" )
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _A ( self : List[str] ):
_UpperCAmelCase : Tuple = self.get_image_processor()
_UpperCAmelCase : str = self.get_feature_extractor()
_UpperCAmelCase : str = TvltProcessor(image_processor=A , feature_extractor=A )
_UpperCAmelCase : Any = np.ones([12000] )
_UpperCAmelCase : Optional[Any] = np.ones([3, 224, 224] )
_UpperCAmelCase : Any = processor(audio=A , images=A )
self.assertListEqual(list(inputs.keys() ) , ["audio_values", "audio_mask", "pixel_values", "pixel_mask"] )
# test if it raises when no input is passed
with pytest.raises(A ):
processor()
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[int] = self.get_image_processor()
_UpperCAmelCase : Tuple = self.get_feature_extractor()
_UpperCAmelCase : List[Any] = TvltProcessor(image_processor=A , feature_extractor=A )
self.assertListEqual(
processor.model_input_names , image_processor.model_input_names + feature_extractor.model_input_names , msg="`processor` and `image_processor`+`feature_extractor` model input names do not match" , )
| 31
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : Dict = {
"""vocab_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-german-cased""": (
"""https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
),
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
),
},
}
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""distilbert-base-uncased""": 512,
"""distilbert-base-uncased-distilled-squad""": 512,
"""distilbert-base-cased""": 512,
"""distilbert-base-cased-distilled-squad""": 512,
"""distilbert-base-german-cased""": 512,
"""distilbert-base-multilingual-cased""": 512,
}
__SCREAMING_SNAKE_CASE : List[Any] = {
"""distilbert-base-uncased""": {"""do_lower_case""": True},
"""distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
"""distilbert-base-cased""": {"""do_lower_case""": False},
"""distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
"""distilbert-base-german-cased""": {"""do_lower_case""": False},
"""distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase: str = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Any = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase: str = ["input_ids", "attention_mask"]
__UpperCamelCase: List[str] = DistilBertTokenizer
def __init__( self : str , A : int=None , A : Tuple=None , A : Tuple=True , A : Dict="[UNK]" , A : List[Any]="[SEP]" , A : Optional[Any]="[PAD]" , A : Dict="[CLS]" , A : Tuple="[MASK]" , A : str=True , A : Dict=None , **A : List[Any] , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Dict = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : int = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : str = tokenize_chinese_chars
_UpperCAmelCase : List[Any] = normalizer_class(**A )
_UpperCAmelCase : Dict = do_lower_case
def _A ( self : List[Any] , A : Tuple , A : Any=None ):
_UpperCAmelCase : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _A ( self : int , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _A ( self : Dict , A : str , A : Optional[str] = None ):
_UpperCAmelCase : Any = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 31
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = "yolos"
def __init__( self : Optional[Any] , A : Optional[int]=768 , A : List[str]=12 , A : Tuple=12 , A : List[str]=3072 , A : str="gelu" , A : Dict=0.0 , A : str=0.0 , A : int=0.02 , A : List[Any]=1E-12 , A : Optional[Any]=[512, 864] , A : Union[str, Any]=16 , A : Union[str, Any]=3 , A : int=True , A : Dict=100 , A : List[str]=True , A : Optional[int]=False , A : int=1 , A : List[str]=5 , A : Optional[int]=2 , A : Tuple=5 , A : Optional[int]=2 , A : Optional[int]=0.1 , **A : str , ):
super().__init__(**A )
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : Union[str, Any] = num_attention_heads
_UpperCAmelCase : str = intermediate_size
_UpperCAmelCase : Any = hidden_act
_UpperCAmelCase : Union[str, Any] = hidden_dropout_prob
_UpperCAmelCase : Optional[int] = attention_probs_dropout_prob
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Tuple = layer_norm_eps
_UpperCAmelCase : List[str] = image_size
_UpperCAmelCase : List[str] = patch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : int = qkv_bias
_UpperCAmelCase : List[str] = num_detection_tokens
_UpperCAmelCase : List[str] = use_mid_position_embeddings
_UpperCAmelCase : List[Any] = auxiliary_loss
# Hungarian matcher
_UpperCAmelCase : List[str] = class_cost
_UpperCAmelCase : Optional[Any] = bbox_cost
_UpperCAmelCase : Optional[Any] = giou_cost
# Loss coefficients
_UpperCAmelCase : int = bbox_loss_coefficient
_UpperCAmelCase : Optional[Any] = giou_loss_coefficient
_UpperCAmelCase : List[str] = eos_coefficient
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Any = version.parse("1.11" )
@property
def _A ( self : List[Any] ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _A ( self : Optional[int] ):
return 1E-4
@property
def _A ( self : Optional[int] ):
return 12
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
def _A ( self : Any , A : Union[str, Any] , A : Optional[int] , A : List[str] , **A : Tuple ):
self.events.append("on_init_end" )
def _A ( self : Any , A : str , A : List[Any] , A : List[Any] , **A : Tuple ):
self.events.append("on_train_begin" )
def _A ( self : Tuple , A : List[str] , A : Tuple , A : int , **A : List[str] ):
self.events.append("on_train_end" )
def _A ( self : Optional[Any] , A : Dict , A : Any , A : Optional[Any] , **A : List[Any] ):
self.events.append("on_epoch_begin" )
def _A ( self : Optional[Any] , A : List[Any] , A : List[str] , A : Optional[int] , **A : Optional[int] ):
self.events.append("on_epoch_end" )
def _A ( self : List[str] , A : Optional[int] , A : List[Any] , A : Union[str, Any] , **A : Any ):
self.events.append("on_step_begin" )
def _A ( self : Tuple , A : Union[str, Any] , A : int , A : Optional[int] , **A : int ):
self.events.append("on_step_end" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Union[str, Any] , A : str , **A : Union[str, Any] ):
self.events.append("on_evaluate" )
def _A ( self : Optional[Any] , A : Optional[int] , A : Dict , A : List[Any] , **A : Dict ):
self.events.append("on_predict" )
def _A ( self : Dict , A : Dict , A : List[Any] , A : Dict , **A : str ):
self.events.append("on_save" )
def _A ( self : Tuple , A : Optional[Any] , A : Union[str, Any] , A : Optional[int] , **A : Dict ):
self.events.append("on_log" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Tuple , A : Tuple , **A : List[str] ):
self.events.append("on_prediction_step" )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
def _A ( self : List[Any] ):
shutil.rmtree(self.output_dir )
def _A ( self : Union[str, Any] , A : Optional[int]=0 , A : Optional[Any]=0 , A : Optional[Any]=64 , A : Dict=64 , A : Any=None , A : Tuple=False , **A : Optional[int] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
_UpperCAmelCase : str = RegressionDataset(length=A )
_UpperCAmelCase : Union[str, Any] = RegressionDataset(length=A )
_UpperCAmelCase : Any = RegressionModelConfig(a=A , b=A )
_UpperCAmelCase : List[Any] = RegressionPreTrainedModel(A )
_UpperCAmelCase : Dict = TrainingArguments(self.output_dir , disable_tqdm=A , report_to=[] , **A )
return Trainer(
A , A , train_dataset=A , eval_dataset=A , callbacks=A , )
def _A ( self : str , A : List[str] , A : List[str] ):
self.assertEqual(len(A ) , len(A ) )
# Order doesn't matter
_UpperCAmelCase : Tuple = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
_UpperCAmelCase : Any = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
for cba, cba in zip(A , A ):
if isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(A , A )
elif isinstance(A , A ) and not isinstance(A , A ):
self.assertEqual(A , cba.__class__ )
elif not isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(cba.__class__ , A )
else:
self.assertEqual(A , A )
def _A ( self : int , A : List[str] ):
_UpperCAmelCase : List[str] = ["on_init_end", "on_train_begin"]
_UpperCAmelCase : str = 0
_UpperCAmelCase : Optional[Any] = len(trainer.get_eval_dataloader() )
_UpperCAmelCase : Optional[int] = ["on_prediction_step"] * len(trainer.get_eval_dataloader() ) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("on_epoch_begin" )
for _ in range(A ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save" )
expected_events.append("on_epoch_end" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _A ( self : str ):
_UpperCAmelCase : Any = self.get_trainer()
_UpperCAmelCase : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# Callbacks passed at init are added to the default callbacks
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_UpperCAmelCase : List[Any] = self.get_trainer(disable_tqdm=A )
_UpperCAmelCase : Tuple = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_UpperCAmelCase : Dict = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : Optional[Any] = self.get_trainer()
_UpperCAmelCase : Any = trainer.pop_callback(A )
self.assertEqual(cb.__class__ , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# We can also add, pop, or remove by instance
_UpperCAmelCase : Union[str, Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : List[Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
_UpperCAmelCase : Union[str, Any] = trainer.pop_callback(A )
self.assertEqual(A , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" , category=A )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_UpperCAmelCase : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# Independent log/save/eval
_UpperCAmelCase : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_UpperCAmelCase : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="steps" )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="epoch" )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# A bit of everything
_UpperCAmelCase : int = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="steps" , )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning" ) as warn_mock:
_UpperCAmelCase : Optional[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(A ) in warn_mock.call_args[0][0]
| 31
| 1
|
'''simple docstring'''
from math import isqrt
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> list[int]:
"""simple docstring"""
_UpperCAmelCase : str = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , _UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : str = False
return [i for i in range(2 , _UpperCAmelCase ) if is_prime[i]]
def UpperCamelCase_ ( _UpperCAmelCase : int = 10**8 ) -> int:
"""simple docstring"""
_UpperCAmelCase : Dict = calculate_prime_numbers(max_number // 2 )
_UpperCAmelCase : Union[str, Any] = 0
_UpperCAmelCase : Any = 0
_UpperCAmelCase : List[str] = len(_UpperCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : Dict , A : Optional[int]=7 , A : Tuple=3 , A : Optional[Any]=10 , A : int=18 , A : Dict=30 , A : List[str]=400 , A : int=True , A : Optional[Any]=None , A : Optional[Any]=True , A : List[Any]=[0.5, 0.5, 0.5] , A : List[str]=[0.5, 0.5, 0.5] , A : Optional[int]=None , ):
_UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 18}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : Optional[Any] = num_frames
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : Any = max_resolution
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : List[Any] = do_normalize
_UpperCAmelCase : Any = image_mean
_UpperCAmelCase : Tuple = image_std
_UpperCAmelCase : Any = crop_size
def _A ( self : List[Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = VivitImageProcessor if is_vision_available() else None
def _A ( self : int ):
_UpperCAmelCase : Tuple = VivitImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "size" ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Tuple ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_UpperCAmelCase : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_UpperCAmelCase : str = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_UpperCAmelCase : Tuple = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
__SCREAMING_SNAKE_CASE : Optional[Any] = [8, 5, 9, 7]
__SCREAMING_SNAKE_CASE : Optional[int] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__SCREAMING_SNAKE_CASE : List[Any] = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : str , A : list[int] , A : list[list[int]] , A : list[list[int]] , ):
_UpperCAmelCase : List[str] = claim_vector
_UpperCAmelCase : Optional[Any] = allocated_resources_table
_UpperCAmelCase : Any = maximum_claim_table
def _A ( self : Union[str, Any] ):
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def _A ( self : List[Any] ):
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def _A ( self : str ):
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(A ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def _A ( self : Dict ):
return {self.__need().index(A ): i for i in self.__need()}
def _A ( self : str , **A : Optional[int] ):
_UpperCAmelCase : str = self.__need()
_UpperCAmelCase : Tuple = self.__allocated_resources_table
_UpperCAmelCase : Tuple = self.__available_resources()
_UpperCAmelCase : Any = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("_" * 50 + "\n" )
while need_list:
_UpperCAmelCase : Dict = False
for each_need in need_list:
_UpperCAmelCase : Optional[Any] = True
for index, need in enumerate(A ):
if need > available_resources[index]:
_UpperCAmelCase : Optional[int] = False
break
if execution:
_UpperCAmelCase : Dict = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
_UpperCAmelCase : Tuple = original_need_index
print(F"""Process {process_number + 1} is executing.""" )
# remove the process run from stack
need_list.remove(A )
# update available/freed resources stack
_UpperCAmelCase : Tuple = np.array(A ) + np.array(
alloc_resources_table[process_number] )
print(
"Updated available resource stack for processes: "
+ " ".join([str(A ) for x in available_resources] ) )
break
if safe:
print("The process is in a safe state.\n" )
else:
print("System in unsafe state. Aborting...\n" )
break
def _A ( self : Tuple ):
print(" " * 9 + "Allocated Resource Table" )
for item in self.__allocated_resources_table:
print(
F"""P{self.__allocated_resources_table.index(A ) + 1}"""
+ " ".join(F"""{it:>8}""" for it in item )
+ "\n" )
print(" " * 9 + "System Resource Table" )
for item in self.__maximum_claim_table:
print(
F"""P{self.__maximum_claim_table.index(A ) + 1}"""
+ " ".join(F"""{it:>8}""" for it in item )
+ "\n" )
print(
"Current Usage by Active Processes: "
+ " ".join(str(A ) for x in self.__claim_vector ) )
print(
"Initial Available Resources: "
+ " ".join(str(A ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "encodec"
def __init__( self : Optional[int] , A : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , A : List[Any]=24000 , A : Union[str, Any]=1 , A : List[Any]=False , A : Optional[int]=None , A : int=None , A : str=128 , A : List[Any]=32 , A : List[Any]=1 , A : int=[8, 5, 4, 2] , A : Optional[int]="weight_norm" , A : List[Any]=7 , A : Any=7 , A : Dict=3 , A : Optional[int]=2 , A : Dict=True , A : Dict="reflect" , A : Any=2 , A : Dict=2 , A : str=1.0 , A : Optional[int]=1024 , A : Any=None , A : Any=True , **A : str , ):
_UpperCAmelCase : Optional[int] = target_bandwidths
_UpperCAmelCase : List[str] = sampling_rate
_UpperCAmelCase : Optional[int] = audio_channels
_UpperCAmelCase : str = normalize
_UpperCAmelCase : int = chunk_length_s
_UpperCAmelCase : str = overlap
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : int = num_filters
_UpperCAmelCase : Optional[Any] = num_residual_layers
_UpperCAmelCase : Optional[int] = upsampling_ratios
_UpperCAmelCase : int = norm_type
_UpperCAmelCase : List[Any] = kernel_size
_UpperCAmelCase : List[Any] = last_kernel_size
_UpperCAmelCase : List[Any] = residual_kernel_size
_UpperCAmelCase : List[str] = dilation_growth_rate
_UpperCAmelCase : Dict = use_causal_conv
_UpperCAmelCase : Tuple = pad_mode
_UpperCAmelCase : Tuple = compress
_UpperCAmelCase : List[str] = num_lstm_layers
_UpperCAmelCase : List[Any] = trim_right_ratio
_UpperCAmelCase : int = codebook_size
_UpperCAmelCase : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size
_UpperCAmelCase : Optional[int] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**A )
@property
def _A ( self : Any ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A ( self : Union[str, Any] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A ( self : str ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 31
| 1
|
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate
@dataclass(frozen=snake_case__ )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = field(default="audio-classification" , metadata={"include_in_asdict_even_if_is_default": True} )
__UpperCamelCase: ClassVar[Features] = Features({"audio": Audio()} )
__UpperCamelCase: ClassVar[Features] = Features({"labels": ClassLabel} )
__UpperCamelCase: str = "audio"
__UpperCamelCase: str = "labels"
def _A ( self : int , A : List[Any] ):
if self.label_column not in features:
raise ValueError(F"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] , A ):
raise ValueError(F"""Column {self.label_column} is not a ClassLabel.""" )
_UpperCAmelCase : Optional[int] = copy.deepcopy(self )
_UpperCAmelCase : List[str] = self.label_schema.copy()
_UpperCAmelCase : List[str] = features[self.label_column]
_UpperCAmelCase : str = label_schema
return task_template
@property
def _A ( self : int ):
return {
self.audio_column: "audio",
self.label_column: "labels",
}
| 31
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Any , A : Optional[int]=None , A : Tuple=None , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
if config is None:
assert isinstance(self.model , A ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
_UpperCAmelCase : str = self.model.config
else:
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : List[Any] = data_args
_UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , A ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
_UpperCAmelCase : Optional[Any] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase : Dict = label_smoothed_nll_loss
def _A ( self : Tuple , A : int ):
if self.optimizer is None:
_UpperCAmelCase : Tuple = ["bias", "LayerNorm.weight"]
_UpperCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
_UpperCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase : List[str] = Adafactor
_UpperCAmelCase : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
_UpperCAmelCase : List[str] = AdamW
_UpperCAmelCase : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
_UpperCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase : List[Any] = OSS(
params=A , optim=A , **A , )
else:
_UpperCAmelCase : Union[str, Any] = optimizer_cls(A , **A )
if self.lr_scheduler is None:
_UpperCAmelCase : List[str] = self._get_lr_scheduler(A )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : List[str] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A )
return scheduler
def _A ( self : Tuple ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _A ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : List[Any] ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase : List[str] = model(**A , use_cache=A )[0]
_UpperCAmelCase : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase : Any = model(**A , labels=A , use_cache=A )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase : Optional[int] = model(**A , use_cache=A )[0]
_UpperCAmelCase : List[str] = torch.nn.functional.log_softmax(A , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.loss_fn(A , A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _A ( self : List[str] , A : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Union[str, Any] = inputs.pop("labels" )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self._compute_loss(A , A , A )
return loss
def _A ( self : List[str] , A : nn.Module , A : Dict[str, Union[torch.Tensor, Any]] , A : bool , A : Optional[List[str]] = None , ):
_UpperCAmelCase : List[str] = self._prepare_inputs(A )
_UpperCAmelCase : Dict = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : int = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
_UpperCAmelCase : Any = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase : str = self._compute_loss(A , A , A )
_UpperCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
return (loss, logits, labels)
def _A ( self : Dict , A : int , A : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
_UpperCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase : Tuple = tensor
return padded_tensor
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| 1
|
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@property
def _A ( self : List[str] ):
torch.manual_seed(0 )
_UpperCAmelCase : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def _A ( self : int ):
_UpperCAmelCase : Optional[Any] = self.dummy_uncond_unet
_UpperCAmelCase : Any = ScoreSdeVeScheduler()
_UpperCAmelCase : str = ScoreSdeVePipeline(unet=A , scheduler=A )
sde_ve.to(A )
sde_ve.set_progress_bar_config(disable=A )
_UpperCAmelCase : List[str] = torch.manual_seed(0 )
_UpperCAmelCase : List[str] = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=A ).images
_UpperCAmelCase : Optional[int] = torch.manual_seed(0 )
_UpperCAmelCase : List[Any] = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=A , return_dict=A )[
0
]
_UpperCAmelCase : Tuple = image[0, -3:, -3:, -1]
_UpperCAmelCase : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
_UpperCAmelCase : List[Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : int ):
_UpperCAmelCase : int = "google/ncsnpp-church-256"
_UpperCAmelCase : Any = UNetaDModel.from_pretrained(A )
_UpperCAmelCase : List[Any] = ScoreSdeVeScheduler.from_pretrained(A )
_UpperCAmelCase : Union[str, Any] = ScoreSdeVePipeline(unet=A , scheduler=A )
sde_ve.to(A )
sde_ve.set_progress_bar_config(disable=A )
_UpperCAmelCase : int = torch.manual_seed(0 )
_UpperCAmelCase : Tuple = sde_ve(num_inference_steps=10 , output_type="numpy" , generator=A ).images
_UpperCAmelCase : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
_UpperCAmelCase : int = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
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'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["input_features", "is_longer"]
def __init__( self : str , A : int=64 , A : Dict=48000 , A : str=480 , A : List[Any]=10 , A : Optional[Any]=1024 , A : Tuple=0.0 , A : List[Any]=False , A : float = 0 , A : float = 14000 , A : int = None , A : str = "fusion" , A : str = "repeatpad" , **A : Dict , ):
super().__init__(
feature_size=A , sampling_rate=A , padding_value=A , return_attention_mask=A , **A , )
_UpperCAmelCase : Optional[Any] = top_db
_UpperCAmelCase : Dict = truncation
_UpperCAmelCase : List[Any] = padding
_UpperCAmelCase : Optional[Any] = fft_window_size
_UpperCAmelCase : Dict = (fft_window_size >> 1) + 1
_UpperCAmelCase : Any = hop_length
_UpperCAmelCase : Tuple = max_length_s
_UpperCAmelCase : str = max_length_s * sampling_rate
_UpperCAmelCase : Any = sampling_rate
_UpperCAmelCase : Optional[int] = frequency_min
_UpperCAmelCase : str = frequency_max
_UpperCAmelCase : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm=A , mel_scale="htk" , )
_UpperCAmelCase : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm="slaney" , mel_scale="slaney" , )
def _A ( self : List[str] ):
_UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : Dict = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _A ( self : Optional[Any] , A : np.array , A : Optional[np.array] = None ):
_UpperCAmelCase : Dict = spectrogram(
A , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=A , log_mel="dB" , )
return log_mel_spectrogram.T
def _A ( self : str , A : str , A : List[str] , A : List[Any] ):
_UpperCAmelCase : List[str] = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Optional[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Tuple = [0]
# randomly choose index for each part
_UpperCAmelCase : Dict = np.random.choice(ranges[0] )
_UpperCAmelCase : str = np.random.choice(ranges[1] )
_UpperCAmelCase : Tuple = np.random.choice(ranges[2] )
_UpperCAmelCase : str = mel[idx_front : idx_front + chunk_frames, :]
_UpperCAmelCase : str = mel[idx_middle : idx_middle + chunk_frames, :]
_UpperCAmelCase : List[Any] = mel[idx_back : idx_back + chunk_frames, :]
_UpperCAmelCase : Dict = torch.tensor(mel[None, None, :] )
_UpperCAmelCase : Optional[Any] = torch.nn.functional.interpolate(
A , size=[chunk_frames, 64] , mode="bilinear" , align_corners=A )
_UpperCAmelCase : List[str] = mel_shrink[0][0].numpy()
_UpperCAmelCase : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _A ( self : List[Any] , A : np.array , A : List[str] , A : Any , A : Optional[int] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
_UpperCAmelCase : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
_UpperCAmelCase : str = len(A ) - max_length
_UpperCAmelCase : str = np.random.randint(0 , overflow + 1 )
_UpperCAmelCase : int = waveform[idx : idx + max_length]
_UpperCAmelCase : Any = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
_UpperCAmelCase : Tuple = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : List[str] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
_UpperCAmelCase : Optional[Any] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
_UpperCAmelCase : Any = np.stack([mel, mel, mel, mel] , axis=0 )
_UpperCAmelCase : int = False
else:
_UpperCAmelCase : Tuple = self._random_mel_fusion(A , A , A )
_UpperCAmelCase : Any = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
_UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
_UpperCAmelCase : str = int(max_length / len(A ) )
_UpperCAmelCase : Dict = np.stack(np.tile(A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
_UpperCAmelCase : Dict = int(max_length / len(A ) )
_UpperCAmelCase : List[str] = np.stack(np.tile(A , A ) )
_UpperCAmelCase : Optional[Any] = np.pad(A , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
_UpperCAmelCase : str = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : Optional[int] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
_UpperCAmelCase : List[str] = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : Union[str, Any] , A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A : str = None , A : Optional[str] = None , A : Optional[int] = None , A : Optional[int] = None , A : Optional[Union[str, TensorType]] = None , **A : List[str] , ):
_UpperCAmelCase : int = truncation if truncation is not None else self.truncation
_UpperCAmelCase : Optional[int] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : Optional[Any] = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : int = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
_UpperCAmelCase : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[str] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
_UpperCAmelCase : Dict = [
self._get_input_mel(A , max_length if max_length else self.nb_max_samples , A , A )
for waveform in raw_speech
]
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[Any] = []
for mel, longer in padded_inputs:
input_mel.append(A )
is_longer.append(A )
if truncation == "fusion" and sum(A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
_UpperCAmelCase : Union[str, Any] = np.random.randint(0 , len(A ) )
_UpperCAmelCase : Optional[Any] = True
if isinstance(input_mel[0] , A ):
_UpperCAmelCase : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
_UpperCAmelCase : Tuple = [[longer] for longer in is_longer]
_UpperCAmelCase : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
_UpperCAmelCase : Tuple = BatchFeature(A )
if return_tensors is not None:
_UpperCAmelCase : List[Any] = input_features.convert_to_tensors(A )
return input_features
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| 1
|
'''simple docstring'''
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def UpperCamelCase_ ( _UpperCAmelCase : List[str] ) -> str:
"""simple docstring"""
_UpperCAmelCase : Tuple = []
for line in lines:
_UpperCAmelCase : Optional[Any] = re.sub(R"#.*" , "" , _UpperCAmelCase ) # remove comments
if line:
filtered_lines.append(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = "\n".join(_UpperCAmelCase )
# Make a hash from all this code
_UpperCAmelCase : Optional[int] = full_str.encode("utf-8" )
return shaaaa(_UpperCAmelCase ).hexdigest()
# get importable module names and hash for caching
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""csv""": (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
"""json""": (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
"""pandas""": (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
"""parquet""": (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
"""arrow""": (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
"""text""": (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
"""imagefolder""": (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
"""audiofolder""": (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
__SCREAMING_SNAKE_CASE : Tuple = {
""".csv""": ("""csv""", {}),
""".tsv""": ("""csv""", {"""sep""": """\t"""}),
""".json""": ("""json""", {}),
""".jsonl""": ("""json""", {}),
""".parquet""": ("""parquet""", {}),
""".arrow""": ("""arrow""", {}),
""".txt""": ("""text""", {}),
}
_EXTENSION_TO_MODULE.update({ext: ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""imagefolder""", {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ("""audiofolder""", {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
__SCREAMING_SNAKE_CASE : str = {"""imagefolder""", """audiofolder"""}
# Used to filter data files based on extensions given a module name
__SCREAMING_SNAKE_CASE : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append(""".zip""")
_MODULE_TO_EXTENSIONS["audiofolder"].append(""".zip""")
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'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""GPTNeoXTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXForCausalLM""",
"""GPTNeoXForQuestionAnswering""",
"""GPTNeoXForSequenceClassification""",
"""GPTNeoXForTokenClassification""",
"""GPTNeoXLayer""",
"""GPTNeoXModel""",
"""GPTNeoXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
| 1
|
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = prime_factors(_UpperCAmelCase )
if is_square_free(_UpperCAmelCase ):
return -1 if len(_UpperCAmelCase ) % 2 else 1
return 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , A : Any , A : str , A : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = graph
self._normalize_graph(A , A )
_UpperCAmelCase : List[str] = len(A )
_UpperCAmelCase : Tuple = None
def _A ( self : Any , A : List[Any] , A : str ):
if sources is int:
_UpperCAmelCase : List[Any] = [sources]
if sinks is int:
_UpperCAmelCase : List[Any] = [sinks]
if len(A ) == 0 or len(A ) == 0:
return
_UpperCAmelCase : str = sources[0]
_UpperCAmelCase : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A ) > 1 or len(A ) > 1:
_UpperCAmelCase : Dict = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCAmelCase : Optional[Any] = max_input_flow
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCAmelCase : Dict = max_input_flow
_UpperCAmelCase : List[Any] = size - 1
def _A ( self : Union[str, Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _A ( self : Tuple , A : Dict ):
_UpperCAmelCase : str = algorithm(self )
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : str ):
_UpperCAmelCase : Optional[int] = flow_network
_UpperCAmelCase : Any = flow_network.verticesCount
_UpperCAmelCase : List[str] = flow_network.sourceIndex
_UpperCAmelCase : Union[str, Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCAmelCase : Any = flow_network.graph
_UpperCAmelCase : Union[str, Any] = False
def _A ( self : List[str] ):
if not self.executed:
self._algorithm()
_UpperCAmelCase : int = True
def _A ( self : List[Any] ):
pass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[str, Any] ):
super().__init__(A )
# use this to save your result
_UpperCAmelCase : Any = -1
def _A ( self : Union[str, Any] ):
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : int ):
super().__init__(A )
_UpperCAmelCase : List[str] = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCAmelCase : Union[str, Any] = [0] * self.verticies_count
_UpperCAmelCase : int = [0] * self.verticies_count
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCAmelCase : Optional[int] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCAmelCase : Any = 0
while i < len(A ):
_UpperCAmelCase : int = vertices_list[i]
_UpperCAmelCase : int = self.heights[vertex_index]
self.process_vertex(A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A ) )
_UpperCAmelCase : Union[str, Any] = 0
else:
i += 1
_UpperCAmelCase : List[Any] = sum(self.preflow[self.source_index] )
def _A ( self : Union[str, Any] , A : str ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A , A )
self.relabel(A )
def _A ( self : int , A : Dict , A : List[str] ):
_UpperCAmelCase : int = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _A ( self : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : str = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCAmelCase : Tuple = self.heights[to_index]
if min_height is not None:
_UpperCAmelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__SCREAMING_SNAKE_CASE : List[Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__SCREAMING_SNAKE_CASE : Optional[Any] = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 31
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|
'''simple docstring'''
import darl # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline
__SCREAMING_SNAKE_CASE : Any = {
"""n_samples""": 64,
"""horizon""": 32,
"""num_inference_steps""": 20,
"""n_guide_steps""": 2, # can set to 0 for faster sampling, does not use value network
"""scale_grad_by_std""": True,
"""scale""": 0.1,
"""eta""": 0.0,
"""t_grad_cutoff""": 2,
"""device""": """cpu""",
}
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = """hopper-medium-v2"""
__SCREAMING_SNAKE_CASE : Optional[int] = gym.make(env_name)
__SCREAMING_SNAKE_CASE : Any = ValueGuidedRLPipeline.from_pretrained(
"""bglick13/hopper-medium-v2-value-function-hor32""",
env=env,
)
env.seed(0)
__SCREAMING_SNAKE_CASE : Any = env.reset()
__SCREAMING_SNAKE_CASE : str = 0
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : Any = 1_000
__SCREAMING_SNAKE_CASE : str = [obs.copy()]
try:
for t in tqdm.tqdm(range(T)):
# call the policy
__SCREAMING_SNAKE_CASE : int = pipeline(obs, planning_horizon=32)
# execute action in environment
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Any = env.step(denorm_actions)
__SCREAMING_SNAKE_CASE : int = env.get_normalized_score(total_reward)
# update return
total_reward += reward
total_score += score
print(
F'Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:'
F' {total_score}'
)
# save observations for rendering
rollout.append(next_observation.copy())
__SCREAMING_SNAKE_CASE : int = next_observation
except KeyboardInterrupt:
pass
print(F'Total reward: {total_reward}')
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> float:
"""simple docstring"""
def get_matched_characters(_UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_UpperCAmelCase : int = int(max(0 , i - limit ) )
_UpperCAmelCase : Any = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = F"""{_stra[0:_stra.index(_UpperCAmelCase )]} {_stra[_stra.index(_UpperCAmelCase ) + 1:]}"""
return "".join(_UpperCAmelCase )
# matching characters
_UpperCAmelCase : Union[str, Any] = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = len(_UpperCAmelCase )
# transposition
_UpperCAmelCase : Optional[Any] = (
len([(ca, ca) for ca, ca in zip(_UpperCAmelCase , _UpperCAmelCase ) if ca != ca] ) // 2
)
if not match_count:
_UpperCAmelCase : Dict = 0.0
else:
_UpperCAmelCase : Optional[int] = (
1
/ 3
* (
match_count / len(_UpperCAmelCase )
+ match_count / len(_UpperCAmelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_UpperCAmelCase : str = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 31
| 1
|
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : int ):
_UpperCAmelCase : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : Optional[int] = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(A )
_UpperCAmelCase : Any = -1
_UpperCAmelCase : Any = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(A )
_UpperCAmelCase : str = model.generate(A , max_new_tokens=10 , do_sample=A )
_UpperCAmelCase : Dict = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
_UpperCAmelCase : str = TextStreamer(A )
model.generate(A , max_new_tokens=10 , do_sample=A , streamer=A )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_UpperCAmelCase : int = cs.out[:-1]
self.assertEqual(A , A )
def _A ( self : Any ):
_UpperCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : Dict = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(A )
_UpperCAmelCase : List[str] = -1
_UpperCAmelCase : Union[str, Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(A )
_UpperCAmelCase : Tuple = model.generate(A , max_new_tokens=10 , do_sample=A )
_UpperCAmelCase : List[str] = tokenizer.decode(greedy_ids[0] )
_UpperCAmelCase : Union[str, Any] = TextIteratorStreamer(A )
_UpperCAmelCase : Union[str, Any] = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_UpperCAmelCase : Dict = Thread(target=model.generate , kwargs=A )
thread.start()
_UpperCAmelCase : List[Any] = ""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(A , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : Optional[Any] = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(A )
_UpperCAmelCase : Dict = -1
_UpperCAmelCase : Tuple = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(A )
_UpperCAmelCase : str = model.generate(A , max_new_tokens=10 , do_sample=A )
_UpperCAmelCase : List[Any] = greedy_ids[:, input_ids.shape[1] :]
_UpperCAmelCase : str = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
_UpperCAmelCase : int = TextStreamer(A , skip_prompt=A )
model.generate(A , max_new_tokens=10 , do_sample=A , streamer=A )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_UpperCAmelCase : Optional[Any] = cs.out[:-1]
self.assertEqual(A , A )
def _A ( self : Dict ):
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
_UpperCAmelCase : int = AutoTokenizer.from_pretrained("distilgpt2" )
_UpperCAmelCase : List[str] = AutoModelForCausalLM.from_pretrained("distilgpt2" ).to(A )
_UpperCAmelCase : Optional[int] = -1
_UpperCAmelCase : int = torch.ones((1, 5) , device=A ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_UpperCAmelCase : int = TextStreamer(A , skip_special_tokens=A )
model.generate(A , max_new_tokens=1 , do_sample=A , streamer=A )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
_UpperCAmelCase : str = cs.out[:-1] # Remove the final "\n"
_UpperCAmelCase : int = tokenizer(A , return_tensors="pt" )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_UpperCAmelCase : Dict = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(A )
_UpperCAmelCase : str = -1
_UpperCAmelCase : List[Any] = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(A )
_UpperCAmelCase : List[str] = TextIteratorStreamer(A , timeout=0.001 )
_UpperCAmelCase : List[str] = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_UpperCAmelCase : Any = Thread(target=model.generate , kwargs=A )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(A ):
_UpperCAmelCase : Tuple = ""
for new_text in streamer:
streamer_text += new_text
| 31
|
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = 1
@register_to_config
def __init__( self : Optional[int] , A : int = 1000 , A : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(A )
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_UpperCAmelCase : int = 4
# running values
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : int , A : Union[str, torch.device] = None ):
_UpperCAmelCase : int = num_inference_steps
_UpperCAmelCase : Union[str, Any] = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_UpperCAmelCase : Any = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_UpperCAmelCase : str = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_UpperCAmelCase : Dict = torch.sin(steps * math.pi / 2 ) ** 2
_UpperCAmelCase : List[Any] = (1.0 - self.betas**2) ** 0.5
_UpperCAmelCase : List[str] = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_UpperCAmelCase : Dict = timesteps.to(A )
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : torch.FloatTensor , A : int , A : torch.FloatTensor , A : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )
_UpperCAmelCase : Tuple = (self.timesteps == timestep).nonzero().item()
_UpperCAmelCase : Optional[Any] = timestep_index + 1
_UpperCAmelCase : int = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(A )
if len(self.ets ) == 1:
_UpperCAmelCase : List[Any] = self.ets[-1]
elif len(self.ets ) == 2:
_UpperCAmelCase : str = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_UpperCAmelCase : Tuple = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_UpperCAmelCase : Union[str, Any] = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_UpperCAmelCase : Union[str, Any] = self._get_prev_sample(A , A , A , A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def _A ( self : Union[str, Any] , A : torch.FloatTensor , *A : Union[str, Any] , **A : Dict ):
return sample
def _A ( self : Optional[Any] , A : Optional[int] , A : int , A : Optional[Any] , A : List[str] ):
_UpperCAmelCase : List[str] = self.alphas[timestep_index]
_UpperCAmelCase : List[Any] = self.betas[timestep_index]
_UpperCAmelCase : Optional[Any] = self.alphas[prev_timestep_index]
_UpperCAmelCase : Dict = self.betas[prev_timestep_index]
_UpperCAmelCase : Tuple = (sample - sigma * ets) / max(A , 1E-8 )
_UpperCAmelCase : List[str] = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Union[str, Any] ):
return self.config.num_train_timesteps
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : Dict , _UpperCAmelCase : str , _UpperCAmelCase : Optional[int] ) -> Any: # noqa: E741
"""simple docstring"""
while r - l > 1:
_UpperCAmelCase : List[Any] = (l + r) // 2
if v[m] >= key:
_UpperCAmelCase : Tuple = m
else:
_UpperCAmelCase : List[Any] = m # noqa: E741
return r
def UpperCamelCase_ ( _UpperCAmelCase : list[int] ) -> int:
"""simple docstring"""
if len(_UpperCAmelCase ) == 0:
return 0
_UpperCAmelCase : Any = [0] * len(_UpperCAmelCase )
_UpperCAmelCase : str = 1
_UpperCAmelCase : List[str] = v[0]
for i in range(1 , len(_UpperCAmelCase ) ):
if v[i] < tail[0]:
_UpperCAmelCase : Tuple = v[i]
elif v[i] > tail[length - 1]:
_UpperCAmelCase : Tuple = v[i]
length += 1
else:
_UpperCAmelCase : str = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase_ ( _UpperCAmelCase : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
_UpperCAmelCase : Any = XGBClassifier()
classifier.fit(_UpperCAmelCase , _UpperCAmelCase )
return classifier
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : List[str] = load_iris()
_UpperCAmelCase , _UpperCAmelCase : Dict = data_handling(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = train_test_split(
_UpperCAmelCase , _UpperCAmelCase , test_size=0.2_5 )
_UpperCAmelCase : Optional[Any] = iris["target_names"]
# Create an XGBoost Classifier from the training data
_UpperCAmelCase : Tuple = xgboost(_UpperCAmelCase , _UpperCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , display_labels=_UpperCAmelCase , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 31
| 1
|
'''simple docstring'''
import argparse
__SCREAMING_SNAKE_CASE : List[str] = """docs/source/_static/js/custom.js"""
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] ) -> List[str]:
"""simple docstring"""
with open(_UpperCAmelCase , encoding="utf-8" , newline="\n" ) as f:
_UpperCAmelCase : List[Any] = f.readlines()
_UpperCAmelCase : Union[str, Any] = 0
# First let's put the right version
while not lines[index].startswith("const stableVersion =" ):
index += 1
_UpperCAmelCase : List[str] = F"""const stableVersion = \"v{version}\"\n"""
# Then update the dictionary
while not lines[index].startswith("const versionMapping = {" ):
index += 1
# We go until the end
while not lines[index].startswith("}" ):
index += 1
# We add the new version at the end
lines[index - 1] += F""" \"v{version}\": \"v{version}\",\n"""
with open(_UpperCAmelCase , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(_UpperCAmelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
parser.add_argument("""--version""", help="""Release version.""")
__SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
update_custom_js(args.version)
| 31
|
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : Optional[Any]=13 , A : Optional[Any]=7 , A : Union[str, Any]=True , A : Optional[Any]=True , A : int=False , A : str=True , A : Optional[Any]=99 , A : Union[str, Any]=32 , A : int=5 , A : Tuple=4 , A : Union[str, Any]=37 , A : Dict="gelu" , A : Union[str, Any]=0.1 , A : str=0.1 , A : Union[str, Any]=512 , A : int=16 , A : List[str]=2 , A : Tuple=0.02 , A : int=3 , A : List[str]=4 , A : str=None , ):
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : str = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : List[str] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Dict ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _A ( self : int , A : List[Any] , A : Any , A : int , A : Union[str, Any] , A : Dict , A : List[Any] , A : Dict ):
_UpperCAmelCase : List[str] = BioGptModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : List[Any] , A : str , A : List[Any] , A : Dict , A : List[Any] , A : List[str] , A : Union[str, Any] , A : int , A : List[str] , A : Dict , ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : List[Any] , A : str , A : str , A : str , A : Any , A : List[str] , *A : Optional[int] ):
_UpperCAmelCase : str = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
_UpperCAmelCase : Optional[int] = self.seq_length // 2
_UpperCAmelCase : List[Any] = 0
# first forward pass
_UpperCAmelCase , _UpperCAmelCase : List[str] = model(A , attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_UpperCAmelCase : List[str] = ids_tensor((1,) , A ).item() + 1
_UpperCAmelCase : str = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_UpperCAmelCase : Any = random_other_next_tokens
# append to next input_ids and attn_mask
_UpperCAmelCase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=A )] , dim=1 , )
# get two different outputs
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Optional[Any] = model(A , past_key_values=A , attention_mask=A )["last_hidden_state"]
# select random slice
_UpperCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : int , A : Dict , A : str , A : Dict , A : Union[str, Any] , A : Any , *A : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = BioGptModel(config=A ).to(A ).eval()
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase , _UpperCAmelCase : Dict = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase : Any = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Dict = model(A , attention_mask=A , past_key_values=A )[
"last_hidden_state"
]
# select random slice
_UpperCAmelCase : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : Optional[Any] , A : Tuple , A : List[str] , A : Tuple , A : Dict , A : List[Any] , *A : Tuple , A : List[str]=False ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_UpperCAmelCase : Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _A ( self : Optional[Any] , A : Any , *A : Optional[Any] ):
_UpperCAmelCase : Tuple = BioGptModel(A )
_UpperCAmelCase : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def _A ( self : Optional[int] , A : Dict , A : Tuple , A : Optional[int] , A : int , A : List[str] , *A : Dict ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Any = BioGptForTokenClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
__UpperCamelCase: List[str] = (BioGptForCausalLM,) if is_torch_available() else ()
__UpperCamelCase: str = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase: Union[str, Any] = False
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = BioGptModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def _A ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : Tuple = type
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A , gradient_checkpointing=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
_UpperCAmelCase : Tuple = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : str = "left"
# Define PAD Token = EOS Token = 50256
_UpperCAmelCase : Any = tokenizer.eos_token
_UpperCAmelCase : int = model.config.eos_token_id
# use different length sentences to test batching
_UpperCAmelCase : Any = [
"Hello, my dog is a little",
"Today, I",
]
_UpperCAmelCase : Tuple = tokenizer(A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Optional[Any] = inputs["input_ids"].to(A )
_UpperCAmelCase : Any = model.generate(
input_ids=A , attention_mask=inputs["attention_mask"].to(A ) , )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : List[Any] = model.generate(input_ids=A )
_UpperCAmelCase : List[Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
_UpperCAmelCase : int = tokenizer(sentences[1] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : int = model.generate(input_ids=A , max_length=model.config.max_length - num_paddings )
_UpperCAmelCase : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
_UpperCAmelCase : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : str = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(A , A )
self.assertListEqual(A , [non_padded_sentence, padded_sentence] )
@slow
def _A ( self : str ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _A ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : List[str] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : List[str] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = 3
_UpperCAmelCase : Dict = "multi_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Optional[int] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : Optional[Any] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
_UpperCAmelCase : List[Any] = model(A )[0]
_UpperCAmelCase : int = 42384
_UpperCAmelCase : int = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@slow
def _A ( self : Any ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Tuple = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = tokenizer("COVID-19 is" , return_tensors="pt" ).to(A )
_UpperCAmelCase : Dict = model.generate(
**A , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=A , )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=A )
_UpperCAmelCase : List[str] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(A , A )
| 31
| 1
|
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
if openai_config_file == "":
_UpperCAmelCase : Tuple = OpenAIGPTConfig()
else:
_UpperCAmelCase : List[str] = OpenAIGPTConfig.from_json_file(_UpperCAmelCase )
_UpperCAmelCase : int = OpenAIGPTModel(_UpperCAmelCase )
# Load weights from numpy
load_tf_weights_in_openai_gpt(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Save pytorch-model
_UpperCAmelCase : Any = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
_UpperCAmelCase : Dict = pytorch_dump_folder_path + "/" + CONFIG_NAME
print(F"""Save PyTorch model to {pytorch_weights_dump_path}""" )
torch.save(model.state_dict() , _UpperCAmelCase )
print(F"""Save configuration file to {pytorch_config_dump_path}""" )
with open(_UpperCAmelCase , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--openai_checkpoint_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the TensorFlow checkpoint path.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
parser.add_argument(
"""--openai_config_file""",
default="""""",
type=str,
help=(
"""An optional config json file corresponding to the pre-trained OpenAI model. \n"""
"""This specifies the model architecture."""
),
)
__SCREAMING_SNAKE_CASE : Any = parser.parse_args()
convert_openai_checkpoint_to_pytorch(
args.openai_checkpoint_folder_path, args.openai_config_file, args.pytorch_dump_folder_path
)
| 31
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {
"""microsoft/resnet-50""": """https://huggingface.co/microsoft/resnet-50/blob/main/config.json""",
}
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Tuple = "resnet"
__UpperCamelCase: Union[str, Any] = ["basic", "bottleneck"]
def __init__( self : Tuple , A : Optional[int]=3 , A : Union[str, Any]=64 , A : Dict=[256, 512, 1024, 2048] , A : Tuple=[3, 4, 6, 3] , A : Optional[Any]="bottleneck" , A : int="relu" , A : List[Any]=False , A : Optional[int]=None , A : Union[str, Any]=None , **A : List[Any] , ):
super().__init__(**A )
if layer_type not in self.layer_types:
raise ValueError(F"""layer_type={layer_type} is not one of {','.join(self.layer_types )}""" )
_UpperCAmelCase : Tuple = num_channels
_UpperCAmelCase : int = embedding_size
_UpperCAmelCase : Union[str, Any] = hidden_sizes
_UpperCAmelCase : int = depths
_UpperCAmelCase : Any = layer_type
_UpperCAmelCase : Optional[Any] = hidden_act
_UpperCAmelCase : Union[str, Any] = downsample_in_first_stage
_UpperCAmelCase : Dict = ["stem"] + [F"""stage{idx}""" for idx in range(1 , len(A ) + 1 )]
_UpperCAmelCase , _UpperCAmelCase : str = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[str] = version.parse("1.11" )
@property
def _A ( self : Dict ):
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def _A ( self : Dict ):
return 1E-3
| 31
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = """▁"""
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : int = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
__SCREAMING_SNAKE_CASE : str = {
"""google/pegasus-xsum""": 512,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase: Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Optional[int] = PegasusTokenizer
__UpperCamelCase: Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : Dict , A : List[str]=None , A : Union[str, Any]=None , A : Optional[int]="<pad>" , A : Tuple="</s>" , A : Union[str, Any]="<unk>" , A : Union[str, Any]="<mask_2>" , A : Dict="<mask_1>" , A : Union[str, Any]=None , A : int=103 , **A : Optional[Any] , ):
_UpperCAmelCase : Dict = offset
if additional_special_tokens is not None:
if not isinstance(A , A ):
raise TypeError(
F"""additional_special_tokens should be of type {type(A )}, but is"""
F""" {type(A )}""" )
_UpperCAmelCase : Optional[int] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(A ) , self.offset - 1 )
]
if len(set(A ) ) != len(A ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
_UpperCAmelCase : Any = additional_special_tokens_extended
else:
_UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
A , tokenizer_file=A , pad_token=A , eos_token=A , unk_token=A , mask_token=A , mask_token_sent=A , offset=A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[Any] = vocab_file
_UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
def _A ( self : List[str] , A : Optional[Any] ):
_UpperCAmelCase : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : str , A : List , A : Optional[List] = None , A : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(A )
elif token_ids_a is None:
return self._special_token_mask(A ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : Optional[int] , A : Union[str, Any] , A : int=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : Union[str, Any] , A : str , A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 31
| 1
|
'''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : int , A : int ):
# we need a list not a string, so do something to change the type
_UpperCAmelCase : int = arr.split("," )
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = [int(self.array[0] )] * len(self.array )
_UpperCAmelCase : List[str] = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
_UpperCAmelCase : Optional[int] = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
_UpperCAmelCase : List[str] = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Any = input("""please input some numbers:""")
__SCREAMING_SNAKE_CASE : Optional[int] = SubArray(whole_array)
__SCREAMING_SNAKE_CASE : Any = array.solve_sub_array()
print(("""the results is:""", re))
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__SCREAMING_SNAKE_CASE : Optional[int] = 256_047
__SCREAMING_SNAKE_CASE : Optional[int] = 256_145
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = NllbTokenizer
__UpperCamelCase: Tuple = NllbTokenizerFast
__UpperCamelCase: Union[str, Any] = True
__UpperCamelCase: Dict = True
__UpperCamelCase: Optional[Any] = {}
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
_UpperCAmelCase : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase : List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Dict = tokenizer_r.save_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
_UpperCAmelCase : Optional[int] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : List[str] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
_UpperCAmelCase : str = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : Optional[int] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
def _A ( self : Tuple ):
if not self.test_seqaseq:
return
_UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_UpperCAmelCase : Optional[Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
_UpperCAmelCase : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=A , tgt_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_UpperCAmelCase : Tuple = tokenizer.prepare_seqaseq_batch(
A , tgt_texts=A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_UpperCAmelCase : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def _A ( self : List[Any] ):
pass
def _A ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Any = [AddedToken("<special>" , lstrip=A )]
_UpperCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Dict = tokenizer_r.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_r.encode("<special>" , add_special_tokens=A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = "facebook/nllb-200-distilled-600M"
__UpperCamelCase: Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__UpperCamelCase: str = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__UpperCamelCase: str = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _A ( cls : int ):
_UpperCAmelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
_UpperCAmelCase : Union[str, Any] = 1
return cls
def _A ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 256057 )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def _A ( self : Tuple ):
self.assertIn(A , self.tokenizer.all_special_ids )
# fmt: off
_UpperCAmelCase : List[Any] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_UpperCAmelCase : Tuple = self.tokenizer.decode(A , skip_special_tokens=A )
_UpperCAmelCase : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , A )
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Tuple = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , A )
self.assertEqual(len(A ) , A )
def _A ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [256203, 3] )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
_UpperCAmelCase : Tuple = NllbTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def _A ( self : Dict ):
_UpperCAmelCase : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Tuple = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(A , A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_UpperCAmelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : Dict = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=10 , return_tensors="pt" )
_UpperCAmelCase : List[Any] = targets["input_ids"]
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(
A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
} , )
@require_torch
def _A ( self : Any ):
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Any = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : str = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 31
| 1
|
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_UpperCAmelCase , _UpperCAmelCase : int = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = list(range(10, 0, -1))
print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
__SCREAMING_SNAKE_CASE : List[str] = typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
__SCREAMING_SNAKE_CASE : List[str] = typing.Union[np.floataa, int, float] # noqa: UP007
def UpperCamelCase_ ( _UpperCAmelCase : Vector , _UpperCAmelCase : Vector ) -> VectorOut:
"""simple docstring"""
return np.sqrt(np.sum((np.asarray(_UpperCAmelCase ) - np.asarray(_UpperCAmelCase )) ** 2 ) )
def UpperCamelCase_ ( _UpperCAmelCase : Vector , _UpperCAmelCase : Vector ) -> VectorOut:
"""simple docstring"""
return sum((va - va) ** 2 for va, va in zip(_UpperCAmelCase , _UpperCAmelCase ) ) ** (1 / 2)
if __name__ == "__main__":
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
from timeit import timeit
print("Without Numpy" )
print(
timeit(
"euclidean_distance_no_np([1, 2, 3], [4, 5, 6])" , number=10_000 , globals=globals() , ) )
print("With Numpy" )
print(
timeit(
"euclidean_distance([1, 2, 3], [4, 5, 6])" , number=10_000 , globals=globals() , ) )
benchmark()
| 31
|
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
_UpperCAmelCase : Optional[int] = nn.ModuleList(A )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : torch.Tensor , A : List[torch.tensor] , A : List[float] , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[Dict[str, Any]] = None , A : bool = False , A : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(A , A , self.nets ) ):
_UpperCAmelCase , _UpperCAmelCase : str = controlnet(
A , A , A , A , A , A , A , A , A , A , A , )
# merge samples
if i == 0:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = down_samples, mid_sample
else:
_UpperCAmelCase : Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A , A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _A ( self : List[str] , A : Union[str, os.PathLike] , A : bool = True , A : Callable = None , A : bool = False , A : Optional[str] = None , ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A , is_main_process=A , save_function=A , safe_serialization=A , variant=A , )
idx += 1
_UpperCAmelCase : Tuple = model_path_to_save + F"""_{idx}"""
@classmethod
def _A ( cls : int , A : Optional[Union[str, os.PathLike]] , **A : Tuple ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_UpperCAmelCase : int = pretrained_model_path
while os.path.isdir(A ):
_UpperCAmelCase : List[str] = ControlNetModel.from_pretrained(A , **A )
controlnets.append(A )
idx += 1
_UpperCAmelCase : Dict = pretrained_model_path + F"""_{idx}"""
logger.info(F"""{len(A )} controlnets loaded from {pretrained_model_path}.""" )
if len(A ) == 0:
raise ValueError(
F"""No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(A )
| 31
| 1
|
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
__SCREAMING_SNAKE_CASE : Any = imread(R"""digital_image_processing/image_data/lena_small.jpg""")
__SCREAMING_SNAKE_CASE : Tuple = cvtColor(img, COLOR_BGR2GRAY)
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[Any] = cn.convert_to_negative(_UpperCAmelCase )
# assert negative_img array for at least one True
assert negative_img.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
with Image.open("digital_image_processing/image_data/lena_small.jpg" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(_UpperCAmelCase , 110 ) ).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at" )
def UpperCamelCase_ ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : int = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def UpperCamelCase_ ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : str = imread("digital_image_processing/image_data/lena_small.jpg" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
_UpperCAmelCase : str = canny.canny(_UpperCAmelCase )
# assert canny array for at least one True
assert canny_array.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
assert gg.gaussian_filter(_UpperCAmelCase , 5 , sigma=0.9 ).all()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = array([[0.2_5, 0.5, 0.2_5], [0.5, -3, 0.5], [0.2_5, 0.5, 0.2_5]] )
_UpperCAmelCase : List[str] = conv.img_convolve(_UpperCAmelCase , _UpperCAmelCase ).astype(_UpperCAmelCase )
assert res.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
assert med.median_filter(_UpperCAmelCase , 3 ).any()
def UpperCamelCase_ ( ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase , _UpperCAmelCase : Any = sob.sobel_filter(_UpperCAmelCase )
assert grad.any() and theta.any()
def UpperCamelCase_ ( ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = sp.make_sepia(_UpperCAmelCase , 20 )
assert sepia.all()
def UpperCamelCase_ ( _UpperCAmelCase : str = "digital_image_processing/image_data/lena_small.jpg" ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : Tuple = bs.Burkes(imread(_UpperCAmelCase , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def UpperCamelCase_ ( _UpperCAmelCase : str = "digital_image_processing/image_data/lena_small.jpg" , ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Tuple = rs.NearestNeighbour(imread(_UpperCAmelCase , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def UpperCamelCase_ ( ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : str = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
_UpperCAmelCase : Tuple = imread(_UpperCAmelCase , 0 )
# Test for get_neighbors_pixel function() return not None
_UpperCAmelCase : Any = 0
_UpperCAmelCase : str = 0
_UpperCAmelCase : Tuple = image[x_coordinate][y_coordinate]
_UpperCAmelCase : Dict = lbp.get_neighbors_pixel(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
_UpperCAmelCase : Dict = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
_UpperCAmelCase : List[str] = lbp.local_binary_value(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
assert lbp_image.any()
| 31
|
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_UpperCAmelCase : List[Any] = MaskFormerConfig(backbone_config=_UpperCAmelCase )
_UpperCAmelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_UpperCAmelCase : Dict = 847
_UpperCAmelCase : Any = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_UpperCAmelCase : Any = 150
_UpperCAmelCase : Any = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_UpperCAmelCase : Tuple = 171
_UpperCAmelCase : Union[str, Any] = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_UpperCAmelCase : Any = 133
_UpperCAmelCase : int = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 19
_UpperCAmelCase : str = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 65
_UpperCAmelCase : Tuple = "mapillary-vistas-id2label.json"
_UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : List[str] = val
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase : Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase : Any = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_UpperCAmelCase : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[:dim, :]
_UpperCAmelCase : Tuple = in_proj_bias[: dim]
_UpperCAmelCase : List[Any] = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase : List[str] = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase : Optional[Any] = in_proj_weight[
-dim :, :
]
_UpperCAmelCase : Dict = in_proj_bias[-dim :]
# fmt: on
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : int = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : int = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Optional[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Any = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Tuple = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : Optional[int] = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[-hidden_size :]
# fmt: on
def UpperCamelCase_ ( ) -> torch.Tensor:
"""simple docstring"""
_UpperCAmelCase : int = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : Any = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = get_maskformer_config(_UpperCAmelCase )
# load original state_dict
with open(_UpperCAmelCase , "rb" ) as f:
_UpperCAmelCase : Optional[int] = pickle.load(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_UpperCAmelCase : Any = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_swin_q_k_v(_UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_UpperCAmelCase , _UpperCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_UpperCAmelCase : Tuple = torch.from_numpy(_UpperCAmelCase )
# load 🤗 model
_UpperCAmelCase : Union[str, Any] = MaskFormerForInstanceSegmentation(_UpperCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_UpperCAmelCase , param.shape )
_UpperCAmelCase , _UpperCAmelCase : Any = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_UpperCAmelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_UpperCAmelCase : Optional[int] = prepare_img()
if "vistas" in model_name:
_UpperCAmelCase : int = 65
elif "cityscapes" in model_name:
_UpperCAmelCase : Tuple = 65_535
else:
_UpperCAmelCase : Any = 255
_UpperCAmelCase : Optional[Any] = True if "ade" in model_name else False
_UpperCAmelCase : Optional[int] = MaskFormerImageProcessor(ignore_index=_UpperCAmelCase , reduce_labels=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = image_processor(_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : List[Any] = model(**_UpperCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_UpperCAmelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 31
| 1
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Any , A : Optional[int]=None , A : Tuple=None , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
if config is None:
assert isinstance(self.model , A ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
_UpperCAmelCase : str = self.model.config
else:
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : List[Any] = data_args
_UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , A ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
_UpperCAmelCase : Optional[Any] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase : Dict = label_smoothed_nll_loss
def _A ( self : Tuple , A : int ):
if self.optimizer is None:
_UpperCAmelCase : Tuple = ["bias", "LayerNorm.weight"]
_UpperCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
_UpperCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase : List[str] = Adafactor
_UpperCAmelCase : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
_UpperCAmelCase : List[str] = AdamW
_UpperCAmelCase : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
_UpperCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase : List[Any] = OSS(
params=A , optim=A , **A , )
else:
_UpperCAmelCase : Union[str, Any] = optimizer_cls(A , **A )
if self.lr_scheduler is None:
_UpperCAmelCase : List[str] = self._get_lr_scheduler(A )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : List[str] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A )
return scheduler
def _A ( self : Tuple ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _A ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : List[Any] ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase : List[str] = model(**A , use_cache=A )[0]
_UpperCAmelCase : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase : Any = model(**A , labels=A , use_cache=A )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase : Optional[int] = model(**A , use_cache=A )[0]
_UpperCAmelCase : List[str] = torch.nn.functional.log_softmax(A , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.loss_fn(A , A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _A ( self : List[str] , A : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Union[str, Any] = inputs.pop("labels" )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self._compute_loss(A , A , A )
return loss
def _A ( self : List[str] , A : nn.Module , A : Dict[str, Union[torch.Tensor, Any]] , A : bool , A : Optional[List[str]] = None , ):
_UpperCAmelCase : List[str] = self._prepare_inputs(A )
_UpperCAmelCase : Dict = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : int = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
_UpperCAmelCase : Any = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase : str = self._compute_loss(A , A , A )
_UpperCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
return (loss, logits, labels)
def _A ( self : Dict , A : int , A : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
_UpperCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase : Tuple = tensor
return padded_tensor
| 31
|
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : Optional[str] = None ):
_UpperCAmelCase : Dict = (
os.path.join(A , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_UpperCAmelCase : Union[str, Any] = Extractor
def _A ( self : Tuple , A : str ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_UpperCAmelCase : Dict = os.path.abspath(A )
return os.path.join(self.extract_dir , hash_url_to_filename(A ) )
def _A ( self : int , A : str , A : bool ):
return force_extract or (
not os.path.isfile(A ) and not (os.path.isdir(A ) and os.listdir(A ))
)
def _A ( self : Optional[int] , A : str , A : bool = False ):
_UpperCAmelCase : Union[str, Any] = self.extractor.infer_extractor_format(A )
if not extractor_format:
return input_path
_UpperCAmelCase : Optional[Any] = self._get_output_path(A )
if self._do_extract(A , A ):
self.extractor.extract(A , A , A )
return output_path
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
@abstractmethod
def _A ( cls : str , A : Union[Path, str] , **A : Dict ):
...
@staticmethod
@abstractmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
...
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[bytes] = []
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
with open(A , "rb" ) as f:
return f.read(A )
@classmethod
def _A ( cls : Any , A : Union[Path, str] , A : bytes = b"" ):
if not magic_number:
_UpperCAmelCase : Any = max(len(A ) for cls_magic_number in cls.magic_numbers )
try:
_UpperCAmelCase : int = cls.read_magic_number(A , A )
except OSError:
return False
return any(magic_number.startswith(A ) for cls_magic_number in cls.magic_numbers )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
def _A ( cls : str , A : Union[Path, str] , **A : List[Any] ):
return tarfile.is_tarfile(A )
@staticmethod
def _A ( A : Union[str, Any] , A : str ):
def resolved(A : str ) -> str:
return os.path.realpath(os.path.abspath(A ) )
def badpath(A : str , A : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(A , A ) ).startswith(A )
def badlink(A : str , A : str ) -> bool:
# Links are interpreted relative to the directory containing the link
_UpperCAmelCase : List[str] = resolved(os.path.join(A , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=A )
_UpperCAmelCase : Optional[int] = resolved(A )
for finfo in members:
if badpath(finfo.name , A ):
logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : int = tarfile.open(A )
tar_file.extractall(A , members=TarExtractor.safemembers(A , A ) )
tar_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = [b"\x1F\x8B"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with gzip.open(A , "rb" ) as gzip_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [
b"PK\x03\x04",
b"PK\x05\x06", # empty archive
b"PK\x07\x08", # spanned archive
]
@classmethod
def _A ( cls : Dict , A : Union[Path, str] , A : bytes = b"" ):
if super().is_extractable(A , magic_number=A ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(A , "rb" ) as fp:
_UpperCAmelCase : Tuple = _EndRecData(A )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_UpperCAmelCase : Dict = fp.read(A ) # CD is where we expect it to be
if len(A ) == sizeCentralDir:
_UpperCAmelCase : Any = struct.unpack(A , A ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
with zipfile.ZipFile(A , "r" ) as zip_file:
zip_file.extractall(A )
zip_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [b"\xFD\x37\x7A\x58\x5A\x00"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with lzma.open(A ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[str] = [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : List[str] = rarfile.RarFile(A )
rf.extractall(A )
rf.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x28\xb5\x2F\xFD"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
_UpperCAmelCase : Optional[Any] = zstd.ZstdDecompressor()
with open(A , "rb" ) as ifh, open(A , "wb" ) as ofh:
dctx.copy_stream(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x42\x5A\x68"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with bza.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = [b"\x37\x7A\xBC\xAF\x27\x1C"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr" )
import pyazr
os.makedirs(A , exist_ok=A )
with pyazr.SevenZipFile(A , "r" ) as archive:
archive.extractall(A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = [b"\x04\x22\x4D\x18"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4" )
import lza.frame
with lza.frame.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _A ( cls : List[Any] ):
return max(
len(A )
for extractor in cls.extractors.values()
if issubclass(A , A )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
try:
return MagicNumberBaseExtractor.read_magic_number(A , magic_number_length=A )
except OSError:
return b""
@classmethod
def _A ( cls : Optional[Any] , A : Union[Path, str] , A : bool = False ):
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead." , category=A , )
_UpperCAmelCase : Union[str, Any] = cls.infer_extractor_format(A )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _A ( cls : Dict , A : Union[Path, str] ): # <Added version="2.4.0"/>
_UpperCAmelCase : Optional[int] = cls._get_magic_number_max_length()
_UpperCAmelCase : str = cls._read_magic_number(A , A )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(A , magic_number=A ):
return extractor_format
@classmethod
def _A ( cls : List[str] , A : Union[Path, str] , A : Union[Path, str] , A : Optional[str] = None , A : Optional[BaseExtractor] = "deprecated" , ):
os.makedirs(os.path.dirname(A ) , exist_ok=A )
# Prevent parallel extractions
_UpperCAmelCase : Tuple = str(Path(A ).with_suffix(".lock" ) )
with FileLock(A ):
shutil.rmtree(A , ignore_errors=A )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(A , A ): # passed as positional arg
warnings.warn(
"Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'extractor_format' instead." , category=A , )
_UpperCAmelCase : Tuple = extractor if extractor != "deprecated" else extractor_format
else:
_UpperCAmelCase : Tuple = cls.extractors[extractor_format]
return extractor.extract(A , A )
else:
warnings.warn(
"Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
"exception in 3.0.0." , category=A , )
for extractor in cls.extractors.values():
if extractor.is_extractable(A ):
return extractor.extract(A , A )
| 31
| 1
|
'''simple docstring'''
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : Union[int, Iterable[int]] , _UpperCAmelCase : bool , _UpperCAmelCase : int ) -> Tuple[int, int]:
"""simple docstring"""
def constraint_to_multiple_of(_UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Any=0 , _UpperCAmelCase : List[Any]=None ):
_UpperCAmelCase : Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_UpperCAmelCase : Any = math.floor(val / multiple ) * multiple
if x < min_val:
_UpperCAmelCase : Optional[Any] = math.ceil(val / multiple ) * multiple
return x
_UpperCAmelCase : str = (output_size, output_size) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else output_size
_UpperCAmelCase , _UpperCAmelCase : Dict = get_image_size(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase : Dict = output_size
# determine new height and width
_UpperCAmelCase : List[str] = output_height / input_height
_UpperCAmelCase : str = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_UpperCAmelCase : Any = scale_width
else:
# fit height
_UpperCAmelCase : List[Any] = scale_height
_UpperCAmelCase : Union[str, Any] = constraint_to_multiple_of(scale_height * input_height , multiple=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = constraint_to_multiple_of(scale_width * input_width , multiple=_UpperCAmelCase )
return (new_height, new_width)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = ["pixel_values"]
def __init__( self : List[str] , A : bool = True , A : Dict[str, int] = None , A : PILImageResampling = PILImageResampling.BILINEAR , A : bool = False , A : int = 1 , A : bool = True , A : Union[int, float] = 1 / 255 , A : bool = True , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , **A : Dict , ):
super().__init__(**A )
_UpperCAmelCase : Optional[Any] = size if size is not None else {"height": 384, "width": 384}
_UpperCAmelCase : List[Any] = get_size_dict(A )
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : Any = keep_aspect_ratio
_UpperCAmelCase : Any = ensure_multiple_of
_UpperCAmelCase : Dict = resample
_UpperCAmelCase : List[str] = do_rescale
_UpperCAmelCase : List[Any] = rescale_factor
_UpperCAmelCase : str = do_normalize
_UpperCAmelCase : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_UpperCAmelCase : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def _A ( self : List[str] , A : np.ndarray , A : Dict[str, int] , A : bool = False , A : int = 1 , A : PILImageResampling = PILImageResampling.BICUBIC , A : Optional[Union[str, ChannelDimension]] = None , **A : int , ):
_UpperCAmelCase : Optional[Any] = get_size_dict(A )
if "height" not in size or "width" not in size:
raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}""" )
_UpperCAmelCase : Optional[Any] = get_resize_output_image_size(
A , output_size=(size["height"], size["width"]) , keep_aspect_ratio=A , multiple=A , )
return resize(A , size=A , resample=A , data_format=A , **A )
def _A ( self : List[Any] , A : np.ndarray , A : Union[int, float] , A : Optional[Union[str, ChannelDimension]] = None , **A : Union[str, Any] , ):
return rescale(A , scale=A , data_format=A , **A )
def _A ( self : List[Any] , A : np.ndarray , A : Union[float, List[float]] , A : Union[float, List[float]] , A : Optional[Union[str, ChannelDimension]] = None , **A : Any , ):
return normalize(A , mean=A , std=A , data_format=A , **A )
def _A ( self : List[Any] , A : ImageInput , A : bool = None , A : int = None , A : bool = None , A : int = None , A : PILImageResampling = None , A : bool = None , A : float = None , A : bool = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[float, List[float]]] = None , A : Optional[Union[str, TensorType]] = None , A : ChannelDimension = ChannelDimension.FIRST , **A : Any , ):
_UpperCAmelCase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
_UpperCAmelCase : List[Any] = size if size is not None else self.size
_UpperCAmelCase : Optional[int] = get_size_dict(A )
_UpperCAmelCase : Optional[Any] = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_UpperCAmelCase : List[str] = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_UpperCAmelCase : List[Any] = resample if resample is not None else self.resample
_UpperCAmelCase : Tuple = do_rescale if do_rescale is not None else self.do_rescale
_UpperCAmelCase : str = rescale_factor if rescale_factor is not None else self.rescale_factor
_UpperCAmelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
_UpperCAmelCase : List[Any] = image_mean if image_mean is not None else self.image_mean
_UpperCAmelCase : Any = image_std if image_std is not None else self.image_std
_UpperCAmelCase : Optional[int] = make_list_of_images(A )
if not valid_images(A ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
_UpperCAmelCase : Optional[Any] = [to_numpy_array(A ) for image in images]
if do_resize:
_UpperCAmelCase : Dict = [self.resize(image=A , size=A , resample=A ) for image in images]
if do_rescale:
_UpperCAmelCase : Union[str, Any] = [self.rescale(image=A , scale=A ) for image in images]
if do_normalize:
_UpperCAmelCase : Tuple = [self.normalize(image=A , mean=A , std=A ) for image in images]
_UpperCAmelCase : Tuple = [to_channel_dimension_format(A , A ) for image in images]
_UpperCAmelCase : Any = {"pixel_values": images}
return BatchFeature(data=A , tensor_type=A )
def _A ( self : Optional[int] , A : List[str] , A : List[Tuple] = None ):
_UpperCAmelCase : List[str] = outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(A ) != len(A ):
raise ValueError(
"Make sure that you pass in as many target sizes as the batch dimension of the logits" )
if is_torch_tensor(A ):
_UpperCAmelCase : Optional[int] = target_sizes.numpy()
_UpperCAmelCase : Any = []
for idx in range(len(A ) ):
_UpperCAmelCase : List[Any] = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="bilinear" , align_corners=A )
_UpperCAmelCase : Optional[Any] = resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(A )
else:
_UpperCAmelCase : Any = logits.argmax(dim=1 )
_UpperCAmelCase : Dict = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 31
|
'''simple docstring'''
from typing import Any
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : dict , _UpperCAmelCase : dict , _UpperCAmelCase : dict , ) -> list:
"""simple docstring"""
_validation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# Creates data structures and fill initial step
_UpperCAmelCase : dict = {}
_UpperCAmelCase : dict = {}
for state in states_space:
_UpperCAmelCase : Union[str, Any] = observations_space[0]
_UpperCAmelCase : Tuple = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
_UpperCAmelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase : Optional[Any] = observations_space[o]
_UpperCAmelCase : int = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_UpperCAmelCase : str = ""
_UpperCAmelCase : Tuple = -1
for k_state in states_space:
_UpperCAmelCase : Any = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_UpperCAmelCase : Union[str, Any] = probability
_UpperCAmelCase : str = k_state
# Update probabilities and pointers dicts
_UpperCAmelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_UpperCAmelCase : Tuple = arg_max
# The final observation
_UpperCAmelCase : Optional[Any] = observations_space[len(_UpperCAmelCase ) - 1]
# argmax for given final observation
_UpperCAmelCase : List[str] = ""
_UpperCAmelCase : Any = -1
for k_state in states_space:
_UpperCAmelCase : Optional[int] = probabilities[(k_state, final_observation)]
if probability > max_probability:
_UpperCAmelCase : int = probability
_UpperCAmelCase : Dict = k_state
_UpperCAmelCase : Dict = arg_max
# Process pointers backwards
_UpperCAmelCase : List[Any] = last_state
_UpperCAmelCase : str = []
for o in range(len(_UpperCAmelCase ) - 1 , -1 , -1 ):
result.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_not_empty(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
_validate_lists(_UpperCAmelCase , _UpperCAmelCase )
_validate_dicts(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any ) -> None:
"""simple docstring"""
_validate_list(_UpperCAmelCase , "observations_space" )
_validate_list(_UpperCAmelCase , "states_space" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list"""
raise ValueError(_UpperCAmelCase )
else:
for x in _object:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list of strings"""
raise ValueError(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_dict(_UpperCAmelCase , "initial_probabilities" , _UpperCAmelCase )
_validate_nested_dict(_UpperCAmelCase , "transition_probabilities" )
_validate_nested_dict(_UpperCAmelCase , "emission_probabilities" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_validate_dict(_object , _UpperCAmelCase , _UpperCAmelCase )
for x in _object.values():
_validate_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : type , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Any = F"""{var_name} must be a dict"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object ):
_UpperCAmelCase : Tuple = F"""{var_name} all keys must be strings"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object.values() ):
_UpperCAmelCase : List[str] = "nested dictionary " if nested else ""
_UpperCAmelCase : List[str] = F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(_UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import SpeechaTextFeatureExtractor
__SCREAMING_SNAKE_CASE : Tuple = random.Random()
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Any=1.0 , _UpperCAmelCase : Optional[Any]=None , _UpperCAmelCase : List[str]=None ) -> str:
"""simple docstring"""
if rng is None:
_UpperCAmelCase : List[Any] = global_rng
_UpperCAmelCase : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : str , A : Dict=7 , A : List[Any]=400 , A : Union[str, Any]=2000 , A : str=24 , A : Optional[Any]=24 , A : Optional[Any]=0.0 , A : Optional[int]=16000 , A : str=True , A : Optional[Any]=True , ):
_UpperCAmelCase : Optional[int] = parent
_UpperCAmelCase : int = batch_size
_UpperCAmelCase : Tuple = min_seq_length
_UpperCAmelCase : List[str] = max_seq_length
_UpperCAmelCase : Tuple = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
_UpperCAmelCase : Dict = feature_size
_UpperCAmelCase : List[Any] = num_mel_bins
_UpperCAmelCase : Union[str, Any] = padding_value
_UpperCAmelCase : Optional[Any] = sampling_rate
_UpperCAmelCase : Optional[int] = return_attention_mask
_UpperCAmelCase : Tuple = do_normalize
def _A ( self : Union[str, Any] ):
return {
"feature_size": self.feature_size,
"num_mel_bins": self.num_mel_bins,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _A ( self : str , A : Tuple=False , A : Any=False ):
def _flatten(A : Optional[Any] ):
return list(itertools.chain(*A ) )
if equal_length:
_UpperCAmelCase : List[Any] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
_UpperCAmelCase : Dict = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
_UpperCAmelCase : Optional[int] = [np.asarray(A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = SpeechaTextFeatureExtractor if is_speech_available() else None
def _A ( self : Any ):
_UpperCAmelCase : Optional[Any] = SpeechaTextFeatureExtractionTester(self )
def _A ( self : Optional[Any] , A : Any ):
self.assertTrue(np.all(np.mean(A , axis=0 ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(A , axis=0 ) - 1 ) < 1E-3 ) )
def _A ( self : Any ):
# Tests that all call wrap to encode_plus and batch_encode_plus
_UpperCAmelCase : str = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
_UpperCAmelCase : Optional[Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_UpperCAmelCase : int = [np.asarray(A ) for speech_input in speech_inputs]
# Test feature size
_UpperCAmelCase : Tuple = feature_extractor(A , padding=A , return_tensors="np" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size )
# Test not batched input
_UpperCAmelCase : Optional[Any] = feature_extractor(speech_inputs[0] , return_tensors="np" ).input_features
_UpperCAmelCase : str = feature_extractor(np_speech_inputs[0] , return_tensors="np" ).input_features
self.assertTrue(np.allclose(A , A , atol=1E-3 ) )
# Test batched
_UpperCAmelCase : List[str] = feature_extractor(A , return_tensors="np" ).input_features
_UpperCAmelCase : List[str] = feature_extractor(A , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(A , A ):
self.assertTrue(np.allclose(A , A , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
_UpperCAmelCase : str = [floats_list((1, x) )[0] for x in (800, 800, 800)]
_UpperCAmelCase : Tuple = np.asarray(A )
_UpperCAmelCase : Union[str, Any] = feature_extractor(A , return_tensors="np" ).input_features
_UpperCAmelCase : Any = feature_extractor(A , return_tensors="np" ).input_features
for enc_seq_a, enc_seq_a in zip(A , A ):
self.assertTrue(np.allclose(A , A , atol=1E-3 ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase : str = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_UpperCAmelCase : Tuple = ["longest", "max_length", "do_not_pad"]
_UpperCAmelCase : int = [None, 16, None]
for max_length, padding in zip(A , A ):
_UpperCAmelCase : str = feature_extractor(
A , padding=A , max_length=A , return_attention_mask=A )
_UpperCAmelCase : int = inputs.input_features
_UpperCAmelCase : Any = inputs.attention_mask
_UpperCAmelCase : Dict = [np.sum(A ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase : Dict = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_UpperCAmelCase : Optional[int] = ["longest", "max_length", "do_not_pad"]
_UpperCAmelCase : Optional[int] = [None, 16, None]
for max_length, padding in zip(A , A ):
_UpperCAmelCase : List[Any] = feature_extractor(
A , max_length=A , padding=A , return_tensors="np" , return_attention_mask=A )
_UpperCAmelCase : Dict = inputs.input_features
_UpperCAmelCase : Dict = inputs.attention_mask
_UpperCAmelCase : str = [np.sum(A ) for x in attention_mask]
self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]] )
self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]] )
self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6 )
self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]] )
def _A ( self : Dict ):
_UpperCAmelCase : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase : Any = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_UpperCAmelCase : Optional[int] = feature_extractor(
A , padding="max_length" , max_length=4 , truncation=A , return_tensors="np" , return_attention_mask=A , )
_UpperCAmelCase : List[Any] = inputs.input_features
_UpperCAmelCase : List[Any] = inputs.attention_mask
_UpperCAmelCase : List[str] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1] )
self._check_zero_mean_unit_variance(input_features[2] )
def _A ( self : Optional[int] ):
_UpperCAmelCase : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase : Union[str, Any] = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_UpperCAmelCase : Optional[int] = feature_extractor(
A , padding="longest" , max_length=4 , truncation=A , return_tensors="np" , return_attention_mask=A , )
_UpperCAmelCase : Dict = inputs.input_features
_UpperCAmelCase : Tuple = inputs.attention_mask
_UpperCAmelCase : List[Any] = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 4, 24) )
_UpperCAmelCase : Tuple = [floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
_UpperCAmelCase : Dict = feature_extractor(
A , padding="longest" , max_length=16 , truncation=A , return_tensors="np" , return_attention_mask=A , )
_UpperCAmelCase : int = inputs.input_features
_UpperCAmelCase : Tuple = inputs.attention_mask
_UpperCAmelCase : Any = np.sum(attention_mask == 1 , axis=1 )
self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]] )
self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]] )
self._check_zero_mean_unit_variance(input_features[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertEqual(input_features.shape , (3, 6, 24) )
def _A ( self : Optional[int] ):
import torch
_UpperCAmelCase : List[str] = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase : str = np.random.rand(100 , 32 ).astype(np.floataa )
_UpperCAmelCase : Dict = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
_UpperCAmelCase : Tuple = feature_extractor.pad([{"input_features": inputs}] , return_tensors="np" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
_UpperCAmelCase : List[str] = feature_extractor.pad([{"input_features": inputs}] , return_tensors="pt" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _A ( self : Dict , A : List[str] ):
from datasets import load_dataset
_UpperCAmelCase : List[str] = load_dataset("hf-internal-testing/librispeech_asr_dummy" , "clean" , split="validation" )
# automatic decoding with librispeech
_UpperCAmelCase : Dict = ds.sort("id" ).select(range(A ) )[:num_samples]["audio"]
return [x["array"] for x in speech_samples]
def _A ( self : str ):
# fmt: off
_UpperCAmelCase : str = np.array([
-1.5_745, -1.7_713, -1.7_020, -1.6_069, -1.2_250, -1.1_105, -0.9_072, -0.8_241,
-1.2_310, -0.8_098, -0.3_320, -0.4_101, -0.7_985, -0.4_996, -0.8_213, -0.9_128,
-1.0_420, -1.1_286, -1.0_440, -0.7_999, -0.8_405, -1.2_275, -1.5_443, -1.4_625,
] )
# fmt: on
_UpperCAmelCase : int = self._load_datasamples(1 )
_UpperCAmelCase : Tuple = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
_UpperCAmelCase : Optional[int] = feature_extractor(A , return_tensors="pt" ).input_features
self.assertEquals(input_features.shape , (1, 584, 24) )
self.assertTrue(np.allclose(input_features[0, 0, :30] , A , atol=1E-4 ) )
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , A : Dict , A : Dict=7 , A : Optional[int]=3 , A : Optional[int]=18 , A : Dict=30 , A : List[Any]=400 , A : Union[str, Any]=True , A : Tuple=None , A : List[Any]=True , A : int=None , A : Optional[int]=True , ):
_UpperCAmelCase : Optional[int] = size if size is not None else {"shortest_edge": 20}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : Optional[Any] = num_channels
_UpperCAmelCase : Union[str, Any] = image_size
_UpperCAmelCase : int = min_resolution
_UpperCAmelCase : Optional[int] = max_resolution
_UpperCAmelCase : List[str] = do_resize
_UpperCAmelCase : Optional[Any] = size
_UpperCAmelCase : Tuple = do_center_crop
_UpperCAmelCase : Optional[int] = crop_size
_UpperCAmelCase : Optional[Any] = do_flip_channel_order
def _A ( self : Dict ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Tuple = MobileViTImageProcessor if is_vision_available() else None
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = MobileViTImageProcessingTester(self )
@property
def _A ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Tuple ):
_UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "size" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "center_crop" ) )
self.assertTrue(hasattr(A , "do_flip_channel_order" ) )
def _A ( self : Any ):
_UpperCAmelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Any ):
pass
def _A ( self : Dict ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_UpperCAmelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Union[str, Any] ):
# Initialize image_processing
_UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_UpperCAmelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Any ):
# Initialize image_processing
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Any = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
execute_subprocess_async,
get_gpu_count,
get_torch_dist_unique_port,
require_apex,
require_bitsandbytes,
require_fairscale,
require_torch,
require_torch_gpu,
require_torch_multi_gpu,
require_torch_non_multi_gpu,
slow,
)
from transformers.trainer_callback import TrainerState
from transformers.trainer_utils import set_seed
__SCREAMING_SNAKE_CASE : str = os.path.abspath(os.path.dirname(__file__))
with ExtendSysPath(F'{bindir}/../../examples/pytorch/translation'):
from run_translation import main # noqa
set_seed(42)
__SCREAMING_SNAKE_CASE : Any = """sshleifer/student_marian_en_ro_6_1"""
__SCREAMING_SNAKE_CASE : int = """sshleifer/tiny-mbart"""
@require_torch
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def _A ( self : Union[str, Any] , A : List[str]=False , A : Union[str, Any]=None , A : Dict=True , A : Any=True , A : Any=True , A : Tuple=True , ):
_UpperCAmelCase : List[str] = self.run_trainer(
eval_steps=1 , max_len=12 , model_name=A , num_train_epochs=1 , distributed=A , extra_args_str=A , predict_with_generate=A , do_train=A , do_eval=A , do_predict=A , )
_UpperCAmelCase : int = TrainerState.load_from_json(os.path.join(A , "trainer_state.json" ) ).log_history
if not do_eval:
return
_UpperCAmelCase : Union[str, Any] = [log for log in logs if "eval_loss" in log.keys()]
_UpperCAmelCase : Optional[int] = eval_metrics[0]
if predict_with_generate:
assert "eval_bleu" in first_step_stats
_UpperCAmelCase : int = eval_metrics[-1]
assert isinstance(last_step_stats["eval_bleu"] , A )
assert not math.isnan(float(last_step_stats["eval_loss"] ) ), "eval_loss must not be `nan`"
@require_torch_non_multi_gpu
def _A ( self : Tuple ):
self.run_seqaseq_quick()
@require_torch_multi_gpu
def _A ( self : int ):
self.run_seqaseq_quick(distributed=A )
@require_torch_multi_gpu
def _A ( self : Any ):
self.run_seqaseq_quick(distributed=A )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def _A ( self : Any ):
self.run_seqaseq_quick(distributed=A , extra_args_str="--sharded_ddp simple" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def _A ( self : Dict ):
self.run_seqaseq_quick(distributed=A , extra_args_str="--sharded_ddp simple --fp16" )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def _A ( self : Optional[int] ):
self.run_seqaseq_quick(distributed=A , extra_args_str="--sharded_ddp zero_dp_2" , predict_with_generate=A )
@unittest.skip("Requires an update of the env running those tests" )
@require_torch_multi_gpu
@require_fairscale
def _A ( self : Tuple ):
self.run_seqaseq_quick(
distributed=A , extra_args_str="--sharded_ddp zero_dp_2 --fp16" , predict_with_generate=A )
@require_apex
@require_torch_gpu
def _A ( self : Tuple ):
