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import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
_snake_case = logging.get_logger(__name__)
_snake_case = {
"vocab_file": "vocab.json",
"merges_file": "merges.txt",
"tokenizer_config_file": "tokenizer_config.json",
}
_snake_case = {
"vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
"merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
"tokenizer_config_file": {
"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
},
}
_snake_case = {"facebook/blenderbot-3B": 128}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = (
list(range(ord("!" ) , ord("~" ) + 1 ) ) + list(range(ord("¡" ) , ord("¬" ) + 1 ) ) + list(range(ord("®" ) , ord("ÿ" ) + 1 ) )
)
_lowerCAmelCase : List[str] = bs[:]
_lowerCAmelCase : int = 0
for b in range(2**8 ):
if b not in bs:
bs.append(_lowerCamelCase )
cs.append(2**8 + n )
n += 1
_lowerCAmelCase : Optional[Any] = [chr(_lowerCamelCase ) for n in cs]
return dict(zip(_lowerCamelCase , _lowerCamelCase ) )
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = set()
_lowerCAmelCase : int = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
_lowerCAmelCase : str = char
return pairs
class UpperCAmelCase_ ( a):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['input_ids', 'attention_mask']
def __init__( self, __a, __a, __a="replace", __a="<s>", __a="</s>", __a="</s>", __a="<s>", __a="<unk>", __a="<pad>", __a="<mask>", __a=False, **__a, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = AddedToken(__a, lstrip=__a, rstrip=__a) if isinstance(__a, __a) else bos_token
_lowerCAmelCase : int = AddedToken(__a, lstrip=__a, rstrip=__a) if isinstance(__a, __a) else eos_token
_lowerCAmelCase : Union[str, Any] = AddedToken(__a, lstrip=__a, rstrip=__a) if isinstance(__a, __a) else sep_token
_lowerCAmelCase : Any = AddedToken(__a, lstrip=__a, rstrip=__a) if isinstance(__a, __a) else cls_token
_lowerCAmelCase : Dict = AddedToken(__a, lstrip=__a, rstrip=__a) if isinstance(__a, __a) else unk_token
_lowerCAmelCase : List[str] = 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
_lowerCAmelCase : Union[str, Any] = 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:
_lowerCAmelCase : int = json.load(__a)
_lowerCAmelCase : List[Any] = {v: k for k, v in self.encoder.items()}
_lowerCAmelCase : Any = errors # how to handle errors in decoding
_lowerCAmelCase : int = bytes_to_unicode()
_lowerCAmelCase : Dict = {v: k for k, v in self.byte_encoder.items()}
with open(__a, encoding="utf-8") as merges_handle:
_lowerCAmelCase : int = merges_handle.read().split("\n")[1:-1]
_lowerCAmelCase : Any = [tuple(merge.split()) for merge in bpe_merges]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[int] = {}
_lowerCAmelCase : int = add_prefix_space
# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
_lowerCAmelCase : Tuple = re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+")
@property
# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
def snake_case__ ( self):
'''simple docstring'''
return len(self.encoder)
def snake_case__ ( self):
'''simple docstring'''
return dict(self.encoder, **self.added_tokens_encoder)
def snake_case__ ( self, __a):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
_lowerCAmelCase : Any = tuple(__a)
_lowerCAmelCase : List[str] = get_pairs(__a)
if not pairs:
return token
while True:
_lowerCAmelCase : List[Any] = min(__a, key=lambda __a: self.bpe_ranks.get(__a, float("inf")))
if bigram not in self.bpe_ranks:
break
_lowerCAmelCase , _lowerCAmelCase : str = bigram
_lowerCAmelCase : Any = []
_lowerCAmelCase : Any = 0
while i < len(__a):
try:
_lowerCAmelCase : List[str] = word.index(__a, __a)
except ValueError:
new_word.extend(word[i:])
break
else:
new_word.extend(word[i:j])
_lowerCAmelCase : Tuple = 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
_lowerCAmelCase : int = tuple(__a)
_lowerCAmelCase : List[Any] = new_word
if len(__a) == 1:
break
else:
_lowerCAmelCase : Any = get_pairs(__a)
_lowerCAmelCase : Union[str, Any] = " ".join(__a)
_lowerCAmelCase : List[str] = word
return word
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : int = []
for token in re.findall(self.pat, __a):
_lowerCAmelCase : int = "".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 snake_case__ ( self, __a):
'''simple docstring'''
return self.encoder.get(__a, self.encoder.get(self.unk_token))
def snake_case__ ( self, __a):
'''simple docstring'''
return self.decoder.get(__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : int = "".join(__a)
_lowerCAmelCase : Optional[Any] = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
return text
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
if not os.path.isdir(__a):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
_lowerCAmelCase : int = os.path.join(
__a, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
_lowerCAmelCase : 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")
_lowerCAmelCase : int = 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!")
_lowerCAmelCase : Dict = token_index
writer.write(" ".join(__a) + "\n")
index += 1
return vocab_file, merge_file
def snake_case__ ( self, __a, __a = None, __a = False):
'''simple docstring'''
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 snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Any = [self.sep_token_id]
_lowerCAmelCase : int = [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 snake_case__ ( self, __a, __a=False, **__a):
'''simple docstring'''
_lowerCAmelCase : str = 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()):
_lowerCAmelCase : Tuple = " " + text
return (text, kwargs)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
return token_ids_a + [self.eos_token_id]
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(" " + text)
else:
# Generated responses should contain them already.
inputs.append(__a)
_lowerCAmelCase : Optional[Any] = " ".join(__a)
_lowerCAmelCase : Tuple = self.encode(__a)
if len(__a) > self.model_max_length:
_lowerCAmelCase : str = input_ids[-self.model_max_length :]
logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens.")
return input_ids
| 36
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'wav2vec2'
def __init__( self, __a=32, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.1, __a=0.1, __a=0.1, __a=0.0, __a=0.0, __a=0.1, __a=0.1, __a=0.02, __a=1E-5, __a="group", __a="gelu", __a=(512, 512, 512, 512, 512, 512, 512), __a=(5, 2, 2, 2, 2, 2, 2), __a=(10, 3, 3, 3, 3, 2, 2), __a=False, __a=128, __a=16, __a=False, __a=True, __a=0.05, __a=10, __a=2, __a=0.0, __a=10, __a=0, __a=320, __a=2, __a=0.1, __a=100, __a=256, __a=256, __a=0.1, __a="sum", __a=False, __a=False, __a=256, __a=(512, 512, 512, 512, 1500), __a=(5, 3, 3, 1, 1), __a=(1, 2, 3, 1, 1), __a=512, __a=0, __a=1, __a=2, __a=False, __a=3, __a=2, __a=3, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a, pad_token_id=__a, bos_token_id=__a, eos_token_id=__a)
_lowerCAmelCase : str = hidden_size
_lowerCAmelCase : Optional[int] = feat_extract_norm
_lowerCAmelCase : Union[str, Any] = feat_extract_activation
_lowerCAmelCase : Optional[Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : List[str] = conv_bias
_lowerCAmelCase : str = num_conv_pos_embeddings
_lowerCAmelCase : List[Any] = num_conv_pos_embedding_groups
_lowerCAmelCase : str = len(self.conv_dim)
_lowerCAmelCase : List[str] = num_hidden_layers
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : Any = hidden_act
_lowerCAmelCase : int = num_attention_heads
_lowerCAmelCase : Optional[Any] = hidden_dropout
_lowerCAmelCase : List[str] = attention_dropout
_lowerCAmelCase : Tuple = activation_dropout
_lowerCAmelCase : int = feat_proj_dropout
_lowerCAmelCase : List[str] = final_dropout
_lowerCAmelCase : int = layerdrop
_lowerCAmelCase : int = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Optional[Any] = do_stable_layer_norm
_lowerCAmelCase : Any = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_lowerCAmelCase : str = apply_spec_augment
_lowerCAmelCase : Optional[Any] = mask_time_prob
_lowerCAmelCase : Optional[int] = mask_time_length
_lowerCAmelCase : List[str] = mask_time_min_masks
_lowerCAmelCase : Optional[int] = mask_feature_prob
_lowerCAmelCase : Optional[int] = mask_feature_length
_lowerCAmelCase : List[str] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_lowerCAmelCase : Union[str, Any] = num_codevectors_per_group
_lowerCAmelCase : str = num_codevector_groups
_lowerCAmelCase : Optional[int] = contrastive_logits_temperature
_lowerCAmelCase : Optional[int] = feat_quantizer_dropout
_lowerCAmelCase : Optional[int] = num_negatives
_lowerCAmelCase : Union[str, Any] = codevector_dim
_lowerCAmelCase : Any = proj_codevector_dim
_lowerCAmelCase : Optional[int] = diversity_loss_weight
# ctc loss
_lowerCAmelCase : Tuple = ctc_loss_reduction
_lowerCAmelCase : Tuple = ctc_zero_infinity
# adapter
_lowerCAmelCase : List[Any] = add_adapter
_lowerCAmelCase : List[str] = adapter_kernel_size
_lowerCAmelCase : str = adapter_stride
_lowerCAmelCase : List[str] = num_adapter_layers
_lowerCAmelCase : str = output_hidden_size or hidden_size
_lowerCAmelCase : Tuple = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCAmelCase : str = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : Union[str, Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : Tuple = xvector_output_dim
@property
def snake_case__ ( self):
'''simple docstring'''
return functools.reduce(operator.mul, self.conv_stride, 1)
| 36
| 1
|
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
slow,
torch_device,
)
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = LDMTextToImagePipeline
lowerCamelCase__ = TEXT_TO_IMAGE_PARAMS - {
'negative_prompt',
'negative_prompt_embeds',
'cross_attention_kwargs',
'prompt_embeds',
}
lowerCamelCase__ = PipelineTesterMixin.required_optional_params - {
'num_images_per_prompt',
'callback',
'callback_steps',
}
lowerCamelCase__ = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
torch.manual_seed(0)
_lowerCAmelCase : Union[str, Any] = UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=4, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, )
_lowerCAmelCase : Optional[int] = DDIMScheduler(
beta_start=0.00_085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=__a, set_alpha_to_one=__a, )
torch.manual_seed(0)
_lowerCAmelCase : int = AutoencoderKL(
block_out_channels=(32, 64), in_channels=3, out_channels=3, down_block_types=("DownEncoderBlock2D", "DownEncoderBlock2D"), up_block_types=("UpDecoderBlock2D", "UpDecoderBlock2D"), latent_channels=4, )
torch.manual_seed(0)
_lowerCAmelCase : str = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, )
_lowerCAmelCase : Any = CLIPTextModel(__a)
_lowerCAmelCase : Optional[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
_lowerCAmelCase : List[Any] = {
"unet": unet,
"scheduler": scheduler,
"vqvae": vae,
"bert": text_encoder,
"tokenizer": tokenizer,
}
return components
def snake_case__ ( self, __a, __a=0):
'''simple docstring'''
if str(__a).startswith("mps"):
_lowerCAmelCase : Any = torch.manual_seed(__a)
else:
_lowerCAmelCase : Optional[Any] = torch.Generator(device=__a).manual_seed(__a)
_lowerCAmelCase : Dict = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : Dict = self.get_dummy_components()
_lowerCAmelCase : Optional[Any] = LDMTextToImagePipeline(**__a)
pipe.to(__a)
pipe.set_progress_bar_config(disable=__a)
_lowerCAmelCase : str = self.get_dummy_inputs(__a)
_lowerCAmelCase : Optional[int] = pipe(**__a).images
_lowerCAmelCase : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 16, 16, 3)
_lowerCAmelCase : Any = np.array([0.6_101, 0.6_156, 0.5_622, 0.4_895, 0.6_661, 0.3_804, 0.5_748, 0.6_136, 0.5_014])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-3
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self, __a, __a=torch.floataa, __a=0):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = torch.manual_seed(__a)
_lowerCAmelCase : Any = np.random.RandomState(__a).standard_normal((1, 4, 32, 32))
_lowerCAmelCase : Optional[int] = torch.from_numpy(__a).to(device=__a, dtype=__a)
_lowerCAmelCase : Tuple = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 3,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(__a)
pipe.set_progress_bar_config(disable=__a)
_lowerCAmelCase : Union[str, Any] = self.get_inputs(__a)
_lowerCAmelCase : List[Any] = pipe(**__a).images
_lowerCAmelCase : str = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 256, 256, 3)
_lowerCAmelCase : List[str] = np.array([0.51_825, 0.52_850, 0.52_543, 0.54_258, 0.52_304, 0.52_569, 0.54_363, 0.55_276, 0.56_878])
_lowerCAmelCase : str = np.abs(expected_slice - image_slice).max()
assert max_diff < 1E-3
@nightly
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self, __a, __a=torch.floataa, __a=0):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.manual_seed(__a)
_lowerCAmelCase : Dict = np.random.RandomState(__a).standard_normal((1, 4, 32, 32))
_lowerCAmelCase : str = torch.from_numpy(__a).to(device=__a, dtype=__a)
_lowerCAmelCase : Dict = {
"prompt": "A painting of a squirrel eating a burger",
"latents": latents,
"generator": generator,
"num_inference_steps": 50,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = LDMTextToImagePipeline.from_pretrained("CompVis/ldm-text2im-large-256").to(__a)
pipe.set_progress_bar_config(disable=__a)
_lowerCAmelCase : Optional[Any] = self.get_inputs(__a)
_lowerCAmelCase : List[str] = pipe(**__a).images[0]
_lowerCAmelCase : Optional[int] = load_numpy(
"https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/ldm_text2img/ldm_large_256_ddim.npy")
_lowerCAmelCase : Dict = np.abs(expected_image - image).max()
assert max_diff < 1E-3
| 36
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'The RoBERTa Model transformer with early exiting (DeeRoBERTa). ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[Any] = RobertaEmbeddings(__a)
self.init_weights()
@add_start_docstrings(
'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[int] = config.num_labels
_lowerCAmelCase : Optional[int] = config.num_hidden_layers
_lowerCAmelCase : Optional[int] = DeeRobertaModel(__a)
_lowerCAmelCase : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob)
_lowerCAmelCase : List[str] = nn.Linear(config.hidden_size, self.config.num_labels)
@add_start_docstrings_to_model_forward(__a)
def snake_case__ ( self, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=-1, __a=False, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.num_layers
try:
_lowerCAmelCase : List[Any] = self.roberta(
__a, attention_mask=__a, token_type_ids=__a, position_ids=__a, head_mask=__a, inputs_embeds=__a, )
_lowerCAmelCase : List[Any] = outputs[1]
_lowerCAmelCase : Dict = self.dropout(__a)
_lowerCAmelCase : Dict = self.classifier(__a)
_lowerCAmelCase : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase : Tuple = e.message
_lowerCAmelCase : Union[str, Any] = e.exit_layer
_lowerCAmelCase : List[Any] = outputs[0]
if not self.training:
_lowerCAmelCase : int = entropy(__a)
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : str = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : Optional[Any] = MSELoss()
_lowerCAmelCase : int = loss_fct(logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Optional[Any] = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
_lowerCAmelCase : Optional[int] = []
for highway_exit in outputs[-1]:
_lowerCAmelCase : Any = highway_exit[0]
if not self.training:
highway_logits_all.append(__a)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : List[str] = MSELoss()
_lowerCAmelCase : List[Any] = loss_fct(highway_logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Dict = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
highway_losses.append(__a)
if train_highway:
_lowerCAmelCase : int = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase : Any = (loss,) + outputs
if not self.training:
_lowerCAmelCase : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 36
| 1
|
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
def A ( _lowerCamelCase ):
'''simple docstring'''
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(_lowerCamelCase ):
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 A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
_lowerCAmelCase : int = try_infer_format_from_ext(args.input ) if args.format == "infer" else args.format
_lowerCAmelCase : Any = PipelineDataFormat.from_str(
format=_lowerCamelCase , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(_lowerCamelCase , _lowerCamelCase )
class UpperCAmelCase_ ( a):
def __init__( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[str] = nlp
_lowerCAmelCase : Tuple = reader
@staticmethod
def snake_case__ ( __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = 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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = self._nlp, []
for entry in self._reader:
_lowerCAmelCase : Any = 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:
_lowerCAmelCase : Tuple = 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)
| 36
|
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'vision-encoder-decoder'
lowerCamelCase__ = True
def __init__( self, **__a):
'''simple docstring'''
super().__init__(**__a)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")
_lowerCAmelCase : str = kwargs.pop("encoder")
_lowerCAmelCase : Any = encoder_config.pop("model_type")
_lowerCAmelCase : str = kwargs.pop("decoder")
_lowerCAmelCase : List[str] = decoder_config.pop("model_type")
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[int] = True
@classmethod
def snake_case__ ( cls, __a, __a, **__a):
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : str = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = copy.deepcopy(self.__dict__)
_lowerCAmelCase : List[str] = self.encoder.to_dict()
_lowerCAmelCase : List[str] = self.decoder.to_dict()
_lowerCAmelCase : Any = self.__class__.model_type
return output
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : List[str] = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : Optional[Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def snake_case__ ( self, __a, __a = -1, __a = -1, __a = False, __a = None, ):
'''simple docstring'''
import torch
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : List[str] = super().generate_dummy_inputs(
__a, batch_size=__a, seq_length=__a, is_pair=__a, framework=__a)
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = dummy_input["input_ids"].shape
_lowerCAmelCase : str = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowerCAmelCase : List[str] = dummy_input.pop("input_ids")
_lowerCAmelCase : List[str] = dummy_input.pop("attention_mask")
_lowerCAmelCase : Optional[int] = torch.zeros(__a)
return common_inputs
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self, __a):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(__a)
def snake_case__ ( self, __a, __a, __a = "default"):
'''simple docstring'''
_lowerCAmelCase : Dict = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__a, __a)
| 36
| 1
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = _distribute_shards(**_lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = _split_gen_kwargs(_lowerCamelCase , _lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(_lowerCamelCase ):
_number_of_shards_in_gen_kwargs(_lowerCamelCase )
else:
_lowerCAmelCase : Optional[int] = _number_of_shards_in_gen_kwargs(_lowerCamelCase )
assert out == expected
| 36
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( a):
def __get__( self, __a, __a=None):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
_lowerCAmelCase : List[Any] = "__cached_" + self.fget.__name__
_lowerCAmelCase : Dict = getattr(__a, __a, __a)
if cached is None:
_lowerCAmelCase : str = self.fget(__a)
setattr(__a, __a, __a)
return cached
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"invalid truth value {val!r}" )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.device )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if hasattr(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase , torch.dtype )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase , tf.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase , jnp.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = fields(self)
# Safety and consistency checks
if not len(__a):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
_lowerCAmelCase : Dict = getattr(self, class_fields[0].name)
_lowerCAmelCase : str = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(__a):
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = first_field.items()
_lowerCAmelCase : Dict = True
else:
try:
_lowerCAmelCase : Dict = iter(__a)
_lowerCAmelCase : Any = True
except TypeError:
_lowerCAmelCase : Any = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a):
if (
not isinstance(__a, (list, tuple))
or not len(__a) == 2
or not isinstance(element[0], __a)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value).")
break
setattr(self, element[0], element[1])
if element[1] is not None:
_lowerCAmelCase : Any = element[1]
elif first_field is not None:
_lowerCAmelCase : Any = first_field
else:
for field in class_fields:
_lowerCAmelCase : Dict = getattr(self, field.name)
if v is not None:
_lowerCAmelCase : Union[str, Any] = v
def __delitem__( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__( self, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Optional[int] = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self, __a, __a):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a, __a)
super().__setattr__(__a, __a)
def __setitem__( self, __a, __a):
'''simple docstring'''
super().__setitem__(__a, __a)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
return tuple(self[k] for k in self.keys())
class UpperCAmelCase_ ( a , a):
@classmethod
def snake_case__ ( cls, __a):
'''simple docstring'''
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'longest'
lowerCamelCase__ = 'max_length'
lowerCamelCase__ = 'do_not_pad'
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'pt'
lowerCamelCase__ = 'tf'
lowerCamelCase__ = 'np'
lowerCamelCase__ = 'jax'
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = context_managers
_lowerCAmelCase : Dict = ExitStack()
def __enter__( self):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a)
def __exit__( self, *__a, **__a):
'''simple docstring'''
self.stack.__exit__(*__a, **__a)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : str = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = model_class.__name__
_lowerCAmelCase : Optional[Any] = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( _lowerCamelCase , _lowerCamelCase = "" , _lowerCamelCase = "." ):
'''simple docstring'''
def _flatten_dict(_lowerCamelCase , _lowerCamelCase="" , _lowerCamelCase="." ):
for k, v in d.items():
_lowerCAmelCase : Dict = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase , _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
@contextmanager
def A ( _lowerCamelCase , _lowerCamelCase = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase , axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase , _lowerCamelCase )
else:
raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase , (tuple, list) ):
_lowerCAmelCase : List[Any] = [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase : Tuple = F"{repo_id}--{value}"
return auto_map
def A ( _lowerCamelCase ):
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
_lowerCAmelCase : Tuple = base_class.__module__
_lowerCAmelCase : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"Could not infer framework from class {model_class}." )
| 36
| 1
|
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_lowerCAmelCase : Optional[int] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 512
_lowerCAmelCase : Union[str, Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : int = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : str = np.abs(np.fft.fft(_lowerCamelCase ) )
_lowerCAmelCase : Union[str, Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_lowerCAmelCase : List[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_lowerCamelCase )
plt.show()
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 512
_lowerCAmelCase : Optional[Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : str = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : Optional[Any] = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 36
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = _distribute_shards(**_lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = _split_gen_kwargs(_lowerCamelCase , _lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(_lowerCamelCase ):
_number_of_shards_in_gen_kwargs(_lowerCamelCase )
else:
_lowerCAmelCase : Optional[int] = _number_of_shards_in_gen_kwargs(_lowerCamelCase )
assert out == expected
| 36
| 1
|
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = LxmertConfig.from_json_file(_lowerCamelCase )
print(F"Building PyTorch model from configuration: {config}" )
_lowerCAmelCase : int = LxmertForPreTraining(_lowerCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_lxmert(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained model. \nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
_snake_case = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 36
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCAmelCase_ :
def __init__( self, __a = "cpu", __a = "openai/clip-vit-large-patch14"):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = device
_lowerCAmelCase : Optional[int] = CLIPTokenizerFast.from_pretrained(__a)
_lowerCAmelCase : Any = [0.48_145_466, 0.4_578_275, 0.40_821_073]
_lowerCAmelCase : Union[str, Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]
_lowerCAmelCase : Tuple = torchvision.transforms.Normalize(self.image_mean, self.image_std)
_lowerCAmelCase : Optional[int] = torchvision.transforms.Resize(224)
_lowerCAmelCase : Dict = torchvision.transforms.CenterCrop(224)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.resize(__a)
_lowerCAmelCase : List[str] = self.center_crop(__a)
_lowerCAmelCase : Optional[Any] = self.normalize(__a)
return images
def __call__( self, __a=None, __a=None, **__a):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(text=__a, **__a)
_lowerCAmelCase : List[str] = self.preprocess_img(__a)
_lowerCAmelCase : Tuple = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a=10, __a=0.01, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=False, __a=True, __a="image", __a=True, __a=False, __a=False, __a=False, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : List[str] = None
_lowerCAmelCase : List[str] = device if device else get_device()
if vqgan:
_lowerCAmelCase : Union[str, Any] = vqgan
else:
_lowerCAmelCase : Optional[Any] = load_vqgan(self.device, conf_path=__a, ckpt_path=__a)
self.vqgan.eval()
if clip:
_lowerCAmelCase : str = clip
else:
_lowerCAmelCase : int = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
_lowerCAmelCase : Optional[int] = ProcessorGradientFlow(device=self.device)
_lowerCAmelCase : Any = iterations
_lowerCAmelCase : List[Any] = lr
_lowerCAmelCase : Tuple = log
_lowerCAmelCase : List[str] = make_grid
_lowerCAmelCase : int = return_val
_lowerCAmelCase : Dict = quantize
_lowerCAmelCase : Any = self.vqgan.decoder.z_shape
def snake_case__ ( self, __a=None, __a=None, __a=5, __a=True):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = []
if output_path is None:
_lowerCAmelCase : List[Any] = "./animation.gif"
if input_path is None:
_lowerCAmelCase : str = self.save_path
_lowerCAmelCase : str = sorted(glob(input_path + "/*"))
if not len(__a):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__a) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
_lowerCAmelCase : Optional[int] = total_duration / len(__a)
_lowerCAmelCase : Union[str, Any] = [frame_duration] * len(__a)
if extend_frames:
_lowerCAmelCase : Any = 1.5
_lowerCAmelCase : List[str] = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__a))
imageio.mimsave(__a, __a, duration=__a)
print(f"gif saved to {output_path}")
def snake_case__ ( self, __a=None, __a=None):
'''simple docstring'''
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
_lowerCAmelCase : Dict = preprocess(Image.open(__a), target_image_size=256).to(self.device)
_lowerCAmelCase : Dict = preprocess_vqgan(__a)
_lowerCAmelCase , *_lowerCAmelCase : str = self.vqgan.encode(__a)
return z
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.latent.detach().requires_grad_()
_lowerCAmelCase : Dict = base_latent + transform_vector
if self.quantize:
_lowerCAmelCase , *_lowerCAmelCase : List[Any] = self.vqgan.quantize(__a)
else:
_lowerCAmelCase : Any = trans_latent
return self.vqgan.decode(__a)
def snake_case__ ( self, __a, __a, __a=None):
'''simple docstring'''
_lowerCAmelCase : int = self.clip_preprocessor(text=__a, images=__a, return_tensors="pt", padding=__a)
_lowerCAmelCase : Optional[int] = self.clip(**__a)
_lowerCAmelCase : Any = clip_outputs.logits_per_image
if weights is not None:
_lowerCAmelCase : Tuple = similarity_logits * weights
return similarity_logits.sum()
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self._get_clip_similarity(pos_prompts["prompts"], __a, weights=(1 / pos_prompts["weights"]))
if neg_prompts:
_lowerCAmelCase : List[Any] = self._get_clip_similarity(neg_prompts["prompts"], __a, weights=neg_prompts["weights"])
else:
_lowerCAmelCase : Union[str, Any] = torch.tensor([1], device=self.device)
_lowerCAmelCase : List[str] = -torch.log(__a) + torch.log(__a)
return loss
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.randn_like(self.latent, requires_grad=__a, device=self.device)
_lowerCAmelCase : Optional[int] = torch.optim.Adam([vector], lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
_lowerCAmelCase : Any = self._add_vector(__a)
_lowerCAmelCase : Optional[Any] = loop_post_process(__a)
_lowerCAmelCase : Optional[Any] = self._get_CLIP_loss(__a, __a, __a)
print("CLIP loss", __a)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__a)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
wandb.init(reinit=__a, project="face-editor")
wandb.config.update({"Positive Prompts": positive_prompts})
wandb.config.update({"Negative Prompts": negative_prompts})
wandb.config.update({"lr": self.lr, "iterations": self.iterations})
if image_path:
_lowerCAmelCase : str = Image.open(__a)
_lowerCAmelCase : int = image.resize((256, 256))
wandb.log("Original Image", wandb.Image(__a))
def snake_case__ ( self, __a):
'''simple docstring'''
if not prompts:
return []
_lowerCAmelCase : int = []
_lowerCAmelCase : List[str] = []
if isinstance(__a, __a):
_lowerCAmelCase : Union[str, Any] = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__a, (tuple, list)):
_lowerCAmelCase : Optional[Any] = prompt[0]
_lowerCAmelCase : Union[str, Any] = float(prompt[1])
elif ":" in prompt:
_lowerCAmelCase , _lowerCAmelCase : int = prompt.split(":")
_lowerCAmelCase : Optional[Any] = float(__a)
else:
_lowerCAmelCase : Optional[int] = prompt
_lowerCAmelCase : List[Any] = 1.0
processed_prompts.append(__a)
weights.append(__a)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a, device=self.device),
}
def snake_case__ ( self, __a, __a=None, __a=None, __a=True, __a=False, __a=True, __a=True, __a=None, ):
'''simple docstring'''
if image_path:
_lowerCAmelCase : List[Any] = self._get_latent(__a)
else:
_lowerCAmelCase : Any = torch.randn(self.latent_dim, device=self.device)
if self.log:
self._init_logging(__a, __a, __a)
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCAmelCase : int = self.process_prompts(__a)
_lowerCAmelCase : List[str] = self.process_prompts(__a)
if save_final and save_path is None:
_lowerCAmelCase : int = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
if not os.path.exists(__a):
os.makedirs(__a)
else:
_lowerCAmelCase : Tuple = save_path + "_" + get_timestamp()
os.makedirs(__a)
_lowerCAmelCase : Tuple = save_path
_lowerCAmelCase : List[Any] = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__a))
_lowerCAmelCase : int = loop_post_process(__a)
for iter, transformed_img in enumerate(self._optimize_CLIP(__a, __a, __a)):
if show_intermediate:
show_pil(__a)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
if self.log:
wandb.log({"Image": wandb.Image(__a)})
if show_final:
show_pil(__a)
if save_final:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))
| 36
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
_snake_case = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 36
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_snake_case = get_tests_dir("fixtures")
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = mock.Mock()
_lowerCAmelCase : int = 500
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : str = HTTPError
_lowerCAmelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__a) as mock_head:
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def snake_case__ ( self):
'''simple docstring'''
with self.assertRaises(__a):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : int = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
_lowerCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor")
self.assertIsNotNone(__a)
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TOKEN
HfFolder.save_token(__a)
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : str = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="test-image-processor", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="valid_org/test-image-processor-org", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowerCAmelCase : List[str] = CustomImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
_lowerCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=__a)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 36
| 1
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_fnet import FNetTokenizer
else:
_snake_case = None
_snake_case = logging.get_logger(__name__)
_snake_case = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
_snake_case = {
"vocab_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
},
"tokenizer_file": {
"google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json",
"google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json",
},
}
_snake_case = {
"google/fnet-base": 512,
"google/fnet-large": 512,
}
_snake_case = "▁"
class UpperCAmelCase_ ( a):
lowerCamelCase__ = VOCAB_FILES_NAMES
lowerCamelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__ = ['input_ids', 'token_type_ids']
lowerCamelCase__ = FNetTokenizer
def __init__( self, __a=None, __a=None, __a=False, __a=True, __a=True, __a="<unk>", __a="[SEP]", __a="<pad>", __a="[CLS]", __a="[MASK]", **__a, ):
'''simple docstring'''
_lowerCAmelCase : str = (
AddedToken(__a, lstrip=__a, rstrip=__a, normalized=__a)
if isinstance(__a, __a)
else mask_token
)
super().__init__(
__a, tokenizer_file=__a, do_lower_case=__a, remove_space=__a, keep_accents=__a, unk_token=__a, sep_token=__a, pad_token=__a, cls_token=__a, mask_token=__a, **__a, )
_lowerCAmelCase : Optional[int] = do_lower_case
_lowerCAmelCase : int = remove_space
_lowerCAmelCase : Dict = keep_accents
_lowerCAmelCase : Dict = vocab_file
_lowerCAmelCase : Any = False if not self.vocab_file else True
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : List[Any] = [self.sep_token_id]
_lowerCAmelCase : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = [self.sep_token_id]
_lowerCAmelCase : int = [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 snake_case__ ( self, __a, __a = None):
'''simple docstring'''
if not os.path.isdir(__a):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
_lowerCAmelCase : 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,)
| 36
|
import unittest
from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=24, __a=2, __a=6, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=3, __a=None, __a=1000, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : List[str] = batch_size
_lowerCAmelCase : int = seq_length
_lowerCAmelCase : Optional[int] = is_training
_lowerCAmelCase : Dict = use_input_mask
_lowerCAmelCase : List[str] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : List[Any] = num_hidden_layers
_lowerCAmelCase : Optional[Any] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : List[str] = hidden_act
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Any = attention_probs_dropout_prob
_lowerCAmelCase : int = max_position_embeddings