# XXX: apex breaks the trainer if it's run twice e.g. run_seq2seq.main() from the same
# program and it breaks other tests that run from the same pytest worker, therefore until this is
# sorted out it must be run only in an external program, that is distributed=True in this
# test and only under one or more gpus - if we want cpu will need to make a special test
#
# specifically to the problem traced it to self.optimizer.step() - if it's run 2nd time via
# 2nd main() call it botches the future eval.
#
self.run_seqaseq_quick(distributed=A , extra_args_str="--fp16 --fp16_backend=apex" )
# test 2nd time - was getting eval_loss': nan'
# to reproduce the problem set distributed=False
self.run_seqaseq_quick(distributed=A , extra_args_str="--fp16 --fp16_backend=apex" )
@parameterized.expand(["base", "low", "high", "mixed"] )
@require_torch_multi_gpu
def _A ( self : Tuple , A : int ):
# as each sub-test is slow-ish split into multiple sub-tests to avoid CI timeout
_UpperCAmelCase : str = {
# test with the default log_level - should be info and thus log info once
"base": {"extra_args_str": "", "n_matches": 1},
# test with low log_level and log_level_replica - should be noisy on all processes
# now the info string should appear twice on 2 processes
"low": {"extra_args_str": "--log_level debug --log_level_replica debug", "n_matches": 2},
# test with high log_level and low log_level_replica
# now the info string should appear once only on the replica
"high": {"extra_args_str": "--log_level error --log_level_replica debug", "n_matches": 1},
# test with high log_level and log_level_replica - should be quiet on all processes
"mixed": {"extra_args_str": "--log_level error --log_level_replica error", "n_matches": 0},
}
_UpperCAmelCase : List[Any] = experiments[experiment_id]
_UpperCAmelCase : Dict = {"distributed": True, "predict_with_generate": False, "do_eval": False, "do_predict": False}
_UpperCAmelCase : List[str] = "Running training"
with CaptureStderr() as cl:
self.run_seqaseq_quick(**A , extra_args_str=data["extra_args_str"] )
_UpperCAmelCase : Tuple = len(re.findall(A , cl.err ) )
self.assertEqual(A , data["n_matches"] )
@slow
def _A ( self : int ):
_UpperCAmelCase : List[str] = self.run_trainer(
eval_steps=2 , max_len=128 , model_name=A , learning_rate=3E-4 , num_train_epochs=10 , distributed=A , )
# Check metrics
_UpperCAmelCase : Any = TrainerState.load_from_json(os.path.join(A , "trainer_state.json" ) ).log_history
_UpperCAmelCase : Tuple = [log for log in logs if "eval_loss" in log.keys()]
_UpperCAmelCase : Union[str, Any] = eval_metrics[0]
_UpperCAmelCase : List[str] = eval_metrics[-1]
assert first_step_stats["eval_loss"] > last_step_stats["eval_loss"], "model learned nothing"
assert isinstance(last_step_stats["eval_bleu"] , A )
# test if do_predict saves generations and metrics
_UpperCAmelCase : Optional[int] = os.listdir(A )
_UpperCAmelCase : str = {os.path.basename(A ) for p in contents}
assert "generated_predictions.txt" in contents
assert "predict_results.json" in contents
@slow
@require_bitsandbytes
def _A ( self : int ):
from transformers.training_args import OptimizerNames
def train_and_return_metrics(A : str ) -> Tuple[int, float]:
_UpperCAmelCase : int = "--skip_memory_metrics 0"
_UpperCAmelCase : Tuple = self.run_trainer(
max_len=128 , model_name=A , learning_rate=3E-4 , num_train_epochs=1 , optim=A , distributed=A , extra_args_str=A , do_eval=A , do_predict=A , n_gpus_to_use=1 , )
# Check metrics
_UpperCAmelCase : Optional[Any] = TrainerState.load_from_json(Path(A , "trainer_state.json" ) ).log_history
_UpperCAmelCase : int = int(logs[0]["train_mem_gpu_peaked_delta"] / 2**20 )
_UpperCAmelCase : Union[str, Any] = int(logs[0]["train_mem_gpu_alloc_delta"] / 2**20 )
_UpperCAmelCase : Optional[int] = logs[0]["train_loss"]
return gpu_peak_mem_mb, gpu_alloc_mem_mb, loss
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = train_and_return_metrics(OptimizerNames.ADAMW_TORCH.value )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : List[str] = train_and_return_metrics(OptimizerNames.ADAMW_BNB.value )
_UpperCAmelCase : str = gpu_alloc_mem_orig - gpu_alloc_mem_bnb
_UpperCAmelCase : Optional[Any] = gpu_peak_mem_orig + gpu_alloc_mem_orig
_UpperCAmelCase : Union[str, Any] = gpu_peak_mem_bnb + gpu_alloc_mem_bnb
_UpperCAmelCase : int = gpu_total_mem_orig - gpu_total_mem_bnb
# sshleifer/student_marian_en_ro_6_1 has 54M parameter, 29M of which is `nn.Embedding` which
# doesn't get quantized and remains in fp32. Therefore we only have 25M parameters quantized
# in 2 bytes and the diff in optim memory usage is derived as so:
#
# - normal 25*8=~200MB (8 bytes per param)
# - bnb 25*2= ~50MB (2 bytes per param)
#
# Thus we should expect ~150MB total memory saved.
#
# Peak memory should be the same - the total should be different by about that same margin
#
# After leaving a small margin to accommodate for differences between gpus let's check
# that we have at least 120MB in savings
_UpperCAmelCase : List[str] = 120
# uncomment the following if this test starts failing - requires py38 for a new print feature
# gpu_peak_mem_diff = gpu_peak_mem_orig - gpu_peak_mem_bnb
# print(f"{gpu_alloc_mem_orig=}MB {gpu_peak_mem_orig=}MB {gpu_alloc_mem_orig+gpu_peak_mem_orig=}MB")
# print(f" {gpu_alloc_mem_bnb=}MB {gpu_peak_mem_bnb=}MB {gpu_alloc_mem_bnb+gpu_peak_mem_bnb=}MB")
# print(f"{gpu_alloc_mem_diff=}MB")
# print(f"{gpu_peak_mem_diff=}MB")
# print(f"{gpu_total_mem_orig=}MB, {gpu_total_mem_bnb=}MB")
# print(f"{gpu_total_mem_diff=}MB, {gpu_total_mem_diff=}MB")
self.assertGreater(
A , A , "should use ~150MB less alloc gpu memory with BNB, compared to without it for this model but got"
F""" a difference of {gpu_alloc_mem_diff}MB, with gpu_alloc_mem_orig={gpu_alloc_mem_orig}MB and"""
F""" gpu_alloc_mem_bnb={gpu_alloc_mem_bnb}MB""" , )
self.assertGreater(
A , A , "should use ~150MB less total gpu memory with BNB, compared to without it for this model but got"
F""" a difference of {gpu_total_mem_diff}MB, with gpu_total_mem_orig={gpu_total_mem_orig}MB and"""
F""" gpu_total_mem_bnb={gpu_total_mem_bnb}MB""" , )
self.assertEqual(
A , A , F"""loss should be the same, but got loss_orig={loss_orig}, loss_bnb={loss_bnb}""" )
def _A ( self : Dict , A : int , A : str , A : int , A : float = 3E-3 , A : str = "adafactor" , A : bool = False , A : str = None , A : int = 0 , A : bool = True , A : bool = True , A : bool = True , A : bool = True , A : int = None , ):
_UpperCAmelCase : Optional[int] = self.test_file_dir / "../fixtures/tests_samples/wmt_en_ro"
_UpperCAmelCase : List[str] = self.get_auto_remove_tmp_dir()
_UpperCAmelCase : List[Any] = F"""
--model_name_or_path {model_name}
--train_file {data_dir}/train.json
--validation_file {data_dir}/val.json
--test_file {data_dir}/test.json
--output_dir {output_dir}
--overwrite_output_dir
--max_train_samples 8
--max_source_length {max_len}
--max_target_length {max_len}
--do_train
--num_train_epochs {str(A )}
--per_device_train_batch_size 4
--learning_rate {learning_rate}
--warmup_steps 8
--logging_steps 0
--logging_strategy no
--save_steps {str(A )}
--group_by_length
--label_smoothing_factor 0.1
--target_lang ro_RO
--source_lang en_XX
""".split()
_UpperCAmelCase : List[Any] = F"""
--do_eval
--per_device_eval_batch_size 4
--max_eval_samples 8
--val_max_target_length {max_len}
--evaluation_strategy steps
--eval_steps {str(A )}
""".split()
_UpperCAmelCase : Tuple = "\n --do_predict\n ".split()
_UpperCAmelCase : Union[str, Any] = []
if do_train:
args += args_train
if do_eval:
args += args_eval
if do_predict:
args += args_predict
if predict_with_generate:
args += "--predict_with_generate".split()
if do_train:
if optim == "adafactor":
args += "--adafactor".split()
else:
args += F"""--optim {optim}""".split()
if extra_args_str is not None:
args += extra_args_str.split()
if distributed:
if n_gpus_to_use is None:
_UpperCAmelCase : Dict = get_gpu_count()
_UpperCAmelCase : Dict = get_torch_dist_unique_port()
_UpperCAmelCase : List[str] = F"""
-m torch.distributed.run
--nproc_per_node={n_gpus_to_use}
--master_port={master_port}
{self.examples_dir_str}/pytorch/translation/run_translation.py
""".split()
_UpperCAmelCase : str = [sys.executable] + distributed_args + args
# keep for quick debug
# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
execute_subprocess_async(A , env=self.get_env() )
else:
_UpperCAmelCase : List[str] = ["run_translation.py"] + args
with patch.object(A , "argv" , A ):
main()
return output_dir
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Any = n - k
# Calculate C(n,k)
for i in range(_UpperCAmelCase ):
result *= n - i
result //= i + 1
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , _UpperCAmelCase ) // (node_count + 1)
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
_UpperCAmelCase : List[str] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return catalan_number(_UpperCAmelCase ) * factorial(_UpperCAmelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
F'binary trees and {catalan_number(node_count)} binary search trees.'
)
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list ) -> float:
"""simple docstring"""
_validate_point(_UpperCAmelCase )
_validate_point(_UpperCAmelCase )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(a - b ) for a, b in zip(_UpperCAmelCase , _UpperCAmelCase ) ) )
def UpperCamelCase_ ( _UpperCAmelCase : list[float] ) -> None:
"""simple docstring"""
if point:
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
for item in point:
if not isinstance(_UpperCAmelCase , (int, float) ):
_UpperCAmelCase : Optional[int] = (
"Expected a list of numbers as input, found "
F"""{type(_UpperCAmelCase ).__name__}"""
)
raise TypeError(_UpperCAmelCase )
else:
_UpperCAmelCase : Any = F"""Expected a list of numbers as input, found {type(_UpperCAmelCase ).__name__}"""
raise TypeError(_UpperCAmelCase )
else:
raise ValueError("Missing an input" )
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list ) -> float:
"""simple docstring"""
_validate_point(_UpperCAmelCase )
_validate_point(_UpperCAmelCase )
if len(_UpperCAmelCase ) != len(_UpperCAmelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )
return float(sum(abs(x - y ) for x, y in zip(_UpperCAmelCase , _UpperCAmelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : Dict = {
"""vocab_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-german-cased""": (
"""https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
),
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
),
},
}
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""distilbert-base-uncased""": 512,
"""distilbert-base-uncased-distilled-squad""": 512,
"""distilbert-base-cased""": 512,
"""distilbert-base-cased-distilled-squad""": 512,
"""distilbert-base-german-cased""": 512,
"""distilbert-base-multilingual-cased""": 512,
}
__SCREAMING_SNAKE_CASE : List[Any] = {
"""distilbert-base-uncased""": {"""do_lower_case""": True},
"""distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
"""distilbert-base-cased""": {"""do_lower_case""": False},
"""distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
"""distilbert-base-german-cased""": {"""do_lower_case""": False},
"""distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase: str = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Any = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase: str = ["input_ids", "attention_mask"]
__UpperCamelCase: List[str] = DistilBertTokenizer
def __init__( self : str , A : int=None , A : Tuple=None , A : Tuple=True , A : Dict="[UNK]" , A : List[Any]="[SEP]" , A : Optional[Any]="[PAD]" , A : Dict="[CLS]" , A : Tuple="[MASK]" , A : str=True , A : Dict=None , **A : List[Any] , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Dict = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : int = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : str = tokenize_chinese_chars
_UpperCAmelCase : List[Any] = normalizer_class(**A )
_UpperCAmelCase : Dict = do_lower_case
def _A ( self : List[Any] , A : Tuple , A : Any=None ):
_UpperCAmelCase : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _A ( self : int , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _A ( self : Dict , A : str , A : Optional[str] = None ):
_UpperCAmelCase : Any = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Optional[int] = 10
def UpperCamelCase_ ( _UpperCAmelCase : list[int] ) -> list[int]:
"""simple docstring"""
_UpperCAmelCase : Dict = 1
_UpperCAmelCase : List[str] = max(_UpperCAmelCase )
while placement <= max_digit:
# declare and initialize empty buckets
_UpperCAmelCase : list[list] = [[] for _ in range(_UpperCAmelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
_UpperCAmelCase : List[Any] = int((i / placement) % RADIX )
buckets[tmp].append(_UpperCAmelCase )
# put each buckets' contents into list_of_ints
_UpperCAmelCase : List[str] = 0
for b in range(_UpperCAmelCase ):
for i in buckets[b]:
_UpperCAmelCase : Optional[int] = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
def _A ( self : Any , A : Union[str, Any] , A : Optional[int] , A : List[str] , **A : Tuple ):
self.events.append("on_init_end" )
def _A ( self : Any , A : str , A : List[Any] , A : List[Any] , **A : Tuple ):
self.events.append("on_train_begin" )
def _A ( self : Tuple , A : List[str] , A : Tuple , A : int , **A : List[str] ):
self.events.append("on_train_end" )
def _A ( self : Optional[Any] , A : Dict , A : Any , A : Optional[Any] , **A : List[Any] ):
self.events.append("on_epoch_begin" )
def _A ( self : Optional[Any] , A : List[Any] , A : List[str] , A : Optional[int] , **A : Optional[int] ):
self.events.append("on_epoch_end" )
def _A ( self : List[str] , A : Optional[int] , A : List[Any] , A : Union[str, Any] , **A : Any ):
self.events.append("on_step_begin" )
def _A ( self : Tuple , A : Union[str, Any] , A : int , A : Optional[int] , **A : int ):
self.events.append("on_step_end" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Union[str, Any] , A : str , **A : Union[str, Any] ):
self.events.append("on_evaluate" )
def _A ( self : Optional[Any] , A : Optional[int] , A : Dict , A : List[Any] , **A : Dict ):
self.events.append("on_predict" )
def _A ( self : Dict , A : Dict , A : List[Any] , A : Dict , **A : str ):
self.events.append("on_save" )
def _A ( self : Tuple , A : Optional[Any] , A : Union[str, Any] , A : Optional[int] , **A : Dict ):
self.events.append("on_log" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Tuple , A : Tuple , **A : List[str] ):
self.events.append("on_prediction_step" )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
def _A ( self : List[Any] ):
shutil.rmtree(self.output_dir )
def _A ( self : Union[str, Any] , A : Optional[int]=0 , A : Optional[Any]=0 , A : Optional[Any]=64 , A : Dict=64 , A : Any=None , A : Tuple=False , **A : Optional[int] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
_UpperCAmelCase : str = RegressionDataset(length=A )
_UpperCAmelCase : Union[str, Any] = RegressionDataset(length=A )
_UpperCAmelCase : Any = RegressionModelConfig(a=A , b=A )
_UpperCAmelCase : List[Any] = RegressionPreTrainedModel(A )
_UpperCAmelCase : Dict = TrainingArguments(self.output_dir , disable_tqdm=A , report_to=[] , **A )
return Trainer(
A , A , train_dataset=A , eval_dataset=A , callbacks=A , )
def _A ( self : str , A : List[str] , A : List[str] ):
self.assertEqual(len(A ) , len(A ) )
# Order doesn't matter
_UpperCAmelCase : Tuple = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
_UpperCAmelCase : Any = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
for cba, cba in zip(A , A ):
if isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(A , A )
elif isinstance(A , A ) and not isinstance(A , A ):
self.assertEqual(A , cba.__class__ )
elif not isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(cba.__class__ , A )
else:
self.assertEqual(A , A )
def _A ( self : int , A : List[str] ):
_UpperCAmelCase : List[str] = ["on_init_end", "on_train_begin"]
_UpperCAmelCase : str = 0
_UpperCAmelCase : Optional[Any] = len(trainer.get_eval_dataloader() )
_UpperCAmelCase : Optional[int] = ["on_prediction_step"] * len(trainer.get_eval_dataloader() ) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("on_epoch_begin" )
for _ in range(A ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save" )
expected_events.append("on_epoch_end" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _A ( self : str ):
_UpperCAmelCase : Any = self.get_trainer()
_UpperCAmelCase : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# Callbacks passed at init are added to the default callbacks
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_UpperCAmelCase : List[Any] = self.get_trainer(disable_tqdm=A )
_UpperCAmelCase : Tuple = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_UpperCAmelCase : Dict = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : Optional[Any] = self.get_trainer()
_UpperCAmelCase : Any = trainer.pop_callback(A )
self.assertEqual(cb.__class__ , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# We can also add, pop, or remove by instance
_UpperCAmelCase : Union[str, Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : List[Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
_UpperCAmelCase : Union[str, Any] = trainer.pop_callback(A )
self.assertEqual(A , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" , category=A )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_UpperCAmelCase : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# Independent log/save/eval
_UpperCAmelCase : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_UpperCAmelCase : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="steps" )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="epoch" )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# A bit of everything
_UpperCAmelCase : int = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="steps" , )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning" ) as warn_mock:
_UpperCAmelCase : Optional[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(A ) in warn_mock.call_args[0][0]
| 31
| 1
|
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin
@skip_mps
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = IFInpaintingPipeline
__UpperCamelCase: List[str] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
__UpperCamelCase: Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__UpperCamelCase: List[Any] = PipelineTesterMixin.required_optional_params - {"latents"}
def _A ( self : Tuple ):
return self._get_dummy_components()
def _A ( self : int , A : List[str] , A : int=0 ):
if str(A ).startswith("mps" ):
_UpperCAmelCase : str = torch.manual_seed(A )
else:
_UpperCAmelCase : Union[str, Any] = torch.Generator(device=A ).manual_seed(A )
_UpperCAmelCase : Optional[Any] = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_UpperCAmelCase : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(A ) ).to(A )
_UpperCAmelCase : Optional[int] = {
"prompt": "A painting of a squirrel eating a burger",
"image": image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def _A ( self : List[str] ):
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
def _A ( self : Any ):
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != "cuda" , reason="float16 requires CUDA" )
def _A ( self : List[str] ):
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1E-1 )
def _A ( self : List[str] ):
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def _A ( self : Union[str, Any] ):
self._test_save_load_local()
def _A ( self : Dict ):
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : Dict , A : Optional[int]=7 , A : Tuple=3 , A : Optional[Any]=10 , A : int=18 , A : Dict=30 , A : List[str]=400 , A : int=True , A : Optional[Any]=None , A : Optional[Any]=True , A : List[Any]=[0.5, 0.5, 0.5] , A : List[str]=[0.5, 0.5, 0.5] , A : Optional[int]=None , ):
_UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 18}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : Optional[Any] = num_frames
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : Any = max_resolution
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : List[Any] = do_normalize
_UpperCAmelCase : Any = image_mean
_UpperCAmelCase : Tuple = image_std
_UpperCAmelCase : Any = crop_size
def _A ( self : List[Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = VivitImageProcessor if is_vision_available() else None
def _A ( self : int ):
_UpperCAmelCase : Tuple = VivitImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "size" ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Tuple ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_UpperCAmelCase : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_UpperCAmelCase : str = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_UpperCAmelCase : Tuple = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST, TFRegNetForImageClassification, TFRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : List[Any] , A : List[str]=3 , A : List[str]=32 , A : Optional[int]=3 , A : str=10 , A : Any=[10, 20, 30, 40] , A : int=[1, 1, 2, 1] , A : int=True , A : Dict=True , A : Optional[int]="relu" , A : Optional[int]=3 , A : Tuple=None , ):
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : List[Any] = image_size
_UpperCAmelCase : Dict = num_channels
_UpperCAmelCase : List[str] = embeddings_size
_UpperCAmelCase : Tuple = hidden_sizes
_UpperCAmelCase : Optional[int] = depths
_UpperCAmelCase : Tuple = is_training
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : Optional[int] = num_labels
_UpperCAmelCase : Dict = scope
_UpperCAmelCase : List[Any] = len(A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_UpperCAmelCase : Optional[Any] = None
if self.use_labels:
_UpperCAmelCase : Dict = ids_tensor([self.batch_size] , self.num_labels )
_UpperCAmelCase : Tuple = self.get_config()
return config, pixel_values, labels
def _A ( self : str ):
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def _A ( self : Optional[int] , A : Any , A : int , A : str ):
_UpperCAmelCase : int = TFRegNetModel(config=A )
_UpperCAmelCase : Optional[int] = model(A , training=A )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _A ( self : Union[str, Any] , A : str , A : Optional[Any] , A : Optional[int] ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : str = TFRegNetForImageClassification(A )
_UpperCAmelCase : Tuple = model(A , labels=A , training=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : int = self.prepare_config_and_inputs()
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : Optional[int] = config_and_inputs
_UpperCAmelCase : Optional[Any] = {"pixel_values": pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = (TFRegNetModel, TFRegNetForImageClassification) if is_tf_available() else ()
__UpperCamelCase: Union[str, Any] = (
{"feature-extraction": TFRegNetModel, "image-classification": TFRegNetForImageClassification}
if is_tf_available()
else {}
)
__UpperCamelCase: Dict = False
__UpperCamelCase: Optional[int] = False
__UpperCamelCase: Any = False
__UpperCamelCase: Optional[Any] = False
__UpperCamelCase: Tuple = False
def _A ( self : Any ):
_UpperCAmelCase : Any = TFRegNetModelTester(self )
_UpperCAmelCase : List[str] = ConfigTester(self , config_class=A , has_text_modality=A )
def _A ( self : List[Any] ):
return
@unittest.skip(reason="RegNet does not use inputs_embeds" )
def _A ( self : List[Any] ):
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("GPU" ) ) == 0 , reason="TF does not support backprop for grouped convolutions on CPU." , )
@slow
def _A ( self : Optional[Any] ):
super().test_keras_fit()
@unittest.skip(reason="RegNet does not support input and output embeddings" )
def _A ( self : str ):
pass
def _A ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_UpperCAmelCase : Union[str, Any] = model_class(A )
_UpperCAmelCase : List[str] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_UpperCAmelCase : Union[str, Any] = [*signature.parameters.keys()]
_UpperCAmelCase : Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : List[Any] ):
def check_hidden_states_output(A : Any , A : Any , A : Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = model_class(A )
_UpperCAmelCase : Optional[int] = model(**self._prepare_for_class(A , A ) , training=A )
_UpperCAmelCase : Optional[Any] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_UpperCAmelCase : int = self.model_tester.num_stages
self.assertEqual(len(A ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : List[str] = ["basic", "bottleneck"]
for model_class in self.all_model_classes:
for layer_type in layers_type:
_UpperCAmelCase : List[str] = layer_type
_UpperCAmelCase : Dict = True
check_hidden_states_output(A , A , A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_UpperCAmelCase : int = True
check_hidden_states_output(A , A , A )
def _A ( self : Tuple ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
def check_equivalence(A : Optional[int] , A : Union[str, Any] , A : Optional[Any] , A : str={} ):
_UpperCAmelCase : Tuple = model(A , return_dict=A , **A )
_UpperCAmelCase : Any = model(A , return_dict=A , **A ).to_tuple()
def recursive_check(A : Tuple , A : Tuple ):
if isinstance(A , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(A , A ):
recursive_check(A , A )
elif tuple_object is None:
return
else:
self.assertTrue(
all(tf.equal(A , A ) ) , msg=(
"Tuple and dict output are not equal. Difference:"
F""" {tf.math.reduce_max(tf.abs(tuple_object - dict_object ) )}"""
) , )
recursive_check(A , A )
for model_class in self.all_model_classes:
_UpperCAmelCase : Tuple = model_class(A )
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : str = self._prepare_for_class(A , A )
check_equivalence(A , A , A )
_UpperCAmelCase : Optional[int] = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : Optional[int] = self._prepare_for_class(A , A , return_labels=A )
check_equivalence(A , A , A )
_UpperCAmelCase : Optional[Any] = self._prepare_for_class(A , A )
_UpperCAmelCase : Tuple = self._prepare_for_class(A , A )
check_equivalence(A , A , A , {"output_hidden_states": True} )
_UpperCAmelCase : int = self._prepare_for_class(A , A , return_labels=A )
_UpperCAmelCase : Union[str, Any] = self._prepare_for_class(A , A , return_labels=A )
check_equivalence(A , A , A , {"output_hidden_states": True} )
def _A ( self : Tuple ):
_UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*A )
@slow
def _A ( self : List[str] ):
for model_name in TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Any = TFRegNetModel.from_pretrained(A )
self.assertIsNotNone(A )
def UpperCamelCase_ ( ) -> str:
"""simple docstring"""
_UpperCAmelCase : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
@require_vision
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@cached_property
def _A ( self : Optional[int] ):
return (
AutoImageProcessor.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Dict = TFRegNetForImageClassification.from_pretrained(TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
_UpperCAmelCase : Tuple = self.default_image_processor
_UpperCAmelCase : int = prepare_img()
_UpperCAmelCase : Optional[Any] = image_processor(images=A , return_tensors="tf" )
# forward pass
_UpperCAmelCase : Optional[Any] = model(**A , training=A )
# verify the logits
_UpperCAmelCase : Union[str, Any] = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , A )
_UpperCAmelCase : Optional[Any] = tf.constant([-0.4_180, -1.5_051, -3.4_836] )
tf.debugging.assert_near(outputs.logits[0, :3] , A , atol=1E-4 )
| 31
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "encodec"
def __init__( self : Optional[int] , A : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , A : List[Any]=24000 , A : Union[str, Any]=1 , A : List[Any]=False , A : Optional[int]=None , A : int=None , A : str=128 , A : List[Any]=32 , A : List[Any]=1 , A : int=[8, 5, 4, 2] , A : Optional[int]="weight_norm" , A : List[Any]=7 , A : Any=7 , A : Dict=3 , A : Optional[int]=2 , A : Dict=True , A : Dict="reflect" , A : Any=2 , A : Dict=2 , A : str=1.0 , A : Optional[int]=1024 , A : Any=None , A : Any=True , **A : str , ):
_UpperCAmelCase : Optional[int] = target_bandwidths
_UpperCAmelCase : List[str] = sampling_rate
_UpperCAmelCase : Optional[int] = audio_channels
_UpperCAmelCase : str = normalize
_UpperCAmelCase : int = chunk_length_s
_UpperCAmelCase : str = overlap
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : int = num_filters
_UpperCAmelCase : Optional[Any] = num_residual_layers
_UpperCAmelCase : Optional[int] = upsampling_ratios
_UpperCAmelCase : int = norm_type
_UpperCAmelCase : List[Any] = kernel_size
_UpperCAmelCase : List[Any] = last_kernel_size
_UpperCAmelCase : List[Any] = residual_kernel_size
_UpperCAmelCase : List[str] = dilation_growth_rate
_UpperCAmelCase : Dict = use_causal_conv
_UpperCAmelCase : Tuple = pad_mode
_UpperCAmelCase : Tuple = compress
_UpperCAmelCase : List[str] = num_lstm_layers
_UpperCAmelCase : List[Any] = trim_right_ratio
_UpperCAmelCase : int = codebook_size
_UpperCAmelCase : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size
_UpperCAmelCase : Optional[int] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**A )
@property
def _A ( self : Any ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A ( self : Union[str, Any] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A ( self : str ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 31
| 1
|
'''simple docstring'''
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
@add_end_docstrings(snake_case__ )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[str] , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
requires_backends(self , "decord" )
self.check_model_type(A )
def _A ( self : Union[str, Any] , A : int=None , A : int=None , A : Any=None ):
_UpperCAmelCase : int = {}
if frame_sampling_rate is not None:
_UpperCAmelCase : Dict = frame_sampling_rate
if num_frames is not None:
_UpperCAmelCase : Optional[int] = num_frames
_UpperCAmelCase : Dict = {}
if top_k is not None:
_UpperCAmelCase : List[str] = top_k
return preprocess_params, {}, postprocess_params
def __call__( self : str , A : Union[str, List[str]] , **A : List[str] ):
return super().__call__(A , **A )
def _A ( self : List[Any] , A : str , A : List[str]=None , A : Optional[int]=1 ):
if num_frames is None:
_UpperCAmelCase : int = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
_UpperCAmelCase : Optional[Any] = BytesIO(requests.get(A ).content )
_UpperCAmelCase : List[Any] = VideoReader(A )
videoreader.seek(0 )
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : int = num_frames * frame_sampling_rate - 1
_UpperCAmelCase : Union[str, Any] = np.linspace(A , A , num=A , dtype=np.intaa )
_UpperCAmelCase : Dict = videoreader.get_batch(A ).asnumpy()
_UpperCAmelCase : Dict = list(A )
_UpperCAmelCase : int = self.image_processor(A , return_tensors=self.framework )
return model_inputs
def _A ( self : Optional[int] , A : List[str] ):
_UpperCAmelCase : Any = self.model(**A )
return model_outputs
def _A ( self : int , A : List[str] , A : str=5 ):
if top_k > self.model.config.num_labels:
_UpperCAmelCase : Optional[int] = self.model.config.num_labels
if self.framework == "pt":
_UpperCAmelCase : Tuple = model_outputs.logits.softmax(-1 )[0]
_UpperCAmelCase , _UpperCAmelCase : int = probs.topk(A )
else:
raise ValueError(F"""Unsupported framework: {self.framework}""" )
_UpperCAmelCase : List[str] = scores.tolist()
_UpperCAmelCase : Any = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(A , A )]
| 31
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Any , A : Optional[int]=None , A : Tuple=None , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
if config is None:
assert isinstance(self.model , A ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
_UpperCAmelCase : str = self.model.config
else:
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : List[Any] = data_args
_UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , A ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
_UpperCAmelCase : Optional[Any] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase : Dict = label_smoothed_nll_loss
def _A ( self : Tuple , A : int ):
if self.optimizer is None:
_UpperCAmelCase : Tuple = ["bias", "LayerNorm.weight"]
_UpperCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
_UpperCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase : List[str] = Adafactor
_UpperCAmelCase : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
_UpperCAmelCase : List[str] = AdamW
_UpperCAmelCase : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
_UpperCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase : List[Any] = OSS(
params=A , optim=A , **A , )
else:
_UpperCAmelCase : Union[str, Any] = optimizer_cls(A , **A )
if self.lr_scheduler is None:
_UpperCAmelCase : List[str] = self._get_lr_scheduler(A )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : List[str] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A )
return scheduler
def _A ( self : Tuple ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _A ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : List[Any] ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase : List[str] = model(**A , use_cache=A )[0]
_UpperCAmelCase : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase : Any = model(**A , labels=A , use_cache=A )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase : Optional[int] = model(**A , use_cache=A )[0]
_UpperCAmelCase : List[str] = torch.nn.functional.log_softmax(A , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.loss_fn(A , A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _A ( self : List[str] , A : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Union[str, Any] = inputs.pop("labels" )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self._compute_loss(A , A , A )
return loss
def _A ( self : List[str] , A : nn.Module , A : Dict[str, Union[torch.Tensor, Any]] , A : bool , A : Optional[List[str]] = None , ):
_UpperCAmelCase : List[str] = self._prepare_inputs(A )
_UpperCAmelCase : Dict = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : int = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
_UpperCAmelCase : Any = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase : str = self._compute_loss(A , A , A )
_UpperCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
return (loss, logits, labels)
def _A ( self : Dict , A : int , A : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
_UpperCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase : Tuple = tensor
return padded_tensor
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'''simple docstring'''
import random
class lowerCamelCase_ :
'''simple docstring'''
@staticmethod
def _A ( A : str ):
_UpperCAmelCase : str = [ord(A ) for i in text]
_UpperCAmelCase : Any = []
_UpperCAmelCase : Tuple = []
for i in plain:
_UpperCAmelCase : Optional[int] = random.randint(1 , 300 )
_UpperCAmelCase : List[Any] = (i + k) * k
cipher.append(A )
key.append(A )
return cipher, key
@staticmethod
def _A ( A : list[int] , A : list[int] ):
_UpperCAmelCase : List[str] = []
for i in range(len(A ) ):
_UpperCAmelCase : str = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(A ) )
return "".join(A )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
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'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["input_features", "is_longer"]
def __init__( self : str , A : int=64 , A : Dict=48000 , A : str=480 , A : List[Any]=10 , A : Optional[Any]=1024 , A : Tuple=0.0 , A : List[Any]=False , A : float = 0 , A : float = 14000 , A : int = None , A : str = "fusion" , A : str = "repeatpad" , **A : Dict , ):
super().__init__(
feature_size=A , sampling_rate=A , padding_value=A , return_attention_mask=A , **A , )
_UpperCAmelCase : Optional[Any] = top_db
_UpperCAmelCase : Dict = truncation
_UpperCAmelCase : List[Any] = padding
_UpperCAmelCase : Optional[Any] = fft_window_size
_UpperCAmelCase : Dict = (fft_window_size >> 1) + 1
_UpperCAmelCase : Any = hop_length
_UpperCAmelCase : Tuple = max_length_s
_UpperCAmelCase : str = max_length_s * sampling_rate
_UpperCAmelCase : Any = sampling_rate
_UpperCAmelCase : Optional[int] = frequency_min
_UpperCAmelCase : str = frequency_max
_UpperCAmelCase : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm=A , mel_scale="htk" , )
_UpperCAmelCase : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm="slaney" , mel_scale="slaney" , )
def _A ( self : List[str] ):
_UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : Dict = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _A ( self : Optional[Any] , A : np.array , A : Optional[np.array] = None ):
_UpperCAmelCase : Dict = spectrogram(
A , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=A , log_mel="dB" , )
return log_mel_spectrogram.T
def _A ( self : str , A : str , A : List[str] , A : List[Any] ):
_UpperCAmelCase : List[str] = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Optional[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Tuple = [0]
# randomly choose index for each part
_UpperCAmelCase : Dict = np.random.choice(ranges[0] )
_UpperCAmelCase : str = np.random.choice(ranges[1] )
_UpperCAmelCase : Tuple = np.random.choice(ranges[2] )
_UpperCAmelCase : str = mel[idx_front : idx_front + chunk_frames, :]
_UpperCAmelCase : str = mel[idx_middle : idx_middle + chunk_frames, :]
_UpperCAmelCase : List[Any] = mel[idx_back : idx_back + chunk_frames, :]
_UpperCAmelCase : Dict = torch.tensor(mel[None, None, :] )
_UpperCAmelCase : Optional[Any] = torch.nn.functional.interpolate(
A , size=[chunk_frames, 64] , mode="bilinear" , align_corners=A )
_UpperCAmelCase : List[str] = mel_shrink[0][0].numpy()
_UpperCAmelCase : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _A ( self : List[Any] , A : np.array , A : List[str] , A : Any , A : Optional[int] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