_lowerCAmelCase : Optional[int] = type_vocab_size
_lowerCAmelCase : Optional[Any] = type_sequence_label_size
_lowerCAmelCase : List[str] = initializer_range
_lowerCAmelCase : List[Any] = num_labels
_lowerCAmelCase : Tuple = scope
_lowerCAmelCase : str = range_bbox
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
_lowerCAmelCase : Dict = bbox[i, j, 3]
_lowerCAmelCase : int = bbox[i, j, 1]
_lowerCAmelCase : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_lowerCAmelCase : str = bbox[i, j, 2]
_lowerCAmelCase : List[Any] = bbox[i, j, 0]
_lowerCAmelCase : str = t
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : Dict = None
if self.use_token_type_ids:
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[int] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case__ ( self):
'''simple docstring'''
return LiltConfig(
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, initializer_range=self.initializer_range, )
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = LiltModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(__a, bbox=__a, attention_mask=__a, token_type_ids=__a)
_lowerCAmelCase : str = model(__a, bbox=__a, token_type_ids=__a)
_lowerCAmelCase : List[Any] = model(__a, bbox=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.num_labels
_lowerCAmelCase : Optional[Any] = LiltForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = LiltForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Dict = config_and_inputs
_lowerCAmelCase : List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
return True
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = LiltModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase : Any = type
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : str = LiltModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)
_lowerCAmelCase : Any = torch.tensor([[1, 2]], device=__a)
_lowerCAmelCase : str = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=__a)
# forward pass
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(input_ids=__a, bbox=__a)
_lowerCAmelCase : Optional[int] = torch.Size([1, 2, 768])
_lowerCAmelCase : List[str] = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]], device=__a, )
self.assertTrue(outputs.last_hidden_state.shape, __a)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3], __a, atol=1E-3))
| 36
| 1
|
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = FunnelTokenizer
lowerCamelCase__ = FunnelTokenizerFast
lowerCamelCase__ = True
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : Any = [
"<unk>",
"<cls>",
"<sep>",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
_lowerCAmelCase : str = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
def snake_case__ ( self, **__a):
'''simple docstring'''
return FunnelTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, **__a):
'''simple docstring'''
return FunnelTokenizerFast.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "UNwant\u00E9d,running"
_lowerCAmelCase : Tuple = "unwanted, running"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.tokenizer_class(self.vocab_file)
_lowerCAmelCase : Dict = tokenizer.tokenize("UNwant\u00E9d,running")
self.assertListEqual(__a, ["un", "##want", "##ed", ",", "runn", "##ing"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), [7, 4, 5, 10, 8, 9])
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.get_tokenizers(do_lower_case=__a)
for tokenizer in tokenizers:
_lowerCAmelCase : Optional[Any] = tokenizer("UNwant\u00E9d,running")
_lowerCAmelCase : Union[str, Any] = len(inputs["input_ids"]) - 1
self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len)
_lowerCAmelCase : Any = tokenizer("UNwant\u00E9d,running", "UNwant\u00E9d,running")
self.assertListEqual(inputs["token_type_ids"], [2] + [0] * sentence_len + [1] * sentence_len)
| 36
|
import argparse
import copy
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = {}
with open(_lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_lowerCAmelCase : Tuple = []
_list.append([line.split()[1], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_lowerCAmelCase : str = []
_list.append([line.split()[0], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase ) as f:
_lowerCAmelCase : str = f.read(1 )
_lowerCAmelCase : str = start_node
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Any = start_node
_lowerCAmelCase : str = 0
while visiting not in first_solution:
_lowerCAmelCase : Dict = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCamelCase ) and k[0] not in first_solution:
_lowerCAmelCase : List[str] = k[1]
_lowerCAmelCase : List[Any] = k[0]
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = distance_of_first_solution + int(_lowerCamelCase )
_lowerCAmelCase : str = best_node
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_lowerCAmelCase : Tuple = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = []
for n in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
for kn in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
if n == kn:
continue
_lowerCAmelCase : Optional[int] = copy.deepcopy(_lowerCamelCase )
_lowerCAmelCase : int = kn
_lowerCAmelCase : Dict = n
_lowerCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_lowerCAmelCase : str = _tmp[_tmp.index(_lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_lowerCAmelCase : Optional[Any] = distance + int(i[1] )
_tmp.append(_lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_lowerCAmelCase : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : int = first_solution
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Tuple = distance_of_first_solution
_lowerCAmelCase : Optional[int] = solution
while count <= iters:
_lowerCAmelCase : int = find_neighborhood(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : Dict = neighborhood[index_of_best_solution]
_lowerCAmelCase : int = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Union[str, Any] = False
while not found:
_lowerCAmelCase : Tuple = 0
while i < len(_lowerCamelCase ):
if best_solution[i] != solution[i]:
_lowerCAmelCase : str = best_solution[i]
_lowerCAmelCase : Tuple = solution[i]
break
_lowerCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : Optional[Any] = best_solution[:-1]
_lowerCAmelCase : Tuple = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_lowerCAmelCase : Union[str, Any] = cost
_lowerCAmelCase : List[Any] = solution
else:
_lowerCAmelCase : Optional[Any] = index_of_best_solution + 1
_lowerCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
if len(_lowerCamelCase ) >= size:
tabu_list.pop(0 )
_lowerCAmelCase : int = count + 1
return best_solution_ever, best_cost
def A ( _lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase : int = generate_neighbours(args.File )
_lowerCAmelCase , _lowerCAmelCase : List[str] = generate_first_solution(
args.File , _lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = tabu_search(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 36
| 1
|
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_trajectory_transformer": [
"TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TrajectoryTransformerConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
"TrajectoryTransformerModel",
"TrajectoryTransformerPreTrainedModel",
"load_tf_weights_in_trajectory_transformer",
]
if TYPE_CHECKING:
from .configuration_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TrajectoryTransformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_trajectory_transformer import (
TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TrajectoryTransformerModel,
TrajectoryTransformerPreTrainedModel,
load_tf_weights_in_trajectory_transformer,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 36
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = BartphoTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : str = ["▁This", "▁is", "▁a", "▁t", "est"]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[Any] = {"unk_token": "<unk>"}
_lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
with open(self.monolingual_vocab_file, "w", encoding="utf-8") as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n")
_lowerCAmelCase : Optional[Any] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BartphoTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "This is a là test"
_lowerCAmelCase : Optional[int] = "This is a<unk><unk> test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
_lowerCAmelCase : List[Any] = "This is a là test"
_lowerCAmelCase : str = "▁This ▁is ▁a ▁l à ▁t est".split()
_lowerCAmelCase : str = tokenizer.tokenize(__a)
self.assertListEqual(__a, __a)
_lowerCAmelCase : Tuple = tokens + [tokenizer.unk_token]
_lowerCAmelCase : List[str] = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), __a)
| 36
| 1
|
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = GPTSwaTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase : Union[str, Any] = GPTSwaTokenizer(__a, eos_token="<unk>", bos_token="<unk>", pad_token="<unk>")
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : List[str] = "This is a test"
_lowerCAmelCase : List[Any] = "This is a test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = "<s>"
_lowerCAmelCase : List[Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a), __a)
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a), __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = list(self.get_tokenizer().get_vocab().keys())
self.assertEqual(vocab_keys[0], "<unk>")
self.assertEqual(vocab_keys[1], "<s>")
self.assertEqual(vocab_keys[-1], "j")
self.assertEqual(len(__a), 2000)
def snake_case__ ( self):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size, 2000)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = GPTSwaTokenizer(__a)
_lowerCAmelCase : Optional[int] = tokenizer.tokenize("This is a test")
self.assertListEqual(__a, ["▁This", "▁is", "▁a", "▁t", "est"])
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), [465, 287, 265, 631, 842])
_lowerCAmelCase : Optional[Any] = tokenizer.tokenize("I was born in 92000, and this is falsé.")
# fmt: off
self.assertListEqual(
__a, ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."], )
# fmt: on
_lowerCAmelCase : str = tokenizer.convert_tokens_to_ids(__a)
self.assertListEqual(
__a, [262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260], )
_lowerCAmelCase : Any = tokenizer.convert_ids_to_tokens(__a)
# fmt: off
self.assertListEqual(
__a, ["▁I", "▁was", "▁bor", "n", "▁in", "▁", "<0x39>", "2", "0", "0", "0", ",", "▁and", "▁this", "▁is", "▁f", "al", "s", "<0xC3>", "<0xA9>", "."])
# fmt: on
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = GPTSwaTokenizer(__a)
_lowerCAmelCase : Optional[int] = ["This is a test", "I was born in 92000, and this is falsé."]
_lowerCAmelCase : Optional[Any] = [
[465, 287, 265, 631, 842],
[262, 272, 1525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
]
# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
for text, expected_ids in zip(__a, __a):
self.assertListEqual(tokenizer.encode_fast(__a), __a)
# Test that decode_fast returns the input text
for text, token_ids in zip(__a, __a):
self.assertEqual(tokenizer.decode_fast(__a), __a)
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = [
"<|python|>def fibonacci(n)\n if n < 0:\n print('Incorrect input')",
"Hey there, how are you doing this fine day?",
"This is a text with a trailing spaces followed by a dot .",
"Häj sväjs lillebrör! =)",
"Det är inget fel på Mr. Cool",
]
# fmt: off
_lowerCAmelCase : Union[str, Any] = {"input_ids": [[6_3423, 5, 6811, 1_4954, 282, 816, 3821, 6_3466, 6_3425, 6_3462, 18, 6_3978, 678, 301, 1320, 6_3423, 6_3455, 6_3458, 18, 6_3982, 4246, 3940, 1901, 4_7789, 5547, 1_8994], [1_9630, 1100, 6_3446, 1342, 633, 544, 4488, 593, 5102, 2416, 6_3495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1652, 428, 268, 1936, 515, 268, 5_8593, 2_2413, 9106, 546, 268, 3_3213, 6_3979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [5_5130, 6_3450, 924, 6_3449, 2249, 4062, 1558, 318, 6_3504, 2_1498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2827, 2559, 332, 6575, 6_3443, 2_6801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "token_type_ids": [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__a, model_name="AI-Sweden/gpt-sw3-126m", sequences=__a, )
| 36
|
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
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def constraint_to_multiple_of(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0 , _lowerCamelCase=None ):
_lowerCAmelCase : Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowerCAmelCase : Optional[int] = math.floor(val / multiple ) * multiple
if x < min_val:
_lowerCAmelCase : List[str] = math.ceil(val / multiple ) * multiple
return x
_lowerCAmelCase : Union[str, Any] = (output_size, output_size) if isinstance(_lowerCamelCase , _lowerCamelCase ) else output_size
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = get_image_size(_lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = output_size
# determine new height and width
_lowerCAmelCase : List[Any] = output_height / input_height
_lowerCAmelCase : Any = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowerCAmelCase : Union[str, Any] = scale_width
else:
# fit height
_lowerCAmelCase : Union[str, Any] = scale_height
_lowerCAmelCase : List[str] = constraint_to_multiple_of(scale_height * input_height , multiple=_lowerCamelCase )
_lowerCAmelCase : Dict = constraint_to_multiple_of(scale_width * input_width , multiple=_lowerCamelCase )
return (new_height, new_width)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['pixel_values']
def __init__( self, __a = True, __a = None, __a = PILImageResampling.BILINEAR, __a = False, __a = 1, __a = True, __a = 1 / 255, __a = True, __a = None, __a = None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = size if size is not None else {"height": 384, "width": 384}
_lowerCAmelCase : Optional[int] = get_size_dict(__a)
_lowerCAmelCase : Optional[Any] = do_resize
_lowerCAmelCase : Dict = size
_lowerCAmelCase : Any = keep_aspect_ratio
_lowerCAmelCase : str = ensure_multiple_of
_lowerCAmelCase : str = resample
_lowerCAmelCase : Dict = do_rescale
_lowerCAmelCase : Optional[int] = rescale_factor
_lowerCAmelCase : Dict = do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self, __a, __a, __a = False, __a = 1, __a = PILImageResampling.BICUBIC, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : List[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()}")
_lowerCAmelCase : List[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 snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return rescale(__a, scale=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return normalize(__a, mean=__a, std=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = ChannelDimension.FIRST, **__a, ):
'''simple docstring'''
_lowerCAmelCase : int = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : List[Any] = size if size is not None else self.size
_lowerCAmelCase : str = get_size_dict(__a)
_lowerCAmelCase : Dict = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowerCAmelCase : Any = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowerCAmelCase : int = resample if resample is not None else self.resample
_lowerCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase : List[str] = image_std if image_std is not None else self.image_std
_lowerCAmelCase : Optional[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_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.
_lowerCAmelCase : List[Any] = [to_numpy_array(__a) for image in images]
if do_resize:
_lowerCAmelCase : Any = [self.resize(image=__a, size=__a, resample=__a) for image in images]
if do_rescale:
_lowerCAmelCase : List[str] = [self.rescale(image=__a, scale=__a) for image in images]
if do_normalize:
_lowerCAmelCase : Dict = [self.normalize(image=__a, mean=__a, std=__a) for image in images]
_lowerCAmelCase : List[str] = [to_channel_dimension_format(__a, __a) for image in images]
_lowerCAmelCase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=__a, tensor_type=__a)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = 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):
_lowerCAmelCase : List[Any] = target_sizes.numpy()
_lowerCAmelCase : Dict = []
for idx in range(len(__a)):
_lowerCAmelCase : int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=__a)
_lowerCAmelCase : int = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(__a)
else:
_lowerCAmelCase : Dict = logits.argmax(dim=1)
_lowerCAmelCase : str = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 36
| 1
|
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
_snake_case = logging.getLogger(__name__)
class UpperCAmelCase_ ( a):
def __init__( self, __a=-1):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = label_idx
def snake_case__ ( self, __a, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Dict = mode.value
_lowerCAmelCase : Optional[int] = os.path.join(__a, f"{mode}.txt")
_lowerCAmelCase : Optional[int] = 1
_lowerCAmelCase : str = []
with open(__a, encoding="utf-8") as f:
_lowerCAmelCase : Any = []
_lowerCAmelCase : int = []
for line in f:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=f"{mode}-{guid_index}", words=__a, labels=__a))
guid_index += 1
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : Any = []
else:
_lowerCAmelCase : int = line.split(" ")
words.append(splits[0])
if len(__a) > 1:
labels.append(splits[self.label_idx].replace("\n", ""))
else:
# Examples could have no label for mode = "test"
labels.append("O")
if words:
examples.append(InputExample(guid=f"{mode}-{guid_index}", words=__a, labels=__a))
return examples
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Any = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-") or line == "" or line == "\n":
writer.write(__a)
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
_lowerCAmelCase : int = line.split()[0] + " " + preds_list[example_id].pop(0) + "\n"
writer.write(__a)
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'.", line.split()[0])
def snake_case__ ( self, __a):
'''simple docstring'''
if path:
with open(__a, "r") as f:
_lowerCAmelCase : Dict = f.read().splitlines()
if "O" not in labels:
_lowerCAmelCase : List[Any] = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class UpperCAmelCase_ ( a):
def __init__( self):
'''simple docstring'''
super().__init__(label_idx=-2)
def snake_case__ ( self, __a):
'''simple docstring'''
if path:
with open(__a, "r") as f:
_lowerCAmelCase : Any = f.read().splitlines()
if "O" not in labels:
_lowerCAmelCase : Optional[Any] = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : int = mode.value
_lowerCAmelCase : List[str] = os.path.join(__a, f"{mode}.txt")
_lowerCAmelCase : Dict = 1
_lowerCAmelCase : Optional[int] = []
with open(__a, encoding="utf-8") as f:
for sentence in parse_incr(__a):
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Union[str, Any] = []
for token in sentence:
words.append(token["form"])
labels.append(token["upos"])
assert len(__a) == len(__a)
if words:
examples.append(InputExample(guid=f"{mode}-{guid_index}", words=__a, labels=__a))
guid_index += 1
return examples
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = 0
for sentence in parse_incr(__a):
_lowerCAmelCase : List[Any] = preds_list[example_id]
_lowerCAmelCase : Union[str, Any] = ""
for token in sentence:
out += f"{token['form']} ({token['upos']}|{s_p.pop(0)}) "
out += "\n"
writer.write(__a)
example_id += 1
def snake_case__ ( self, __a):
'''simple docstring'''
if path:
with open(__a, "r") as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 36
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = "huggingface/label-files"
_lowerCAmelCase : int = "imagenet-1k-id2label.json"
_lowerCAmelCase : Tuple = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
_lowerCAmelCase : Tuple = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_lowerCAmelCase : Tuple = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
_lowerCAmelCase : Optional[int] = BitConfig(
conv_layer=_lowerCamelCase , num_labels=1_000 , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase , )
return config
def A ( _lowerCamelCase ):
'''simple docstring'''
if "stem.conv" in name:
_lowerCAmelCase : List[str] = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
_lowerCAmelCase : Any = name.replace("blocks" , "layers" )
if "head.fc" in name:
_lowerCAmelCase : Optional[Any] = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
_lowerCAmelCase : Any = "bit." + name
if "bit" not in name and "classifier" not in name:
_lowerCAmelCase : Dict = "bit.encoder." + name
return name
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCAmelCase : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = get_config(_lowerCamelCase )
# load original model from timm
_lowerCAmelCase : int = create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model
_lowerCAmelCase : Any = timm_model.state_dict()
for key in state_dict.copy().keys():
_lowerCAmelCase : Dict = state_dict.pop(_lowerCamelCase )
_lowerCAmelCase : Tuple = val.squeeze() if "head" in key else val
# load HuggingFace model
_lowerCAmelCase : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
_lowerCAmelCase : Dict = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
_lowerCAmelCase : Optional[int] = transform.transforms
_lowerCAmelCase : Tuple = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
_lowerCAmelCase : Tuple = BitImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_lowerCAmelCase : Optional[int] = prepare_img()
_lowerCAmelCase : Any = transform(_lowerCamelCase ).unsqueeze(0 )
_lowerCAmelCase : Optional[int] = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
_lowerCAmelCase : Tuple = model(_lowerCamelCase )
_lowerCAmelCase : str = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
_lowerCAmelCase : Union[str, Any] = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
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 to push the model to the hub.",
)
_snake_case = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36
| 1
|
def A ( _lowerCamelCase ):
'''simple docstring'''
if n == 1 or not isinstance(_lowerCamelCase , _lowerCamelCase ):
return 0
elif n == 2:
return 1
else:
_lowerCAmelCase : Dict = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : List[str] = 2
while digits < n:
index += 1
_lowerCAmelCase : int = len(str(fibonacci(_lowerCamelCase ) ) )
return index
def A ( _lowerCamelCase = 1_000 ):
'''simple docstring'''
return fibonacci_digits_index(_lowerCamelCase )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 36
|
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCAmelCase_ ( a , a):
lowerCamelCase__ = 'swin'
lowerCamelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self, __a=224, __a=4, __a=3, __a=96, __a=[2, 2, 6, 2], __a=[3, 6, 12, 24], __a=7, __a=4.0, __a=True, __a=0.0, __a=0.0, __a=0.1, __a="gelu", __a=False, __a=0.02, __a=1E-5, __a=32, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = image_size
_lowerCAmelCase : Union[str, Any] = patch_size
_lowerCAmelCase : Tuple = num_channels
_lowerCAmelCase : List[Any] = embed_dim
_lowerCAmelCase : Tuple = depths
_lowerCAmelCase : Optional[Any] = len(__a)
_lowerCAmelCase : int = num_heads
_lowerCAmelCase : int = window_size
_lowerCAmelCase : int = mlp_ratio
_lowerCAmelCase : List[Any] = qkv_bias
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = drop_path_rate
_lowerCAmelCase : int = hidden_act
_lowerCAmelCase : Tuple = use_absolute_embeddings
_lowerCAmelCase : Optional[int] = layer_norm_eps
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Tuple = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCAmelCase : List[str] = int(embed_dim * 2 ** (len(__a) - 1))
_lowerCAmelCase : List[Any] = ["stem"] + [f"stage{idx}" for idx in range(1, len(__a) + 1)]
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
| 36
| 1
|
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
_snake_case = importlib.util.find_spec("s3fs") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
_snake_case = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(f'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def A ( _lowerCamelCase ):
'''simple docstring'''
if "://" in dataset_path:
_lowerCAmelCase : Any = dataset_path.split("://" )[1]
return dataset_path
def A ( _lowerCamelCase ):
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = not is_remote_filesystem(_lowerCamelCase )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_lowerCamelCase ) , fs._strip_protocol(_lowerCamelCase ) )
else:
fs.mv(_lowerCamelCase , _lowerCamelCase , recursive=_lowerCamelCase )
def A ( ):
'''simple docstring'''
if hasattr(fsspec.asyn , "reset_lock" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
_lowerCAmelCase : Any = None
_lowerCAmelCase : int = None
_lowerCAmelCase : List[Any] = threading.Lock()
| 36
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 36
| 1
|
def A ( _lowerCamelCase = 50 ):
'''simple docstring'''
_lowerCAmelCase : Dict = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 36
|
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_snake_case = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if got_ver is None or want_ver is None:
raise ValueError(
F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
F" reinstalling {pkg}." )
if not ops[op](version.parse(_lowerCamelCase ) , version.parse(_lowerCamelCase ) ):
raise ImportError(
F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}" )
def A ( _lowerCamelCase , _lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase : List[str] = F"\n{hint}" if hint is not None else ""
# non-versioned check
if re.match(r"^[\w_\-\d]+$" , _lowerCamelCase ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = requirement, None, None
else:
_lowerCAmelCase : Optional[int] = re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
F" got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Dict = match[0]
_lowerCAmelCase : Any = want_full.split("," ) # there could be multiple requirements
_lowerCAmelCase : Optional[int] = {}
for w in want_range:
_lowerCAmelCase : Any = re.findall(r"^([\s!=<>]{1,2})(.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
F" but got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Tuple = match[0]
_lowerCAmelCase : Union[str, Any] = want_ver
if op not in ops:
raise ValueError(F"{requirement}: need one of {list(ops.keys() )}, but got {op}" )
# special case
if pkg == "python":
_lowerCAmelCase : Tuple = ".".join([str(_lowerCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return
# check if any version is installed
try:
_lowerCAmelCase : Any = importlib.metadata.version(_lowerCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F"The '{requirement}' distribution was not found and is required by this application. {hint}" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
return require_version(_lowerCamelCase , _lowerCamelCase )
| 36
| 1
|
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
if not is_sharded:
_lowerCAmelCase : List[Any] = os.path.abspath(_lowerCamelCase )
logger.info(F"Loading PyTorch weights from {pt_path}" )
_lowerCAmelCase : str = torch.load(_lowerCamelCase , map_location="cpu" )
logger.info(F"PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters." )
_lowerCAmelCase : List[Any] = convert_pytorch_state_dict_to_flax(_lowerCamelCase , _lowerCamelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
_lowerCAmelCase : Any = convert_pytorch_sharded_state_dict_to_flax(_lowerCamelCase , _lowerCamelCase )
return flax_state_dict
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
'''simple docstring'''
def is_key_or_prefix_key_in_dict(_lowerCamelCase ) -> bool:
return len(set(_lowerCamelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
_lowerCAmelCase : Tuple = pt_tuple_key[:-1] + ("scale",)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
_lowerCAmelCase : Dict = pt_tuple_key[:-1] + ("mean",)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
_lowerCAmelCase : Optional[Any] = pt_tuple_key[:-1] + ("var",)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
_lowerCAmelCase : Any = pt_tuple_key[:-1] + ("embedding",)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCamelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
_lowerCAmelCase : List[str] = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
_lowerCAmelCase : Any = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
_lowerCAmelCase : Any = pt_tuple_key[:-1] + ("kernel",)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCamelCase ):
_lowerCAmelCase : Any = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
_lowerCAmelCase : Dict = pt_tuple_key[:-1] + ("weight",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
_lowerCAmelCase : Dict = pt_tuple_key[:-1] + ("bias",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
_lowerCAmelCase : Tuple = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
_lowerCAmelCase : Any = pt_tuple_key[-2] + "_g"
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
_lowerCAmelCase : Any = pt_tuple_key[-2] + "_v"
if name is not None:
_lowerCAmelCase : List[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = {k: v.numpy() for k, v in pt_state_dict.items()}
_lowerCAmelCase : Optional[Any] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
_lowerCAmelCase : List[Any] = flax_model.params["params"]
else:
_lowerCAmelCase : Any = flax_model.params
_lowerCAmelCase : Union[str, Any] = flatten_dict(_lowerCamelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_lowerCAmelCase : int = flatten_dict(flax_model.params["batch_stats"] )
random_flax_state_dict.update(_lowerCamelCase )
_lowerCAmelCase : List[str] = {}
_lowerCAmelCase : Dict = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
_lowerCAmelCase : List[Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_lowerCAmelCase : List[str] = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
_lowerCAmelCase : Any = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_lowerCAmelCase : List[str] = pt_tuple_key[1:]
# Correctly rename weight parameters
_lowerCAmelCase , _lowerCAmelCase : List[Any] = rename_key_and_reshape_tensor(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# add model prefix if necessary
_lowerCAmelCase : Any = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_lowerCAmelCase : Optional[int] = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
_lowerCAmelCase : Union[str, Any] = jnp.asarray(_lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_lowerCamelCase , _lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
_lowerCAmelCase : Optional[Any] = jnp.asarray(_lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
_lowerCAmelCase : Any = jnp.asarray(_lowerCamelCase )
return unflatten_dict(_lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
import torch
# Load the index
_lowerCAmelCase : int = {}
for shard_file in shard_filenames:
# load using msgpack utils
_lowerCAmelCase : Union[str, Any] = torch.load(_lowerCamelCase )
_lowerCAmelCase : Dict = {k: v.numpy() for k, v in pt_state_dict.items()}
_lowerCAmelCase : Optional[Any] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
_lowerCAmelCase : Optional[int] = flax_model.params["params"]
_lowerCAmelCase : Dict = flatten_dict(_lowerCamelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"] ) )
else:
_lowerCAmelCase : str = flax_model.params
_lowerCAmelCase : Tuple = flatten_dict(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split("." )[0] for k in pt_state_dict.keys()}
)
_lowerCAmelCase : int = (model_prefix in flax_model_params) and (
model_prefix not in {k.split("." )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
_lowerCAmelCase : Dict = tuple(pt_key.split("." ) )
# remove base model prefix if necessary
_lowerCAmelCase : Dict = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
_lowerCAmelCase : List[str] = pt_tuple_key[1:]
# Correctly rename weight parameters
_lowerCAmelCase , _lowerCAmelCase : List[str] = rename_key_and_reshape_tensor(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# add model prefix if necessary
_lowerCAmelCase : Any = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
_lowerCAmelCase : str = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
F"PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape "
F"{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}." )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
_lowerCAmelCase : Any = jnp.asarray(_lowerCamelCase )
continue
if "var" in flax_key[-1]:
_lowerCAmelCase : Tuple = jnp.asarray(_lowerCamelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_lowerCamelCase , _lowerCamelCase )
continue
# also add unexpected weight so that warning is thrown
_lowerCAmelCase : Optional[int] = jnp.asarray(_lowerCamelCase )
else:
# also add unexpected weight so that warning is thrown
_lowerCAmelCase : Tuple = jnp.asarray(_lowerCamelCase )
return unflatten_dict(_lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = os.path.abspath(_lowerCamelCase )
logger.info(F"Loading Flax weights from {flax_checkpoint_path}" )
# import correct flax class
_lowerCAmelCase : Dict = getattr(_lowerCamelCase , "Flax" + model.__class__.__name__ )
# load flax weight dict
with open(_lowerCamelCase , "rb" ) as state_f:
try:
_lowerCAmelCase : Optional[Any] = from_bytes(_lowerCamelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(F"Unable to convert {flax_checkpoint_path} to Flax deserializable object. " )
return load_flax_weights_in_pytorch_model(_lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
try:
import torch # noqa: F401
except ImportError:
logger.error(
"Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
" https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
" instructions." )
raise
# check if we have bf16 weights
_lowerCAmelCase : List[Any] = flatten_dict(jax.tree_util.tree_map(lambda _lowerCamelCase : x.dtype == jnp.bfloataa , _lowerCamelCase ) ).values()
if any(_lowerCamelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
"Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
"before loading those in PyTorch model." )
_lowerCAmelCase : Tuple = jax.tree_util.tree_map(
lambda _lowerCamelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _lowerCamelCase )
_lowerCAmelCase : Optional[int] = flatten_dict(_lowerCamelCase )
_lowerCAmelCase : Any = pt_model.state_dict()
_lowerCAmelCase : Tuple = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split("." )[0] for k in pt_model_dict.keys()}
)