_UpperCAmelCase : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
_UpperCAmelCase : str = len(A ) - max_length
_UpperCAmelCase : str = np.random.randint(0 , overflow + 1 )
_UpperCAmelCase : int = waveform[idx : idx + max_length]
_UpperCAmelCase : Any = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
_UpperCAmelCase : Tuple = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : List[str] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
_UpperCAmelCase : Optional[Any] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
_UpperCAmelCase : Any = np.stack([mel, mel, mel, mel] , axis=0 )
_UpperCAmelCase : int = False
else:
_UpperCAmelCase : Tuple = self._random_mel_fusion(A , A , A )
_UpperCAmelCase : Any = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
_UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
_UpperCAmelCase : str = int(max_length / len(A ) )
_UpperCAmelCase : Dict = np.stack(np.tile(A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
_UpperCAmelCase : Dict = int(max_length / len(A ) )
_UpperCAmelCase : List[str] = np.stack(np.tile(A , A ) )
_UpperCAmelCase : Optional[Any] = np.pad(A , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
_UpperCAmelCase : str = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : Optional[int] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
_UpperCAmelCase : List[str] = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : Union[str, Any] , A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A : str = None , A : Optional[str] = None , A : Optional[int] = None , A : Optional[int] = None , A : Optional[Union[str, TensorType]] = None , **A : List[str] , ):
_UpperCAmelCase : int = truncation if truncation is not None else self.truncation
_UpperCAmelCase : Optional[int] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : Optional[Any] = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : int = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
_UpperCAmelCase : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[str] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
_UpperCAmelCase : Dict = [
self._get_input_mel(A , max_length if max_length else self.nb_max_samples , A , A )
for waveform in raw_speech
]
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[Any] = []
for mel, longer in padded_inputs:
input_mel.append(A )
is_longer.append(A )
if truncation == "fusion" and sum(A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
_UpperCAmelCase : Union[str, Any] = np.random.randint(0 , len(A ) )
_UpperCAmelCase : Optional[Any] = True
if isinstance(input_mel[0] , A ):
_UpperCAmelCase : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
_UpperCAmelCase : Tuple = [[longer] for longer in is_longer]
_UpperCAmelCase : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
_UpperCAmelCase : Tuple = BatchFeature(A )
if return_tensors is not None:
_UpperCAmelCase : List[Any] = input_features.convert_to_tensors(A )
return input_features
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'''simple docstring'''
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
__SCREAMING_SNAKE_CASE : Optional[Any] = pytest.mark.integration
@require_faiss
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def _A ( self : str ):
_UpperCAmelCase : str = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(A ) for x in np.arange(30 ).tolist()]} )
return dset
def _A ( self : Tuple ):
import faiss
_UpperCAmelCase : Dataset = self._create_dummy_dataset()
_UpperCAmelCase : Dict = dset.map(
lambda A , A : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=A , keep_in_memory=A )
_UpperCAmelCase : Tuple = dset.add_faiss_index("vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
dset.drop_index("vecs" )
def _A ( self : List[str] ):
import faiss
_UpperCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def _A ( self : Tuple ):
import faiss
_UpperCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A ) as tmp_file:
dset.save_faiss_index("vecs" , tmp_file.name )
dset.load_faiss_index("vecs2" , tmp_file.name )
os.unlink(tmp_file.name )
_UpperCAmelCase , _UpperCAmelCase : Any = dset.get_nearest_examples("vecs2" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def _A ( self : List[Any] ):
_UpperCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" )
dset.drop_index("vecs" )
self.assertRaises(A , partial(dset.get_nearest_examples , "vecs2" , np.ones(5 , dtype=np.floataa ) ) )
def _A ( self : str ):
from elasticsearch import Elasticsearch
_UpperCAmelCase : Dataset = self._create_dummy_dataset()
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
_UpperCAmelCase : Optional[int] = {"acknowledged": True}
mocked_bulk.return_value([(True, None)] * 30 )
_UpperCAmelCase : List[str] = {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
_UpperCAmelCase : Any = Elasticsearch()
dset.add_elasticsearch_index("filename" , es_client=A )
_UpperCAmelCase , _UpperCAmelCase : str = dset.get_nearest_examples("filename" , "my_name-train_29" )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
@require_faiss
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def _A ( self : int ):
import faiss
_UpperCAmelCase : Tuple = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
_UpperCAmelCase : Optional[Any] = np.zeros(5 , dtype=np.floataa )
_UpperCAmelCase : int = 1
_UpperCAmelCase , _UpperCAmelCase : int = index.search(A )
self.assertRaises(A , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
_UpperCAmelCase : str = np.eye(5 , dtype=np.floataa )[::-1]
_UpperCAmelCase , _UpperCAmelCase : Any = index.search_batch(A )
self.assertRaises(A , index.search_batch , queries[0] )
_UpperCAmelCase : Optional[int] = [scores[0] for scores in total_scores]
_UpperCAmelCase : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , A )
def _A ( self : Any ):
import faiss
_UpperCAmelCase : Tuple = FaissIndex(string_factory="Flat" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
_UpperCAmelCase : Optional[Any] = FaissIndex(string_factory="LSH" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(A ):
_UpperCAmelCase : List[str] = FaissIndex(string_factory="Flat" , custom_index=faiss.IndexFlat(5 ) )
def _A ( self : Any ):
import faiss
_UpperCAmelCase : Any = faiss.IndexFlat(5 )
_UpperCAmelCase : Any = FaissIndex(custom_index=A )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def _A ( self : Any ):
import faiss
_UpperCAmelCase : Union[str, Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=A ) as tmp_file:
index.save(tmp_file.name )
_UpperCAmelCase : List[Any] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
_UpperCAmelCase : Optional[Any] = np.zeros(5 , dtype=np.floataa )
_UpperCAmelCase : List[Any] = 1
_UpperCAmelCase , _UpperCAmelCase : Tuple = index.search(A )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def UpperCamelCase_ ( _UpperCAmelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
import faiss
_UpperCAmelCase : Union[str, Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
_UpperCAmelCase : List[str] = "index.faiss"
_UpperCAmelCase : Tuple = F"""mock://{index_name}"""
index.save(_UpperCAmelCase , storage_options=mockfs.storage_options )
_UpperCAmelCase : str = FaissIndex.load(_UpperCAmelCase , storage_options=mockfs.storage_options )
_UpperCAmelCase : str = np.zeros(5 , dtype=np.floataa )
_UpperCAmelCase : Union[str, Any] = 1
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = index.search(_UpperCAmelCase )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def _A ( self : List[str] ):
from elasticsearch import Elasticsearch
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
_UpperCAmelCase : Optional[int] = Elasticsearch()
_UpperCAmelCase : Any = {"acknowledged": True}
_UpperCAmelCase : Optional[Any] = ElasticSearchIndex(es_client=A )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["foo", "bar", "foobar"] )
# single query
_UpperCAmelCase : Optional[int] = "foo"
_UpperCAmelCase : Dict = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = index.search(A )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
_UpperCAmelCase : Union[str, Any] = "foo"
_UpperCAmelCase : int = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
_UpperCAmelCase , _UpperCAmelCase : Tuple = index.search(A , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
_UpperCAmelCase : List[str] = ["foo", "bar", "foobar"]
_UpperCAmelCase : List[str] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
_UpperCAmelCase , _UpperCAmelCase : List[str] = index.search_batch(A )
_UpperCAmelCase : Dict = [scores[0] for scores in total_scores]
_UpperCAmelCase : Dict = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A ) , 0 )
self.assertListEqual([1, 1, 1] , A )
# batched queries with timeout
_UpperCAmelCase : Dict = ["foo", "bar", "foobar"]
_UpperCAmelCase : Union[str, Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
_UpperCAmelCase , _UpperCAmelCase : Dict = index.search_batch(A , request_timeout=30 )
_UpperCAmelCase : Union[str, Any] = [scores[0] for scores in total_scores]
_UpperCAmelCase : Tuple = [indices[0] for indices in total_indices]
self.assertGreater(np.min(A ) , 0 )
self.assertListEqual([1, 1, 1] , A )
| 31
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""GPTNeoXTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXForCausalLM""",
"""GPTNeoXForQuestionAnswering""",
"""GPTNeoXForSequenceClassification""",
"""GPTNeoXForTokenClassification""",
"""GPTNeoXLayer""",
"""GPTNeoXModel""",
"""GPTNeoXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
| 1
|
'''simple docstring'''
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : Optional[str] = None ) -> str:
"""simple docstring"""
if version.parse(hfh.__version__ ).release < version.parse("0.11.0" ).release:
# old versions of hfh don't url-encode the file path
_UpperCAmelCase : Union[str, Any] = quote(_UpperCAmelCase )
return hfh.hf_hub_url(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" , revision=_UpperCAmelCase )
| 31
|
'''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , A : Any , A : str , A : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = graph
self._normalize_graph(A , A )
_UpperCAmelCase : List[str] = len(A )
_UpperCAmelCase : Tuple = None
def _A ( self : Any , A : List[Any] , A : str ):
if sources is int:
_UpperCAmelCase : List[Any] = [sources]
if sinks is int:
_UpperCAmelCase : List[Any] = [sinks]
if len(A ) == 0 or len(A ) == 0:
return
_UpperCAmelCase : str = sources[0]
_UpperCAmelCase : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A ) > 1 or len(A ) > 1:
_UpperCAmelCase : Dict = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCAmelCase : Optional[Any] = max_input_flow
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCAmelCase : Dict = max_input_flow
_UpperCAmelCase : List[Any] = size - 1
def _A ( self : Union[str, Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _A ( self : Tuple , A : Dict ):
_UpperCAmelCase : str = algorithm(self )
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : str ):
_UpperCAmelCase : Optional[int] = flow_network
_UpperCAmelCase : Any = flow_network.verticesCount
_UpperCAmelCase : List[str] = flow_network.sourceIndex
_UpperCAmelCase : Union[str, Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCAmelCase : Any = flow_network.graph
_UpperCAmelCase : Union[str, Any] = False
def _A ( self : List[str] ):
if not self.executed:
self._algorithm()
_UpperCAmelCase : int = True
def _A ( self : List[Any] ):
pass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[str, Any] ):
super().__init__(A )
# use this to save your result
_UpperCAmelCase : Any = -1
def _A ( self : Union[str, Any] ):
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : int ):
super().__init__(A )
_UpperCAmelCase : List[str] = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCAmelCase : Union[str, Any] = [0] * self.verticies_count
_UpperCAmelCase : int = [0] * self.verticies_count
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCAmelCase : Optional[int] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCAmelCase : Any = 0
while i < len(A ):
_UpperCAmelCase : int = vertices_list[i]
_UpperCAmelCase : int = self.heights[vertex_index]
self.process_vertex(A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A ) )
_UpperCAmelCase : Union[str, Any] = 0
else:
i += 1
_UpperCAmelCase : List[Any] = sum(self.preflow[self.source_index] )
def _A ( self : Union[str, Any] , A : str ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A , A )
self.relabel(A )
def _A ( self : int , A : Dict , A : List[str] ):
_UpperCAmelCase : int = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _A ( self : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : str = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCAmelCase : Tuple = self.heights[to_index]
if min_height is not None:
_UpperCAmelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__SCREAMING_SNAKE_CASE : List[Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__SCREAMING_SNAKE_CASE : Optional[Any] = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 31
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Any = {
"""configuration_time_series_transformer""": [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TimeSeriesTransformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Optional[int] = [
"""TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TimeSeriesTransformerForPrediction""",
"""TimeSeriesTransformerModel""",
"""TimeSeriesTransformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TimeSeriesTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_time_series_transformer import (
TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TimeSeriesTransformerForPrediction,
TimeSeriesTransformerModel,
TimeSeriesTransformerPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> float:
"""simple docstring"""
def get_matched_characters(_UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_UpperCAmelCase : int = int(max(0 , i - limit ) )
_UpperCAmelCase : Any = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = F"""{_stra[0:_stra.index(_UpperCAmelCase )]} {_stra[_stra.index(_UpperCAmelCase ) + 1:]}"""
return "".join(_UpperCAmelCase )
# matching characters
_UpperCAmelCase : Union[str, Any] = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = len(_UpperCAmelCase )
# transposition
_UpperCAmelCase : Optional[Any] = (
len([(ca, ca) for ca, ca in zip(_UpperCAmelCase , _UpperCAmelCase ) if ca != ca] ) // 2
)
if not match_count:
_UpperCAmelCase : Dict = 0.0
else:
_UpperCAmelCase : Optional[int] = (
1
/ 3
* (
match_count / len(_UpperCAmelCase )
+ match_count / len(_UpperCAmelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_UpperCAmelCase : str = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
__SCREAMING_SNAKE_CASE : Dict = """#"""
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Union[str, Any] ):
_UpperCAmelCase : dict = {}
def _A ( self : int , A : str ):
_UpperCAmelCase : Optional[Any] = self._trie
for char in text:
if char not in trie:
_UpperCAmelCase : Dict = {}
_UpperCAmelCase : Tuple = trie[char]
_UpperCAmelCase : Tuple = True
def _A ( self : Optional[Any] , A : str ):
_UpperCAmelCase : str = self._trie
for char in prefix:
if char in trie:
_UpperCAmelCase : Optional[int] = trie[char]
else:
return []
return self._elements(A )
def _A ( self : str , A : dict ):
_UpperCAmelCase : Tuple = []
for c, v in d.items():
_UpperCAmelCase : List[Any] = [" "] if c == END else [(c + s) for s in self._elements(A )]
result.extend(A )
return tuple(A )
__SCREAMING_SNAKE_CASE : Any = Trie()
__SCREAMING_SNAKE_CASE : Optional[Any] = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> tuple:
"""simple docstring"""
_UpperCAmelCase : List[Any] = trie.find_word(_UpperCAmelCase )
return tuple(string + word for word in suffixes )
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 31
|
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = 1
@register_to_config
def __init__( self : Optional[int] , A : int = 1000 , A : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(A )
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_UpperCAmelCase : int = 4
# running values
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : int , A : Union[str, torch.device] = None ):
_UpperCAmelCase : int = num_inference_steps
_UpperCAmelCase : Union[str, Any] = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_UpperCAmelCase : Any = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_UpperCAmelCase : str = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_UpperCAmelCase : Dict = torch.sin(steps * math.pi / 2 ) ** 2
_UpperCAmelCase : List[Any] = (1.0 - self.betas**2) ** 0.5
_UpperCAmelCase : List[str] = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_UpperCAmelCase : Dict = timesteps.to(A )
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : torch.FloatTensor , A : int , A : torch.FloatTensor , A : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )
_UpperCAmelCase : Tuple = (self.timesteps == timestep).nonzero().item()
_UpperCAmelCase : Optional[Any] = timestep_index + 1
_UpperCAmelCase : int = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(A )
if len(self.ets ) == 1:
_UpperCAmelCase : List[Any] = self.ets[-1]
elif len(self.ets ) == 2:
_UpperCAmelCase : str = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_UpperCAmelCase : Tuple = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_UpperCAmelCase : Union[str, Any] = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_UpperCAmelCase : Union[str, Any] = self._get_prev_sample(A , A , A , A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def _A ( self : Union[str, Any] , A : torch.FloatTensor , *A : Union[str, Any] , **A : Dict ):
return sample
def _A ( self : Optional[Any] , A : Optional[int] , A : int , A : Optional[Any] , A : List[str] ):
_UpperCAmelCase : List[str] = self.alphas[timestep_index]
_UpperCAmelCase : List[Any] = self.betas[timestep_index]
_UpperCAmelCase : Optional[Any] = self.alphas[prev_timestep_index]
_UpperCAmelCase : Dict = self.betas[prev_timestep_index]
_UpperCAmelCase : Tuple = (sample - sigma * ets) / max(A , 1E-8 )
_UpperCAmelCase : List[str] = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Union[str, Any] ):
return self.config.num_train_timesteps
| 31
| 1
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__SCREAMING_SNAKE_CASE : Optional[int] = 256_047
__SCREAMING_SNAKE_CASE : Optional[int] = 256_145
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = NllbTokenizer
__UpperCamelCase: Tuple = NllbTokenizerFast
__UpperCamelCase: Union[str, Any] = True
__UpperCamelCase: Dict = True
__UpperCamelCase: Optional[Any] = {}
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
_UpperCAmelCase : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase : List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Dict = tokenizer_r.save_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
_UpperCAmelCase : Optional[int] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : List[str] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
_UpperCAmelCase : str = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : Optional[int] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
def _A ( self : Tuple ):
if not self.test_seqaseq:
return
_UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_UpperCAmelCase : Optional[Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
_UpperCAmelCase : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=A , tgt_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_UpperCAmelCase : Tuple = tokenizer.prepare_seqaseq_batch(
A , tgt_texts=A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_UpperCAmelCase : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def _A ( self : List[Any] ):
pass
def _A ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Any = [AddedToken("<special>" , lstrip=A )]
_UpperCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Dict = tokenizer_r.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_r.encode("<special>" , add_special_tokens=A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = "facebook/nllb-200-distilled-600M"
__UpperCamelCase: Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__UpperCamelCase: str = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__UpperCamelCase: str = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _A ( cls : int ):
_UpperCAmelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
_UpperCAmelCase : Union[str, Any] = 1
return cls
def _A ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 256057 )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def _A ( self : Tuple ):
self.assertIn(A , self.tokenizer.all_special_ids )
# fmt: off
_UpperCAmelCase : List[Any] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_UpperCAmelCase : Tuple = self.tokenizer.decode(A , skip_special_tokens=A )
_UpperCAmelCase : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , A )
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Tuple = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , A )
self.assertEqual(len(A ) , A )
def _A ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [256203, 3] )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
_UpperCAmelCase : Tuple = NllbTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def _A ( self : Dict ):
_UpperCAmelCase : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Tuple = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(A , A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_UpperCAmelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : Dict = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=10 , return_tensors="pt" )
_UpperCAmelCase : List[Any] = targets["input_ids"]
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(
A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
} , )
@require_torch
def _A ( self : Any ):
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Any = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : str = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 31
|
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase_ ( _UpperCAmelCase : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
_UpperCAmelCase : Any = XGBClassifier()
classifier.fit(_UpperCAmelCase , _UpperCAmelCase )
return classifier
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : List[str] = load_iris()
_UpperCAmelCase , _UpperCAmelCase : Dict = data_handling(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = train_test_split(
_UpperCAmelCase , _UpperCAmelCase , test_size=0.2_5 )
_UpperCAmelCase : Optional[Any] = iris["target_names"]
# Create an XGBoost Classifier from the training data
_UpperCAmelCase : Tuple = xgboost(_UpperCAmelCase , _UpperCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , display_labels=_UpperCAmelCase , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 31
| 1
|
'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: torch.FloatTensor
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
@register_to_config
def __init__( self : Union[str, Any] , A : int = 65536 , A : Optional[int] = None , A : int = 2 , A : int = 2 , A : int = 0 , A : str = "fourier" , A : bool = True , A : bool = False , A : float = 0.0 , A : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , A : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , A : Tuple[str] = "UNetMidBlock1D" , A : str = None , A : Tuple[int] = (32, 32, 64) , A : str = None , A : int = 8 , A : int = 1 , A : bool = False , ):
super().__init__()
_UpperCAmelCase : List[str] = sample_size
# time
if time_embedding_type == "fourier":
_UpperCAmelCase : List[str] = GaussianFourierProjection(
embedding_size=8 , set_W_to_weight=A , log=A , flip_sin_to_cos=A )
_UpperCAmelCase : Union[str, Any] = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
_UpperCAmelCase : Dict = Timesteps(
block_out_channels[0] , flip_sin_to_cos=A , downscale_freq_shift=A )
_UpperCAmelCase : str = block_out_channels[0]
if use_timestep_embedding:
_UpperCAmelCase : List[str] = block_out_channels[0] * 4
_UpperCAmelCase : List[str] = TimestepEmbedding(
in_channels=A , time_embed_dim=A , act_fn=A , out_dim=block_out_channels[0] , )
_UpperCAmelCase : Optional[int] = nn.ModuleList([] )
_UpperCAmelCase : Tuple = None
_UpperCAmelCase : Optional[Any] = nn.ModuleList([] )
_UpperCAmelCase : str = None
# down
_UpperCAmelCase : Optional[int] = in_channels
for i, down_block_type in enumerate(A ):
_UpperCAmelCase : Union[str, Any] = output_channel
_UpperCAmelCase : Tuple = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
_UpperCAmelCase : str = i == len(A ) - 1
_UpperCAmelCase : Union[str, Any] = get_down_block(
A , num_layers=A , in_channels=A , out_channels=A , temb_channels=block_out_channels[0] , add_downsample=not is_final_block or downsample_each_block , )
self.down_blocks.append(A )
# mid
_UpperCAmelCase : Optional[int] = get_mid_block(
A , in_channels=block_out_channels[-1] , mid_channels=block_out_channels[-1] , out_channels=block_out_channels[-1] , embed_dim=block_out_channels[0] , num_layers=A , add_downsample=A , )
# up
_UpperCAmelCase : int = list(reversed(A ) )
_UpperCAmelCase : Union[str, Any] = reversed_block_out_channels[0]
if out_block_type is None:
_UpperCAmelCase : Optional[Any] = out_channels
else:
_UpperCAmelCase : Any = block_out_channels[0]
for i, up_block_type in enumerate(A ):
_UpperCAmelCase : Dict = output_channel
_UpperCAmelCase : int = (
reversed_block_out_channels[i + 1] if i < len(A ) - 1 else final_upsample_channels
)
_UpperCAmelCase : Optional[Any] = i == len(A ) - 1
_UpperCAmelCase : Optional[Any] = get_up_block(
A , num_layers=A , in_channels=A , out_channels=A , temb_channels=block_out_channels[0] , add_upsample=not is_final_block , )
self.up_blocks.append(A )
_UpperCAmelCase : Tuple = output_channel
# out
_UpperCAmelCase : Optional[int] = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 , 32 )
_UpperCAmelCase : List[str] = get_out_block(
out_block_type=A , num_groups_out=A , embed_dim=block_out_channels[0] , out_channels=A , act_fn=A , fc_dim=block_out_channels[-1] // 4 , )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : bool = True , ):
_UpperCAmelCase : List[Any] = timestep
if not torch.is_tensor(A ):
_UpperCAmelCase : Optional[int] = torch.tensor([timesteps] , dtype=torch.long , device=sample.device )
elif torch.is_tensor(A ) and len(timesteps.shape ) == 0:
_UpperCAmelCase : Tuple = timesteps[None].to(sample.device )
_UpperCAmelCase : Dict = self.time_proj(A )
if self.config.use_timestep_embedding:
_UpperCAmelCase : Union[str, Any] = self.time_mlp(A )
else:
_UpperCAmelCase : str = timestep_embed[..., None]
_UpperCAmelCase : Tuple = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
_UpperCAmelCase : List[Any] = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
_UpperCAmelCase : Dict = ()
for downsample_block in self.down_blocks:
_UpperCAmelCase , _UpperCAmelCase : List[str] = downsample_block(hidden_states=A , temb=A )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
_UpperCAmelCase : Optional[int] = self.mid_block(A , A )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
_UpperCAmelCase : int = down_block_res_samples[-1:]
_UpperCAmelCase : Optional[int] = down_block_res_samples[:-1]
_UpperCAmelCase : Optional[Any] = upsample_block(A , res_hidden_states_tuple=A , temb=A )
# 5. post-process
if self.out_block:
_UpperCAmelCase : Optional[Any] = self.out_block(A , A )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=A )
| 31
|
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : Optional[Any]=13 , A : Optional[Any]=7 , A : Union[str, Any]=True , A : Optional[Any]=True , A : int=False , A : str=True , A : Optional[Any]=99 , A : Union[str, Any]=32 , A : int=5 , A : Tuple=4 , A : Union[str, Any]=37 , A : Dict="gelu" , A : Union[str, Any]=0.1 , A : str=0.1 , A : Union[str, Any]=512 , A : int=16 , A : List[str]=2 , A : Tuple=0.02 , A : int=3 , A : List[str]=4 , A : str=None , ):
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : str = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : List[str] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Dict ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _A ( self : int , A : List[Any] , A : Any , A : int , A : Union[str, Any] , A : Dict , A : List[Any] , A : Dict ):
_UpperCAmelCase : List[str] = BioGptModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : List[Any] , A : str , A : List[Any] , A : Dict , A : List[Any] , A : List[str] , A : Union[str, Any] , A : int , A : List[str] , A : Dict , ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : List[Any] , A : str , A : str , A : str , A : Any , A : List[str] , *A : Optional[int] ):
_UpperCAmelCase : str = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
_UpperCAmelCase : Optional[int] = self.seq_length // 2
_UpperCAmelCase : List[Any] = 0
# first forward pass
_UpperCAmelCase , _UpperCAmelCase : List[str] = model(A , attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_UpperCAmelCase : List[str] = ids_tensor((1,) , A ).item() + 1
_UpperCAmelCase : str = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_UpperCAmelCase : Any = random_other_next_tokens
# append to next input_ids and attn_mask
_UpperCAmelCase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=A )] , dim=1 , )
# get two different outputs
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Optional[Any] = model(A , past_key_values=A , attention_mask=A )["last_hidden_state"]
# select random slice
_UpperCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : int , A : Dict , A : str , A : Dict , A : Union[str, Any] , A : Any , *A : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = BioGptModel(config=A ).to(A ).eval()
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase , _UpperCAmelCase : Dict = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase : Any = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Dict = model(A , attention_mask=A , past_key_values=A )[
"last_hidden_state"
]
# select random slice
_UpperCAmelCase : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : Optional[Any] , A : Tuple , A : List[str] , A : Tuple , A : Dict , A : List[Any] , *A : Tuple , A : List[str]=False ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_UpperCAmelCase : Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _A ( self : Optional[Any] , A : Any , *A : Optional[Any] ):
_UpperCAmelCase : Tuple = BioGptModel(A )
_UpperCAmelCase : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def _A ( self : Optional[int] , A : Dict , A : Tuple , A : Optional[int] , A : int , A : List[str] , *A : Dict ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Any = BioGptForTokenClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
__UpperCamelCase: List[str] = (BioGptForCausalLM,) if is_torch_available() else ()
__UpperCamelCase: str = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase: Union[str, Any] = False
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = BioGptModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def _A ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : Tuple = type
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A , gradient_checkpointing=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
_UpperCAmelCase : Tuple = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : str = "left"
# Define PAD Token = EOS Token = 50256
_UpperCAmelCase : Any = tokenizer.eos_token
_UpperCAmelCase : int = model.config.eos_token_id
# use different length sentences to test batching
_UpperCAmelCase : Any = [
"Hello, my dog is a little",
"Today, I",
]
_UpperCAmelCase : Tuple = tokenizer(A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Optional[Any] = inputs["input_ids"].to(A )
_UpperCAmelCase : Any = model.generate(
input_ids=A , attention_mask=inputs["attention_mask"].to(A ) , )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : List[Any] = model.generate(input_ids=A )
_UpperCAmelCase : List[Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
_UpperCAmelCase : int = tokenizer(sentences[1] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : int = model.generate(input_ids=A , max_length=model.config.max_length - num_paddings )
_UpperCAmelCase : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
_UpperCAmelCase : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : str = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(A , A )
self.assertListEqual(A , [non_padded_sentence, padded_sentence] )
@slow
def _A ( self : str ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _A ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : List[str] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : List[str] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = 3
_UpperCAmelCase : Dict = "multi_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Optional[int] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : Optional[Any] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
_UpperCAmelCase : List[Any] = model(A )[0]
_UpperCAmelCase : int = 42384
_UpperCAmelCase : int = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@slow
def _A ( self : Any ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Tuple = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = tokenizer("COVID-19 is" , return_tensors="pt" ).to(A )
_UpperCAmelCase : Dict = model.generate(
**A , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=A , )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=A )
_UpperCAmelCase : List[str] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(A , A )
| 31
| 1
|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = {
"""alibaba-damo/mgp-str-base""": """https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "mgp-str"
def __init__( self : List[str] , A : str=[32, 128] , A : str=4 , A : Optional[Any]=3 , A : str=27 , A : List[str]=38 , A : Dict=50257 , A : List[Any]=30522 , A : int=768 , A : Any=12 , A : List[str]=12 , A : Tuple=4.0 , A : str=True , A : str=False , A : List[str]=1E-5 , A : Union[str, Any]=0.0 , A : Tuple=0.0 , A : str=0.0 , A : Any=False , A : int=0.02 , **A : Optional[int] , ):
super().__init__(**A )
_UpperCAmelCase : Tuple = image_size
_UpperCAmelCase : str = patch_size
_UpperCAmelCase : int = num_channels
_UpperCAmelCase : int = max_token_length
_UpperCAmelCase : Union[str, Any] = num_character_labels
_UpperCAmelCase : Optional[int] = num_bpe_labels
_UpperCAmelCase : Optional[int] = num_wordpiece_labels
_UpperCAmelCase : List[str] = hidden_size
_UpperCAmelCase : Optional[int] = num_hidden_layers
_UpperCAmelCase : List[str] = num_attention_heads
_UpperCAmelCase : Dict = mlp_ratio
_UpperCAmelCase : int = distilled
_UpperCAmelCase : int = layer_norm_eps
_UpperCAmelCase : Optional[int] = drop_rate
_UpperCAmelCase : Optional[int] = qkv_bias
_UpperCAmelCase : Optional[Any] = attn_drop_rate
_UpperCAmelCase : Optional[Any] = drop_path_rate
_UpperCAmelCase : Optional[int] = output_aa_attentions
_UpperCAmelCase : Dict = initializer_range
| 31
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
import os
from tempfile import TemporaryDirectory
from unittest import TestCase
import pytest
from absl.testing import parameterized
from datasets import config
from datasets.arrow_reader import HF_GCP_BASE_URL
from datasets.builder import DatasetBuilder
from datasets.dataset_dict import IterableDatasetDict
from datasets.iterable_dataset import IterableDataset
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.file_utils import cached_path
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
{"""dataset""": """wikipedia""", """config_name""": """20220301.de"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.en"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.fr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.frr"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.it"""},
{"""dataset""": """wikipedia""", """config_name""": """20220301.simple"""},
{"""dataset""": """snli""", """config_name""": """plain_text"""},
{"""dataset""": """eli5""", """config_name""": """LFQA_reddit"""},
{"""dataset""": """wiki40b""", """config_name""": """en"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.compressed"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.nq.no_index"""},
{"""dataset""": """wiki_dpr""", """config_name""": """psgs_w100.multiset.no_index"""},
{"""dataset""": """natural_questions""", """config_name""": """default"""},
]
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int]=True ) -> Tuple:
"""simple docstring"""
if with_config:
return [
{
"testcase_name": d["dataset"] + "/" + d["config_name"],
"dataset": d["dataset"],
"config_name": d["config_name"],
}
for d in DATASETS_ON_HF_GCP
]
else:
return [
{"testcase_name": dataset, "dataset": dataset} for dataset in {d["dataset"] for d in DATASETS_ON_HF_GCP}
]
@parameterized.named_parameters(list_datasets_on_hf_gcp_parameters(with_config=snake_case__ ) )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = None
__UpperCamelCase: int = None
def _A ( self : str , A : str , A : List[Any] ):
with TemporaryDirectory() as tmp_dir:
_UpperCAmelCase : int = dataset_module_factory(A , cache_dir=A )
_UpperCAmelCase : List[Any] = import_main_class(dataset_module.module_path , dataset=A )
_UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=A , config_name=A , hash=dataset_module.hash , )
_UpperCAmelCase : Tuple = "/".join(
[
HF_GCP_BASE_URL,
builder_instance._relative_data_dir(with_hash=A ).replace(os.sep , "/" ),
config.DATASET_INFO_FILENAME,
] )
_UpperCAmelCase : Optional[Any] = cached_path(A , cache_dir=A )
self.assertTrue(os.path.exists(A ) )
@pytest.mark.integration
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
_UpperCAmelCase : int = tmp_path_factory.mktemp("test_hf_gcp" ) / "test_wikipedia_simple"
_UpperCAmelCase : Union[str, Any] = dataset_module_factory("wikipedia" , cache_dir=_UpperCAmelCase )
_UpperCAmelCase : str = import_main_class(dataset_module.module_path )
_UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=_UpperCAmelCase , config_name="20220301.frr" , hash=dataset_module.hash , )
# use the HF cloud storage, not the original download_and_prepare that uses apache-beam
_UpperCAmelCase : Dict = None
builder_instance.download_and_prepare()
_UpperCAmelCase : List[str] = builder_instance.as_dataset()
assert ds
@pytest.mark.integration
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : List[str] = dataset_module_factory("wikipedia" , cache_dir=_UpperCAmelCase )
_UpperCAmelCase : List[Any] = import_main_class(dataset_module.module_path , dataset=_UpperCAmelCase )
_UpperCAmelCase : DatasetBuilder = builder_cls(
cache_dir=_UpperCAmelCase , config_name="20220301.frr" , hash=dataset_module.hash , )
_UpperCAmelCase : Dict = builder_instance.as_streaming_dataset()
assert ds
assert isinstance(_UpperCAmelCase , _UpperCAmelCase )
assert "train" in ds
assert isinstance(ds["train"] , _UpperCAmelCase )
assert next(iter(ds["train"] ) )
| 31
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = """▁"""
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : int = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
__SCREAMING_SNAKE_CASE : str = {
"""google/pegasus-xsum""": 512,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase: Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Optional[int] = PegasusTokenizer
__UpperCamelCase: Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : Dict , A : List[str]=None , A : Union[str, Any]=None , A : Optional[int]="<pad>" , A : Tuple="</s>" , A : Union[str, Any]="<unk>" , A : Union[str, Any]="<mask_2>" , A : Dict="<mask_1>" , A : Union[str, Any]=None , A : int=103 , **A : Optional[Any] , ):
_UpperCAmelCase : Dict = offset
if additional_special_tokens is not None:
if not isinstance(A , A ):
raise TypeError(
F"""additional_special_tokens should be of type {type(A )}, but is"""
F""" {type(A )}""" )
_UpperCAmelCase : Optional[int] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(A ) , self.offset - 1 )
]
if len(set(A ) ) != len(A ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
_UpperCAmelCase : Any = additional_special_tokens_extended
else:
_UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
A , tokenizer_file=A , pad_token=A , eos_token=A , unk_token=A , mask_token=A , mask_token_sent=A , offset=A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[Any] = vocab_file
_UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
def _A ( self : List[str] , A : Optional[Any] ):
_UpperCAmelCase : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : str , A : List , A : Optional[List] = None , A : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(A )
elif token_ids_a is None:
return self._special_token_mask(A ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : Optional[int] , A : Union[str, Any] , A : int=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : Union[str, Any] , A : str , A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 31
| 1
|
'''simple docstring'''
import argparse
import torch
# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./ # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig
from transformers import WavLMConfig, WavLMModel, logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
"""self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
"""self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """ctc_proj""",
"""mask_emb""": """masked_spec_embed""",
}
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
"""ctc_proj""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
]
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str] , _UpperCAmelCase : Dict , _UpperCAmelCase : List[Any] ) -> Optional[Any]:
"""simple docstring"""
for attribute in key.split("." ):
_UpperCAmelCase : List[Any] = getattr(_UpperCAmelCase , _UpperCAmelCase )
if weight_type is not None:
_UpperCAmelCase : Optional[Any] = getattr(_UpperCAmelCase , _UpperCAmelCase ).shape
else:
_UpperCAmelCase : Optional[int] = hf_pointer.shape
assert hf_shape == value.shape, (
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}"""
)
if weight_type == "weight":
_UpperCAmelCase : Optional[int] = value
elif weight_type == "weight_g":
_UpperCAmelCase : Union[str, Any] = value
elif weight_type == "weight_v":
_UpperCAmelCase : List[str] = value
elif weight_type == "bias":
_UpperCAmelCase : str = value
else:
_UpperCAmelCase : Optional[Any] = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def UpperCamelCase_ ( _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Dict ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[Any] = fairseq_model.state_dict()
_UpperCAmelCase : Dict = hf_model.feature_extractor
for name, value in fairseq_dict.items():
_UpperCAmelCase : Optional[int] = False
if "conv_layers" in name:
load_conv_layer(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , hf_model.config.feat_extract_norm == "group" , )
_UpperCAmelCase : Dict = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
_UpperCAmelCase : Dict = True
if "*" in mapped_key:
_UpperCAmelCase : Union[str, Any] = name.split(_UpperCAmelCase )[0].split("." )[-2]
_UpperCAmelCase : Dict = mapped_key.replace("*" , _UpperCAmelCase )
if "weight_g" in name:
_UpperCAmelCase : Any = "weight_g"
elif "weight_v" in name:
_UpperCAmelCase : str = "weight_v"
elif "bias" in name and "relative_attention_bias" not in name:
_UpperCAmelCase : Optional[int] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_UpperCAmelCase : Optional[Any] = "weight"
else:
_UpperCAmelCase : Tuple = None
set_recursively(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
continue
if not is_used:
unused_weights.append(_UpperCAmelCase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def UpperCamelCase_ ( _UpperCAmelCase : List[str] , _UpperCAmelCase : Union[str, Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[Any] , _UpperCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : List[str] = full_name.split("conv_layers." )[-1]
_UpperCAmelCase : str = name.split("." )
_UpperCAmelCase : Any = int(items[0] )
_UpperCAmelCase : Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
_UpperCAmelCase : Optional[int] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
_UpperCAmelCase : Optional[Any] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
_UpperCAmelCase : List[Any] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
_UpperCAmelCase : List[str] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_UpperCAmelCase )
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Optional[Any] , _UpperCAmelCase : List[str]=None ) -> Any:
"""simple docstring"""
_UpperCAmelCase : int = torch.load(_UpperCAmelCase )
_UpperCAmelCase : Tuple = WavLMConfigOrig(checkpoint["cfg"] )
_UpperCAmelCase : Tuple = WavLMOrig(_UpperCAmelCase )
model.load_state_dict(checkpoint["model"] )
model.eval()
if config_path is not None:
_UpperCAmelCase : List[str] = WavLMConfig.from_pretrained(_UpperCAmelCase )
else:
_UpperCAmelCase : List[Any] = WavLMConfig()
_UpperCAmelCase : Optional[Any] = WavLMModel(_UpperCAmelCase )
recursively_load_weights(_UpperCAmelCase , _UpperCAmelCase )
hf_wavlm.save_pretrained(_UpperCAmelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
__SCREAMING_SNAKE_CASE : Tuple = parser.parse_args()
convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__SCREAMING_SNAKE_CASE : Optional[int] = 256_047
__SCREAMING_SNAKE_CASE : Optional[int] = 256_145
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = NllbTokenizer
__UpperCamelCase: Tuple = NllbTokenizerFast
__UpperCamelCase: Union[str, Any] = True
__UpperCamelCase: Dict = True
__UpperCamelCase: Optional[Any] = {}
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
_UpperCAmelCase : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase : List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Dict = tokenizer_r.save_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
_UpperCAmelCase : Optional[int] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : List[str] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
_UpperCAmelCase : str = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : Optional[int] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
def _A ( self : Tuple ):
if not self.test_seqaseq:
return
_UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_UpperCAmelCase : Optional[Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
_UpperCAmelCase : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=A , tgt_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_UpperCAmelCase : Tuple = tokenizer.prepare_seqaseq_batch(
A , tgt_texts=A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_UpperCAmelCase : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def _A ( self : List[Any] ):
pass
def _A ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Any = [AddedToken("<special>" , lstrip=A )]
_UpperCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Dict = tokenizer_r.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_r.encode("<special>" , add_special_tokens=A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = "facebook/nllb-200-distilled-600M"
__UpperCamelCase: Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__UpperCamelCase: str = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__UpperCamelCase: str = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _A ( cls : int ):
_UpperCAmelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
_UpperCAmelCase : Union[str, Any] = 1
return cls
def _A ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 256057 )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def _A ( self : Tuple ):
self.assertIn(A , self.tokenizer.all_special_ids )
# fmt: off
_UpperCAmelCase : List[Any] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_UpperCAmelCase : Tuple = self.tokenizer.decode(A , skip_special_tokens=A )
_UpperCAmelCase : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , A )
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Tuple = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , A )
self.assertEqual(len(A ) , A )
def _A ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [256203, 3] )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
_UpperCAmelCase : Tuple = NllbTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def _A ( self : Dict ):
_UpperCAmelCase : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Tuple = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(A , A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_UpperCAmelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : Dict = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=10 , return_tensors="pt" )
_UpperCAmelCase : List[Any] = targets["input_ids"]
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(
A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
} , )
@require_torch
def _A ( self : Any ):
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Any = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : str = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
__SCREAMING_SNAKE_CASE : List[str] = TypeVar("""T""")
__SCREAMING_SNAKE_CASE : str = TypeVar("""U""")
class lowerCamelCase_ (Generic[T, U] ):
'''simple docstring'''
def __init__( self : Optional[int] , A : T | None , A : U | None ):
_UpperCAmelCase : Any = key
_UpperCAmelCase : List[str] = val
_UpperCAmelCase : DoubleLinkedListNode[T, U] | None = None
_UpperCAmelCase : DoubleLinkedListNode[T, U] | None = None
def __repr__( self : str ):
return (
F"""Node: key: {self.key}, val: {self.val}, """
F"""has next: {bool(self.next )}, has prev: {bool(self.prev )}"""
)
class lowerCamelCase_ (Generic[T, U] ):
'''simple docstring'''
def __init__( self : Any ):
_UpperCAmelCase : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(A , A )
_UpperCAmelCase : DoubleLinkedListNode[T, U] = DoubleLinkedListNode(A , A )
_UpperCAmelCase , _UpperCAmelCase : Dict = self.rear, self.head
def __repr__( self : Optional[int] ):
_UpperCAmelCase : List[str] = ["DoubleLinkedList"]
_UpperCAmelCase : Tuple = self.head
while node.next is not None:
rep.append(str(A ) )
_UpperCAmelCase : str = node.next
rep.append(str(self.rear ) )
return ",\n ".join(A )
def _A ( self : Union[str, Any] , A : DoubleLinkedListNode[T, U] ):
_UpperCAmelCase : Optional[Any] = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
_UpperCAmelCase : Dict = node
_UpperCAmelCase : List[Any] = previous
_UpperCAmelCase : Optional[int] = node
_UpperCAmelCase : str = self.rear
def _A ( self : List[Any] , A : DoubleLinkedListNode[T, U] ):
if node.prev is None or node.next is None:
return None
_UpperCAmelCase : str = node.next
_UpperCAmelCase : List[Any] = node.prev
_UpperCAmelCase : Tuple = None
_UpperCAmelCase : List[str] = None
return node
class lowerCamelCase_ (Generic[T, U] ):
'''simple docstring'''
__UpperCamelCase: dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : Dict , A : int ):
_UpperCAmelCase : DoubleLinkedList[T, U] = DoubleLinkedList()
_UpperCAmelCase : int = capacity
_UpperCAmelCase : Tuple = 0
_UpperCAmelCase : Any = 0
_UpperCAmelCase : str = 0
_UpperCAmelCase : dict[T, DoubleLinkedListNode[T, U]] = {}
def __repr__( self : List[Any] ):
return (
F"""CacheInfo(hits={self.hits}, misses={self.miss}, """
F"""capacity={self.capacity}, current size={self.num_keys})"""
)
def __contains__( self : Optional[Any] , A : T ):
return key in self.cache
def _A ( self : int , A : T ):
# Note: pythonic interface would throw KeyError rather than return None
if key in self.cache:
self.hits += 1
_UpperCAmelCase : DoubleLinkedListNode[T, U] = self.cache[key]
_UpperCAmelCase : Optional[int] = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(A )
return node.val
self.miss += 1
return None
def _A ( self : List[str] , A : T , A : U ):
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
_UpperCAmelCase : Tuple = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(A ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
_UpperCAmelCase : List[str] = DoubleLinkedListNode(A , A )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
_UpperCAmelCase : int = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
_UpperCAmelCase : int = value
self.list.add(A )
@classmethod
def _A ( cls : Optional[int] , A : int = 128 ):
def cache_decorator_inner(A : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*A : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
_UpperCAmelCase : Tuple = LRUCache(A )
_UpperCAmelCase : List[str] = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
_UpperCAmelCase : List[Any] = func(*A )
cls.decorator_function_to_instance_map[func].put(args[0] , A )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(A , "cache_info" , A ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : list ) -> list:
"""simple docstring"""
_UpperCAmelCase : List[Any] = len(_UpperCAmelCase )
for _ in range(_UpperCAmelCase ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
_UpperCAmelCase , _UpperCAmelCase : int = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = list(range(10, 0, -1))
print(F'Original: {arr}. Sorted: {odd_even_transposition(arr)}')
| 31
| 1
|
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
__SCREAMING_SNAKE_CASE : Any = """
Human: <<task>>
Assistant: """
__SCREAMING_SNAKE_CASE : List[str] = """huggingface-tools/default-prompts"""
__SCREAMING_SNAKE_CASE : int = {"""chat""": """chat_prompt_template.txt""", """run""": """run_prompt_template.txt"""}
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : List[Any] , _UpperCAmelCase : List[Any]="run" ) -> int:
"""simple docstring"""
if prompt_or_repo_id is None:
_UpperCAmelCase : List[str] = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search("\\s" , _UpperCAmelCase ) is not None:
return prompt_or_repo_id
_UpperCAmelCase : List[Any] = cached_file(
_UpperCAmelCase , PROMPT_FILES[mode] , repo_type="dataset" , user_agent={"agent": agent_name} )
with open(_UpperCAmelCase , "r" , encoding="utf-8" ) as f:
return f.read()
| 31
|
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
_UpperCAmelCase : Optional[int] = nn.ModuleList(A )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : torch.Tensor , A : List[torch.tensor] , A : List[float] , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[Dict[str, Any]] = None , A : bool = False , A : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(A , A , self.nets ) ):
_UpperCAmelCase , _UpperCAmelCase : str = controlnet(
A , A , A , A , A , A , A , A , A , A , A , )
# merge samples
if i == 0:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = down_samples, mid_sample
else:
_UpperCAmelCase : Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A , A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _A ( self : List[str] , A : Union[str, os.PathLike] , A : bool = True , A : Callable = None , A : bool = False , A : Optional[str] = None , ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A , is_main_process=A , save_function=A , safe_serialization=A , variant=A , )
idx += 1
_UpperCAmelCase : Tuple = model_path_to_save + F"""_{idx}"""
@classmethod
def _A ( cls : int , A : Optional[Union[str, os.PathLike]] , **A : Tuple ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_UpperCAmelCase : int = pretrained_model_path
while os.path.isdir(A ):
_UpperCAmelCase : List[str] = ControlNetModel.from_pretrained(A , **A )
controlnets.append(A )
idx += 1
_UpperCAmelCase : Dict = pretrained_model_path + F"""_{idx}"""
logger.info(F"""{len(A )} controlnets loaded from {pretrained_model_path}.""" )
if len(A ) == 0:
raise ValueError(
F"""No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(A )
| 31
| 1
|
'''simple docstring'''
# Function to print upper half of diamond (pyramid)
def UpperCamelCase_ ( _UpperCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
for i in range(0 , _UpperCAmelCase ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
for _ in range(0 , i + 1 ): # printing stars
print("* " , end="" )
print()
def UpperCamelCase_ ( _UpperCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
for i in range(_UpperCAmelCase , 0 , -1 ):
for _ in range(_UpperCAmelCase , 0 , -1 ): # printing stars
print("* " , end="" )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(" " , end="" )
def UpperCamelCase_ ( _UpperCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
if n <= 0:
print(" ... .... nothing printing :(" )
return
floyd(_UpperCAmelCase ) # upper half
reverse_floyd(_UpperCAmelCase ) # lower half
if __name__ == "__main__":
print(R"""| /\ | |- | |- |--| |\ /| |-""")
print(R"""|/ \| |- |_ |_ |__| | \/ | |_""")
__SCREAMING_SNAKE_CASE : str = 1
while K:
__SCREAMING_SNAKE_CASE : Tuple = int(input("""enter the number and , and see the magic : """))
print()
pretty_print(user_number)
__SCREAMING_SNAKE_CASE : Any = int(input("""press 0 to exit... and 1 to continue..."""))
print("""Good Bye...""")
| 31
|
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_UpperCAmelCase : List[Any] = MaskFormerConfig(backbone_config=_UpperCAmelCase )
_UpperCAmelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_UpperCAmelCase : Dict = 847
_UpperCAmelCase : Any = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_UpperCAmelCase : Any = 150
_UpperCAmelCase : Any = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_UpperCAmelCase : Tuple = 171
_UpperCAmelCase : Union[str, Any] = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_UpperCAmelCase : Any = 133
_UpperCAmelCase : int = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 19
_UpperCAmelCase : str = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 65
_UpperCAmelCase : Tuple = "mapillary-vistas-id2label.json"
_UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : List[str] = val
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase : Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase : Any = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_UpperCAmelCase : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[:dim, :]
_UpperCAmelCase : Tuple = in_proj_bias[: dim]
_UpperCAmelCase : List[Any] = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase : List[str] = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase : Optional[Any] = in_proj_weight[
-dim :, :
]
_UpperCAmelCase : Dict = in_proj_bias[-dim :]
# fmt: on
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : int = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : int = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Optional[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Any = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Tuple = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : Optional[int] = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[-hidden_size :]
# fmt: on
def UpperCamelCase_ ( ) -> torch.Tensor:
"""simple docstring"""
_UpperCAmelCase : int = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : Any = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = get_maskformer_config(_UpperCAmelCase )
# load original state_dict
with open(_UpperCAmelCase , "rb" ) as f:
_UpperCAmelCase : Optional[int] = pickle.load(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_UpperCAmelCase : Any = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_swin_q_k_v(_UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_UpperCAmelCase , _UpperCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_UpperCAmelCase : Tuple = torch.from_numpy(_UpperCAmelCase )
# load 🤗 model
_UpperCAmelCase : Union[str, Any] = MaskFormerForInstanceSegmentation(_UpperCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_UpperCAmelCase , param.shape )
_UpperCAmelCase , _UpperCAmelCase : Any = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_UpperCAmelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_UpperCAmelCase : Optional[int] = prepare_img()
if "vistas" in model_name:
_UpperCAmelCase : int = 65
elif "cityscapes" in model_name:
_UpperCAmelCase : Tuple = 65_535
else:
_UpperCAmelCase : Any = 255
_UpperCAmelCase : Optional[Any] = True if "ade" in model_name else False
_UpperCAmelCase : Optional[int] = MaskFormerImageProcessor(ignore_index=_UpperCAmelCase , reduce_labels=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = image_processor(_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : List[Any] = model(**_UpperCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_UpperCAmelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 31
| 1
|
'''simple docstring'''
import gc
import math
import unittest
import torch
from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
enable_full_determinism()
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = UNetaDModel
__UpperCamelCase: str = "sample"
@property
def _A ( self : str ):
_UpperCAmelCase : int = 4
_UpperCAmelCase : Any = 3
_UpperCAmelCase : List[str] = (32, 32)
_UpperCAmelCase : Optional[int] = floats_tensor((batch_size, num_channels) + sizes ).to(A )
_UpperCAmelCase : Optional[Any] = torch.tensor([10] ).to(A )
return {"sample": noise, "timestep": time_step}
@property
def _A ( self : Optional[int] ):
return (3, 32, 32)
@property
def _A ( self : Optional[Any] ):
return (3, 32, 32)
def _A ( self : Any ):
_UpperCAmelCase : Optional[Any] = {
"block_out_channels": (32, 64),
"down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
"up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
"attention_head_dim": 3,
"out_channels": 3,
"in_channels": 3,
"layers_per_block": 2,
"sample_size": 32,
}
_UpperCAmelCase : Union[str, Any] = self.dummy_input
return init_dict, inputs_dict
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = UNetaDModel
__UpperCamelCase: List[Any] = "sample"
@property
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : Union[str, Any] = 4
_UpperCAmelCase : Optional[Any] = (32, 32)
_UpperCAmelCase : str = floats_tensor((batch_size, num_channels) + sizes ).to(A )
_UpperCAmelCase : Tuple = torch.tensor([10] ).to(A )
return {"sample": noise, "timestep": time_step}
@property
def _A ( self : Dict ):
return (4, 32, 32)
@property
def _A ( self : Dict ):
return (4, 32, 32)
def _A ( self : str ):
_UpperCAmelCase : Tuple = {
"sample_size": 32,
"in_channels": 4,
"out_channels": 4,
"layers_per_block": 2,
"block_out_channels": (32, 64),
"attention_head_dim": 32,
"down_block_types": ("DownBlock2D", "DownBlock2D"),
"up_block_types": ("UpBlock2D", "UpBlock2D"),
}
_UpperCAmelCase : str = self.dummy_input
return init_dict, inputs_dict
def _A ( self : Optional[int] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=A )
self.assertIsNotNone(A )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(A )
_UpperCAmelCase : Optional[int] = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" )
def _A ( self : List[Any] ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=A )
model.to(A )
_UpperCAmelCase : Optional[Any] = model(**self.dummy_input ).sample
assert image is not None, "Make sure output is not None"
@unittest.skipIf(torch_device != "cuda" , "This test is supposed to run on GPU" )
def _A ( self : Dict ):
# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
_UpperCAmelCase , _UpperCAmelCase : List[Any] = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" , output_loading_info=A )
model_accelerate.to(A )
model_accelerate.eval()
_UpperCAmelCase : Optional[Any] = torch.randn(
1 , model_accelerate.config.in_channels , model_accelerate.config.sample_size , model_accelerate.config.sample_size , generator=torch.manual_seed(0 ) , )
_UpperCAmelCase : str = noise.to(A )
_UpperCAmelCase : Optional[Any] = torch.tensor([10] * noise.shape[0] ).to(A )
_UpperCAmelCase : Dict = model_accelerate(A , A )["sample"]
# two models don't need to stay in the device at the same time
del model_accelerate
torch.cuda.empty_cache()
gc.collect()
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = UNetaDModel.from_pretrained(
"fusing/unet-ldm-dummy-update" , output_loading_info=A , low_cpu_mem_usage=A )
model_normal_load.to(A )
model_normal_load.eval()
_UpperCAmelCase : Any = model_normal_load(A , A )["sample"]
assert torch_all_close(A , A , rtol=1E-3 )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[str] = UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update" )
model.eval()
model.to(A )
_UpperCAmelCase : Optional[Any] = torch.randn(
1 , model.config.in_channels , model.config.sample_size , model.config.sample_size , generator=torch.manual_seed(0 ) , )
_UpperCAmelCase : Union[str, Any] = noise.to(A )
_UpperCAmelCase : Optional[Any] = torch.tensor([10] * noise.shape[0] ).to(A )
with torch.no_grad():
_UpperCAmelCase : Dict = model(A , A ).sample
_UpperCAmelCase : Dict = output[0, -1, -3:, -3:].flatten().cpu()
# fmt: off
_UpperCAmelCase : int = torch.tensor([-13.3_258, -20.1_100, -15.9_873, -17.6_617, -23.0_596, -17.9_419, -13.3_675, -16.1_889, -12.3_800] )
# fmt: on
self.assertTrue(torch_all_close(A , A , rtol=1E-3 ) )
class lowerCamelCase_ (snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[Any] = UNetaDModel
__UpperCamelCase: Dict = "sample"
@property
def _A ( self : Optional[Any] , A : Optional[Any]=(32, 32) ):
_UpperCAmelCase : Optional[Any] = 4
_UpperCAmelCase : int = 3
_UpperCAmelCase : Union[str, Any] = floats_tensor((batch_size, num_channels) + sizes ).to(A )
_UpperCAmelCase : Optional[Any] = torch.tensor(batch_size * [10] ).to(dtype=torch.intaa , device=A )
return {"sample": noise, "timestep": time_step}
@property
def _A ( self : str ):
return (3, 32, 32)
@property
def _A ( self : str ):
return (3, 32, 32)
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = {
"block_out_channels": [32, 64, 64, 64],
"in_channels": 3,
"layers_per_block": 1,
"out_channels": 3,
"time_embedding_type": "fourier",
"norm_eps": 1E-6,
"mid_block_scale_factor": math.sqrt(2.0 ),
"norm_num_groups": None,
"down_block_types": [
"SkipDownBlock2D",
"AttnSkipDownBlock2D",
"SkipDownBlock2D",
"SkipDownBlock2D",
],
"up_block_types": [
"SkipUpBlock2D",
"SkipUpBlock2D",
"AttnSkipUpBlock2D",
"SkipUpBlock2D",
],
}
_UpperCAmelCase : str = self.dummy_input
return init_dict, inputs_dict
@slow
def _A ( self : Union[str, Any] ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" , output_loading_info=A )
self.assertIsNotNone(A )
self.assertEqual(len(loading_info["missing_keys"] ) , 0 )
model.to(A )
_UpperCAmelCase : Optional[int] = self.dummy_input
_UpperCAmelCase : Optional[int] = floats_tensor((4, 3) + (256, 256) ).to(A )
_UpperCAmelCase : Union[str, Any] = noise
_UpperCAmelCase : Optional[Any] = model(**A )
assert image is not None, "Make sure output is not None"
@slow
def _A ( self : Tuple ):
_UpperCAmelCase : List[str] = UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256" )
model.to(A )
_UpperCAmelCase : List[Any] = 4
_UpperCAmelCase : List[str] = 3
_UpperCAmelCase : str = (256, 256)
_UpperCAmelCase : List[str] = torch.ones((batch_size, num_channels) + sizes ).to(A )
_UpperCAmelCase : Tuple = torch.tensor(batch_size * [1E-4] ).to(A )
with torch.no_grad():
_UpperCAmelCase : Any = model(A , A ).sample
_UpperCAmelCase : Tuple = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
_UpperCAmelCase : Union[str, Any] = torch.tensor([-4_842.8_691, -6_499.6_631, -3_800.1_953, -7_978.2_686, -10_980.7_129, -20_028.8_535, 8_148.2_822, 2_342.2_905, 567.7_608] )
# fmt: on
self.assertTrue(torch_all_close(A , A , rtol=1E-2 ) )
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[int] = UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update" )
model.to(A )
_UpperCAmelCase : Optional[int] = 4
_UpperCAmelCase : Dict = 3
_UpperCAmelCase : Dict = (32, 32)
_UpperCAmelCase : List[Any] = torch.ones((batch_size, num_channels) + sizes ).to(A )
_UpperCAmelCase : Optional[int] = torch.tensor(batch_size * [1E-4] ).to(A )
with torch.no_grad():
_UpperCAmelCase : Optional[Any] = model(A , A ).sample
_UpperCAmelCase : List[str] = output[0, -3:, -3:, -1].flatten().cpu()
# fmt: off
_UpperCAmelCase : List[str] = torch.tensor([-0.0_325, -0.0_900, -0.0_869, -0.0_332, -0.0_725, -0.0_270, -0.0_101, 0.0_227, 0.0_256] )
# fmt: on
self.assertTrue(torch_all_close(A , A , rtol=1E-2 ) )
def _A ( self : List[Any] ):
# not required for this model
pass
| 31
|
'''simple docstring'''
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[str] , A : Optional[str] = None ):
_UpperCAmelCase : Dict = (
os.path.join(A , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
_UpperCAmelCase : Union[str, Any] = Extractor
def _A ( self : Tuple , A : str ):
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
_UpperCAmelCase : Dict = os.path.abspath(A )
return os.path.join(self.extract_dir , hash_url_to_filename(A ) )
def _A ( self : int , A : str , A : bool ):
return force_extract or (
not os.path.isfile(A ) and not (os.path.isdir(A ) and os.listdir(A ))
)
def _A ( self : Optional[int] , A : str , A : bool = False ):
_UpperCAmelCase : Union[str, Any] = self.extractor.infer_extractor_format(A )
if not extractor_format:
return input_path
_UpperCAmelCase : Optional[Any] = self._get_output_path(A )
if self._do_extract(A , A ):
self.extractor.extract(A , A , A )
return output_path
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
@abstractmethod
def _A ( cls : str , A : Union[Path, str] , **A : Dict ):
...
@staticmethod
@abstractmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
...
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[bytes] = []
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
with open(A , "rb" ) as f:
return f.read(A )
@classmethod
def _A ( cls : Any , A : Union[Path, str] , A : bytes = b"" ):
if not magic_number:
_UpperCAmelCase : Any = max(len(A ) for cls_magic_number in cls.magic_numbers )
try:
_UpperCAmelCase : int = cls.read_magic_number(A , A )
except OSError:
return False
return any(magic_number.startswith(A ) for cls_magic_number in cls.magic_numbers )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@classmethod
def _A ( cls : str , A : Union[Path, str] , **A : List[Any] ):
return tarfile.is_tarfile(A )
@staticmethod
def _A ( A : Union[str, Any] , A : str ):
def resolved(A : str ) -> str:
return os.path.realpath(os.path.abspath(A ) )
def badpath(A : str , A : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(A , A ) ).startswith(A )
def badlink(A : str , A : str ) -> bool:
# Links are interpreted relative to the directory containing the link
_UpperCAmelCase : List[str] = resolved(os.path.join(A , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=A )
_UpperCAmelCase : Optional[int] = resolved(A )
for finfo in members:
if badpath(finfo.name , A ):
logger.error(F"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(A , A ):
logger.error(F"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : int = tarfile.open(A )
tar_file.extractall(A , members=TarExtractor.safemembers(A , A ) )
tar_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = [b"\x1F\x8B"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with gzip.open(A , "rb" ) as gzip_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [
b"PK\x03\x04",
b"PK\x05\x06", # empty archive
b"PK\x07\x08", # spanned archive
]
@classmethod
def _A ( cls : Dict , A : Union[Path, str] , A : bytes = b"" ):
if super().is_extractable(A , magic_number=A ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(A , "rb" ) as fp:
_UpperCAmelCase : Tuple = _EndRecData(A )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
_UpperCAmelCase : Dict = fp.read(A ) # CD is where we expect it to be
if len(A ) == sizeCentralDir:
_UpperCAmelCase : Any = struct.unpack(A , A ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
os.makedirs(A , exist_ok=A )
with zipfile.ZipFile(A , "r" ) as zip_file:
zip_file.extractall(A )
zip_file.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = [b"\xFD\x37\x7A\x58\x5A\x00"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with lzma.open(A ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[str] = [b"Rar!\x1a\x07\x00", b"Rar!\x1a\x07\x01\x00"] # RAR_ID # RAR5_ID
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(A , exist_ok=A )
_UpperCAmelCase : List[str] = rarfile.RarFile(A )
rf.extractall(A )
rf.close()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x28\xb5\x2F\xFD"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
_UpperCAmelCase : Optional[Any] = zstd.ZstdDecompressor()
with open(A , "rb" ) as ifh, open(A , "wb" ) as ofh:
dctx.copy_stream(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = [b"\x42\x5A\x68"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
with bza.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = [b"\x37\x7A\xBC\xAF\x27\x1C"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr" )
import pyazr
os.makedirs(A , exist_ok=A )
with pyazr.SevenZipFile(A , "r" ) as archive:
archive.extractall(A )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = [b"\x04\x22\x4D\x18"]
@staticmethod
def _A ( A : Union[Path, str] , A : Union[Path, str] ):
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4" )
import lza.frame
with lza.frame.open(A , "rb" ) as compressed_file:
with open(A , "wb" ) as extracted_file:
shutil.copyfileobj(A , A )
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _A ( cls : List[Any] ):
return max(
len(A )
for extractor in cls.extractors.values()
if issubclass(A , A )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _A ( A : Union[Path, str] , A : int ):
try:
return MagicNumberBaseExtractor.read_magic_number(A , magic_number_length=A )
except OSError:
return b""
@classmethod
def _A ( cls : Optional[Any] , A : Union[Path, str] , A : bool = False ):
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead." , category=A , )
_UpperCAmelCase : Union[str, Any] = cls.infer_extractor_format(A )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _A ( cls : Dict , A : Union[Path, str] ): # <Added version="2.4.0"/>
_UpperCAmelCase : Optional[int] = cls._get_magic_number_max_length()
_UpperCAmelCase : str = cls._read_magic_number(A , A )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(A , magic_number=A ):
return extractor_format
@classmethod
def _A ( cls : List[str] , A : Union[Path, str] , A : Union[Path, str] , A : Optional[str] = None , A : Optional[BaseExtractor] = "deprecated" , ):
os.makedirs(os.path.dirname(A ) , exist_ok=A )
# Prevent parallel extractions
_UpperCAmelCase : Tuple = str(Path(A ).with_suffix(".lock" ) )
with FileLock(A ):
shutil.rmtree(A , ignore_errors=A )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(A , A ): # passed as positional arg
warnings.warn(
"Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'extractor_format' instead." , category=A , )
_UpperCAmelCase : Tuple = extractor if extractor != "deprecated" else extractor_format
else:
_UpperCAmelCase : Tuple = cls.extractors[extractor_format]
return extractor.extract(A , A )
else:
warnings.warn(
"Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
"exception in 3.0.0." , category=A , )
for extractor in cls.extractors.values():
if extractor.is_extractable(A ):
return extractor.extract(A , A )
| 31
| 1
|
'''simple docstring'''
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> Any:
"""simple docstring"""
_UpperCAmelCase : List[Any] = []
for part_id in partition_order:
_UpperCAmelCase : Any = df.where(F"""SPARK_PARTITION_ID() = {part_id}""" ).collect()
for row_idx, row in enumerate(_UpperCAmelCase ):
expected_row_ids_and_row_dicts.append((F"""{part_id}_{row_idx}""", row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ) -> int:
"""simple docstring"""
_UpperCAmelCase : str = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_UpperCAmelCase : Optional[Any] = spark.range(100 ).repartition(1 )
_UpperCAmelCase : Optional[int] = Spark(_UpperCAmelCase )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : str = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_UpperCAmelCase : Optional[int] = spark.range(10 ).repartition(2 )
_UpperCAmelCase : Union[str, Any] = [1, 0]
_UpperCAmelCase : Dict = _generate_iterable_examples(_UpperCAmelCase , _UpperCAmelCase ) # Reverse the partitions.