_lowerCAmelCase : Optional[Any] = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split("." )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
_lowerCAmelCase : Dict = []
_lowerCAmelCase : Optional[Any] = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
_lowerCAmelCase : int = flax_key_tuple[0] == pt_model.base_model_prefix
_lowerCAmelCase : List[str] = ".".join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
_lowerCAmelCase : Dict = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
_lowerCAmelCase : int = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCamelCase ) not in pt_model_dict:
# conv layer
_lowerCAmelCase : Optional[Any] = flax_key_tuple[:-1] + ("weight",)
_lowerCAmelCase : List[Any] = jnp.transpose(_lowerCamelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCamelCase ) not in pt_model_dict:
# linear layer
_lowerCAmelCase : Any = flax_key_tuple[:-1] + ("weight",)
_lowerCAmelCase : Any = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
_lowerCAmelCase : Optional[int] = flax_key_tuple[:-1] + ("weight",)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
_lowerCAmelCase : Union[str, Any] = flax_key_tuple[:-1] + ("running_mean",)
elif "var" in flax_key_tuple[-1]:
_lowerCAmelCase : Dict = flax_key_tuple[:-1] + ("running_var",)
if "batch_stats" in flax_state:
_lowerCAmelCase : str = ".".join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
_lowerCAmelCase : Optional[int] = ".".join(_lowerCamelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
_lowerCAmelCase : Dict = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
_lowerCAmelCase : Dict = key.split("." )
_lowerCAmelCase : List[Any] = None
if key_components[-3::2] == ["parametrizations", "original0"]:
_lowerCAmelCase : List[Any] = key_components[-2] + "_g"
elif key_components[-3::2] == ["parametrizations", "original1"]:
_lowerCAmelCase : str = key_components[-2] + "_v"
if name is not None:
_lowerCAmelCase : Any = key_components[:-3] + [name]
_lowerCAmelCase : Union[str, Any] = ".".join(_lowerCamelCase )
_lowerCAmelCase : Dict = key
if flax_key in special_pt_names:
_lowerCAmelCase : Optional[int] = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
F"Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected "
F"to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}." )
else:
# add weight to pytorch dict
_lowerCAmelCase : str = np.asarray(_lowerCamelCase ) if not isinstance(_lowerCamelCase , np.ndarray ) else flax_tensor
_lowerCAmelCase : Union[str, Any] = torch.from_numpy(_lowerCamelCase )
# remove from missing keys
missing_keys.remove(_lowerCamelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_lowerCamelCase )
pt_model.load_state_dict(_lowerCamelCase )
# re-transform missing_keys to list
_lowerCAmelCase : Dict = list(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
logger.warning(
"Some weights of the Flax model were not used when initializing the PyTorch model"
F" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"
F" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"
" (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
F" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"
" to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
" FlaxBertForSequenceClassification model)." )
else:
logger.warning(F"All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n" )
if len(_lowerCamelCase ) > 0:
logger.warning(
F"Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"
F" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"
" use it for predictions and inference." )
else:
logger.warning(
F"All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"
"If your task is similar to the task the model of the checkpoint was trained on, "
F"you can already use {pt_model.__class__.__name__} for predictions without further training." )
return pt_model
| 36
|
import argparse
from collections import defaultdict
import yaml
_snake_case = "docs/source/en/_toctree.yml"
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = defaultdict(_lowerCamelCase )
_lowerCAmelCase : Any = []
_lowerCAmelCase : List[str] = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"local": doc["local"], "title": doc["title"]} )
else:
new_doc_list.append(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = new_doc_list
_lowerCAmelCase : List[Any] = [key for key, value in counts.items() if value > 1]
_lowerCAmelCase : str = []
for duplicate_key in duplicates:
_lowerCAmelCase : List[str] = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
if len(_lowerCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
"others." )
# Only add this once
new_doc.append({"local": duplicate_key, "title": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1] )
_lowerCAmelCase : Optional[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(_lowerCamelCase ) > 1:
raise ValueError("{doc_list} has two 'overview' docs which is not allowed." )
overview_doc.extend(_lowerCamelCase )
# Sort
return overview_doc
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : int = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : List[str] = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : Union[str, Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase : Optional[Any] = api_doc[scheduler_idx]["sections"]
_lowerCAmelCase : Optional[Any] = clean_doc_toc(_lowerCamelCase )
_lowerCAmelCase : int = False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase : List[Any] = True
if overwrite:
_lowerCAmelCase : Dict = new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase : Tuple = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : int = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : List[str] = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase : Dict = False
_lowerCAmelCase : Optional[int] = api_doc[pipeline_idx]["sections"]
_lowerCAmelCase : Tuple = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase : List[Any] = pipeline_doc["section"]
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if overwrite:
_lowerCAmelCase : Optional[Any] = new_sub_pipeline_doc
new_pipeline_docs.append(_lowerCamelCase )
# sort overall pipeline doc
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase : Dict = True
if overwrite:
_lowerCAmelCase : Optional[int] = new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase : Optional[int] = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_snake_case = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 36
| 1
|
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = tempfile.mkdtemp()
_lowerCAmelCase : int = 8
# DPR tok
_lowerCAmelCase : List[str] = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
_lowerCAmelCase : Dict = os.path.join(self.tmpdirname, "dpr_tokenizer")
os.makedirs(__a, exist_ok=__a)
_lowerCAmelCase : str = os.path.join(__a, DPR_VOCAB_FILES_NAMES["vocab_file"])
with open(self.vocab_file, "w", encoding="utf-8") as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in vocab_tokens]))
# BART tok
_lowerCAmelCase : Dict = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
_lowerCAmelCase : Optional[int] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[int] = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
_lowerCAmelCase : Any = {"unk_token": "<unk>"}
_lowerCAmelCase : Union[str, Any] = os.path.join(self.tmpdirname, "bart_tokenizer")
os.makedirs(__a, exist_ok=__a)
_lowerCAmelCase : str = os.path.join(__a, BART_VOCAB_FILES_NAMES["vocab_file"])
_lowerCAmelCase : Tuple = os.path.join(__a, BART_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))
def snake_case__ ( self):
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname, "dpr_tokenizer"))
def snake_case__ ( self):
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname, "bart_tokenizer"))
def snake_case__ ( self):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
@require_tokenizers
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = os.path.join(self.tmpdirname, "rag_tokenizer")
_lowerCAmelCase : str = RagConfig(question_encoder=DPRConfig().to_dict(), generator=BartConfig().to_dict())
_lowerCAmelCase : Dict = RagTokenizer(question_encoder=self.get_dpr_tokenizer(), generator=self.get_bart_tokenizer())
rag_config.save_pretrained(__a)
rag_tokenizer.save_pretrained(__a)
_lowerCAmelCase : Optional[int] = RagTokenizer.from_pretrained(__a, config=__a)
self.assertIsInstance(new_rag_tokenizer.question_encoder, __a)
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab(), rag_tokenizer.question_encoder.get_vocab())
self.assertIsInstance(new_rag_tokenizer.generator, __a)
self.assertEqual(new_rag_tokenizer.generator.get_vocab(), rag_tokenizer.generator.get_vocab())
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = RagTokenizer.from_pretrained("facebook/rag-token-nq")
_lowerCAmelCase : int = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
_lowerCAmelCase : Tuple = tokenizer(__a)
self.assertIsNotNone(__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
_lowerCAmelCase : Optional[Any] = [
"who got the first nobel prize in physics",
"when is the next deadpool movie being released",
"which mode is used for short wave broadcast service",
"who is the owner of reading football club",
"when is the next scandal episode coming out",
"when is the last time the philadelphia won the superbowl",
"what is the most current adobe flash player version",
"how many episodes are there in dragon ball z",
"what is the first step in the evolution of the eye",
"where is gall bladder situated in human body",
"what is the main mineral in lithium batteries",
"who is the president of usa right now",
"where do the greasers live in the outsiders",
"panda is a national animal of which country",
"what is the name of manchester united stadium",
]
_lowerCAmelCase : Dict = tokenizer(__a)
self.assertIsNotNone(__a)
| 36
|
def A ( _lowerCamelCase , _lowerCamelCase ):
'''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()
| 36
| 1
|
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class UpperCAmelCase_ ( a , a):
@register_to_config
def __init__( self, *,
__a = 4, __a = 768, __a, __a, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : str = nn.Parameter(torch.zeros(__a))
# parameters for additional clip time embeddings
_lowerCAmelCase : List[str] = nn.Linear(__a, __a)
_lowerCAmelCase : Tuple = nn.Linear(__a, __a)
# parameters for encoder hidden states
_lowerCAmelCase : str = clip_extra_context_tokens
_lowerCAmelCase : Union[str, Any] = nn.Linear(
__a, self.clip_extra_context_tokens * cross_attention_dim)
_lowerCAmelCase : int = nn.Linear(__a, __a)
_lowerCAmelCase : Optional[Any] = nn.LayerNorm(__a)
def snake_case__ ( self, *, __a, __a, __a, __a):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
_lowerCAmelCase : Union[str, Any] = image_embeddings.shape[0]
_lowerCAmelCase : Optional[int] = self.learned_classifier_free_guidance_embeddings.unsqueeze(0)
_lowerCAmelCase : List[Any] = classifier_free_guidance_embeddings.expand(
__a, -1)
_lowerCAmelCase : Dict = torch.cat([classifier_free_guidance_embeddings, image_embeddings], dim=0)
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
_lowerCAmelCase : Any = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
_lowerCAmelCase : str = self.embedding_proj(__a)
_lowerCAmelCase : Optional[int] = self.clip_image_embeddings_project_to_time_embeddings(__a)
_lowerCAmelCase : Optional[Any] = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
_lowerCAmelCase : List[Any] = self.clip_extra_context_tokens_proj(__a)
_lowerCAmelCase : Tuple = clip_extra_context_tokens.reshape(__a, -1, self.clip_extra_context_tokens)
_lowerCAmelCase : str = clip_extra_context_tokens.permute(0, 2, 1)
_lowerCAmelCase : Union[str, Any] = self.encoder_hidden_states_proj(__a)
_lowerCAmelCase : Dict = self.text_encoder_hidden_states_norm(__a)
_lowerCAmelCase : Dict = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states], dim=1)
return text_encoder_hidden_states, additive_clip_time_embeddings
| 36
|
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
_snake_case = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class UpperCAmelCase_ ( a):
def __init__( self, __a = 101):
'''simple docstring'''
_lowerCAmelCase : str = length
def __len__( self):
'''simple docstring'''
return self.length
def __getitem__( self, __a):
'''simple docstring'''
return i
class UpperCAmelCase_ :
def __call__( self, __a):
'''simple docstring'''
return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}
class UpperCAmelCase_ ( nn.Module):
def __init__( self):
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
_lowerCAmelCase : str = nn.Linear(120, 80)
def snake_case__ ( self, __a, __a=None):
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
class UpperCAmelCase_ ( a):
@require_torch_neuroncore
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = f"--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : List[Any] = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class UpperCAmelCase_ ( a):
@require_torch_multi_gpu
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = f"--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Any = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : Any = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
_snake_case = HfArgumentParser((TrainingArguments,))
_snake_case = parser.parse_args_into_dataclasses()[0]
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
_snake_case = DummyDataset(dataset_length)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = list(range(len(_lowerCamelCase ) ) )
_lowerCAmelCase : Union[str, Any] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}" )
return {"success": success}
_snake_case = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = 2
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = None
| 36
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|
from __future__ import annotations
import bisect
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : int = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Optional[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
_lowerCAmelCase : Union[str, Any] = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : str = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Tuple = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
_lowerCAmelCase : Dict = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_left(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_right(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
while left <= right:
_lowerCAmelCase : int = left + (right - left) // 2
_lowerCAmelCase : int = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
_lowerCAmelCase : str = midpoint - 1
else:
_lowerCAmelCase : Any = midpoint + 1
return None
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = bisect.bisect_left(_lowerCamelCase , _lowerCamelCase )
if index != len(_lowerCamelCase ) and sorted_collection[index] == item:
return index
return None
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if right < left:
return None
_lowerCAmelCase : Optional[int] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , midpoint + 1 , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = input("Enter numbers separated by comma:\n").strip()
_snake_case = sorted(int(item) for item in user_input.split(","))
_snake_case = int(input("Enter a single number to be found in the list:\n"))
_snake_case = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
| 36
|
from __future__ import annotations
import bisect
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : int = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Optional[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
_lowerCAmelCase : Union[str, Any] = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : str = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Tuple = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
_lowerCAmelCase : Dict = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_left(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_right(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
while left <= right:
_lowerCAmelCase : int = left + (right - left) // 2
_lowerCAmelCase : int = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
_lowerCAmelCase : str = midpoint - 1
else:
_lowerCAmelCase : Any = midpoint + 1
return None
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = bisect.bisect_left(_lowerCamelCase , _lowerCamelCase )
if index != len(_lowerCamelCase ) and sorted_collection[index] == item:
return index
return None
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if right < left:
return None
_lowerCAmelCase : Optional[int] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , midpoint + 1 , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = input("Enter numbers separated by comma:\n").strip()
_snake_case = sorted(int(item) for item in user_input.split(","))
_snake_case = int(input("Enter a single number to be found in the list:\n"))
_snake_case = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
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|
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
move_disk(_lowerCamelCase , _lowerCamelCase )
move_tower(height - 1 , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
print("moving disk from" , _lowerCamelCase , "to" , _lowerCamelCase )
def A ( ):
'''simple docstring'''
_lowerCAmelCase : int = int(input("Height of hanoi: " ).strip() )
move_tower(_lowerCamelCase , "A" , "B" , "C" )
if __name__ == "__main__":
main()
| 36
|
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_lowerCAmelCase : Optional[int] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 512
_lowerCAmelCase : Union[str, Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : int = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : str = np.abs(np.fft.fft(_lowerCamelCase ) )
_lowerCAmelCase : Union[str, Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_lowerCAmelCase : List[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_lowerCamelCase )
plt.show()
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 512
_lowerCAmelCase : Optional[Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : str = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : Optional[Any] = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 36
| 1
|
import math
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = [True] * n
_lowerCAmelCase : Union[str, Any] = False
_lowerCAmelCase : Optional[Any] = False
_lowerCAmelCase : Tuple = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
_lowerCAmelCase : str = i * 2
while index < n:
_lowerCAmelCase : List[str] = False
_lowerCAmelCase : Optional[int] = index + i
_lowerCAmelCase : Any = [2]
for i in range(3 , _lowerCamelCase , 2 ):
if is_prime[i]:
primes.append(_lowerCamelCase )
return primes
def A ( _lowerCamelCase = 999_966_663_333 ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = math.floor(math.sqrt(_lowerCamelCase ) ) + 100
_lowerCAmelCase : str = prime_sieve(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = 0
_lowerCAmelCase : List[str] = 0
_lowerCAmelCase : List[Any] = primes[prime_index]
while (last_prime**2) <= limit:
_lowerCAmelCase : Tuple = primes[prime_index + 1]
_lowerCAmelCase : List[Any] = last_prime**2
_lowerCAmelCase : Dict = next_prime**2
# Get numbers divisible by lps(current)
_lowerCAmelCase : List[Any] = lower_bound + last_prime
while upper_bound > current <= limit:
matches_sum += current
current += last_prime
# Reset the upper_bound
while (upper_bound - next_prime) > limit:
upper_bound -= next_prime
# Add the numbers divisible by ups(current)
_lowerCAmelCase : List[Any] = upper_bound - next_prime
while current > lower_bound:
matches_sum += current
current -= next_prime
# Remove the numbers divisible by both ups and lps
_lowerCAmelCase : Optional[int] = 0
while upper_bound > current <= limit:
if current <= lower_bound:
# Increment the current number
current += last_prime * next_prime
continue
if current > limit:
break
# Remove twice since it was added by both ups and lps
matches_sum -= current * 2
# Increment the current number
current += last_prime * next_prime
# Setup for next pair
_lowerCAmelCase : Optional[int] = next_prime
prime_index += 1
return matches_sum
if __name__ == "__main__":
print(solution())
| 36
|
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
_lowerCAmelCase : List[str] = gray_code_sequence_string(_lowerCamelCase )
#
# convert them to integers
for i in range(len(_lowerCamelCase ) ):
_lowerCAmelCase : List[str] = int(sequence[i] , 2 )
return sequence
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
_lowerCAmelCase : List[Any] = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
_lowerCAmelCase : Optional[int] = gray_code_sequence_string(bit_count - 1 )
_lowerCAmelCase : str = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
_lowerCAmelCase : Dict = "0" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
_lowerCAmelCase : Optional[Any] = "1" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
| 1
|
import json
import os
import tempfile
from unittest.mock import patch
import torch
from torch.utils.data import DataLoader, TensorDataset
from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment
def A ( ):
'''simple docstring'''
_lowerCAmelCase : List[str] = torch.nn.Linear(2 , 4 )
_lowerCAmelCase : Optional[Any] = torch.optim.AdamW(model.parameters() , lr=1.0 )
_lowerCAmelCase : Optional[Any] = torch.optim.lr_scheduler.OneCycleLR(_lowerCamelCase , max_lr=0.01 , steps_per_epoch=2 , epochs=1 )
_lowerCAmelCase : List[Any] = DataLoader(TensorDataset(torch.tensor([1, 2, 3] ) ) )
_lowerCAmelCase : Optional[int] = DataLoader(TensorDataset(torch.tensor([4, 5, 6] ) ) )
return model, optimizer, scheduler, train_dl, valid_dl
def A ( _lowerCamelCase ):
'''simple docstring'''
return (model.weight.abs().sum() + model.bias.abs().sum()).item()
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = torch.nn.Linear(*tuple(model.weight.T.shape ) ).state_dict()
model.load_state_dict(_lowerCamelCase )
class UpperCAmelCase_ ( a):
@require_cuda
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = Accelerator()
assert PartialState._shared_state["_cpu"] is False
assert PartialState._shared_state["device"].type == "cuda"
with self.assertRaises(__a):
_lowerCAmelCase : List[str] = Accelerator(cpu=__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = Accelerator()
_lowerCAmelCase : Any = GradientState()
assert state.num_steps == 1
_lowerCAmelCase : Any = 4
assert state.num_steps == 4
assert state.sync_gradients is True
_lowerCAmelCase : Any = False
assert state.sync_gradients is False
GradientState._reset_state()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = Accelerator()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Any = create_components()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Union[str, Any] = accelerator.prepare(__a, __a, __a, __a, __a)
self.assertTrue(prepared_model in accelerator._models)
self.assertTrue(prepared_optimizer in accelerator._optimizers)
self.assertTrue(prepared_scheduler in accelerator._schedulers)
self.assertTrue(prepared_train_dl in accelerator._dataloaders)
self.assertTrue(prepared_valid_dl in accelerator._dataloaders)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = Accelerator()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = create_components()
accelerator.prepare(__a, __a, __a, __a, __a)
accelerator.free_memory()
self.assertTrue(len(accelerator._models) == 0)
self.assertTrue(len(accelerator._optimizers) == 0)
self.assertTrue(len(accelerator._schedulers) == 0)
self.assertTrue(len(accelerator._dataloaders) == 0)
def snake_case__ ( self):
'''simple docstring'''
PartialState._reset_state()
# Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
def noop(*__a, **__a):
pass
with patch("torch.cuda.set_device", __a), patch_environment(ACCELERATE_TORCH_DEVICE="cuda:64"):
_lowerCAmelCase : int = Accelerator()
self.assertEqual(str(accelerator.state.device), "cuda:64")
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = Accelerator()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[Any] = create_components()
accelerator.prepare(__a, __a, __a, __a, __a)
_lowerCAmelCase : Optional[Any] = get_signature(__a)
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__a)
# make sure random weights don't match
load_random_weights(__a)
self.assertTrue(abs(model_signature - get_signature(__a)) > 1E-3)
# make sure loaded weights match
accelerator.load_state(__a)
self.assertTrue(abs(model_signature - get_signature(__a)) < 1E-3)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = Accelerator()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[Any] = create_components()
accelerator.prepare(__a, __a, __a, __a, __a)
_lowerCAmelCase : Optional[Any] = get_signature(__a)
# saving hook
def save_config(__a, __a, __a):
_lowerCAmelCase : Optional[int] = {"class_name": models[0].__class__.__name__}
with open(os.path.join(__a, "data.json"), "w") as f:
json.dump(__a, __a)
# loading hook
def load_config(__a, __a):
with open(os.path.join(__a, "data.json"), "r") as f:
_lowerCAmelCase : Optional[int] = json.load(__a)
_lowerCAmelCase : List[Any] = config["class_name"]
_lowerCAmelCase : Optional[int] = accelerator.register_save_state_pre_hook(__a)
_lowerCAmelCase : List[str] = accelerator.register_load_state_pre_hook(__a)
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__a)
# make sure random weights don't match with hooks
load_random_weights(__a)
self.assertTrue(abs(model_signature - get_signature(__a)) > 1E-3)
# random class name to verify correct one is loaded
_lowerCAmelCase : Any = "random"
# make sure loaded weights match with hooks
accelerator.load_state(__a)
self.assertTrue(abs(model_signature - get_signature(__a)) < 1E-3)
# mode.class_name is loaded from config
self.assertTrue(model.class_name == model.__class__.__name__)
# remove hooks
save_hook.remove()
load_hook.remove()
with tempfile.TemporaryDirectory() as tmpdirname:
accelerator.save_state(__a)
# make sure random weights don't match with hooks removed
load_random_weights(__a)
self.assertTrue(abs(model_signature - get_signature(__a)) > 1E-3)
# random class name to verify correct one is loaded
_lowerCAmelCase : int = "random"
# make sure loaded weights match with hooks removed
accelerator.load_state(__a)
self.assertTrue(abs(model_signature - get_signature(__a)) < 1E-3)
# mode.class_name is NOT loaded from config
self.assertTrue(model.class_name != model.__class__.__name__)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = Accelerator()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = create_components()
_lowerCAmelCase : Any = None
# This should work
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Tuple = accelerator.prepare(
__a, __a, __a, __a, __a, __a)
self.assertTrue(dummy_obj is None)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = Accelerator()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Any = create_components()
_lowerCAmelCase : Any = [1, 2, 3]
# This should work
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Dict = accelerator.prepare(
__a, __a, __a, __a, __a, __a)
self.assertEqual(
getattr(__a, "_is_accelerate_prepared", __a), __a, "Dummy object should have `_is_accelerate_prepared` set to `True`", )
self.assertEqual(
getattr(__a, "_is_accelerate_prepared", __a), __a, "Model is missing `_is_accelerator_prepared` or is set to `False`", )
self.assertEqual(
getattr(__a, "_is_accelerate_prepared", __a), __a, "Optimizer is missing `_is_accelerator_prepared` or is set to `False`", )
self.assertEqual(
getattr(__a, "_is_accelerate_prepared", __a), __a, "Scheduler is missing `_is_accelerator_prepared` or is set to `False`", )
self.assertEqual(
getattr(__a, "_is_accelerate_prepared", __a), __a, "Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`", )
self.assertEqual(
getattr(__a, "_is_accelerate_prepared", __a), __a, "Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`", )
@slow
@require_bnb
def snake_case__ ( self):
'''simple docstring'''
from transformers import AutoModelForCausalLM
_lowerCAmelCase : List[str] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", load_in_abit=__a, device_map={"": 0}, )
_lowerCAmelCase : Dict = Accelerator()
# This should work
_lowerCAmelCase : List[str] = accelerator.prepare(__a)
@slow
@require_bnb
def snake_case__ ( self):
'''simple docstring'''
from transformers import AutoModelForCausalLM
_lowerCAmelCase : List[Any] = Accelerator()
with init_empty_weights():
_lowerCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", )
model.tie_weights()
_lowerCAmelCase : Optional[int] = infer_auto_device_map(__a)
_lowerCAmelCase : Dict = "cpu"
_lowerCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", device_map=__a, load_in_abit=__a, llm_inta_enable_fpaa_cpu_offload=__a)
# This should not work and get value error
with self.assertRaises(__a):
_lowerCAmelCase : Optional[Any] = accelerator.prepare(__a)
@slow
@require_bnb
@require_multi_gpu
def snake_case__ ( self):
'''simple docstring'''
from transformers import AutoModelForCausalLM
_lowerCAmelCase : Union[str, Any] = {"distributed_type": DistributedType.MULTI_GPU}
with init_empty_weights():
_lowerCAmelCase : List[Any] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", )
model.tie_weights()
_lowerCAmelCase : Optional[Any] = infer_auto_device_map(__a)
_lowerCAmelCase : Optional[int] = 1
_lowerCAmelCase : int = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", load_in_abit=__a, device_map=__a, )
_lowerCAmelCase : str = Accelerator()
# This should not work and get value error
with self.assertRaises(__a):
_lowerCAmelCase : List[Any] = accelerator.prepare(__a)
PartialState._reset_state()
@slow
@require_bnb
@require_multi_gpu
def snake_case__ ( self):
'''simple docstring'''
from transformers import AutoModelForCausalLM
with init_empty_weights():
_lowerCAmelCase : List[str] = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", )
_lowerCAmelCase : List[Any] = infer_auto_device_map(__a)
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : Tuple = AutoModelForCausalLM.from_pretrained(
"EleutherAI/gpt-neo-125m", load_in_abit=__a, device_map=__a, )
_lowerCAmelCase : Dict = Accelerator()
# This should work
_lowerCAmelCase : Union[str, Any] = accelerator.prepare(__a)
@require_cuda
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = torch.nn.Linear(10, 10)
_lowerCAmelCase : Union[str, Any] = torch.optim.SGD(model.parameters(), lr=0.01)
_lowerCAmelCase : Dict = Accelerator(cpu=__a)
_lowerCAmelCase : Optional[int] = accelerator.prepare(__a)
| 36
|
from PIL import Image
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = image.size
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = image.load()
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(_lowerCamelCase ):
for i in range(_lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_snake_case = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 36
| 1
|
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def A ( _lowerCamelCase , _lowerCamelCase=7 ):
'''simple docstring'''
_lowerCAmelCase : Any = None
if token is not None:
_lowerCAmelCase : Tuple = {"Accept": "application/vnd.github+json", "Authorization": F"Bearer {token}"}
# The id of a workflow (not of a workflow run)
_lowerCAmelCase : Any = "636036"
_lowerCAmelCase : List[Any] = F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"
_lowerCAmelCase : Optional[int] = requests.get(_lowerCamelCase , headers=_lowerCamelCase ).json()
return result["workflow_runs"]
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = get_daily_ci_runs(_lowerCamelCase )
_lowerCAmelCase : List[Any] = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
_lowerCAmelCase : str = workflow_run["id"]
break
return workflow_run_id
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = get_last_daily_ci_runs(_lowerCamelCase )
if workflow_run_id is not None:
_lowerCAmelCase : int = get_artifacts_links(worflow_run_id=_lowerCamelCase , token=_lowerCamelCase )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
_lowerCAmelCase : Optional[int] = artifacts_links[artifact_name]
download_artifact(
artifact_name=_lowerCamelCase , artifact_url=_lowerCamelCase , output_dir=_lowerCamelCase , token=_lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
get_last_daily_ci_artifacts(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : str = {}
for artifact_name in artifact_names:
_lowerCAmelCase : Optional[int] = os.path.join(_lowerCamelCase , F"{artifact_name}.zip" )
if os.path.isfile(_lowerCamelCase ):
_lowerCAmelCase : Dict = {}
with zipfile.ZipFile(_lowerCamelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(_lowerCamelCase ):
# read the file
with z.open(_lowerCamelCase ) as f:
_lowerCAmelCase : str = f.read().decode("UTF-8" )
return results
| 36
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'wav2vec2'
def __init__( self, __a=32, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.1, __a=0.1, __a=0.1, __a=0.0, __a=0.0, __a=0.1, __a=0.1, __a=0.02, __a=1E-5, __a="group", __a="gelu", __a=(512, 512, 512, 512, 512, 512, 512), __a=(5, 2, 2, 2, 2, 2, 2), __a=(10, 3, 3, 3, 3, 2, 2), __a=False, __a=128, __a=16, __a=False, __a=True, __a=0.05, __a=10, __a=2, __a=0.0, __a=10, __a=0, __a=320, __a=2, __a=0.1, __a=100, __a=256, __a=256, __a=0.1, __a="sum", __a=False, __a=False, __a=256, __a=(512, 512, 512, 512, 1500), __a=(5, 3, 3, 1, 1), __a=(1, 2, 3, 1, 1), __a=512, __a=0, __a=1, __a=2, __a=False, __a=3, __a=2, __a=3, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a, pad_token_id=__a, bos_token_id=__a, eos_token_id=__a)
_lowerCAmelCase : str = hidden_size
_lowerCAmelCase : Optional[int] = feat_extract_norm
_lowerCAmelCase : Union[str, Any] = feat_extract_activation
_lowerCAmelCase : Optional[Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : List[str] = conv_bias
_lowerCAmelCase : str = num_conv_pos_embeddings
_lowerCAmelCase : List[Any] = num_conv_pos_embedding_groups
_lowerCAmelCase : str = len(self.conv_dim)
_lowerCAmelCase : List[str] = num_hidden_layers
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : Any = hidden_act
_lowerCAmelCase : int = num_attention_heads
_lowerCAmelCase : Optional[Any] = hidden_dropout
_lowerCAmelCase : List[str] = attention_dropout
_lowerCAmelCase : Tuple = activation_dropout
_lowerCAmelCase : int = feat_proj_dropout
_lowerCAmelCase : List[str] = final_dropout
_lowerCAmelCase : int = layerdrop
_lowerCAmelCase : int = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Optional[Any] = do_stable_layer_norm
_lowerCAmelCase : Any = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_lowerCAmelCase : str = apply_spec_augment
_lowerCAmelCase : Optional[Any] = mask_time_prob
_lowerCAmelCase : Optional[int] = mask_time_length
_lowerCAmelCase : List[str] = mask_time_min_masks
_lowerCAmelCase : Optional[int] = mask_feature_prob
_lowerCAmelCase : Optional[int] = mask_feature_length
_lowerCAmelCase : List[str] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_lowerCAmelCase : Union[str, Any] = num_codevectors_per_group
_lowerCAmelCase : str = num_codevector_groups
_lowerCAmelCase : Optional[int] = contrastive_logits_temperature
_lowerCAmelCase : Optional[int] = feat_quantizer_dropout
_lowerCAmelCase : Optional[int] = num_negatives
_lowerCAmelCase : Union[str, Any] = codevector_dim
_lowerCAmelCase : Any = proj_codevector_dim
_lowerCAmelCase : Optional[int] = diversity_loss_weight
# ctc loss
_lowerCAmelCase : Tuple = ctc_loss_reduction
_lowerCAmelCase : Tuple = ctc_zero_infinity
# adapter
_lowerCAmelCase : List[Any] = add_adapter
_lowerCAmelCase : List[str] = adapter_kernel_size
_lowerCAmelCase : str = adapter_stride
_lowerCAmelCase : List[str] = num_adapter_layers
_lowerCAmelCase : str = output_hidden_size or hidden_size
_lowerCAmelCase : Tuple = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCAmelCase : str = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : Union[str, Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : Tuple = xvector_output_dim
@property
def snake_case__ ( self):
'''simple docstring'''
return functools.reduce(operator.mul, self.conv_stride, 1)
| 36
| 1
|
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
_snake_case = ["gpt2"]
_snake_case = "gpt2"
if is_tf_available():
class UpperCAmelCase_ ( tf.Module):
def __init__( self, __a):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Tuple = tokenizer
_lowerCAmelCase : int = AutoConfig.from_pretrained(__a)
_lowerCAmelCase : Optional[int] = TFGPTaLMHeadModel.from_config(__a)
@tf.function(input_signature=(tf.TensorSpec((None,), tf.string, name="text"),))
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Dict = self.tokenizer(__a)
_lowerCAmelCase : Optional[int] = tokenized["input_ids"].to_tensor()
_lowerCAmelCase : str = tf.cast(input_ids_dense > 0, tf.intaa)
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
_lowerCAmelCase : Optional[int] = self.model(input_ids=__a, attention_mask=__a)["logits"]
return outputs
@require_tf
@require_keras_nlp
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : Tuple = [GPTaTokenizer.from_pretrained(__a) for checkpoint in (TOKENIZER_CHECKPOINTS)]
_lowerCAmelCase : str = [TFGPTaTokenizer.from_pretrained(__a) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers) == len(self.tf_tokenizers)
_lowerCAmelCase : Optional[Any] = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
_lowerCAmelCase : int = list(zip(self.test_sentences, self.test_sentences[::-1]))
def snake_case__ ( self):
'''simple docstring'''
for tokenizer, tf_tokenizer in zip(self.tokenizers, self.tf_tokenizers):
for test_inputs in self.test_sentences:
_lowerCAmelCase : Union[str, Any] = tokenizer([test_inputs], return_tensors="tf")
_lowerCAmelCase : Dict = tf_tokenizer([test_inputs])
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
_lowerCAmelCase : Tuple = python_outputs[key].numpy()
_lowerCAmelCase : List[str] = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape))
self.assertTrue(tf.reduce_all(tf.cast(__a, tf.intaa) == tf_outputs_values))
@slow
def snake_case__ ( self):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
_lowerCAmelCase : str = tf.function(__a)
for test_inputs in self.test_sentences:
_lowerCAmelCase : Any = tf.constant(__a)
_lowerCAmelCase : int = compiled_tokenizer(__a)
_lowerCAmelCase : int = tf_tokenizer(__a)
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key]))
@slow
def snake_case__ ( self):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
_lowerCAmelCase : List[str] = ModelToSave(tokenizer=__a)
_lowerCAmelCase : int = tf.convert_to_tensor([self.test_sentences[0]])
_lowerCAmelCase : str = model.serving(__a) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
_lowerCAmelCase : Optional[Any] = Path(__a) / "saved.model"
tf.saved_model.save(__a, __a, signatures={"serving_default": model.serving})
_lowerCAmelCase : Optional[Any] = tf.saved_model.load(__a)
_lowerCAmelCase : Union[str, Any] = loaded_model.signatures["serving_default"](__a)["output_0"]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output))
@slow
def snake_case__ ( self):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
_lowerCAmelCase : Optional[int] = tf.convert_to_tensor([self.test_sentences[0]])
_lowerCAmelCase : Optional[Any] = tf_tokenizer(__a) # Build model with some sample inputs
_lowerCAmelCase : Optional[Any] = tf_tokenizer.get_config()
_lowerCAmelCase : Optional[Any] = TFGPTaTokenizer.from_config(__a)
_lowerCAmelCase : Tuple = model_from_config(__a)
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key]))
@slow
def snake_case__ ( self):
'''simple docstring'''
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
_lowerCAmelCase : Any = 12_3123
for max_length in [3, 5, 1024]:
_lowerCAmelCase : Optional[Any] = tf.convert_to_tensor([self.test_sentences[0]])
_lowerCAmelCase : Optional[Any] = tf_tokenizer(__a, max_length=__a)
_lowerCAmelCase : Optional[Any] = out["input_ids"].numpy().shape[1]
assert out_length == max_length
| 36
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'The RoBERTa Model transformer with early exiting (DeeRoBERTa). ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[Any] = RobertaEmbeddings(__a)
self.init_weights()
@add_start_docstrings(
'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[int] = config.num_labels
_lowerCAmelCase : Optional[int] = config.num_hidden_layers
_lowerCAmelCase : Optional[int] = DeeRobertaModel(__a)
_lowerCAmelCase : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob)
_lowerCAmelCase : List[str] = nn.Linear(config.hidden_size, self.config.num_labels)
@add_start_docstrings_to_model_forward(__a)
def snake_case__ ( self, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=-1, __a=False, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.num_layers
try:
_lowerCAmelCase : List[Any] = self.roberta(
__a, attention_mask=__a, token_type_ids=__a, position_ids=__a, head_mask=__a, inputs_embeds=__a, )
_lowerCAmelCase : List[Any] = outputs[1]
_lowerCAmelCase : Dict = self.dropout(__a)
_lowerCAmelCase : Dict = self.classifier(__a)
_lowerCAmelCase : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase : Tuple = e.message
_lowerCAmelCase : Union[str, Any] = e.exit_layer
_lowerCAmelCase : List[Any] = outputs[0]
if not self.training:
_lowerCAmelCase : int = entropy(__a)
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : str = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : Optional[Any] = MSELoss()
_lowerCAmelCase : int = loss_fct(logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Optional[Any] = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
_lowerCAmelCase : Optional[int] = []
for highway_exit in outputs[-1]:
_lowerCAmelCase : Any = highway_exit[0]
if not self.training:
highway_logits_all.append(__a)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : List[str] = MSELoss()
_lowerCAmelCase : List[Any] = loss_fct(highway_logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Dict = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
highway_losses.append(__a)
if train_highway:
_lowerCAmelCase : int = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase : Any = (loss,) + outputs
if not self.training:
_lowerCAmelCase : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 36
| 1
|
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'efficientnet'
def __init__( self, __a = 3, __a = 600, __a = 2.0, __a = 3.1, __a = 8, __a = [3, 3, 5, 3, 5, 5, 3], __a = [32, 16, 24, 40, 80, 112, 192], __a = [16, 24, 40, 80, 112, 192, 320], __a = [], __a = [1, 2, 2, 2, 1, 2, 1], __a = [1, 2, 2, 3, 3, 4, 1], __a = [1, 6, 6, 6, 6, 6, 6], __a = 0.25, __a = "swish", __a = 2560, __a = "mean", __a = 0.02, __a = 0.001, __a = 0.99, __a = 0.5, __a = 0.2, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : List[Any] = num_channels
_lowerCAmelCase : Dict = image_size
_lowerCAmelCase : Tuple = width_coefficient
_lowerCAmelCase : Dict = depth_coefficient
_lowerCAmelCase : Optional[int] = depth_divisor
_lowerCAmelCase : Optional[Any] = kernel_sizes
_lowerCAmelCase : Any = in_channels
_lowerCAmelCase : int = out_channels
_lowerCAmelCase : Union[str, Any] = depthwise_padding
_lowerCAmelCase : Union[str, Any] = strides
_lowerCAmelCase : int = num_block_repeats
_lowerCAmelCase : Union[str, Any] = expand_ratios
_lowerCAmelCase : Optional[Any] = squeeze_expansion_ratio
_lowerCAmelCase : int = hidden_act
_lowerCAmelCase : Optional[Any] = hidden_dim
_lowerCAmelCase : str = pooling_type
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : Any = batch_norm_eps
_lowerCAmelCase : Optional[Any] = batch_norm_momentum
_lowerCAmelCase : List[Any] = dropout_rate
_lowerCAmelCase : List[Any] = drop_connect_rate
_lowerCAmelCase : Optional[int] = sum(__a) * 4
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-5
| 36
|
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'vision-encoder-decoder'
lowerCamelCase__ = True
def __init__( self, **__a):
'''simple docstring'''
super().__init__(**__a)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")
_lowerCAmelCase : str = kwargs.pop("encoder")
_lowerCAmelCase : Any = encoder_config.pop("model_type")
_lowerCAmelCase : str = kwargs.pop("decoder")
_lowerCAmelCase : List[str] = decoder_config.pop("model_type")
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[int] = True
@classmethod
def snake_case__ ( cls, __a, __a, **__a):
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : str = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = copy.deepcopy(self.__dict__)
_lowerCAmelCase : List[str] = self.encoder.to_dict()
_lowerCAmelCase : List[str] = self.decoder.to_dict()
_lowerCAmelCase : Any = self.__class__.model_type
return output
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : List[str] = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : Optional[Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def snake_case__ ( self, __a, __a = -1, __a = -1, __a = False, __a = None, ):
'''simple docstring'''
import torch
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : List[str] = super().generate_dummy_inputs(
__a, batch_size=__a, seq_length=__a, is_pair=__a, framework=__a)
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = dummy_input["input_ids"].shape
_lowerCAmelCase : str = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowerCAmelCase : List[str] = dummy_input.pop("input_ids")
_lowerCAmelCase : List[str] = dummy_input.pop("attention_mask")
_lowerCAmelCase : Optional[int] = torch.zeros(__a)
return common_inputs
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self, __a):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(__a)
def snake_case__ ( self, __a, __a, __a = "default"):
'''simple docstring'''
_lowerCAmelCase : Dict = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__a, __a)
| 36
| 1
|
import secrets
from random import shuffle
from string import ascii_letters, ascii_lowercase, ascii_uppercase, digits, punctuation
def A ( _lowerCamelCase = 8 ):
'''simple docstring'''
_lowerCAmelCase : int = ascii_letters + digits + punctuation
return "".join(secrets.choice(_lowerCamelCase ) for _ in range(_lowerCamelCase ) )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
i -= len(_lowerCamelCase )
_lowerCAmelCase : int = i // 3
_lowerCAmelCase : Any = i % 3
# chars = chars_incl + random_letters(ascii_letters, i / 3 + remainder) +
# random_number(digits, i / 3) + random_characters(punctuation, i / 3)
_lowerCAmelCase : Union[str, Any] = (
chars_incl
+ random(_lowerCamelCase , quotient + remainder )
+ random(_lowerCamelCase , _lowerCamelCase )
+ random(_lowerCamelCase , _lowerCamelCase )
)
_lowerCAmelCase : Tuple = list(_lowerCamelCase )
shuffle(_lowerCamelCase )
return "".join(_lowerCamelCase )
# random is a generalised function for letters, characters and numbers
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return "".join(secrets.choice(_lowerCamelCase ) for _ in range(_lowerCamelCase ) )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
pass # Put your code here...
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
pass # Put your code here...
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
pass # Put your code here...
def A ( _lowerCamelCase , _lowerCamelCase = 8 ):
'''simple docstring'''
if len(_lowerCamelCase ) < min_length:
# Your Password must be at least 8 characters long
return False
_lowerCAmelCase : Tuple = any(char in ascii_uppercase for char in password )
_lowerCAmelCase : Optional[int] = any(char in ascii_lowercase for char in password )
_lowerCAmelCase : List[str] = any(char in digits for char in password )
_lowerCAmelCase : Any = any(char in punctuation for char in password )
return upper and lower and num and spec_char
# Passwords should contain UPPERCASE, lowerase
# numbers, and special characters
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = int(input("Please indicate the max length of your password: " ).strip() )
_lowerCAmelCase : Optional[Any] = input(
"Please indicate the characters that must be in your password: " ).strip()
print("Password generated:" , password_generator(_lowerCamelCase ) )
print(
"Alternative Password generated:" , alternative_password_generator(_lowerCamelCase , _lowerCamelCase ) , )
print("[If you are thinking of using this passsword, You better save it.]" )
if __name__ == "__main__":
main()
| 36
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( a):
def __get__( self, __a, __a=None):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
_lowerCAmelCase : List[Any] = "__cached_" + self.fget.__name__
_lowerCAmelCase : Dict = getattr(__a, __a, __a)
if cached is None:
_lowerCAmelCase : str = self.fget(__a)
setattr(__a, __a, __a)
return cached
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"invalid truth value {val!r}" )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.device )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if hasattr(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase , torch.dtype )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase , tf.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase , jnp.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = fields(self)
# Safety and consistency checks
if not len(__a):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
_lowerCAmelCase : Dict = getattr(self, class_fields[0].name)
_lowerCAmelCase : str = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(__a):
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = first_field.items()
_lowerCAmelCase : Dict = True
else:
try:
_lowerCAmelCase : Dict = iter(__a)
_lowerCAmelCase : Any = True
except TypeError:
_lowerCAmelCase : Any = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a):
if (
not isinstance(__a, (list, tuple))
or not len(__a) == 2
or not isinstance(element[0], __a)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value).")
break
setattr(self, element[0], element[1])
if element[1] is not None:
_lowerCAmelCase : Any = element[1]
elif first_field is not None:
_lowerCAmelCase : Any = first_field
else:
for field in class_fields:
_lowerCAmelCase : Dict = getattr(self, field.name)
if v is not None:
_lowerCAmelCase : Union[str, Any] = v
def __delitem__( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__( self, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Optional[int] = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self, __a, __a):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a, __a)
super().__setattr__(__a, __a)
def __setitem__( self, __a, __a):
'''simple docstring'''
super().__setitem__(__a, __a)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
return tuple(self[k] for k in self.keys())
class UpperCAmelCase_ ( a , a):
@classmethod
def snake_case__ ( cls, __a):
'''simple docstring'''
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'longest'
lowerCamelCase__ = 'max_length'
lowerCamelCase__ = 'do_not_pad'
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'pt'
lowerCamelCase__ = 'tf'
lowerCamelCase__ = 'np'
lowerCamelCase__ = 'jax'
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = context_managers
_lowerCAmelCase : Dict = ExitStack()
def __enter__( self):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a)
def __exit__( self, *__a, **__a):
'''simple docstring'''
self.stack.__exit__(*__a, **__a)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : str = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = model_class.__name__
_lowerCAmelCase : Optional[Any] = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( _lowerCamelCase , _lowerCamelCase = "" , _lowerCamelCase = "." ):
'''simple docstring'''
def _flatten_dict(_lowerCamelCase , _lowerCamelCase="" , _lowerCamelCase="." ):
for k, v in d.items():
_lowerCAmelCase : Dict = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase , _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
@contextmanager
def A ( _lowerCamelCase , _lowerCamelCase = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase , axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase , _lowerCamelCase )
else:
raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase , (tuple, list) ):
_lowerCAmelCase : List[Any] = [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase : Tuple = F"{repo_id}--{value}"
return auto_map
def A ( _lowerCamelCase ):
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
_lowerCAmelCase : Tuple = base_class.__module__
_lowerCAmelCase : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"Could not infer framework from class {model_class}." )
| 36
| 1
|
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
super().tearDown()
gc.collect()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
_lowerCAmelCase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
_lowerCAmelCase : Any = "xvjiarui/stable-diffusion-2-inpainting"
_lowerCAmelCase , _lowerCAmelCase : str = FlaxStableDiffusionInpaintPipeline.from_pretrained(__a, safety_checker=__a)
_lowerCAmelCase : List[Any] = "Face of a yellow cat, high resolution, sitting on a park bench"
_lowerCAmelCase : List[Any] = jax.random.PRNGKey(0)
_lowerCAmelCase : int = 50
_lowerCAmelCase : Optional[Any] = jax.device_count()
_lowerCAmelCase : Dict = num_samples * [prompt]
_lowerCAmelCase : Dict = num_samples * [init_image]
_lowerCAmelCase : Any = num_samples * [mask_image]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Optional[Any] = pipeline.prepare_inputs(__a, __a, __a)
# shard inputs and rng
_lowerCAmelCase : Any = replicate(__a)
_lowerCAmelCase : List[Any] = jax.random.split(__a, jax.device_count())
_lowerCAmelCase : Optional[int] = shard(__a)
_lowerCAmelCase : List[str] = shard(__a)
_lowerCAmelCase : Optional[int] = shard(__a)
_lowerCAmelCase : int = pipeline(
__a, __a, __a, __a, __a, __a, jit=__a)
_lowerCAmelCase : Dict = output.images.reshape(__a, 512, 512, 3)
_lowerCAmelCase : Optional[Any] = images[0, 253:256, 253:256, -1]
_lowerCAmelCase : List[str] = jnp.asarray(jax.device_get(image_slice.flatten()))
_lowerCAmelCase : Dict = jnp.array(
[0.3_611_307, 0.37_649_736, 0.3_757_408, 0.38_213_953, 0.39_295_167, 0.3_841_631, 0.41_554_978, 0.4_137_475, 0.4_217_084])
print(f"output_slice: {output_slice}")
assert jnp.abs(output_slice - expected_slice).max() < 1E-2
| 36
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = _distribute_shards(**_lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = _split_gen_kwargs(_lowerCamelCase , _lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(_lowerCamelCase ):
_number_of_shards_in_gen_kwargs(_lowerCamelCase )
else:
_lowerCAmelCase : Optional[int] = _number_of_shards_in_gen_kwargs(_lowerCamelCase )
assert out == expected
| 36
| 1
|
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def A ( _lowerCamelCase , _lowerCamelCase = True , _lowerCamelCase = math.inf , _lowerCamelCase = -math.inf , _lowerCamelCase = math.inf , _lowerCamelCase = -math.inf , _lowerCamelCase = False , _lowerCamelCase = 100 , _lowerCamelCase = 0.01 , _lowerCamelCase = 1 , ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = False
_lowerCAmelCase : Any = search_prob
_lowerCAmelCase : str = start_temperate
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : List[Any] = 0
_lowerCAmelCase : Union[str, Any] = None
while not search_end:
_lowerCAmelCase : Dict = current_state.score()
if best_state is None or current_score > best_state.score():
_lowerCAmelCase : List[str] = current_state
scores.append(_lowerCamelCase )
iterations += 1
_lowerCAmelCase : str = None
_lowerCAmelCase : Tuple = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
_lowerCAmelCase : str = random.randint(0 , len(_lowerCamelCase ) - 1 ) # picking a random neighbor
_lowerCAmelCase : Union[str, Any] = neighbors.pop(_lowerCamelCase )
_lowerCAmelCase : List[Any] = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
_lowerCAmelCase : Union[str, Any] = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
_lowerCAmelCase : Optional[int] = picked_neighbor
else:
_lowerCAmelCase : Optional[int] = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
_lowerCAmelCase : Dict = picked_neighbor
_lowerCAmelCase : Any = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
_lowerCAmelCase : Optional[Any] = True
else:
_lowerCAmelCase : Tuple = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_lowerCamelCase ) , _lowerCamelCase )
plt.xlabel("Iterations" )
plt.ylabel("Function values" )
plt.show()
return best_state
if __name__ == "__main__":
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
_snake_case = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
_snake_case = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
# starting the problem with initial coordinates (12, 47)
_snake_case = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
_snake_case = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f'''and 50 > y > - 5 found via hill climbing: {local_min.score()}'''
)
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (3 * x**2) - (6 * y)
_snake_case = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
_snake_case = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f'''{local_min.score()}'''
)
_snake_case = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
_snake_case = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f'''{local_min.score()}'''
)
| 36