_UpperCAmelCase : Union[str, Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase , _UpperCAmelCase )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
_UpperCAmelCase , _UpperCAmelCase : Tuple = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_UpperCAmelCase : List[str] = spark.range(10 ).repartition(1 )
_UpperCAmelCase : List[str] = SparkExamplesIterable(_UpperCAmelCase )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(_UpperCAmelCase ):
assert row_id == F"""0_{i}"""
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ) -> str:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_UpperCAmelCase : Any = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator" ) as generator_mock:
_UpperCAmelCase : Optional[Any] = lambda _UpperCAmelCase : x.reverse()
_UpperCAmelCase : Optional[Any] = _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase , [2, 1, 0] )
_UpperCAmelCase : Optional[Any] = SparkExamplesIterable(_UpperCAmelCase ).shuffle_data_sources(_UpperCAmelCase )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase : List[str] = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_UpperCAmelCase : Optional[int] = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
_UpperCAmelCase : Dict = SparkExamplesIterable(_UpperCAmelCase ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCAmelCase : int = _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase , [0, 2] )
for i, (row_id, row_dict) in enumerate(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
_UpperCAmelCase : Any = SparkExamplesIterable(_UpperCAmelCase ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
_UpperCAmelCase : int = _get_expected_row_ids_and_row_dicts_for_partition_order(_UpperCAmelCase , [1, 3] )
for i, (row_id, row_dict) in enumerate(_UpperCAmelCase ):
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def UpperCamelCase_ ( ) -> Union[str, Any]:
"""simple docstring"""
_UpperCAmelCase : Optional[Any] = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
_UpperCAmelCase : str = spark.range(100 ).repartition(1 )
_UpperCAmelCase : int = Spark(_UpperCAmelCase )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 31
|
'''simple docstring'''
from typing import Any
def UpperCamelCase_ ( _UpperCAmelCase : list , _UpperCAmelCase : list , _UpperCAmelCase : dict , _UpperCAmelCase : dict , _UpperCAmelCase : dict , ) -> list:
"""simple docstring"""
_validation(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
# Creates data structures and fill initial step
_UpperCAmelCase : dict = {}
_UpperCAmelCase : dict = {}
for state in states_space:
_UpperCAmelCase : Union[str, Any] = observations_space[0]
_UpperCAmelCase : Tuple = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
_UpperCAmelCase : List[str] = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(_UpperCAmelCase ) ):
_UpperCAmelCase : Optional[Any] = observations_space[o]
_UpperCAmelCase : int = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
_UpperCAmelCase : str = ""
_UpperCAmelCase : Tuple = -1
for k_state in states_space:
_UpperCAmelCase : Any = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
_UpperCAmelCase : Union[str, Any] = probability
_UpperCAmelCase : str = k_state
# Update probabilities and pointers dicts
_UpperCAmelCase : Optional[int] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
_UpperCAmelCase : Tuple = arg_max
# The final observation
_UpperCAmelCase : Optional[Any] = observations_space[len(_UpperCAmelCase ) - 1]
# argmax for given final observation
_UpperCAmelCase : List[str] = ""
_UpperCAmelCase : Any = -1
for k_state in states_space:
_UpperCAmelCase : Optional[int] = probabilities[(k_state, final_observation)]
if probability > max_probability:
_UpperCAmelCase : int = probability
_UpperCAmelCase : Dict = k_state
_UpperCAmelCase : Dict = arg_max
# Process pointers backwards
_UpperCAmelCase : List[Any] = last_state
_UpperCAmelCase : str = []
for o in range(len(_UpperCAmelCase ) - 1 , -1 , -1 ):
result.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = pointers[previous, observations_space[o]]
result.reverse()
return result
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_not_empty(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , )
_validate_lists(_UpperCAmelCase , _UpperCAmelCase )
_validate_dicts(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any ) -> None:
"""simple docstring"""
_validate_list(_UpperCAmelCase , "observations_space" )
_validate_list(_UpperCAmelCase , "states_space" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list"""
raise ValueError(_UpperCAmelCase )
else:
for x in _object:
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ):
_UpperCAmelCase : Optional[int] = F"""{var_name} must be a list of strings"""
raise ValueError(_UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : Any , _UpperCAmelCase : Any , ) -> None:
"""simple docstring"""
_validate_dict(_UpperCAmelCase , "initial_probabilities" , _UpperCAmelCase )
_validate_nested_dict(_UpperCAmelCase , "transition_probabilities" )
_validate_nested_dict(_UpperCAmelCase , "emission_probabilities" )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str ) -> None:
"""simple docstring"""
_validate_dict(_object , _UpperCAmelCase , _UpperCAmelCase )
for x in _object.values():
_validate_dict(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
def UpperCamelCase_ ( _UpperCAmelCase : Any , _UpperCAmelCase : str , _UpperCAmelCase : type , _UpperCAmelCase : bool = False ) -> None:
"""simple docstring"""
if not isinstance(_object , _UpperCAmelCase ):
_UpperCAmelCase : Any = F"""{var_name} must be a dict"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object ):
_UpperCAmelCase : Tuple = F"""{var_name} all keys must be strings"""
raise ValueError(_UpperCAmelCase )
if not all(isinstance(_UpperCAmelCase , _UpperCAmelCase ) for x in _object.values() ):
_UpperCAmelCase : List[str] = "nested dictionary " if nested else ""
_UpperCAmelCase : List[str] = F"""{var_name} {nested_text}all values must be {value_type.__name__}"""
raise ValueError(_UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int = 1_000 ) -> int:
"""simple docstring"""
_UpperCAmelCase : int = -1
_UpperCAmelCase : Optional[int] = 0
for a in range(1 , n // 3 ):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
_UpperCAmelCase : Tuple = (n * n - 2 * a * n) // (2 * n - 2 * a)
_UpperCAmelCase : List[str] = n - a - b
if c * c == (a * a + b * b):
_UpperCAmelCase : Optional[int] = a * b * c
if candidate >= product:
_UpperCAmelCase : int = candidate
return product
if __name__ == "__main__":
print(F'{solution() = }')
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] , A : Dict , A : Dict=7 , A : Optional[int]=3 , A : Optional[int]=18 , A : Dict=30 , A : List[Any]=400 , A : Union[str, Any]=True , A : Tuple=None , A : List[Any]=True , A : int=None , A : Optional[int]=True , ):
_UpperCAmelCase : Optional[int] = size if size is not None else {"shortest_edge": 20}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Union[str, Any] = batch_size
_UpperCAmelCase : Optional[Any] = num_channels
_UpperCAmelCase : Union[str, Any] = image_size
_UpperCAmelCase : int = min_resolution
_UpperCAmelCase : Optional[int] = max_resolution
_UpperCAmelCase : List[str] = do_resize
_UpperCAmelCase : Optional[Any] = size
_UpperCAmelCase : Tuple = do_center_crop
_UpperCAmelCase : Optional[int] = crop_size
_UpperCAmelCase : Optional[Any] = do_flip_channel_order
def _A ( self : Dict ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Tuple = MobileViTImageProcessor if is_vision_available() else None
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = MobileViTImageProcessingTester(self )
@property
def _A ( self : int ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Tuple ):
_UpperCAmelCase : int = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "size" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "center_crop" ) )
self.assertTrue(hasattr(A , "do_flip_channel_order" ) )
def _A ( self : Any ):
_UpperCAmelCase : List[str] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Any ):
pass
def _A ( self : Dict ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_UpperCAmelCase : List[Any] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Union[str, Any] ):
# Initialize image_processing
_UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_UpperCAmelCase : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : Any ):
# Initialize image_processing
_UpperCAmelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_UpperCAmelCase : List[str] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Any = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
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|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( _UpperCAmelCase : int | float | str , _UpperCAmelCase : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
_UpperCAmelCase : Optional[int] = int(_UpperCAmelCase )
_UpperCAmelCase : Dict = int(_UpperCAmelCase )
_UpperCAmelCase : list[str] = []
for temp in range(int(_UpperCAmelCase ) ):
series.append(F"""1 / {pow(temp + 1 , int(_UpperCAmelCase ) )}""" if series else "1" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
__SCREAMING_SNAKE_CASE : List[Any] = int(input("""Enter the last number (nth term) of the P-Series"""))
__SCREAMING_SNAKE_CASE : Tuple = int(input("""Enter the power for P-Series"""))
print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
print(p_series(nth_term, power))
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : int ) -> int:
"""simple docstring"""
_UpperCAmelCase : List[str] = 1 # To kept the Calculated Value
# Since C(n, k) = C(n, n-k)
if k > (n - k):
_UpperCAmelCase : Any = n - k
# Calculate C(n,k)
for i in range(_UpperCAmelCase ):
result *= n - i
result //= i + 1
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return binomial_coefficient(2 * node_count , _UpperCAmelCase ) // (node_count + 1)
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
if n < 0:
raise ValueError("factorial() not defined for negative values" )
_UpperCAmelCase : List[str] = 1
for i in range(1 , n + 1 ):
result *= i
return result
def UpperCamelCase_ ( _UpperCAmelCase : int ) -> int:
"""simple docstring"""
return catalan_number(_UpperCAmelCase ) * factorial(_UpperCAmelCase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Dict = int(input("""Enter the number of nodes: """).strip() or 0)
if node_count <= 0:
raise ValueError("""We need some nodes to work with.""")
print(
F'Given {node_count} nodes, there are {binary_tree_count(node_count)} '
F'binary trees and {catalan_number(node_count)} binary search trees.'
)
| 31
| 1
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[Any] = {"""vocab_file""": """spm_char.model"""}
__SCREAMING_SNAKE_CASE : List[str] = {
"""vocab_file""": {
"""microsoft/speecht5_asr""": """https://huggingface.co/microsoft/speecht5_asr/resolve/main/spm_char.model""",
"""microsoft/speecht5_tts""": """https://huggingface.co/microsoft/speecht5_tts/resolve/main/spm_char.model""",
"""microsoft/speecht5_vc""": """https://huggingface.co/microsoft/speecht5_vc/resolve/main/spm_char.model""",
}
}
__SCREAMING_SNAKE_CASE : Any = {
"""microsoft/speecht5_asr""": 1_024,
"""microsoft/speecht5_tts""": 1_024,
"""microsoft/speecht5_vc""": 1_024,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = VOCAB_FILES_NAMES
__UpperCamelCase: List[str] = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: List[Any] = ["input_ids", "attention_mask"]
def __init__( self : Tuple , A : Dict , A : Optional[int]="<s>" , A : Any="</s>" , A : int="<unk>" , A : int="<pad>" , A : Optional[Dict[str, Any]] = None , **A : List[Any] , ):
_UpperCAmelCase : Union[str, Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=A , eos_token=A , unk_token=A , pad_token=A , sp_model_kwargs=self.sp_model_kwargs , **A , )
_UpperCAmelCase : int = vocab_file
_UpperCAmelCase : Any = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(A )
@property
def _A ( self : Optional[int] ):
return self.sp_model.get_piece_size()
def _A ( self : Optional[int] ):
_UpperCAmelCase : Any = {self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
_UpperCAmelCase : List[Any] = self.__dict__.copy()
_UpperCAmelCase : Any = None
return state
def __setstate__( self : int , A : List[Any] ):
_UpperCAmelCase : List[str] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_UpperCAmelCase : Union[str, Any] = {}
_UpperCAmelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def _A ( self : List[Any] , A : str ):
return self.sp_model.encode(A , out_type=A )
def _A ( self : Any , A : Tuple ):
return self.sp_model.piece_to_id(A )
def _A ( self : int , A : Optional[Any] ):
_UpperCAmelCase : int = self.sp_model.IdToPiece(A )
return token
def _A ( self : Dict , A : List[str] ):
_UpperCAmelCase : Optional[int] = []
_UpperCAmelCase : Dict = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(A ) + token
_UpperCAmelCase : Any = []
else:
current_sub_tokens.append(A )
out_string += self.sp_model.decode(A )
return out_string.strip()
def _A ( self : Dict , A : Any , A : int=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : Tuple , A : List[int] , A : Optional[List[int]] = None , A : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=A , token_ids_a=A , already_has_special_tokens=A )
_UpperCAmelCase : int = [1]
if token_ids_a is None:
return ([0] * len(A )) + suffix_ones
return ([0] * len(A )) + ([0] * len(A )) + suffix_ones
def _A ( self : Any , A : str , A : Optional[str] = None ):
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[str] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , A )
elif not os.path.isfile(self.vocab_file ):
with open(A , "wb" ) as fi:
_UpperCAmelCase : List[Any] = self.sp_model.serialized_model_proto()
fi.write(A )
return (out_vocab_file,)
| 31
|
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : Dict = {
"""vocab_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt"""
),
"""distilbert-base-german-cased""": """https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt""",
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""distilbert-base-uncased""": """https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json""",
"""distilbert-base-uncased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-cased""": """https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json""",
"""distilbert-base-cased-distilled-squad""": (
"""https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json"""
),
"""distilbert-base-german-cased""": (
"""https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json"""
),
"""distilbert-base-multilingual-cased""": (
"""https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json"""
),
},
}
__SCREAMING_SNAKE_CASE : Optional[Any] = {
"""distilbert-base-uncased""": 512,
"""distilbert-base-uncased-distilled-squad""": 512,
"""distilbert-base-cased""": 512,
"""distilbert-base-cased-distilled-squad""": 512,
"""distilbert-base-german-cased""": 512,
"""distilbert-base-multilingual-cased""": 512,
}
__SCREAMING_SNAKE_CASE : List[Any] = {
"""distilbert-base-uncased""": {"""do_lower_case""": True},
"""distilbert-base-uncased-distilled-squad""": {"""do_lower_case""": True},
"""distilbert-base-cased""": {"""do_lower_case""": False},
"""distilbert-base-cased-distilled-squad""": {"""do_lower_case""": False},
"""distilbert-base-german-cased""": {"""do_lower_case""": False},
"""distilbert-base-multilingual-cased""": {"""do_lower_case""": False},
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = VOCAB_FILES_NAMES
__UpperCamelCase: str = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Any = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase: str = ["input_ids", "attention_mask"]
__UpperCamelCase: List[str] = DistilBertTokenizer
def __init__( self : str , A : int=None , A : Tuple=None , A : Tuple=True , A : Dict="[UNK]" , A : List[Any]="[SEP]" , A : Optional[Any]="[PAD]" , A : Dict="[CLS]" , A : Tuple="[MASK]" , A : str=True , A : Dict=None , **A : List[Any] , ):
super().__init__(
A , tokenizer_file=A , do_lower_case=A , unk_token=A , sep_token=A , pad_token=A , cls_token=A , mask_token=A , tokenize_chinese_chars=A , strip_accents=A , **A , )
_UpperCAmelCase : str = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , A ) != do_lower_case
or normalizer_state.get("strip_accents" , A ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , A ) != tokenize_chinese_chars
):
_UpperCAmelCase : Dict = getattr(A , normalizer_state.pop("type" ) )
_UpperCAmelCase : int = do_lower_case
_UpperCAmelCase : Optional[int] = strip_accents
_UpperCAmelCase : str = tokenize_chinese_chars
_UpperCAmelCase : List[Any] = normalizer_class(**A )
_UpperCAmelCase : Dict = do_lower_case
def _A ( self : List[Any] , A : Tuple , A : Any=None ):
_UpperCAmelCase : Optional[int] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _A ( self : int , A : List[int] , A : Optional[List[int]] = None ):
_UpperCAmelCase : Any = [self.sep_token_id]
_UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _A ( self : Dict , A : str , A : Optional[str] = None ):
_UpperCAmelCase : Any = self._tokenizer.model.save(A , name=A )
return tuple(A )
| 31
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|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : bytes ) -> str:
"""simple docstring"""
return "".join([hex(_UpperCAmelCase )[2:].zfill(2 ).upper() for byte in list(_UpperCAmelCase )] )
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> bytes:
"""simple docstring"""
if (len(_UpperCAmelCase ) % 2) != 0:
raise ValueError(
"Base16 encoded data is invalid:\nData does not have an even number of hex digits." )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(_UpperCAmelCase ) <= set("0123456789ABCDEF" ):
raise ValueError(
"Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters." )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(_UpperCAmelCase ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from transformers.testing_utils import require_torch
if is_torch_available():
from transformers.trainer import DEFAULT_CALLBACKS
from .test_trainer import RegressionDataset, RegressionModelConfig, RegressionPreTrainedModel
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = []
def _A ( self : Any , A : Union[str, Any] , A : Optional[int] , A : List[str] , **A : Tuple ):
self.events.append("on_init_end" )
def _A ( self : Any , A : str , A : List[Any] , A : List[Any] , **A : Tuple ):
self.events.append("on_train_begin" )
def _A ( self : Tuple , A : List[str] , A : Tuple , A : int , **A : List[str] ):
self.events.append("on_train_end" )
def _A ( self : Optional[Any] , A : Dict , A : Any , A : Optional[Any] , **A : List[Any] ):
self.events.append("on_epoch_begin" )
def _A ( self : Optional[Any] , A : List[Any] , A : List[str] , A : Optional[int] , **A : Optional[int] ):
self.events.append("on_epoch_end" )
def _A ( self : List[str] , A : Optional[int] , A : List[Any] , A : Union[str, Any] , **A : Any ):
self.events.append("on_step_begin" )
def _A ( self : Tuple , A : Union[str, Any] , A : int , A : Optional[int] , **A : int ):
self.events.append("on_step_end" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Union[str, Any] , A : str , **A : Union[str, Any] ):
self.events.append("on_evaluate" )
def _A ( self : Optional[Any] , A : Optional[int] , A : Dict , A : List[Any] , **A : Dict ):
self.events.append("on_predict" )
def _A ( self : Dict , A : Dict , A : List[Any] , A : Dict , **A : str ):
self.events.append("on_save" )
def _A ( self : Tuple , A : Optional[Any] , A : Union[str, Any] , A : Optional[int] , **A : Dict ):
self.events.append("on_log" )
def _A ( self : Optional[int] , A : Optional[Any] , A : Tuple , A : Tuple , **A : List[str] ):
self.events.append("on_prediction_step" )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : Optional[int] ):
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
def _A ( self : List[Any] ):
shutil.rmtree(self.output_dir )
def _A ( self : Union[str, Any] , A : Optional[int]=0 , A : Optional[Any]=0 , A : Optional[Any]=64 , A : Dict=64 , A : Any=None , A : Tuple=False , **A : Optional[int] ):
# disable_tqdm in TrainingArguments has a flaky default since it depends on the level of logging. We make sure
# its set to False since the tests later on depend on its value.
_UpperCAmelCase : str = RegressionDataset(length=A )
_UpperCAmelCase : Union[str, Any] = RegressionDataset(length=A )
_UpperCAmelCase : Any = RegressionModelConfig(a=A , b=A )
_UpperCAmelCase : List[Any] = RegressionPreTrainedModel(A )
_UpperCAmelCase : Dict = TrainingArguments(self.output_dir , disable_tqdm=A , report_to=[] , **A )
return Trainer(
A , A , train_dataset=A , eval_dataset=A , callbacks=A , )
def _A ( self : str , A : List[str] , A : List[str] ):
self.assertEqual(len(A ) , len(A ) )
# Order doesn't matter
_UpperCAmelCase : Tuple = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
_UpperCAmelCase : Any = sorted(A , key=lambda A : cb.__name__ if isinstance(A , A ) else cb.__class__.__name__ )
for cba, cba in zip(A , A ):
if isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(A , A )
elif isinstance(A , A ) and not isinstance(A , A ):
self.assertEqual(A , cba.__class__ )
elif not isinstance(A , A ) and isinstance(A , A ):
self.assertEqual(cba.__class__ , A )
else:
self.assertEqual(A , A )
def _A ( self : int , A : List[str] ):
_UpperCAmelCase : List[str] = ["on_init_end", "on_train_begin"]
_UpperCAmelCase : str = 0
_UpperCAmelCase : Optional[Any] = len(trainer.get_eval_dataloader() )
_UpperCAmelCase : Optional[int] = ["on_prediction_step"] * len(trainer.get_eval_dataloader() ) + ["on_log", "on_evaluate"]
for _ in range(trainer.state.num_train_epochs ):
expected_events.append("on_epoch_begin" )
for _ in range(A ):
step += 1
expected_events += ["on_step_begin", "on_step_end"]
if step % trainer.args.logging_steps == 0:
expected_events.append("on_log" )
if trainer.args.evaluation_strategy == IntervalStrategy.STEPS and step % trainer.args.eval_steps == 0:
expected_events += evaluation_events.copy()
if step % trainer.args.save_steps == 0:
expected_events.append("on_save" )
expected_events.append("on_epoch_end" )
if trainer.args.evaluation_strategy == IntervalStrategy.EPOCH:
expected_events += evaluation_events.copy()
expected_events += ["on_log", "on_train_end"]
return expected_events
def _A ( self : str ):
_UpperCAmelCase : Any = self.get_trainer()
_UpperCAmelCase : int = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# Callbacks passed at init are added to the default callbacks
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
expected_callbacks.append(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# TrainingArguments.disable_tqdm controls if use ProgressCallback or PrinterCallback
_UpperCAmelCase : List[Any] = self.get_trainer(disable_tqdm=A )
_UpperCAmelCase : Tuple = DEFAULT_CALLBACKS.copy() + [PrinterCallback]
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = DEFAULT_CALLBACKS.copy() + [ProgressCallback]
_UpperCAmelCase : Dict = self.get_trainer()
# We can add, pop, or remove by class name
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : Optional[Any] = self.get_trainer()
_UpperCAmelCase : Any = trainer.pop_callback(A )
self.assertEqual(cb.__class__ , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
# We can also add, pop, or remove by instance
_UpperCAmelCase : Union[str, Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
trainer.remove_callback(A )
expected_callbacks.remove(A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
_UpperCAmelCase : List[Any] = self.get_trainer()
_UpperCAmelCase : List[Any] = trainer.callback_handler.callbacks[0]
_UpperCAmelCase : Union[str, Any] = trainer.pop_callback(A )
self.assertEqual(A , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
trainer.add_callback(A )
expected_callbacks.insert(0 , A )
self.check_callbacks_equality(trainer.callback_handler.callbacks , A )
def _A ( self : Optional[Any] ):
import warnings
# XXX: for now ignore scatter_gather warnings in this test since it's not relevant to what's being tested
warnings.simplefilter(action="ignore" , category=A )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] )
trainer.train()
_UpperCAmelCase : Union[str, Any] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# Independent log/save/eval
_UpperCAmelCase : Tuple = self.get_trainer(callbacks=[MyTestTrainerCallback] , logging_steps=5 )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : List[str] = self.get_trainer(callbacks=[MyTestTrainerCallback] , save_steps=5 )
trainer.train()
_UpperCAmelCase : Tuple = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : int = self.get_trainer(callbacks=[MyTestTrainerCallback] , eval_steps=5 , evaluation_strategy="steps" )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
_UpperCAmelCase : Optional[int] = self.get_trainer(callbacks=[MyTestTrainerCallback] , evaluation_strategy="epoch" )
trainer.train()
_UpperCAmelCase : int = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# A bit of everything
_UpperCAmelCase : int = self.get_trainer(
callbacks=[MyTestTrainerCallback] , logging_steps=3 , save_steps=10 , eval_steps=5 , evaluation_strategy="steps" , )
trainer.train()
_UpperCAmelCase : Optional[int] = trainer.callback_handler.callbacks[-2].events
self.assertEqual(A , self.get_expected_events(A ) )
# warning should be emitted for duplicated callbacks
with patch("transformers.trainer_callback.logger.warning" ) as warn_mock:
_UpperCAmelCase : Optional[Any] = self.get_trainer(
callbacks=[MyTestTrainerCallback, MyTestTrainerCallback] , )
assert str(A ) in warn_mock.call_args[0][0]
| 31
| 1
|
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def _A ( self : int ):
_UpperCAmelCase : Union[str, Any] = tempfile.mkdtemp()
# fmt: off
_UpperCAmelCase : Dict = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
_UpperCAmelCase : Optional[int] = dict(zip(A , range(len(A ) ) ) )
_UpperCAmelCase : Tuple = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
_UpperCAmelCase : int = {"unk_token": "<unk>"}
_UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
_UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(A ) + "\n" )
with open(self.merges_file , "w" , encoding="utf-8" ) as fp:
fp.write("\n".join(A ) )
_UpperCAmelCase : str = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48_145_466, 0.4_578_275, 0.40_821_073],
"image_std": [0.26_862_954, 0.26_130_258, 0.27_577_711],
}
_UpperCAmelCase : List[Any] = os.path.join(self.tmpdirname , A )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(A , A )
def _A ( self : Union[str, Any] , **A : Dict ):
return CLIPTokenizer.from_pretrained(self.tmpdirname , **A )
def _A ( self : Optional[int] , **A : List[Any] ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **A )
def _A ( self : Optional[Any] , **A : Union[str, Any] ):
return CLIPImageProcessor.from_pretrained(self.tmpdirname , **A )
def _A ( self : Dict ):
shutil.rmtree(self.tmpdirname )
def _A ( self : str ):
_UpperCAmelCase : List[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_UpperCAmelCase : List[Any] = [Image.fromarray(np.moveaxis(A , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
_UpperCAmelCase : Tuple = self.get_rust_tokenizer()
_UpperCAmelCase : Any = self.get_image_processor()
_UpperCAmelCase : int = CLIPProcessor(tokenizer=A , image_processor=A )
processor_slow.save_pretrained(self.tmpdirname )
_UpperCAmelCase : int = CLIPProcessor.from_pretrained(self.tmpdirname , use_fast=A )
_UpperCAmelCase : Optional[int] = CLIPProcessor(tokenizer=A , image_processor=A )
processor_fast.save_pretrained(self.tmpdirname )
_UpperCAmelCase : Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , A )
self.assertIsInstance(processor_fast.tokenizer , A )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , A )
self.assertIsInstance(processor_fast.image_processor , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = CLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase : Union[str, Any] = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase : List[str] = self.get_image_processor(do_normalize=A , padding_value=1.0 )
_UpperCAmelCase : List[Any] = CLIPProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=A , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , A )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Union[str, Any] = self.get_image_processor()
_UpperCAmelCase : List[str] = self.get_tokenizer()
_UpperCAmelCase : Union[str, Any] = CLIPProcessor(tokenizer=A , image_processor=A )
_UpperCAmelCase : Optional[int] = self.prepare_image_inputs()
_UpperCAmelCase : List[Any] = image_processor(A , return_tensors="np" )
_UpperCAmelCase : Tuple = processor(images=A , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _A ( self : str ):
_UpperCAmelCase : Any = self.get_image_processor()
_UpperCAmelCase : Union[str, Any] = self.get_tokenizer()
_UpperCAmelCase : int = CLIPProcessor(tokenizer=A , image_processor=A )
_UpperCAmelCase : Tuple = "lower newer"
_UpperCAmelCase : str = processor(text=A )
_UpperCAmelCase : List[str] = tokenizer(A )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _A ( self : int ):
_UpperCAmelCase : str = self.get_image_processor()
_UpperCAmelCase : List[str] = self.get_tokenizer()
_UpperCAmelCase : int = CLIPProcessor(tokenizer=A , image_processor=A )
_UpperCAmelCase : Any = "lower newer"
_UpperCAmelCase : List[str] = self.prepare_image_inputs()
_UpperCAmelCase : int = processor(text=A , images=A )
self.assertListEqual(list(inputs.keys() ) , ["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(A ):
processor()
def _A ( self : Optional[Any] ):
_UpperCAmelCase : int = self.get_image_processor()
_UpperCAmelCase : Dict = self.get_tokenizer()
_UpperCAmelCase : Dict = CLIPProcessor(tokenizer=A , image_processor=A )
_UpperCAmelCase : int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_UpperCAmelCase : int = processor.batch_decode(A )
_UpperCAmelCase : Tuple = tokenizer.batch_decode(A )
self.assertListEqual(A , A )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Tuple = self.get_image_processor()
_UpperCAmelCase : str = self.get_tokenizer()
_UpperCAmelCase : List[Any] = CLIPProcessor(tokenizer=A , image_processor=A )
_UpperCAmelCase : Union[str, Any] = "lower newer"
_UpperCAmelCase : Optional[Any] = self.prepare_image_inputs()
_UpperCAmelCase : Union[str, Any] = processor(text=A , images=A )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 31
|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : Dict , A : Optional[int]=7 , A : Tuple=3 , A : Optional[Any]=10 , A : int=18 , A : Dict=30 , A : List[str]=400 , A : int=True , A : Optional[Any]=None , A : Optional[Any]=True , A : List[Any]=[0.5, 0.5, 0.5] , A : List[str]=[0.5, 0.5, 0.5] , A : Optional[int]=None , ):
_UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 18}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : Optional[Any] = num_frames
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : Any = max_resolution
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : List[Any] = do_normalize
_UpperCAmelCase : Any = image_mean
_UpperCAmelCase : Tuple = image_std
_UpperCAmelCase : Any = crop_size
def _A ( self : List[Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = VivitImageProcessor if is_vision_available() else None
def _A ( self : int ):
_UpperCAmelCase : Tuple = VivitImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "size" ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Tuple ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_UpperCAmelCase : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_UpperCAmelCase : str = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_UpperCAmelCase : Tuple = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
| 1
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : int ) -> float:
"""simple docstring"""
if digit_amount > 0:
return round(number - int(_UpperCAmelCase ) , _UpperCAmelCase )
return number - int(_UpperCAmelCase )
if __name__ == "__main__":
print(decimal_isolate(1.5_3, 0))
print(decimal_isolate(3_5.3_4_5, 1))
print(decimal_isolate(3_5.3_4_5, 2))
print(decimal_isolate(3_5.3_4_5, 3))
print(decimal_isolate(-1_4.7_8_9, 3))
print(decimal_isolate(0, 2))
print(decimal_isolate(-1_4.1_2_3, 1))
print(decimal_isolate(-1_4.1_2_3, 2))
print(decimal_isolate(-1_4.1_2_3, 3))
| 31
|
'''simple docstring'''
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = {
"""facebook/encodec_24khz""": """https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json""",
"""facebook/encodec_48khz""": """https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json""",
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "encodec"
def __init__( self : Optional[int] , A : Union[str, Any]=[1.5, 3.0, 6.0, 12.0, 24.0] , A : List[Any]=24000 , A : Union[str, Any]=1 , A : List[Any]=False , A : Optional[int]=None , A : int=None , A : str=128 , A : List[Any]=32 , A : List[Any]=1 , A : int=[8, 5, 4, 2] , A : Optional[int]="weight_norm" , A : List[Any]=7 , A : Any=7 , A : Dict=3 , A : Optional[int]=2 , A : Dict=True , A : Dict="reflect" , A : Any=2 , A : Dict=2 , A : str=1.0 , A : Optional[int]=1024 , A : Any=None , A : Any=True , **A : str , ):
_UpperCAmelCase : Optional[int] = target_bandwidths
_UpperCAmelCase : List[str] = sampling_rate
_UpperCAmelCase : Optional[int] = audio_channels
_UpperCAmelCase : str = normalize
_UpperCAmelCase : int = chunk_length_s
_UpperCAmelCase : str = overlap
_UpperCAmelCase : Optional[Any] = hidden_size
_UpperCAmelCase : int = num_filters
_UpperCAmelCase : Optional[Any] = num_residual_layers
_UpperCAmelCase : Optional[int] = upsampling_ratios
_UpperCAmelCase : int = norm_type
_UpperCAmelCase : List[Any] = kernel_size
_UpperCAmelCase : List[Any] = last_kernel_size
_UpperCAmelCase : List[Any] = residual_kernel_size
_UpperCAmelCase : List[str] = dilation_growth_rate
_UpperCAmelCase : Dict = use_causal_conv
_UpperCAmelCase : Tuple = pad_mode
_UpperCAmelCase : Tuple = compress
_UpperCAmelCase : List[str] = num_lstm_layers
_UpperCAmelCase : List[Any] = trim_right_ratio
_UpperCAmelCase : int = codebook_size
_UpperCAmelCase : Optional[Any] = codebook_dim if codebook_dim is not None else hidden_size
_UpperCAmelCase : Optional[int] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**A )
@property
def _A ( self : Any ):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def _A ( self : Union[str, Any] ):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def _A ( self : str ):
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 31
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
"""microsoft/deberta-v2-xlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
),
"""microsoft/deberta-v2-xxlarge-mnli""": (
"""https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
),
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = "deberta-v2"
def __init__( self : List[str] , A : Tuple=128100 , A : Any=1536 , A : Union[str, Any]=24 , A : Optional[Any]=24 , A : Any=6144 , A : Optional[Any]="gelu" , A : List[Any]=0.1 , A : Tuple=0.1 , A : List[str]=512 , A : List[Any]=0 , A : Tuple=0.02 , A : Optional[Any]=1E-7 , A : List[str]=False , A : int=-1 , A : str=0 , A : List[str]=True , A : int=None , A : Union[str, Any]=0 , A : str="gelu" , **A : Optional[int] , ):
super().__init__(**A )
_UpperCAmelCase : Dict = hidden_size
_UpperCAmelCase : int = num_hidden_layers
_UpperCAmelCase : List[str] = num_attention_heads
_UpperCAmelCase : Tuple = intermediate_size
_UpperCAmelCase : Tuple = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : Any = attention_probs_dropout_prob
_UpperCAmelCase : List[str] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : Union[str, Any] = initializer_range
_UpperCAmelCase : int = relative_attention
_UpperCAmelCase : Optional[int] = max_relative_positions
_UpperCAmelCase : Dict = pad_token_id
_UpperCAmelCase : str = position_biased_input
# Backwards compatibility
if type(A ) == str:
_UpperCAmelCase : Any = [x.strip() for x in pos_att_type.lower().split("|" )]
_UpperCAmelCase : Optional[Any] = pos_att_type
_UpperCAmelCase : int = vocab_size
_UpperCAmelCase : Dict = layer_norm_eps
_UpperCAmelCase : int = kwargs.get("pooler_hidden_size" , A )
_UpperCAmelCase : List[Any] = pooler_dropout
_UpperCAmelCase : List[Any] = pooler_hidden_act
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@property
def _A ( self : str ):
if self.task == "multiple-choice":
_UpperCAmelCase : str = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCAmelCase : Any = {0: "batch", 1: "sequence"}
if self._config.type_vocab_size > 0:
return OrderedDict(
[("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)] )
else:
return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)] )
@property
def _A ( self : int ):
return 12
def _A ( self : Optional[int] , A : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , A : int = -1 , A : int = -1 , A : int = -1 , A : bool = False , A : Optional["TensorType"] = None , A : int = 3 , A : int = 40 , A : int = 40 , A : "PreTrainedTokenizerBase" = None , ):
_UpperCAmelCase : List[Any] = super().generate_dummy_inputs(preprocessor=A , framework=A )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 31
|
'''simple docstring'''
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Any , A : Optional[int]=None , A : Tuple=None , *A : Tuple , **A : List[str] ):
super().__init__(*A , **A )
if config is None:
assert isinstance(self.model , A ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
F""" {self.model.__class__}"""
)
_UpperCAmelCase : str = self.model.config
else:
_UpperCAmelCase : List[str] = config
_UpperCAmelCase : List[Any] = data_args
_UpperCAmelCase : str = self.config.tgt_vocab_size if isinstance(self.config , A ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
F"""The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"""
" padding.." )
if self.args.label_smoothing == 0:
_UpperCAmelCase : Optional[Any] = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
_UpperCAmelCase : Dict = label_smoothed_nll_loss
def _A ( self : Tuple , A : int ):
if self.optimizer is None:
_UpperCAmelCase : Tuple = ["bias", "LayerNorm.weight"]
_UpperCAmelCase : str = [
{
"params": [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
"weight_decay": self.args.weight_decay,
},
{
"params": [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
"weight_decay": 0.0,
},
]
_UpperCAmelCase : int = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
_UpperCAmelCase : List[str] = Adafactor
_UpperCAmelCase : List[Any] = {"scale_parameter": False, "relative_step": False}
else:
_UpperCAmelCase : List[str] = AdamW
_UpperCAmelCase : List[str] = {
"betas": (self.args.adam_betaa, self.args.adam_betaa),
"eps": self.args.adam_epsilon,
}
_UpperCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
_UpperCAmelCase : List[Any] = OSS(
params=A , optim=A , **A , )
else:
_UpperCAmelCase : Union[str, Any] = optimizer_cls(A , **A )
if self.lr_scheduler is None:
_UpperCAmelCase : List[str] = self._get_lr_scheduler(A )
else: # ignoring --lr_scheduler
logger.warning("scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored." )
def _A ( self : List[str] , A : Optional[int] ):
_UpperCAmelCase : List[str] = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
_UpperCAmelCase : Optional[Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
_UpperCAmelCase : str = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
_UpperCAmelCase : str = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=A )
return scheduler
def _A ( self : Tuple ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def _A ( self : Any , A : Union[str, Any] , A : Union[str, Any] , A : List[Any] ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
_UpperCAmelCase : List[str] = model(**A , use_cache=A )[0]
_UpperCAmelCase : int = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
_UpperCAmelCase , _UpperCAmelCase : Any = model(**A , labels=A , use_cache=A )[:2]
else:
# compute label smoothed loss
_UpperCAmelCase : Optional[int] = model(**A , use_cache=A )[0]
_UpperCAmelCase : List[str] = torch.nn.functional.log_softmax(A , dim=-1 )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.loss_fn(A , A , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def _A ( self : List[str] , A : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Union[str, Any] = inputs.pop("labels" )
_UpperCAmelCase , _UpperCAmelCase : Optional[int] = self._compute_loss(A , A , A )
return loss
def _A ( self : List[str] , A : nn.Module , A : Dict[str, Union[torch.Tensor, Any]] , A : bool , A : Optional[List[str]] = None , ):
_UpperCAmelCase : List[str] = self._prepare_inputs(A )
_UpperCAmelCase : Dict = {
"max_length": self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
"num_beams": self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
_UpperCAmelCase : Dict = self.model.generate(
inputs["input_ids"] , attention_mask=inputs["attention_mask"] , **A , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : int = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
_UpperCAmelCase : Any = inputs.pop("labels" )
with torch.no_grad():
# compute loss on predict data
_UpperCAmelCase , _UpperCAmelCase : str = self._compute_loss(A , A , A )
_UpperCAmelCase : List[str] = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
_UpperCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
_UpperCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(A , gen_kwargs["max_length"] )
return (loss, logits, labels)
def _A ( self : Dict , A : int , A : List[str] ):
# If PAD token is not defined at least EOS token has to be defined
_UpperCAmelCase : Union[str, Any] = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
"Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be"
F""" padded to `max_length`={max_length}""" )
_UpperCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
_UpperCAmelCase : Tuple = tensor
return padded_tensor
| 31
| 1
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
__SCREAMING_SNAKE_CASE : Dict = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = ["""DeiTFeatureExtractor"""]
__SCREAMING_SNAKE_CASE : List[Any] = ["""DeiTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Union[str, Any] = [
"""DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DeiTForImageClassification""",
"""DeiTForImageClassificationWithTeacher""",
"""DeiTForMaskedImageModeling""",
"""DeiTModel""",
"""DeiTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = [
"""TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDeiTForImageClassification""",
"""TFDeiTForImageClassificationWithTeacher""",
"""TFDeiTForMaskedImageModeling""",
"""TFDeiTModel""",
"""TFDeiTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
|
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
__SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["input_features", "is_longer"]
def __init__( self : str , A : int=64 , A : Dict=48000 , A : str=480 , A : List[Any]=10 , A : Optional[Any]=1024 , A : Tuple=0.0 , A : List[Any]=False , A : float = 0 , A : float = 14000 , A : int = None , A : str = "fusion" , A : str = "repeatpad" , **A : Dict , ):
super().__init__(
feature_size=A , sampling_rate=A , padding_value=A , return_attention_mask=A , **A , )
_UpperCAmelCase : Optional[Any] = top_db
_UpperCAmelCase : Dict = truncation
_UpperCAmelCase : List[Any] = padding
_UpperCAmelCase : Optional[Any] = fft_window_size
_UpperCAmelCase : Dict = (fft_window_size >> 1) + 1
_UpperCAmelCase : Any = hop_length
_UpperCAmelCase : Tuple = max_length_s
_UpperCAmelCase : str = max_length_s * sampling_rate
_UpperCAmelCase : Any = sampling_rate
_UpperCAmelCase : Optional[int] = frequency_min
_UpperCAmelCase : str = frequency_max
_UpperCAmelCase : Union[str, Any] = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm=A , mel_scale="htk" , )
_UpperCAmelCase : Tuple = mel_filter_bank(
num_frequency_bins=self.nb_frequency_bins , num_mel_filters=A , min_frequency=A , max_frequency=A , sampling_rate=A , norm="slaney" , mel_scale="slaney" , )
def _A ( self : List[str] ):
_UpperCAmelCase : Union[str, Any] = copy.deepcopy(self.__dict__ )
_UpperCAmelCase : Dict = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
if "mel_filters_slaney" in output:
del output["mel_filters_slaney"]
return output
def _A ( self : Optional[Any] , A : np.array , A : Optional[np.array] = None ):
_UpperCAmelCase : Dict = spectrogram(
A , window_function(self.fft_window_size , "hann" ) , frame_length=self.fft_window_size , hop_length=self.hop_length , power=2.0 , mel_filters=A , log_mel="dB" , )
return log_mel_spectrogram.T
def _A ( self : str , A : str , A : List[str] , A : List[Any] ):
_UpperCAmelCase : List[str] = np.array_split(list(range(0 , total_frames - chunk_frames + 1 ) ) , 3 )
if len(ranges[1] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Optional[Any] = [0]
if len(ranges[2] ) == 0:
# if the audio is too short, we just use the first chunk
_UpperCAmelCase : Tuple = [0]
# randomly choose index for each part
_UpperCAmelCase : Dict = np.random.choice(ranges[0] )
_UpperCAmelCase : str = np.random.choice(ranges[1] )
_UpperCAmelCase : Tuple = np.random.choice(ranges[2] )
_UpperCAmelCase : str = mel[idx_front : idx_front + chunk_frames, :]
_UpperCAmelCase : str = mel[idx_middle : idx_middle + chunk_frames, :]
_UpperCAmelCase : List[Any] = mel[idx_back : idx_back + chunk_frames, :]
_UpperCAmelCase : Dict = torch.tensor(mel[None, None, :] )
_UpperCAmelCase : Optional[Any] = torch.nn.functional.interpolate(
A , size=[chunk_frames, 64] , mode="bilinear" , align_corners=A )
_UpperCAmelCase : List[str] = mel_shrink[0][0].numpy()
_UpperCAmelCase : str = np.stack([mel_shrink, mel_chunk_front, mel_chunk_middle, mel_chunk_back] , axis=0 )
return mel_fusion
def _A ( self : List[Any] , A : np.array , A : List[str] , A : Any , A : Optional[int] ):
if waveform.shape[0] > max_length:
if truncation == "rand_trunc":
_UpperCAmelCase : int = True
# random crop to max_length (for compatibility) -> this should be handled by self.pad
_UpperCAmelCase : str = len(A ) - max_length
_UpperCAmelCase : str = np.random.randint(0 , overflow + 1 )
_UpperCAmelCase : int = waveform[idx : idx + max_length]
_UpperCAmelCase : Any = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
elif truncation == "fusion":
_UpperCAmelCase : Tuple = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : List[str] = max_length // self.hop_length + 1 # the +1 related to how the spectrogram is computed
_UpperCAmelCase : Optional[Any] = mel.shape[0]
if chunk_frames == total_frames:
# there is a corner case where the audio length is larger than max_length but smaller than max_length+hop_length.
# In this case, we just use the whole audio.
_UpperCAmelCase : Any = np.stack([mel, mel, mel, mel] , axis=0 )
_UpperCAmelCase : int = False
else:
_UpperCAmelCase : Tuple = self._random_mel_fusion(A , A , A )
_UpperCAmelCase : Any = True
else:
raise NotImplementedError(F"""data_truncating {truncation} not implemented""" )
else:
_UpperCAmelCase : Optional[Any] = False
# only use repeat as a new possible value for padding. you repeat the audio before applying the usual max_length padding
if waveform.shape[0] < max_length:
if padding == "repeat":
_UpperCAmelCase : str = int(max_length / len(A ) )
_UpperCAmelCase : Dict = np.stack(np.tile(A , n_repeat + 1 ) )[:max_length]
if padding == "repeatpad":
_UpperCAmelCase : Dict = int(max_length / len(A ) )
_UpperCAmelCase : List[str] = np.stack(np.tile(A , A ) )
_UpperCAmelCase : Optional[Any] = np.pad(A , (0, max_length - waveform.shape[0]) , mode="constant" , constant_values=0 )
if truncation == "fusion":
_UpperCAmelCase : str = self._np_extract_fbank_features(A , self.mel_filters )
_UpperCAmelCase : Optional[int] = np.stack([input_mel, input_mel, input_mel, input_mel] , axis=0 )
else:
_UpperCAmelCase : List[str] = self._np_extract_fbank_features(A , self.mel_filters_slaney )[None, :]
return input_mel, longer
def __call__( self : Union[str, Any] , A : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] , A : str = None , A : Optional[str] = None , A : Optional[int] = None , A : Optional[int] = None , A : Optional[Union[str, TensorType]] = None , **A : List[str] , ):
_UpperCAmelCase : int = truncation if truncation is not None else self.truncation
_UpperCAmelCase : Optional[int] = padding if padding else self.padding
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"It is strongly recommended to pass the `sampling_rate` argument to this function. "
"Failing to do so can result in silent errors that might be hard to debug." )
_UpperCAmelCase : Any = isinstance(A , np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
_UpperCAmelCase : Optional[Any] = is_batched_numpy or (
isinstance(A , (list, tuple) ) and (isinstance(raw_speech[0] , (np.ndarray, tuple, list) ))
)
if is_batched:
_UpperCAmelCase : int = [np.asarray(A , dtype=np.floataa ) for speech in raw_speech]
elif not is_batched and not isinstance(A , np.ndarray ):
_UpperCAmelCase : List[str] = np.asarray(A , dtype=np.floataa )
elif isinstance(A , np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
_UpperCAmelCase : Any = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
_UpperCAmelCase : List[str] = [np.asarray(A )]
# convert to mel spectrogram, truncate and pad if needed.
_UpperCAmelCase : Dict = [
self._get_input_mel(A , max_length if max_length else self.nb_max_samples , A , A )
for waveform in raw_speech
]
_UpperCAmelCase : int = []
_UpperCAmelCase : Optional[Any] = []
for mel, longer in padded_inputs:
input_mel.append(A )
is_longer.append(A )
if truncation == "fusion" and sum(A ) == 0:
# if no audio is longer than 10s, then randomly select one audio to be longer
_UpperCAmelCase : Union[str, Any] = np.random.randint(0 , len(A ) )
_UpperCAmelCase : Optional[Any] = True
if isinstance(input_mel[0] , A ):
_UpperCAmelCase : List[str] = [np.asarray(A , dtype=np.floataa ) for feature in input_mel]
# is_longer is a list of bool
_UpperCAmelCase : Tuple = [[longer] for longer in is_longer]
_UpperCAmelCase : Optional[Any] = {"input_features": input_mel, "is_longer": is_longer}
_UpperCAmelCase : Tuple = BatchFeature(A )
if return_tensors is not None:
_UpperCAmelCase : List[Any] = input_features.convert_to_tensors(A )
return input_features
| 31
| 1
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCamelCase_ (metaclass=snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = ["torch", "scipy"]
def __init__( self : Any , *A : List[str] , **A : Tuple ):
requires_backends(self , ["torch", "scipy"] )
@classmethod
def _A ( cls : Dict , *A : Union[str, Any] , **A : int ):
requires_backends(cls , ["torch", "scipy"] )
@classmethod
def _A ( cls : Dict , *A : int , **A : Optional[Any] ):
requires_backends(cls , ["torch", "scipy"] )
| 31
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__SCREAMING_SNAKE_CASE : Optional[int] = {"""configuration_gpt_neox""": ["""GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXConfig"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = ["""GPTNeoXTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Dict = [
"""GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GPTNeoXForCausalLM""",
"""GPTNeoXForQuestionAnswering""",
"""GPTNeoXForSequenceClassification""",
"""GPTNeoXForTokenClassification""",
"""GPTNeoXLayer""",
"""GPTNeoXModel""",
"""GPTNeoXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31
| 1
|
'''simple docstring'''
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[List[ControlNetModel], Tuple[ControlNetModel]] ):
super().__init__()
_UpperCAmelCase : Optional[int] = nn.ModuleList(A )
def _A ( self : Dict , A : torch.FloatTensor , A : Union[torch.Tensor, float, int] , A : torch.Tensor , A : List[torch.tensor] , A : List[float] , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[torch.Tensor] = None , A : Optional[Dict[str, Any]] = None , A : bool = False , A : bool = True , ):
for i, (image, scale, controlnet) in enumerate(zip(A , A , self.nets ) ):
_UpperCAmelCase , _UpperCAmelCase : str = controlnet(
A , A , A , A , A , A , A , A , A , A , A , )
# merge samples
if i == 0:
_UpperCAmelCase , _UpperCAmelCase : List[Any] = down_samples, mid_sample
else:
_UpperCAmelCase : Optional[int] = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(A , A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _A ( self : List[str] , A : Union[str, os.PathLike] , A : bool = True , A : Callable = None , A : bool = False , A : Optional[str] = None , ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : str = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
A , is_main_process=A , save_function=A , safe_serialization=A , variant=A , )
idx += 1
_UpperCAmelCase : Tuple = model_path_to_save + F"""_{idx}"""
@classmethod
def _A ( cls : int , A : Optional[Union[str, os.PathLike]] , **A : Tuple ):
_UpperCAmelCase : str = 0
_UpperCAmelCase : int = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
_UpperCAmelCase : int = pretrained_model_path
while os.path.isdir(A ):
_UpperCAmelCase : List[str] = ControlNetModel.from_pretrained(A , **A )
controlnets.append(A )
idx += 1
_UpperCAmelCase : Dict = pretrained_model_path + F"""_{idx}"""
logger.info(F"""{len(A )} controlnets loaded from {pretrained_model_path}.""" )
if len(A ) == 0:
raise ValueError(
F"""No ControlNets found under {os.path.dirname(A )}. Expected at least {pretrained_model_path + '_0'}.""" )
return cls(A )
| 31
|
'''simple docstring'''
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Tuple , A : Any , A : str , A : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = graph
self._normalize_graph(A , A )
_UpperCAmelCase : List[str] = len(A )
_UpperCAmelCase : Tuple = None
def _A ( self : Any , A : List[Any] , A : str ):
if sources is int:
_UpperCAmelCase : List[Any] = [sources]
if sinks is int:
_UpperCAmelCase : List[Any] = [sinks]
if len(A ) == 0 or len(A ) == 0:
return
_UpperCAmelCase : str = sources[0]
_UpperCAmelCase : Union[str, Any] = sinks[0]
# make fake vertex if there are more
# than one source or sink
if len(A ) > 1 or len(A ) > 1:
_UpperCAmelCase : Dict = 0
for i in sources:
max_input_flow += sum(self.graph[i] )
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.insert(0 , 0 )
self.graph.insert(0 , [0] * size )
for i in sources:
_UpperCAmelCase : Optional[Any] = max_input_flow
_UpperCAmelCase : List[str] = 0
_UpperCAmelCase : str = len(self.graph ) + 1
for room in self.graph:
room.append(0 )
self.graph.append([0] * size )
for i in sinks:
_UpperCAmelCase : Dict = max_input_flow
_UpperCAmelCase : List[Any] = size - 1
def _A ( self : Union[str, Any] ):
if self.maximum_flow_algorithm is None:
raise Exception("You need to set maximum flow algorithm before." )
if self.source_index is None or self.sink_index is None:
return 0
self.maximum_flow_algorithm.execute()
return self.maximum_flow_algorithm.getMaximumFlow()
def _A ( self : Tuple , A : Dict ):
_UpperCAmelCase : str = algorithm(self )
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Any , A : str ):
_UpperCAmelCase : Optional[int] = flow_network
_UpperCAmelCase : Any = flow_network.verticesCount
_UpperCAmelCase : List[str] = flow_network.sourceIndex
_UpperCAmelCase : Union[str, Any] = flow_network.sinkIndex
# it's just a reference, so you shouldn't change
# it in your algorithms, use deep copy before doing that
_UpperCAmelCase : Any = flow_network.graph
_UpperCAmelCase : Union[str, Any] = False
def _A ( self : List[str] ):
if not self.executed:
self._algorithm()
_UpperCAmelCase : int = True
def _A ( self : List[Any] ):
pass
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Union[str, Any] ):
super().__init__(A )
# use this to save your result
_UpperCAmelCase : Any = -1
def _A ( self : Union[str, Any] ):
if not self.executed:
raise Exception("You should execute algorithm before using its result!" )
return self.maximum_flow
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : int ):
super().__init__(A )
_UpperCAmelCase : List[str] = [[0] * self.verticies_count for i in range(self.verticies_count )]
_UpperCAmelCase : Union[str, Any] = [0] * self.verticies_count
_UpperCAmelCase : int = [0] * self.verticies_count
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.verticies_count
# push some substance to graph
for nextvertex_index, bandwidth in enumerate(self.graph[self.source_index] ):
self.preflow[self.source_index][nextvertex_index] += bandwidth
self.preflow[nextvertex_index][self.source_index] -= bandwidth
self.excesses[nextvertex_index] += bandwidth
# Relabel-to-front selection rule
_UpperCAmelCase : Optional[int] = [
i
for i in range(self.verticies_count )
if i != self.source_index and i != self.sink_index
]
# move through list
_UpperCAmelCase : Any = 0
while i < len(A ):
_UpperCAmelCase : int = vertices_list[i]
_UpperCAmelCase : int = self.heights[vertex_index]
self.process_vertex(A )
if self.heights[vertex_index] > previous_height:
# if it was relabeled, swap elements
# and start from 0 index
vertices_list.insert(0 , vertices_list.pop(A ) )
_UpperCAmelCase : Union[str, Any] = 0
else:
i += 1
_UpperCAmelCase : List[Any] = sum(self.preflow[self.source_index] )
def _A ( self : Union[str, Any] , A : str ):
while self.excesses[vertex_index] > 0:
for neighbour_index in range(self.verticies_count ):
# if it's neighbour and current vertex is higher
if (
self.graph[vertex_index][neighbour_index]
- self.preflow[vertex_index][neighbour_index]
> 0
and self.heights[vertex_index] > self.heights[neighbour_index]
):
self.push(A , A )
self.relabel(A )
def _A ( self : int , A : Dict , A : List[str] ):
_UpperCAmelCase : int = min(
self.excesses[from_index] , self.graph[from_index][to_index] - self.preflow[from_index][to_index] , )
self.preflow[from_index][to_index] += preflow_delta
self.preflow[to_index][from_index] -= preflow_delta
self.excesses[from_index] -= preflow_delta
self.excesses[to_index] += preflow_delta
def _A ( self : Optional[int] , A : Union[str, Any] ):
_UpperCAmelCase : str = None
for to_index in range(self.verticies_count ):
if (
self.graph[vertex_index][to_index]
- self.preflow[vertex_index][to_index]
> 0
) and (min_height is None or self.heights[to_index] < min_height):
_UpperCAmelCase : Tuple = self.heights[to_index]
if min_height is not None:
_UpperCAmelCase : Optional[Any] = min_height + 1
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[int] = [0]
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3]
# graph = [
# [0, 0, 4, 6, 0, 0],
# [0, 0, 5, 2, 0, 0],
# [0, 0, 0, 0, 4, 4],
# [0, 0, 0, 0, 6, 6],
# [0, 0, 0, 0, 0, 0],
# [0, 0, 0, 0, 0, 0],
# ]
__SCREAMING_SNAKE_CASE : List[Any] = [[0, 7, 0, 0], [0, 0, 6, 0], [0, 0, 0, 8], [9, 0, 0, 0]]
# prepare our network
__SCREAMING_SNAKE_CASE : Union[str, Any] = FlowNetwork(graph, entrances, exits)
# set algorithm
flow_network.set_maximum_flow_algorithm(PushRelabelExecutor)
# and calculate
__SCREAMING_SNAKE_CASE : Optional[Any] = flow_network.find_maximum_flow()
print(F'maximum flow is {maximum_flow}')
| 31
| 1
|
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = (CMStochasticIterativeScheduler,)
__UpperCamelCase: Optional[int] = 1_0
def _A ( self : int , **A : int ):
_UpperCAmelCase : Any = {
"num_train_timesteps": 201,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
config.update(**A )
return config
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Optional[Any] = self.get_scheduler_config()
_UpperCAmelCase : str = self.scheduler_classes[0](**A )
scheduler.set_timesteps(A )
_UpperCAmelCase : Tuple = scheduler.timesteps[0]
_UpperCAmelCase : Tuple = scheduler.timesteps[1]
_UpperCAmelCase : List[Any] = self.dummy_sample
_UpperCAmelCase : Optional[int] = 0.1 * sample
_UpperCAmelCase : List[Any] = scheduler.step(A , A , A ).prev_sample
_UpperCAmelCase : List[str] = scheduler.step(A , A , A ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def _A ( self : Any ):
for timesteps in [10, 50, 100, 1000]:
self.check_over_configs(num_train_timesteps=A )
def _A ( self : Dict ):
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=A )
def _A ( self : str ):
_UpperCAmelCase : Optional[int] = self.scheduler_classes[0]
_UpperCAmelCase : str = self.get_scheduler_config()
_UpperCAmelCase : Tuple = scheduler_class(**A )
_UpperCAmelCase : Tuple = 1
scheduler.set_timesteps(A )
_UpperCAmelCase : Any = scheduler.timesteps
_UpperCAmelCase : List[str] = torch.manual_seed(0 )
_UpperCAmelCase : Union[str, Any] = self.dummy_model()
_UpperCAmelCase : str = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(A ):
# 1. scale model input
_UpperCAmelCase : str = scheduler.scale_model_input(A , A )
# 2. predict noise residual
_UpperCAmelCase : List[Any] = model(A , A )
# 3. predict previous sample x_t-1
_UpperCAmelCase : Any = scheduler.step(A , A , A , generator=A ).prev_sample
_UpperCAmelCase : List[Any] = pred_prev_sample
_UpperCAmelCase : int = torch.sum(torch.abs(A ) )
_UpperCAmelCase : str = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 192.7_614 ) < 1E-2
assert abs(result_mean.item() - 0.2_510 ) < 1E-3
def _A ( self : str ):
_UpperCAmelCase : str = self.scheduler_classes[0]
_UpperCAmelCase : Dict = self.get_scheduler_config()
_UpperCAmelCase : Any = scheduler_class(**A )
_UpperCAmelCase : List[str] = [106, 0]
scheduler.set_timesteps(timesteps=A )
_UpperCAmelCase : Tuple = scheduler.timesteps
_UpperCAmelCase : Optional[Any] = torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = self.dummy_model()
_UpperCAmelCase : Dict = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
_UpperCAmelCase : str = scheduler.scale_model_input(A , A )
# 2. predict noise residual
_UpperCAmelCase : Optional[Any] = model(A , A )
# 3. predict previous sample x_t-1
_UpperCAmelCase : List[str] = scheduler.step(A , A , A , generator=A ).prev_sample
_UpperCAmelCase : int = pred_prev_sample
_UpperCAmelCase : Union[str, Any] = torch.sum(torch.abs(A ) )
_UpperCAmelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 347.6_357 ) < 1E-2
assert abs(result_mean.item() - 0.4_527 ) < 1E-3
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Tuple = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : Optional[int] = scheduler_class(**A )
_UpperCAmelCase : str = [39, 30, 12, 15, 0]
with self.assertRaises(A , msg="`timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.scheduler_classes[0]
_UpperCAmelCase : List[Any] = self.get_scheduler_config()
_UpperCAmelCase : Any = scheduler_class(**A )
_UpperCAmelCase : Dict = [39, 30, 12, 1, 0]
_UpperCAmelCase : Dict = len(A )
with self.assertRaises(A , msg="Can only pass one of `num_inference_steps` or `timesteps`." ):
scheduler.set_timesteps(num_inference_steps=A , timesteps=A )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.scheduler_classes[0]
_UpperCAmelCase : str = self.get_scheduler_config()
_UpperCAmelCase : Tuple = scheduler_class(**A )
_UpperCAmelCase : Dict = [scheduler.config.num_train_timesteps]
with self.assertRaises(
A , msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" , ):
scheduler.set_timesteps(timesteps=A )
| 31
|
'''simple docstring'''
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str ) -> float:
"""simple docstring"""
def get_matched_characters(_UpperCAmelCase : str , _UpperCAmelCase : str ) -> str:
_UpperCAmelCase : Tuple = []
_UpperCAmelCase : Dict = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
_UpperCAmelCase : int = int(max(0 , i - limit ) )
_UpperCAmelCase : Any = int(min(i + limit + 1 , len(_stra ) ) )
if l in _stra[left:right]:
matched.append(_UpperCAmelCase )
_UpperCAmelCase : List[Any] = F"""{_stra[0:_stra.index(_UpperCAmelCase )]} {_stra[_stra.index(_UpperCAmelCase ) + 1:]}"""
return "".join(_UpperCAmelCase )
# matching characters
_UpperCAmelCase : Union[str, Any] = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = get_matched_characters(_UpperCAmelCase , _UpperCAmelCase )
_UpperCAmelCase : Tuple = len(_UpperCAmelCase )
# transposition
_UpperCAmelCase : Optional[Any] = (
len([(ca, ca) for ca, ca in zip(_UpperCAmelCase , _UpperCAmelCase ) if ca != ca] ) // 2
)
if not match_count:
_UpperCAmelCase : Dict = 0.0
else:
_UpperCAmelCase : Optional[int] = (
1
/ 3
* (
match_count / len(_UpperCAmelCase )
+ match_count / len(_UpperCAmelCase )
+ (match_count - transpositions) / match_count
)
)
# common prefix up to 4 characters
_UpperCAmelCase : str = 0
for ca, ca in zip(stra[:4] , stra[:4] ):
if ca == ca:
prefix_len += 1
else:
break
return jaro + 0.1 * prefix_len * (1 - jaro)
if __name__ == "__main__":
import doctest
doctest.testmod()
print(jaro_winkler("""hello""", """world"""))
| 31
| 1
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float , ) -> tuple:
"""simple docstring"""
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor" )
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor" )
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor" )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 31
|
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class lowerCamelCase_ (snake_case__ , snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[Any] = 1
@register_to_config
def __init__( self : Optional[int] , A : int = 1000 , A : Optional[Union[np.ndarray, List[float]]] = None ):
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(A )
# standard deviation of the initial noise distribution
_UpperCAmelCase : int = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
_UpperCAmelCase : int = 4
# running values
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : int , A : Union[str, torch.device] = None ):
_UpperCAmelCase : int = num_inference_steps
_UpperCAmelCase : Union[str, Any] = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
_UpperCAmelCase : Any = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
_UpperCAmelCase : str = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
_UpperCAmelCase : Dict = torch.sin(steps * math.pi / 2 ) ** 2
_UpperCAmelCase : List[Any] = (1.0 - self.betas**2) ** 0.5
_UpperCAmelCase : List[str] = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
_UpperCAmelCase : Dict = timesteps.to(A )
_UpperCAmelCase : Dict = []
def _A ( self : Optional[int] , A : torch.FloatTensor , A : int , A : torch.FloatTensor , A : bool = True , ):
if self.num_inference_steps is None:
raise ValueError(
"Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler" )
_UpperCAmelCase : Tuple = (self.timesteps == timestep).nonzero().item()
_UpperCAmelCase : Optional[Any] = timestep_index + 1
_UpperCAmelCase : int = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(A )
if len(self.ets ) == 1:
_UpperCAmelCase : List[Any] = self.ets[-1]
elif len(self.ets ) == 2:
_UpperCAmelCase : str = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
_UpperCAmelCase : Tuple = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
_UpperCAmelCase : Union[str, Any] = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
_UpperCAmelCase : Union[str, Any] = self._get_prev_sample(A , A , A , A )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A )
def _A ( self : Union[str, Any] , A : torch.FloatTensor , *A : Union[str, Any] , **A : Dict ):
return sample
def _A ( self : Optional[Any] , A : Optional[int] , A : int , A : Optional[Any] , A : List[str] ):
_UpperCAmelCase : List[str] = self.alphas[timestep_index]
_UpperCAmelCase : List[Any] = self.betas[timestep_index]
_UpperCAmelCase : Optional[Any] = self.alphas[prev_timestep_index]
_UpperCAmelCase : Dict = self.betas[prev_timestep_index]
_UpperCAmelCase : Tuple = (sample - sigma * ets) / max(A , 1E-8 )
_UpperCAmelCase : List[str] = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Union[str, Any] ):
return self.config.num_train_timesteps
| 31
| 1
|
'''simple docstring'''
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
def UpperCamelCase_ ( _UpperCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCAmelCase : int = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
_UpperCAmelCase : List[Any] = MaskFormerConfig(backbone_config=_UpperCAmelCase )
_UpperCAmelCase : Tuple = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
_UpperCAmelCase : Dict = 847
_UpperCAmelCase : Any = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
_UpperCAmelCase : Any = 150
_UpperCAmelCase : Any = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
_UpperCAmelCase : Tuple = 171
_UpperCAmelCase : Union[str, Any] = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
_UpperCAmelCase : Any = 133
_UpperCAmelCase : int = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 19
_UpperCAmelCase : str = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
_UpperCAmelCase : Optional[int] = 65
_UpperCAmelCase : Tuple = "mapillary-vistas-id2label.json"
_UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(_UpperCAmelCase , _UpperCAmelCase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase : Tuple = {int(_UpperCAmelCase ): v for k, v in idalabel.items()}
return config
def UpperCamelCase_ ( _UpperCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
_UpperCAmelCase : Dict = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.relative_position_index""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.attn.proj.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.norm2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc1.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight""") )
rename_keys.append((F"""backbone.layers.{i}.blocks.{j}.mlp.fc2.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias""") )
if i < 3:
rename_keys.append((F"""backbone.layers.{i}.downsample.reduction.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.weight""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight""") )
rename_keys.append((F"""backbone.layers.{i}.downsample.norm.bias""", F"""model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias""") )
rename_keys.append((F"""backbone.norm{i}.weight""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.weight""") )
rename_keys.append((F"""backbone.norm{i}.bias""", F"""model.pixel_level_module.encoder.hidden_states_norms.{i}.bias""") )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight""") )
rename_keys.append((F"""sem_seg_head.adapter_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.weight""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight""") )
rename_keys.append((F"""sem_seg_head.layer_{source_index}.norm.bias""", F"""model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias""") )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias""") )
# cross-attention out projection
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias""") )
# MLP 1
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc1.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc1.bias""") )
# MLP 2
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight""", F"""model.transformer_module.decoder.layers.{idx}.fc2.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias""", F"""model.transformer_module.decoder.layers.{idx}.fc2.bias""") )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias""", F"""model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias""") )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias""", F"""model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias""") )
# layernorm 3 (final layernorm)
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias""", F"""model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias""") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.weight""", F"""mask_embedder.{i}.0.weight""") )
rename_keys.append((F"""sem_seg_head.predictor.mask_embed.layers.{i}.bias""", F"""mask_embedder.{i}.0.bias""") )
# fmt: on
return rename_keys
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : Tuple , _UpperCAmelCase : List[Any] ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = dct.pop(_UpperCAmelCase )
_UpperCAmelCase : List[str] = val
def UpperCamelCase_ ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
_UpperCAmelCase : List[str] = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
_UpperCAmelCase : Optional[int] = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
_UpperCAmelCase : Any = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.weight""" )
_UpperCAmelCase : Optional[int] = state_dict.pop(F"""backbone.layers.{i}.blocks.{j}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : List[str] = in_proj_weight[:dim, :]
_UpperCAmelCase : Tuple = in_proj_bias[: dim]
_UpperCAmelCase : List[Any] = in_proj_weight[
dim : dim * 2, :
]
_UpperCAmelCase : List[str] = in_proj_bias[
dim : dim * 2
]
_UpperCAmelCase : Optional[Any] = in_proj_weight[
-dim :, :
]
_UpperCAmelCase : Dict = in_proj_bias[-dim :]
# fmt: on
def UpperCamelCase_ ( _UpperCAmelCase : Dict , _UpperCAmelCase : str ) -> Dict:
"""simple docstring"""
_UpperCAmelCase : Union[str, Any] = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight""" )
_UpperCAmelCase : Dict = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : int = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : List[str] = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : List[str] = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : int = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Optional[Any] = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
_UpperCAmelCase : Optional[Any] = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase : Tuple = state_dict.pop(F"""sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase : Any = in_proj_weight[: hidden_size, :]
_UpperCAmelCase : Tuple = in_proj_bias[:config.hidden_size]
_UpperCAmelCase : Dict = in_proj_weight[hidden_size : hidden_size * 2, :]
_UpperCAmelCase : Dict = in_proj_bias[hidden_size : hidden_size * 2]
_UpperCAmelCase : Optional[int] = in_proj_weight[-hidden_size :, :]
_UpperCAmelCase : Union[str, Any] = in_proj_bias[-hidden_size :]
# fmt: on
def UpperCamelCase_ ( ) -> torch.Tensor:
"""simple docstring"""
_UpperCAmelCase : int = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase : Any = Image.open(requests.get(_UpperCAmelCase , stream=_UpperCAmelCase ).raw )
return im
@torch.no_grad()
def UpperCamelCase_ ( _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : str , _UpperCAmelCase : bool = False ) -> List[str]:
"""simple docstring"""
_UpperCAmelCase : Optional[int] = get_maskformer_config(_UpperCAmelCase )
# load original state_dict
with open(_UpperCAmelCase , "rb" ) as f:
_UpperCAmelCase : Optional[int] = pickle.load(_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
_UpperCAmelCase : Any = create_rename_keys(_UpperCAmelCase )
for src, dest in rename_keys:
rename_key(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
read_in_swin_q_k_v(_UpperCAmelCase , config.backbone_config )
read_in_decoder_q_k_v(_UpperCAmelCase , _UpperCAmelCase )
# update to torch tensors
for key, value in state_dict.items():
_UpperCAmelCase : Tuple = torch.from_numpy(_UpperCAmelCase )
# load 🤗 model
_UpperCAmelCase : Union[str, Any] = MaskFormerForInstanceSegmentation(_UpperCAmelCase )
model.eval()
for name, param in model.named_parameters():
print(_UpperCAmelCase , param.shape )
_UpperCAmelCase , _UpperCAmelCase : Any = model.load_state_dict(_UpperCAmelCase , strict=_UpperCAmelCase )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(_UpperCAmelCase ) == 0, F"""Unexpected keys: {unexpected_keys}"""
# verify results
_UpperCAmelCase : Optional[int] = prepare_img()
if "vistas" in model_name:
_UpperCAmelCase : int = 65
elif "cityscapes" in model_name:
_UpperCAmelCase : Tuple = 65_535
else:
_UpperCAmelCase : Any = 255
_UpperCAmelCase : Optional[Any] = True if "ade" in model_name else False
_UpperCAmelCase : Optional[int] = MaskFormerImageProcessor(ignore_index=_UpperCAmelCase , reduce_labels=_UpperCAmelCase )
_UpperCAmelCase : Optional[int] = image_processor(_UpperCAmelCase , return_tensors="pt" )
_UpperCAmelCase : List[Any] = model(**_UpperCAmelCase )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
_UpperCAmelCase : Tuple = torch.tensor(
[[3.6_3_5_3, -4.4_7_7_0, -2.6_0_6_5], [0.5_0_8_1, -4.2_3_9_4, -3.5_3_4_3], [2.1_9_0_9, -5.0_3_5_3, -1.9_3_2_3]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , _UpperCAmelCase , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F"""Saving model and image processor to {pytorch_dump_folder_path}""" )
Path(_UpperCAmelCase ).mkdir(exist_ok=_UpperCAmelCase )
model.save_pretrained(_UpperCAmelCase )
image_processor.save_pretrained(_UpperCAmelCase )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F"""nielsr/{model_name}""" )
image_processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""maskformer-swin-tiny-ade""",
type=str,
help=("""Name of the MaskFormer model you'd like to convert""",),
)
parser.add_argument(
"""--checkpoint_path""",
default="""/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl""",
type=str,
help="""Path to the original state dict (.pth file).""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 31
|
'''simple docstring'''
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase_ ( _UpperCAmelCase : dict ) -> tuple:
"""simple docstring"""
return (data["data"], data["target"])
def UpperCamelCase_ ( _UpperCAmelCase : np.ndarray , _UpperCAmelCase : np.ndarray ) -> XGBClassifier:
"""simple docstring"""
_UpperCAmelCase : Any = XGBClassifier()
classifier.fit(_UpperCAmelCase , _UpperCAmelCase )
return classifier
def UpperCamelCase_ ( ) -> None:
"""simple docstring"""
_UpperCAmelCase : List[str] = load_iris()
_UpperCAmelCase , _UpperCAmelCase : Dict = data_handling(_UpperCAmelCase )
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase : str = train_test_split(
_UpperCAmelCase , _UpperCAmelCase , test_size=0.2_5 )
_UpperCAmelCase : Optional[Any] = iris["target_names"]
# Create an XGBoost Classifier from the training data
_UpperCAmelCase : Tuple = xgboost(_UpperCAmelCase , _UpperCAmelCase )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , display_labels=_UpperCAmelCase , cmap="Blues" , normalize="true" , )
plt.title("Normalized Confusion Matrix - IRIS Dataset" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 31
| 1
|
'''simple docstring'''
from math import pi, sqrt, tan
def UpperCamelCase_ ( _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError("surface_area_cube() only accepts non-negative values" )
return 6 * side_length**2
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError("surface_area_cuboid() only accepts non-negative values" )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def UpperCamelCase_ ( _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError("surface_area_sphere() only accepts non-negative values" )
return 4 * pi * radius**2
def UpperCamelCase_ ( _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError("surface_area_hemisphere() only accepts non-negative values" )
return 3 * pi * radius**2
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cone() only accepts non-negative values" )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
"surface_area_conical_frustum() only accepts non-negative values" )
_UpperCAmelCase : int = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError("surface_area_cylinder() only accepts non-negative values" )
return 2 * pi * radius * (height + radius)
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError("surface_area_torus() only accepts non-negative values" )
if torus_radius < tube_radius:
raise ValueError(
"surface_area_torus() does not support spindle or self intersecting tori" )
return 4 * pow(_UpperCAmelCase , 2 ) * torus_radius * tube_radius
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError("area_rectangle() only accepts non-negative values" )
return length * width
def UpperCamelCase_ ( _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if side_length < 0:
raise ValueError("area_square() only accepts non-negative values" )
return side_length**2
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError("area_triangle() only accepts non-negative values" )
return (base * height) / 2
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError("area_triangle_three_sides() only accepts non-negative values" )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError("Given three sides do not form a triangle" )
_UpperCAmelCase : Dict = (sidea + sidea + sidea) / 2
_UpperCAmelCase : Tuple = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError("area_parallelogram() only accepts non-negative values" )
return base * height
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if basea < 0 or basea < 0 or height < 0:
raise ValueError("area_trapezium() only accepts non-negative values" )
return 1 / 2 * (basea + basea) * height
def UpperCamelCase_ ( _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius < 0:
raise ValueError("area_circle() only accepts non-negative values" )
return pi * radius**2
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if radius_x < 0 or radius_y < 0:
raise ValueError("area_ellipse() only accepts non-negative values" )
return pi * radius_x * radius_y
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError("area_rhombus() only accepts non-negative values" )
return 1 / 2 * diagonal_a * diagonal_a
def UpperCamelCase_ ( _UpperCAmelCase : int , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if not isinstance(_UpperCAmelCase , _UpperCAmelCase ) or sides < 3:
raise ValueError(
"area_reg_polygon() only accepts integers greater than or \
equal to three as number of sides" )
elif length < 0:
raise ValueError(
"area_reg_polygon() only accepts non-negative values as \
length of a side" )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print("""[DEMO] Areas of various geometric shapes: \n""")
print(F'Rectangle: {area_rectangle(10, 20) = }')
print(F'Square: {area_square(10) = }')
print(F'Triangle: {area_triangle(10, 10) = }')
print(F'Triangle: {area_triangle_three_sides(5, 12, 13) = }')
print(F'Parallelogram: {area_parallelogram(10, 20) = }')
print(F'Rhombus: {area_rhombus(10, 20) = }')
print(F'Trapezium: {area_trapezium(10, 20, 30) = }')
print(F'Circle: {area_circle(20) = }')
print(F'Ellipse: {area_ellipse(10, 20) = }')
print("""\nSurface Areas of various geometric shapes: \n""")
print(F'Cube: {surface_area_cube(20) = }')
print(F'Cuboid: {surface_area_cuboid(10, 20, 30) = }')
print(F'Sphere: {surface_area_sphere(20) = }')
print(F'Hemisphere: {surface_area_hemisphere(20) = }')
print(F'Cone: {surface_area_cone(10, 20) = }')
print(F'Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }')
print(F'Cylinder: {surface_area_cylinder(10, 20) = }')
print(F'Torus: {surface_area_torus(20, 10) = }')
print(F'Equilateral Triangle: {area_reg_polygon(3, 10) = }')
print(F'Square: {area_reg_polygon(4, 10) = }')
print(F'Reqular Pentagon: {area_reg_polygon(5, 10) = }')
| 31
|
'''simple docstring'''
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : Optional[Any]=13 , A : Optional[Any]=7 , A : Union[str, Any]=True , A : Optional[Any]=True , A : int=False , A : str=True , A : Optional[Any]=99 , A : Union[str, Any]=32 , A : int=5 , A : Tuple=4 , A : Union[str, Any]=37 , A : Dict="gelu" , A : Union[str, Any]=0.1 , A : str=0.1 , A : Union[str, Any]=512 , A : int=16 , A : List[str]=2 , A : Tuple=0.02 , A : int=3 , A : List[str]=4 , A : str=None , ):
_UpperCAmelCase : List[Any] = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : int = seq_length
_UpperCAmelCase : Union[str, Any] = is_training
_UpperCAmelCase : Any = use_input_mask
_UpperCAmelCase : Optional[Any] = use_token_type_ids
_UpperCAmelCase : str = use_labels
_UpperCAmelCase : Union[str, Any] = vocab_size
_UpperCAmelCase : Tuple = hidden_size
_UpperCAmelCase : Union[str, Any] = num_hidden_layers
_UpperCAmelCase : Optional[Any] = num_attention_heads
_UpperCAmelCase : Union[str, Any] = intermediate_size
_UpperCAmelCase : Union[str, Any] = hidden_act
_UpperCAmelCase : List[Any] = hidden_dropout_prob
_UpperCAmelCase : List[Any] = attention_probs_dropout_prob
_UpperCAmelCase : Optional[int] = max_position_embeddings
_UpperCAmelCase : str = type_vocab_size
_UpperCAmelCase : str = type_sequence_label_size
_UpperCAmelCase : int = initializer_range
_UpperCAmelCase : Optional[Any] = num_labels
_UpperCAmelCase : List[str] = num_choices
_UpperCAmelCase : List[str] = scope
def _A ( self : Optional[int] ):
_UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCAmelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCAmelCase : List[Any] = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCAmelCase : Any = None
if self.use_token_type_ids:
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_UpperCAmelCase : Optional[int] = None
_UpperCAmelCase : Any = None
_UpperCAmelCase : Optional[int] = None
if self.use_labels:
_UpperCAmelCase : Optional[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCAmelCase : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCAmelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCAmelCase : List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _A ( self : Dict ):
return BioGptConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A , initializer_range=self.initializer_range , )
def _A ( self : int , A : List[Any] , A : Any , A : int , A : Union[str, Any] , A : Dict , A : List[Any] , A : Dict ):
_UpperCAmelCase : List[str] = BioGptModel(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A )
_UpperCAmelCase : int = model(A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _A ( self : List[Any] , A : str , A : List[Any] , A : Dict , A : List[Any] , A : List[str] , A : Union[str, Any] , A : int , A : List[str] , A : Dict , ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(config=A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A , labels=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _A ( self : List[Any] , A : str , A : str , A : str , A : Any , A : List[str] , *A : Optional[int] ):
_UpperCAmelCase : str = BioGptModel(config=A )
model.to(A )
model.eval()
# create attention mask
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
_UpperCAmelCase : Optional[int] = self.seq_length // 2
_UpperCAmelCase : List[Any] = 0
# first forward pass
_UpperCAmelCase , _UpperCAmelCase : List[str] = model(A , attention_mask=A ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_UpperCAmelCase : List[str] = ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_UpperCAmelCase : List[str] = ids_tensor((1,) , A ).item() + 1
_UpperCAmelCase : str = ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_UpperCAmelCase : Any = random_other_next_tokens
# append to next input_ids and attn_mask
_UpperCAmelCase : List[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Optional[int] = torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=A )] , dim=1 , )
# get two different outputs
_UpperCAmelCase : List[Any] = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Optional[Any] = model(A , past_key_values=A , attention_mask=A )["last_hidden_state"]
# select random slice
_UpperCAmelCase : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Optional[Any] = output_from_no_past[:, -1, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : int , A : Dict , A : str , A : Dict , A : Union[str, Any] , A : Any , *A : Union[str, Any] ):
_UpperCAmelCase : Optional[Any] = BioGptModel(config=A ).to(A ).eval()
_UpperCAmelCase : List[Any] = torch.ones(input_ids.shape , dtype=torch.long , device=A )
# first forward pass
_UpperCAmelCase : Union[str, Any] = model(A , attention_mask=A , use_cache=A )
_UpperCAmelCase , _UpperCAmelCase : Dict = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_UpperCAmelCase : str = ids_tensor((self.batch_size, 3) , config.vocab_size )
_UpperCAmelCase : Any = ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_UpperCAmelCase : Optional[Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
_UpperCAmelCase : Dict = torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_UpperCAmelCase : Any = model(A , attention_mask=A )["last_hidden_state"]
_UpperCAmelCase : Dict = model(A , attention_mask=A , past_key_values=A )[
"last_hidden_state"
]
# select random slice
_UpperCAmelCase : Dict = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_UpperCAmelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
_UpperCAmelCase : Any = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A , A , atol=1E-3 ) )
def _A ( self : Optional[Any] , A : Tuple , A : List[str] , A : Tuple , A : Dict , A : List[Any] , *A : Tuple , A : List[str]=False ):
_UpperCAmelCase : Optional[int] = BioGptForCausalLM(A )
model.to(A )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_UpperCAmelCase : Union[str, Any] = model(A , labels=A )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def _A ( self : Optional[Any] , A : Any , *A : Optional[Any] ):
_UpperCAmelCase : Tuple = BioGptModel(A )
_UpperCAmelCase : int = model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def _A ( self : Optional[int] , A : Dict , A : Tuple , A : Optional[int] , A : int , A : List[str] , *A : Dict ):
_UpperCAmelCase : Any = self.num_labels
_UpperCAmelCase : Any = BioGptForTokenClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Optional[int] = model(A , attention_mask=A , token_type_ids=A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _A ( self : int ):
_UpperCAmelCase : Dict = self.prepare_config_and_inputs()
(
(
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) , (
_UpperCAmelCase
) ,
) : List[str] = config_and_inputs
_UpperCAmelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase_ (snake_case__ , snake_case__ , snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: List[str] = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
__UpperCamelCase: List[str] = (BioGptForCausalLM,) if is_torch_available() else ()
__UpperCamelCase: str = (
{
"feature-extraction": BioGptModel,
"text-classification": BioGptForSequenceClassification,
"text-generation": BioGptForCausalLM,
"token-classification": BioGptForTokenClassification,
"zero-shot": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase: Union[str, Any] = False
def _A ( self : Optional[Any] ):
_UpperCAmelCase : List[Any] = BioGptModelTester(self )
_UpperCAmelCase : str = ConfigTester(self , config_class=A , hidden_size=37 )
def _A ( self : Union[str, Any] ):
self.config_tester.run_common_tests()
def _A ( self : Any ):
_UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A )
def _A ( self : Any ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_UpperCAmelCase : Tuple = type
self.model_tester.create_and_check_model(*A )
def _A ( self : int ):
_UpperCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*A , gradient_checkpointing=A )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*A )
def _A ( self : Dict ):
_UpperCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*A )
@slow
def _A ( self : List[str] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
_UpperCAmelCase : Tuple = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : str = "left"
# Define PAD Token = EOS Token = 50256
_UpperCAmelCase : Any = tokenizer.eos_token
_UpperCAmelCase : int = model.config.eos_token_id
# use different length sentences to test batching
_UpperCAmelCase : Any = [
"Hello, my dog is a little",
"Today, I",
]
_UpperCAmelCase : Tuple = tokenizer(A , return_tensors="pt" , padding=A )
_UpperCAmelCase : Optional[Any] = inputs["input_ids"].to(A )
_UpperCAmelCase : Any = model.generate(
input_ids=A , attention_mask=inputs["attention_mask"].to(A ) , )
_UpperCAmelCase : int = tokenizer(sentences[0] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : List[Any] = model.generate(input_ids=A )
_UpperCAmelCase : List[Any] = inputs_non_padded.shape[-1] - inputs["attention_mask"][-1].long().sum().cpu().item()
_UpperCAmelCase : int = tokenizer(sentences[1] , return_tensors="pt" ).input_ids.to(A )
_UpperCAmelCase : int = model.generate(input_ids=A , max_length=model.config.max_length - num_paddings )
_UpperCAmelCase : Dict = tokenizer.batch_decode(A , skip_special_tokens=A )
_UpperCAmelCase : Any = tokenizer.decode(output_non_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : Optional[int] = tokenizer.decode(output_padded[0] , skip_special_tokens=A )
_UpperCAmelCase : str = [
"Hello, my dog is a little bit bigger than a little bit.",
"Today, I have a good idea of how to use the information",
]
self.assertListEqual(A , A )
self.assertListEqual(A , [non_padded_sentence, padded_sentence] )
@slow
def _A ( self : str ):
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Optional[Any] = BioGptModel.from_pretrained(A )
self.assertIsNotNone(A )
def _A ( self : List[str] ):
_UpperCAmelCase , _UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : str = 3
_UpperCAmelCase : List[str] = input_dict["input_ids"]
_UpperCAmelCase : Dict = input_ids.ne(1 ).to(A )
_UpperCAmelCase : List[Any] = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_UpperCAmelCase : List[str] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : List[str] = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def _A ( self : int ):
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_UpperCAmelCase : int = 3
_UpperCAmelCase : Dict = "multi_label_classification"
_UpperCAmelCase : Optional[Any] = input_dict["input_ids"]
_UpperCAmelCase : Optional[int] = input_ids.ne(1 ).to(A )
_UpperCAmelCase : Tuple = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_UpperCAmelCase : Optional[Any] = BioGptForSequenceClassification(A )
model.to(A )
model.eval()
_UpperCAmelCase : Tuple = model(A , attention_mask=A , labels=A )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
@slow
def _A ( self : List[Any] ):
_UpperCAmelCase : Optional[Any] = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : List[str] = torch.tensor([[2, 4805, 9, 656, 21]] )
_UpperCAmelCase : List[Any] = model(A )[0]
_UpperCAmelCase : int = 42384
_UpperCAmelCase : int = torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , A )
_UpperCAmelCase : Any = torch.tensor(
[[[-9.5_236, -9.8_918, 10.4_557], [-11.0_469, -9.6_423, 8.1_022], [-8.8_664, -7.8_826, 5.5_325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A , atol=1E-4 ) )
@slow
def _A ( self : Any ):
_UpperCAmelCase : str = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
_UpperCAmelCase : Tuple = BioGptForCausalLM.from_pretrained("microsoft/biogpt" )
model.to(A )
torch.manual_seed(0 )
_UpperCAmelCase : Optional[Any] = tokenizer("COVID-19 is" , return_tensors="pt" ).to(A )
_UpperCAmelCase : Dict = model.generate(
**A , min_length=100 , max_length=1024 , num_beams=5 , early_stopping=A , )
_UpperCAmelCase : Optional[Any] = tokenizer.decode(output_ids[0] , skip_special_tokens=A )
_UpperCAmelCase : List[str] = (
"COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the"
" causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and"
" territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),"
" and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and"
" more than 800,000 deaths."
)
self.assertEqual(A , A )
| 31
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'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: int = ""
__UpperCamelCase: str = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
__UpperCamelCase: str = None # compression type in fsspec. ex: "gzip"
__UpperCamelCase: str = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : Union[str, Any] , A : str = "" , A : Optional[str] = None , A : Optional[dict] = None , **A : Dict ):
super().__init__(self , **A )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
_UpperCAmelCase : List[str] = fsspec.open(
A , mode="rb" , protocol=A , compression=self.compression , client_kwargs={
"requote_redirect_url": False, # see https://github.com/huggingface/datasets/pull/5459
"trust_env": True, # Enable reading proxy env variables.
**(target_options or {}).pop("client_kwargs" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
_UpperCAmelCase : Dict = os.path.basename(self.file.path.split("::" )[0] )
_UpperCAmelCase : Dict = (
self.compressed_name[: self.compressed_name.rindex("." )]
if "." in self.compressed_name
else self.compressed_name
)
_UpperCAmelCase : Union[str, Any] = None
@classmethod
def _A ( cls : Optional[int] , A : Union[str, Any] ):
# compressed file paths are always relative to the archive root
return super()._strip_protocol(A ).lstrip("/" )
def _A ( self : int ):
if self.dir_cache is None:
_UpperCAmelCase : int = {**self.file.fs.info(self.file.path ), "name": self.uncompressed_name}
_UpperCAmelCase : str = {f["name"]: f}
def _A ( self : Tuple , A : str ):
return self.file.open().read()
def _A ( self : List[str] , A : str , A : str = "rb" , A : int=None , A : Optional[Any]=True , A : int=None , **A : Any , ):
_UpperCAmelCase : List[Any] = self._strip_protocol(A )
if mode != "rb":
raise ValueError(F"""Tried to read with mode {mode} on file {self.file.path} opened with mode 'rb'""" )
return self.file.open()
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Union[str, Any] = "bz2"
__UpperCamelCase: Optional[int] = "bz2"
__UpperCamelCase: Union[str, Any] = ".bz2"
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Dict = "gzip"
__UpperCamelCase: str = "gzip"
__UpperCamelCase: int = ".gz"
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: List[Any] = "lz4"
__UpperCamelCase: List[Any] = "lz4"
__UpperCamelCase: int = ".lz4"
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: str = "xz"
__UpperCamelCase: Tuple = "xz"
__UpperCamelCase: str = ".xz"
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Any = "zstd"
__UpperCamelCase: Tuple = "zstd"
__UpperCamelCase: Tuple = ".zst"
def __init__( self : Union[str, Any] , A : str , A : str = "rb" , A : Optional[str] = None , A : Optional[dict] = None , A : int = DEFAULT_BLOCK_SIZE , **A : List[str] , ):
super().__init__(
fo=A , mode=A , target_protocol=A , target_options=A , block_size=A , **A , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
_UpperCAmelCase : List[str] = self.file.__enter__
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] , A : Any ):
_UpperCAmelCase : Optional[Any] = file_
def __enter__( self : Tuple ):
self._file.__enter__()
return self
def __exit__( self : str , *A : Optional[Any] , **A : Optional[Any] ):
self._file.__exit__(*A , **A )
def __iter__( self : int ):
return iter(self._file )
def _A ( self : Union[str, Any] ):
return next(self._file )
def __getattr__( self : Union[str, Any] , A : Optional[int] ):
return getattr(self._file , A )
def fixed_enter(*A : int , **A : Any ):
return WrappedFile(_enter(*A , **A ) )
_UpperCAmelCase : Optional[Any] = fixed_enter
| 31
|
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = 8.3_1_4_4_6_2 # Unit - J mol-1 K-1
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def UpperCamelCase_ ( _UpperCAmelCase : float , _UpperCAmelCase : float , _UpperCAmelCase : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("Invalid inputs. Enter positive value." )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 31
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|
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import VivitImageProcessor
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
def __init__( self : int , A : Dict , A : Optional[int]=7 , A : Tuple=3 , A : Optional[Any]=10 , A : int=18 , A : Dict=30 , A : List[str]=400 , A : int=True , A : Optional[Any]=None , A : Optional[Any]=True , A : List[Any]=[0.5, 0.5, 0.5] , A : List[str]=[0.5, 0.5, 0.5] , A : Optional[int]=None , ):
_UpperCAmelCase : Dict = size if size is not None else {"shortest_edge": 18}
_UpperCAmelCase : Optional[Any] = crop_size if crop_size is not None else {"height": 18, "width": 18}
_UpperCAmelCase : Tuple = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : Optional[int] = num_channels
_UpperCAmelCase : Optional[Any] = num_frames
_UpperCAmelCase : Any = image_size
_UpperCAmelCase : Dict = min_resolution
_UpperCAmelCase : Any = max_resolution
_UpperCAmelCase : Optional[int] = do_resize
_UpperCAmelCase : str = size
_UpperCAmelCase : List[Any] = do_normalize
_UpperCAmelCase : Any = image_mean
_UpperCAmelCase : Tuple = image_std
_UpperCAmelCase : Any = crop_size
def _A ( self : List[Any] ):
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = VivitImageProcessor if is_vision_available() else None
def _A ( self : int ):
_UpperCAmelCase : Tuple = VivitImageProcessingTester(self )
@property
def _A ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "do_center_crop" ) )
self.assertTrue(hasattr(A , "size" ) )
def _A ( self : List[Any] ):
_UpperCAmelCase : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
_UpperCAmelCase : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def _A ( self : Tuple ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL videos
_UpperCAmelCase : Any = prepare_video_inputs(self.image_processor_tester , equal_resolution=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , Image.Image )
# Test not batched input
_UpperCAmelCase : str = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : List[Any] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , numpify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , np.ndarray )
# Test not batched input
_UpperCAmelCase : Tuple = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : Optional[int] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def _A ( self : List[Any] ):
# Initialize image_processing
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : Optional[int] = prepare_video_inputs(self.image_processor_tester , equal_resolution=A , torchify=A )
for video in video_inputs:
self.assertIsInstance(A , A )
self.assertIsInstance(video[0] , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processing(video_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
1,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
_UpperCAmelCase : List[Any] = image_processing(A , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_videos.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_frames,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 31
|
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
__SCREAMING_SNAKE_CASE : Optional[Any] = None
__SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Union[str, Any] = """▁"""
__SCREAMING_SNAKE_CASE : str = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__SCREAMING_SNAKE_CASE : int = {
"""vocab_file""": {"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"""},
"""tokenizer_file""": {
"""google/pegasus-xsum""": """https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"""
},
}
__SCREAMING_SNAKE_CASE : str = {
"""google/pegasus-xsum""": 512,
}
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
__UpperCamelCase: Optional[int] = VOCAB_FILES_NAMES
__UpperCamelCase: Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase: List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase: Optional[int] = PegasusTokenizer
__UpperCamelCase: Optional[Any] = ["input_ids", "attention_mask"]
def __init__( self : Dict , A : List[str]=None , A : Union[str, Any]=None , A : Optional[int]="<pad>" , A : Tuple="</s>" , A : Union[str, Any]="<unk>" , A : Union[str, Any]="<mask_2>" , A : Dict="<mask_1>" , A : Union[str, Any]=None , A : int=103 , **A : Optional[Any] , ):
_UpperCAmelCase : Dict = offset
if additional_special_tokens is not None:
if not isinstance(A , A ):
raise TypeError(
F"""additional_special_tokens should be of type {type(A )}, but is"""
F""" {type(A )}""" )
_UpperCAmelCase : Optional[int] = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
F"""<unk_{i}>""" for i in range(len(A ) , self.offset - 1 )
]
if len(set(A ) ) != len(A ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
F""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
_UpperCAmelCase : Any = additional_special_tokens_extended
else:
_UpperCAmelCase : Dict = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [F"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
A , tokenizer_file=A , pad_token=A , eos_token=A , unk_token=A , mask_token=A , mask_token_sent=A , offset=A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[Any] = vocab_file
_UpperCAmelCase : Optional[Any] = False if not self.vocab_file else True
def _A ( self : List[str] , A : Optional[Any] ):
_UpperCAmelCase : Any = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"There should be 3 special tokens: mask_token, pad_token, and eos_token +"
F""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def _A ( self : str , A : List , A : Optional[List] = None , A : bool = False ):
if already_has_special_tokens:
return self._special_token_mask(A )
elif token_ids_a is None:
return self._special_token_mask(A ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def _A ( self : Optional[int] , A : Union[str, Any] , A : int=None ):
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def _A ( self : Union[str, Any] , A : str , A : Optional[str] = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(A ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
_UpperCAmelCase : List[Any] = os.path.join(
A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(A ):
copyfile(self.vocab_file , A )
return (out_vocab_file,)
| 31
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|
'''simple docstring'''
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
__SCREAMING_SNAKE_CASE : List[Any] = get_logger(__name__)
__SCREAMING_SNAKE_CASE : List[Any] = R"""
Args:
input_ids (`jnp.ndarray` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`PreTrainedTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`jnp.ndarray` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be logits for each vocabulary when not using beam
search or log softmax for each vocabulary token when using beam search
kwargs (`Dict[str, Any]`, *optional*):
Additional logits processor specific kwargs.
Return:
`jnp.ndarray` of shape `(batch_size, config.vocab_size)`: The processed prediction scores.
"""
class lowerCamelCase_ :
'''simple docstring'''
@add_start_docstrings(A )
def __call__( self : Dict , A : jnp.ndarray , A : jnp.ndarray ):
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class lowerCamelCase_ :
'''simple docstring'''
@add_start_docstrings(A )
def __call__( self : Dict , A : jnp.ndarray , A : jnp.ndarray ):
raise NotImplementedError(
F"""{self.__class__} is an abstract class. Only classes inheriting this class can be called.""" )
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
@add_start_docstrings(A )
def __call__( self : Union[str, Any] , A : jnp.ndarray , A : jnp.ndarray , A : int , **A : str ):
for processor in self:
_UpperCAmelCase : Optional[int] = inspect.signature(processor.__call__ ).parameters
if len(A ) > 3:
if not all(arg in kwargs for arg in list(function_args.keys() )[2:] ):
raise ValueError(
F"""Make sure that all the required parameters: {list(function_args.keys() )} for """
F"""{processor.__class__} are passed to the logits processor.""" )
_UpperCAmelCase : int = processor(A , A , A , **A )
else:
_UpperCAmelCase : List[str] = processor(A , A , A )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[str] , A : float ):
if not isinstance(A , A ) or not (temperature > 0):
raise ValueError(F"""`temperature` has to be a strictly positive float, but is {temperature}""" )
_UpperCAmelCase : str = temperature
def __call__( self : List[str] , A : jnp.ndarray , A : jnp.ndarray , A : int ):
_UpperCAmelCase : Optional[int] = scores / self.temperature
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Tuple , A : float , A : float = -float("Inf" ) , A : int = 1 ):
if not isinstance(A , A ) or (top_p < 0 or top_p > 1.0):
raise ValueError(F"""`top_p` has to be a float > 0 and < 1, but is {top_p}""" )
if not isinstance(A , A ) or (min_tokens_to_keep < 1):
raise ValueError(F"""`min_tokens_to_keep` has to be a positive integer, but is {min_tokens_to_keep}""" )
_UpperCAmelCase : Dict = top_p
_UpperCAmelCase : Any = filter_value
_UpperCAmelCase : int = min_tokens_to_keep
def __call__( self : Tuple , A : jnp.ndarray , A : jnp.ndarray , A : int ):
_UpperCAmelCase , _UpperCAmelCase : Any = lax.top_k(A , scores.shape[-1] )
_UpperCAmelCase : List[str] = jnp.full_like(A , self.filter_value )
_UpperCAmelCase : int = jax.nn.softmax(A , axis=-1 ).cumsum(axis=-1 )
_UpperCAmelCase : Optional[int] = cumulative_probs < self.top_p
# include the token that is higher than top_p as well
_UpperCAmelCase : Union[str, Any] = jnp.roll(A , 1 )
score_mask |= score_mask.at[:, 0].set(A )
# min tokens to keep
_UpperCAmelCase : Tuple = score_mask.at[:, : self.min_tokens_to_keep].set(A )
_UpperCAmelCase : Dict = jnp.where(A , A , A )
_UpperCAmelCase : Union[str, Any] = jax.lax.sort_key_val(A , A )[-1]
return next_scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , A : int , A : float = -float("Inf" ) , A : int = 1 ):
if not isinstance(A , A ) or top_k <= 0:
raise ValueError(F"""`top_k` has to be a strictly positive integer, but is {top_k}""" )
_UpperCAmelCase : Optional[Any] = max(A , A )
_UpperCAmelCase : List[str] = filter_value
def __call__( self : Dict , A : jnp.ndarray , A : jnp.ndarray , A : int ):
_UpperCAmelCase , _UpperCAmelCase : List[Any] = scores.shape
_UpperCAmelCase : List[Any] = jnp.full(batch_size * vocab_size , self.filter_value )
_UpperCAmelCase : int = min(self.top_k , scores.shape[-1] ) # Safety check
_UpperCAmelCase , _UpperCAmelCase : int = lax.top_k(A , A )
_UpperCAmelCase : Optional[int] = jnp.broadcast_to((jnp.arange(A ) * vocab_size)[:, None] , (batch_size, topk) ).flatten()
_UpperCAmelCase : List[str] = topk_scores.flatten()
_UpperCAmelCase : Tuple = topk_indices.flatten() + shift
_UpperCAmelCase : Optional[int] = next_scores_flat.at[topk_indices_flat].set(A )
_UpperCAmelCase : Any = next_scores_flat.reshape(A , A )
return next_scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[Any] , A : int ):
_UpperCAmelCase : Union[str, Any] = bos_token_id
def __call__( self : Tuple , A : jnp.ndarray , A : jnp.ndarray , A : int ):
_UpperCAmelCase : str = jnp.full(scores.shape , -float("inf" ) )
_UpperCAmelCase : int = 1 - jnp.bool_(cur_len - 1 )
_UpperCAmelCase : List[str] = jnp.where(A , new_scores.at[:, self.bos_token_id].set(0 ) , A )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Dict , A : int , A : int ):
_UpperCAmelCase : Union[str, Any] = max_length
_UpperCAmelCase : Union[str, Any] = eos_token_id
def __call__( self : List[Any] , A : jnp.ndarray , A : jnp.ndarray , A : int ):
_UpperCAmelCase : Tuple = jnp.full(scores.shape , -float("inf" ) )
_UpperCAmelCase : List[str] = 1 - jnp.bool_(cur_len - self.max_length + 1 )
_UpperCAmelCase : str = jnp.where(A , new_scores.at[:, self.eos_token_id].set(0 ) , A )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : int , A : int ):
if not isinstance(A , A ) or min_length < 0:
raise ValueError(F"""`min_length` has to be a positive integer, but is {min_length}""" )
if not isinstance(A , A ) or eos_token_id < 0:
raise ValueError(F"""`eos_token_id` has to be a positive integer, but is {eos_token_id}""" )
_UpperCAmelCase : Tuple = min_length
_UpperCAmelCase : int = eos_token_id
def __call__( self : int , A : jnp.ndarray , A : jnp.ndarray , A : int ):
# create boolean flag to decide if min length penalty should be applied
_UpperCAmelCase : str = 1 - jnp.clip(cur_len - self.min_length , 0 , 1 )
_UpperCAmelCase : str = jnp.where(A , scores.at[:, self.eos_token_id].set(-float("inf" ) ) , A )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : int , A : str , A : Optional[int] ):
_UpperCAmelCase : List[str] = list(A )
_UpperCAmelCase : str = begin_index
def __call__( self : Optional[Any] , A : Optional[Any] , A : List[Any] , A : int ):
_UpperCAmelCase : Union[str, Any] = 1 - jnp.bool_(cur_len - self.begin_index )
_UpperCAmelCase : Any = jnp.where(A , scores.at[:, self.begin_suppress_tokens].set(-float("inf" ) ) , A )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Union[str, Any] , A : list ):
_UpperCAmelCase : Any = list(A )
def __call__( self : str , A : jnp.ndarray , A : jnp.ndarray , A : int ):
_UpperCAmelCase : Optional[int] = scores.at[..., self.suppress_tokens].set(-float("inf" ) )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : Optional[int] , A : Optional[int] ):
_UpperCAmelCase : Optional[Any] = dict(A )
# Converts the dictionary of format {index: token} containing the tokens to be forced to an array, where the
# index of the array corresponds to the index of the token to be forced, for XLA compatibility.
# Indexes without forced tokens will have a negative value.
_UpperCAmelCase : str = jnp.ones((max(force_token_map.keys() ) + 1) , dtype=jnp.intaa ) * -1
for index, token in force_token_map.items():
if token is not None:
_UpperCAmelCase : Dict = force_token_array.at[index].set(A )
_UpperCAmelCase : Any = jnp.intaa(A )
def __call__( self : List[Any] , A : jnp.ndarray , A : jnp.ndarray , A : int ):
def _force_token(A : str ):
_UpperCAmelCase : List[str] = scores.shape[0]
_UpperCAmelCase : Optional[Any] = self.force_token_array[generation_idx]
_UpperCAmelCase : Dict = jnp.ones_like(A , dtype=scores.dtype ) * -float("inf" )
_UpperCAmelCase : List[str] = jnp.zeros((batch_size, 1) , dtype=scores.dtype )
_UpperCAmelCase : int = lax.dynamic_update_slice(A , A , (0, current_token) )
return new_scores
_UpperCAmelCase : Tuple = lax.cond(
cur_len >= self.force_token_array.shape[0] , lambda: scores , lambda: lax.cond(
self.force_token_array[cur_len] >= 0 , lambda: _force_token(A ) , lambda: scores , ) , )
return scores
class lowerCamelCase_ (snake_case__ ):
'''simple docstring'''
def __init__( self : List[Any] , A : Dict , A : int , A : List[Any] ):
_UpperCAmelCase : Optional[Any] = generate_config.eos_token_id
_UpperCAmelCase : List[Any] = generate_config.no_timestamps_token_id
_UpperCAmelCase : Optional[int] = generate_config.no_timestamps_token_id + 1
_UpperCAmelCase : Union[str, Any] = decoder_input_length + 1
if generate_config.is_multilingual:
# room for language token and task token
self.begin_index += 2
if hasattr(A , "max_initial_timestamp_index" ):
_UpperCAmelCase : Optional[int] = generate_config.max_initial_timestamp_index
else:
_UpperCAmelCase : Dict = model_config.vocab_size
if self.max_initial_timestamp_index is None:
_UpperCAmelCase : str = model_config.vocab_size
def __call__( self : str , A : Dict , A : int , A : List[Any] ):
# suppress <|notimestamps|> which is handled by without_timestamps
_UpperCAmelCase : Tuple = scores.at[:, self.no_timestamps_token_id].set(-float("inf" ) )
def handle_pairs(A : Union[str, Any] , A : List[str] ):
_UpperCAmelCase : Optional[Any] = jnp.where((cur_len - self.begin_index) >= 1 , A , A )
_UpperCAmelCase : Union[str, Any] = jnp.where(
input_ids_k[cur_len - 1] >= self.timestamp_begin , True and last_was_timestamp , A , )
_UpperCAmelCase : List[Any] = jnp.where((cur_len - self.begin_index) < 2 , A , A )
_UpperCAmelCase : Union[str, Any] = jnp.where(
input_ids_k[cur_len - 2] >= self.timestamp_begin , A , A , )
return jnp.where(
A , jnp.where(
penultimate_was_timestamp > 0 , scores_k.at[self.timestamp_begin :].set(-float("inf" ) ) , scores_k.at[: self.eos_token_id].set(-float("inf" ) ) , ) , A , )
_UpperCAmelCase : Optional[int] = jax.vmap(A )(A , A )
_UpperCAmelCase : Union[str, Any] = jnp.where(cur_len == self.begin_index , A , A )
_UpperCAmelCase : Union[str, Any] = jnp.where(
self.max_initial_timestamp_index is not None , True and apply_max_initial_timestamp , A , )
_UpperCAmelCase : Tuple = self.timestamp_begin + self.max_initial_timestamp_index
_UpperCAmelCase : Optional[Any] = jnp.where(
A , scores.at[:, last_allowed + 1 :].set(-float("inf" ) ) , A , )
# if sum of probability over timestamps is above any other token, sample timestamp
_UpperCAmelCase : List[str] = jax.nn.log_softmax(A , axis=-1 )
def handle_cumulative_probs(A : str , A : Dict ):
_UpperCAmelCase : Optional[Any] = jax.nn.logsumexp(logprobs_k[self.timestamp_begin :] , axis=-1 )
_UpperCAmelCase : int = jnp.max(logprobs_k[: self.timestamp_begin] )
return jnp.where(
timestamp_logprob > max_text_token_logprob , scores_k.at[: self.timestamp_begin].set(-float("inf" ) ) , A , )
_UpperCAmelCase : str = jax.vmap(A )(A , A )
return scores
| 31
|
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__SCREAMING_SNAKE_CASE : Union[str, Any] = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__SCREAMING_SNAKE_CASE : Optional[int] = 256_047
__SCREAMING_SNAKE_CASE : Optional[int] = 256_145
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (snake_case__ , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: int = NllbTokenizer
__UpperCamelCase: Tuple = NllbTokenizerFast
__UpperCamelCase: Union[str, Any] = True
__UpperCamelCase: Dict = True
__UpperCamelCase: Optional[Any] = {}
def _A ( self : Union[str, Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
tokenizer.save_pretrained(self.tmpdirname )
def _A ( self : Dict ):
_UpperCAmelCase : Tuple = NllbTokenizer(A , keep_accents=A )
_UpperCAmelCase : Optional[Any] = tokenizer.tokenize("This is a test" )
self.assertListEqual(A , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase : List[str] = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
_UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_ids(A )
self.assertListEqual(
A , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
_UpperCAmelCase : Union[str, Any] = tokenizer.convert_ids_to_tokens(A )
self.assertListEqual(
A , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _A ( self : List[Any] ):
_UpperCAmelCase : Any = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-nllb", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : str = self.tokenizer_class.from_pretrained(A , **A )
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Dict = tokenizer_r.save_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
_UpperCAmelCase : Optional[int] = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : List[str] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=True
_UpperCAmelCase : Optional[Any] = tempfile.mkdtemp()
_UpperCAmelCase : str = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : str = tokenizer_p.save_pretrained(A )
# Checks it save with the same files
self.assertSequenceEqual(A , A )
# Checks everything loads correctly in the same way
_UpperCAmelCase : Optional[int] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Dict = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
# Save tokenizer rust, legacy_format=False
_UpperCAmelCase : Optional[int] = tempfile.mkdtemp()
_UpperCAmelCase : Optional[int] = tokenizer_r.save_pretrained(A , legacy_format=A )
_UpperCAmelCase : Dict = tokenizer_p.save_pretrained(A )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_UpperCAmelCase : List[Any] = tokenizer_r.from_pretrained(A )
_UpperCAmelCase : Optional[int] = tokenizer_p.from_pretrained(A )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(A , A ) )
shutil.rmtree(A )
@require_torch
def _A ( self : Tuple ):
if not self.test_seqaseq:
return
_UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_UpperCAmelCase : Optional[Any] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for"
" Syria is that 'there is no military solution' to the nearly five-year conflict and more weapons"
" will only worsen the violence and misery for millions of people.",
]
_UpperCAmelCase : Optional[Any] = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al"
" Rusiei pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi"
" că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
try:
_UpperCAmelCase : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=A , tgt_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="ron_Latn" , )
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 10 )
# max_target_length will default to max_length if not specified
_UpperCAmelCase : Tuple = tokenizer.prepare_seqaseq_batch(
A , tgt_texts=A , max_length=3 , return_tensors="pt" )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.labels.shape[1] , 3 )
_UpperCAmelCase : Union[str, Any] = tokenizer.prepare_seqaseq_batch(
src_texts=A , max_length=3 , max_target_length=10 , return_tensors="pt" )
self.assertEqual(batch_encoder_only.input_ids.shape[1] , 3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] , 3 )
self.assertNotIn("decoder_input_ids" , A )
@unittest.skip("Unfortunately way too slow to build a BPE with SentencePiece." )
def _A ( self : List[Any] ):
pass
def _A ( self : Union[str, Any] ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_UpperCAmelCase : Any = [AddedToken("<special>" , lstrip=A )]
_UpperCAmelCase : List[Any] = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Dict = tokenizer_r.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_r.encode("<special>" , add_special_tokens=A )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_UpperCAmelCase : Dict = self.rust_tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A , )
_UpperCAmelCase : Optional[int] = self.tokenizer_class.from_pretrained(
A , additional_special_tokens=A , **A )
_UpperCAmelCase : Union[str, Any] = tokenizer_p.encode("Hey this is a <special> token" )
_UpperCAmelCase : Any = tokenizer_cr.encode("Hey this is a <special> token" )
self.assertEqual(A , A )
self.assertEqual(A , A )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase_ (unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase: Dict = "facebook/nllb-200-distilled-600M"
__UpperCamelCase: Optional[int] = [
" UN Chief Says There Is No Military Solution in Syria",
" Secretary-General Ban Ki-moon says his response to Russia's stepped up military support for Syria is that \"there is no military solution\" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.",
]
__UpperCamelCase: str = [
"Şeful ONU declară că nu există o soluţie militară în Siria",
"Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei"
" pentru Siria este că \"nu există o soluţie militară\" la conflictul de aproape cinci ani şi că noi arme nu vor"
" face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.",
]
__UpperCamelCase: str = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def _A ( cls : int ):
_UpperCAmelCase : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="eng_Latn" , tgt_lang="ron_Latn" )
_UpperCAmelCase : Union[str, Any] = 1
return cls
def _A ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Arab"] , 256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ace_Latn"] , 256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["fra_Latn"] , 256057 )
def _A ( self : Union[str, Any] ):
_UpperCAmelCase : Optional[int] = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , A )
def _A ( self : Tuple ):
self.assertIn(A , self.tokenizer.all_special_ids )
# fmt: off
_UpperCAmelCase : List[Any] = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_UpperCAmelCase : Tuple = self.tokenizer.decode(A , skip_special_tokens=A )
_UpperCAmelCase : Optional[Any] = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=A )
self.assertEqual(A , A )
self.assertNotIn(self.tokenizer.eos_token , A )
def _A ( self : Optional[int] ):
_UpperCAmelCase : List[Any] = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , A )
_UpperCAmelCase : Dict = 10
_UpperCAmelCase : Tuple = self.tokenizer(A , max_length=A , truncation=A ).input_ids[0]
self.assertEqual(ids[-1] , 2 )
self.assertEqual(ids[0] , A )
self.assertEqual(len(A ) , A )
def _A ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [256203, 3] )
def _A ( self : Optional[Any] ):
_UpperCAmelCase : Dict = tempfile.mkdtemp()
_UpperCAmelCase : str = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(A )
_UpperCAmelCase : Tuple = NllbTokenizer.from_pretrained(A )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , A )
@require_torch
def _A ( self : Dict ):
_UpperCAmelCase : List[str] = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=A , truncation=A , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
_UpperCAmelCase : Tuple = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.lang_code_to_id["ron_Latn"] )
self.assertIsInstance(A , A )
self.assertEqual((2, 15) , batch.input_ids.shape )
self.assertEqual((2, 15) , batch.attention_mask.shape )
_UpperCAmelCase : Dict = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , A )
self.assertEqual(A , batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _A ( self : str ):
_UpperCAmelCase : Optional[Any] = self.tokenizer(self.src_text , padding=A , truncation=A , max_length=3 , return_tensors="pt" )
_UpperCAmelCase : Dict = self.tokenizer(
text_target=self.tgt_text , padding=A , truncation=A , max_length=10 , return_tensors="pt" )
_UpperCAmelCase : List[Any] = targets["input_ids"]
_UpperCAmelCase : Union[str, Any] = shift_tokens_right(
A , self.tokenizer.pad_token_id , decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] , )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _A ( self : List[Any] ):
_UpperCAmelCase : str = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
nested_simplify(A ) , {
# A, test, EOS, en_XX
"input_ids": [[256047, 70, 7356, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 256057,
} , )
@require_torch
def _A ( self : Any ):
_UpperCAmelCase : Dict = True
_UpperCAmelCase : Any = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_UpperCAmelCase : Optional[int] = False
_UpperCAmelCase : str = self.tokenizer(
"UN Chief says there is no military solution in Syria" , src_lang="eng_Latn" , tgt_lang="fra_Latn" )
self.assertEqual(
inputs.input_ids , [256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 31
| 1
|
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