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCAmelCase_ :
def __init__( self, __a = "cpu", __a = "openai/clip-vit-large-patch14"):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = device
_lowerCAmelCase : Optional[int] = CLIPTokenizerFast.from_pretrained(__a)
_lowerCAmelCase : Any = [0.48_145_466, 0.4_578_275, 0.40_821_073]
_lowerCAmelCase : Union[str, Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]
_lowerCAmelCase : Tuple = torchvision.transforms.Normalize(self.image_mean, self.image_std)
_lowerCAmelCase : Optional[int] = torchvision.transforms.Resize(224)
_lowerCAmelCase : Dict = torchvision.transforms.CenterCrop(224)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.resize(__a)
_lowerCAmelCase : List[str] = self.center_crop(__a)
_lowerCAmelCase : Optional[Any] = self.normalize(__a)
return images
def __call__( self, __a=None, __a=None, **__a):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(text=__a, **__a)
_lowerCAmelCase : List[str] = self.preprocess_img(__a)
_lowerCAmelCase : Tuple = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a=10, __a=0.01, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=False, __a=True, __a="image", __a=True, __a=False, __a=False, __a=False, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : List[str] = None
_lowerCAmelCase : List[str] = device if device else get_device()
if vqgan:
_lowerCAmelCase : Union[str, Any] = vqgan
else:
_lowerCAmelCase : Optional[Any] = load_vqgan(self.device, conf_path=__a, ckpt_path=__a)
self.vqgan.eval()
if clip:
_lowerCAmelCase : str = clip
else:
_lowerCAmelCase : int = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
_lowerCAmelCase : Optional[int] = ProcessorGradientFlow(device=self.device)
_lowerCAmelCase : Any = iterations
_lowerCAmelCase : List[Any] = lr
_lowerCAmelCase : Tuple = log
_lowerCAmelCase : List[str] = make_grid
_lowerCAmelCase : int = return_val
_lowerCAmelCase : Dict = quantize
_lowerCAmelCase : Any = self.vqgan.decoder.z_shape
def snake_case__ ( self, __a=None, __a=None, __a=5, __a=True):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = []
if output_path is None:
_lowerCAmelCase : List[Any] = "./animation.gif"
if input_path is None:
_lowerCAmelCase : str = self.save_path
_lowerCAmelCase : str = sorted(glob(input_path + "/*"))
if not len(__a):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__a) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
_lowerCAmelCase : Optional[int] = total_duration / len(__a)
_lowerCAmelCase : Union[str, Any] = [frame_duration] * len(__a)
if extend_frames:
_lowerCAmelCase : Any = 1.5
_lowerCAmelCase : List[str] = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__a))
imageio.mimsave(__a, __a, duration=__a)
print(f"gif saved to {output_path}")
def snake_case__ ( self, __a=None, __a=None):
'''simple docstring'''
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
_lowerCAmelCase : Dict = preprocess(Image.open(__a), target_image_size=256).to(self.device)
_lowerCAmelCase : Dict = preprocess_vqgan(__a)
_lowerCAmelCase , *_lowerCAmelCase : str = self.vqgan.encode(__a)
return z
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.latent.detach().requires_grad_()
_lowerCAmelCase : Dict = base_latent + transform_vector
if self.quantize:
_lowerCAmelCase , *_lowerCAmelCase : List[Any] = self.vqgan.quantize(__a)
else:
_lowerCAmelCase : Any = trans_latent
return self.vqgan.decode(__a)
def snake_case__ ( self, __a, __a, __a=None):
'''simple docstring'''
_lowerCAmelCase : int = self.clip_preprocessor(text=__a, images=__a, return_tensors="pt", padding=__a)
_lowerCAmelCase : Optional[int] = self.clip(**__a)
_lowerCAmelCase : Any = clip_outputs.logits_per_image
if weights is not None:
_lowerCAmelCase : Tuple = similarity_logits * weights
return similarity_logits.sum()
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self._get_clip_similarity(pos_prompts["prompts"], __a, weights=(1 / pos_prompts["weights"]))
if neg_prompts:
_lowerCAmelCase : List[Any] = self._get_clip_similarity(neg_prompts["prompts"], __a, weights=neg_prompts["weights"])
else:
_lowerCAmelCase : Union[str, Any] = torch.tensor([1], device=self.device)
_lowerCAmelCase : List[str] = -torch.log(__a) + torch.log(__a)
return loss
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.randn_like(self.latent, requires_grad=__a, device=self.device)
_lowerCAmelCase : Optional[int] = torch.optim.Adam([vector], lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
_lowerCAmelCase : Any = self._add_vector(__a)
_lowerCAmelCase : Optional[Any] = loop_post_process(__a)
_lowerCAmelCase : Optional[Any] = self._get_CLIP_loss(__a, __a, __a)
print("CLIP loss", __a)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__a)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
wandb.init(reinit=__a, project="face-editor")
wandb.config.update({"Positive Prompts": positive_prompts})
wandb.config.update({"Negative Prompts": negative_prompts})
wandb.config.update({"lr": self.lr, "iterations": self.iterations})
if image_path:
_lowerCAmelCase : str = Image.open(__a)
_lowerCAmelCase : int = image.resize((256, 256))
wandb.log("Original Image", wandb.Image(__a))
def snake_case__ ( self, __a):
'''simple docstring'''
if not prompts:
return []
_lowerCAmelCase : int = []
_lowerCAmelCase : List[str] = []
if isinstance(__a, __a):
_lowerCAmelCase : Union[str, Any] = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__a, (tuple, list)):
_lowerCAmelCase : Optional[Any] = prompt[0]
_lowerCAmelCase : Union[str, Any] = float(prompt[1])
elif ":" in prompt:
_lowerCAmelCase , _lowerCAmelCase : int = prompt.split(":")
_lowerCAmelCase : Optional[Any] = float(__a)
else:
_lowerCAmelCase : Optional[int] = prompt
_lowerCAmelCase : List[Any] = 1.0
processed_prompts.append(__a)
weights.append(__a)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a, device=self.device),
}
def snake_case__ ( self, __a, __a=None, __a=None, __a=True, __a=False, __a=True, __a=True, __a=None, ):
'''simple docstring'''
if image_path:
_lowerCAmelCase : List[Any] = self._get_latent(__a)
else:
_lowerCAmelCase : Any = torch.randn(self.latent_dim, device=self.device)
if self.log:
self._init_logging(__a, __a, __a)
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCAmelCase : int = self.process_prompts(__a)
_lowerCAmelCase : List[str] = self.process_prompts(__a)
if save_final and save_path is None:
_lowerCAmelCase : int = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
if not os.path.exists(__a):
os.makedirs(__a)
else:
_lowerCAmelCase : Tuple = save_path + "_" + get_timestamp()
os.makedirs(__a)
_lowerCAmelCase : Tuple = save_path
_lowerCAmelCase : List[Any] = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__a))
_lowerCAmelCase : int = loop_post_process(__a)
for iter, transformed_img in enumerate(self._optimize_CLIP(__a, __a, __a)):
if show_intermediate:
show_pil(__a)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
if self.log:
wandb.log({"Image": wandb.Image(__a)})
if show_final:
show_pil(__a)
if save_final:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))
| 36
| 1
|
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, 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
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['pixel_values']
def __init__( self, __a = True, __a = None, __a = PIL.Image.BICUBIC, __a = True, __a = None, __a = 1 / 255, __a = True, __a = True, __a = None, __a = None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = size if size is not None else {"height": 256, "width": 256}
_lowerCAmelCase : List[Any] = get_size_dict(__a)
_lowerCAmelCase : Any = crop_size if crop_size is not None else {"height": 224, "width": 224}
_lowerCAmelCase : List[Any] = get_size_dict(__a, param_name="crop_size")
_lowerCAmelCase : int = do_resize
_lowerCAmelCase : List[str] = size
_lowerCAmelCase : str = resample
_lowerCAmelCase : Optional[Any] = do_center_crop
_lowerCAmelCase : Tuple = crop_size
_lowerCAmelCase : List[str] = do_rescale
_lowerCAmelCase : str = rescale_factor
_lowerCAmelCase : Optional[Any] = do_normalize
_lowerCAmelCase : Tuple = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase : Optional[int] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self, __a, __a, __a = PIL.Image.BICUBIC, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : Any = get_size_dict(__a)
if "height" not in size or "width" not in size:
raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
return resize(
__a, size=(size["height"], size["width"]), resample=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : int = get_size_dict(__a)
if "height" not in size or "width" not in size:
raise ValueError(f"The size dictionary must have keys 'height' and 'width'. Got {size.keys()}")
return center_crop(__a, size=(size["height"], size["width"]), data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return rescale(__a, scale=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return normalize(__a, mean=__a, std=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a = None, __a = None, __a=None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = ChannelDimension.FIRST, **__a, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : int = resample if resample is not None else self.resample
_lowerCAmelCase : Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
_lowerCAmelCase : Tuple = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase : Optional[int] = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase : Dict = image_std if image_std is not None else self.image_std
_lowerCAmelCase : Any = size if size is not None else self.size
_lowerCAmelCase : int = get_size_dict(__a)
_lowerCAmelCase : Optional[int] = crop_size if crop_size is not None else self.crop_size
_lowerCAmelCase : Any = get_size_dict(__a, param_name="crop_size")
_lowerCAmelCase : 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_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop 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.
_lowerCAmelCase : List[Any] = [to_numpy_array(__a) for image in images]
if do_resize:
_lowerCAmelCase : str = [self.resize(image=__a, size=__a, resample=__a) for image in images]
if do_center_crop:
_lowerCAmelCase : int = [self.center_crop(image=__a, size=__a) for image in images]
if do_rescale:
_lowerCAmelCase : Optional[int] = [self.rescale(image=__a, scale=__a) for image in images]
if do_normalize:
_lowerCAmelCase : List[Any] = [self.normalize(image=__a, mean=__a, std=__a) for image in images]
_lowerCAmelCase : List[Any] = [to_channel_dimension_format(__a, __a) for image in images]
_lowerCAmelCase : str = {"pixel_values": images}
return BatchFeature(data=__a, tensor_type=__a)
| 36
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_snake_case = get_tests_dir("fixtures")
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = mock.Mock()
_lowerCAmelCase : int = 500
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : str = HTTPError
_lowerCAmelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__a) as mock_head:
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def snake_case__ ( self):
'''simple docstring'''
with self.assertRaises(__a):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : int = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
_lowerCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor")
self.assertIsNotNone(__a)
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TOKEN
HfFolder.save_token(__a)
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : str = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="test-image-processor", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="valid_org/test-image-processor-org", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowerCAmelCase : List[str] = CustomImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
_lowerCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=__a)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 36
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 36
|
import unittest
from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=24, __a=2, __a=6, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=3, __a=None, __a=1000, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : List[str] = batch_size
_lowerCAmelCase : int = seq_length
_lowerCAmelCase : Optional[int] = is_training
_lowerCAmelCase : Dict = use_input_mask
_lowerCAmelCase : List[str] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : List[Any] = num_hidden_layers
_lowerCAmelCase : Optional[Any] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : List[str] = hidden_act
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Any = attention_probs_dropout_prob
_lowerCAmelCase : int = max_position_embeddings
_lowerCAmelCase : Optional[int] = type_vocab_size
_lowerCAmelCase : Optional[Any] = type_sequence_label_size
_lowerCAmelCase : List[str] = initializer_range
_lowerCAmelCase : List[Any] = num_labels
_lowerCAmelCase : Tuple = scope
_lowerCAmelCase : str = range_bbox
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
_lowerCAmelCase : Dict = bbox[i, j, 3]
_lowerCAmelCase : int = bbox[i, j, 1]
_lowerCAmelCase : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_lowerCAmelCase : str = bbox[i, j, 2]
_lowerCAmelCase : List[Any] = bbox[i, j, 0]
_lowerCAmelCase : str = t
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : Dict = None
if self.use_token_type_ids:
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[int] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case__ ( self):
'''simple docstring'''
return LiltConfig(
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, initializer_range=self.initializer_range, )
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = LiltModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(__a, bbox=__a, attention_mask=__a, token_type_ids=__a)
_lowerCAmelCase : str = model(__a, bbox=__a, token_type_ids=__a)
_lowerCAmelCase : List[Any] = model(__a, bbox=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.num_labels
_lowerCAmelCase : Optional[Any] = LiltForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = LiltForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Dict = config_and_inputs
_lowerCAmelCase : List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
return True
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = LiltModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase : Any = type
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : str = LiltModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)
_lowerCAmelCase : Any = torch.tensor([[1, 2]], device=__a)
_lowerCAmelCase : str = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=__a)
# forward pass
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(input_ids=__a, bbox=__a)
_lowerCAmelCase : Optional[int] = torch.Size([1, 2, 768])
_lowerCAmelCase : List[str] = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]], device=__a, )
self.assertTrue(outputs.last_hidden_state.shape, __a)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3], __a, atol=1E-3))
| 36
| 1
|
import math
from collections.abc import Iterator
from itertools import takewhile
def A ( _lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 2
while True:
if is_prime(_lowerCamelCase ):
yield num
num += 1
def A ( _lowerCamelCase = 2_000_000 ):
'''simple docstring'''
return sum(takewhile(lambda _lowerCamelCase : x < n , prime_generator() ) )
if __name__ == "__main__":
print(f'''{solution() = }''')
| 36
|
import argparse
import copy
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = {}
with open(_lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_lowerCAmelCase : Tuple = []
_list.append([line.split()[1], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_lowerCAmelCase : str = []
_list.append([line.split()[0], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase ) as f:
_lowerCAmelCase : str = f.read(1 )
_lowerCAmelCase : str = start_node
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Any = start_node
_lowerCAmelCase : str = 0
while visiting not in first_solution:
_lowerCAmelCase : Dict = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCamelCase ) and k[0] not in first_solution:
_lowerCAmelCase : List[str] = k[1]
_lowerCAmelCase : List[Any] = k[0]
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = distance_of_first_solution + int(_lowerCamelCase )
_lowerCAmelCase : str = best_node
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_lowerCAmelCase : Tuple = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = []
for n in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
for kn in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
if n == kn:
continue
_lowerCAmelCase : Optional[int] = copy.deepcopy(_lowerCamelCase )
_lowerCAmelCase : int = kn
_lowerCAmelCase : Dict = n
_lowerCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_lowerCAmelCase : str = _tmp[_tmp.index(_lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_lowerCAmelCase : Optional[Any] = distance + int(i[1] )
_tmp.append(_lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_lowerCAmelCase : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : int = first_solution
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Tuple = distance_of_first_solution
_lowerCAmelCase : Optional[int] = solution
while count <= iters:
_lowerCAmelCase : int = find_neighborhood(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : Dict = neighborhood[index_of_best_solution]
_lowerCAmelCase : int = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Union[str, Any] = False
while not found:
_lowerCAmelCase : Tuple = 0
while i < len(_lowerCamelCase ):
if best_solution[i] != solution[i]:
_lowerCAmelCase : str = best_solution[i]
_lowerCAmelCase : Tuple = solution[i]
break
_lowerCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : Optional[Any] = best_solution[:-1]
_lowerCAmelCase : Tuple = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_lowerCAmelCase : Union[str, Any] = cost
_lowerCAmelCase : List[Any] = solution
else:
_lowerCAmelCase : Optional[Any] = index_of_best_solution + 1
_lowerCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
if len(_lowerCamelCase ) >= size:
tabu_list.pop(0 )
_lowerCAmelCase : int = count + 1
return best_solution_ever, best_cost
def A ( _lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase : int = generate_neighbours(args.File )
_lowerCAmelCase , _lowerCAmelCase : List[str] = generate_first_solution(
args.File , _lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = tabu_search(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 36
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
_snake_case = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["BeitFeatureExtractor"]
_snake_case = ["BeitImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BeitForImageClassification",
"BeitForMaskedImageModeling",
"BeitForSemanticSegmentation",
"BeitModel",
"BeitPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"FlaxBeitForImageClassification",
"FlaxBeitForMaskedImageModeling",
"FlaxBeitModel",
"FlaxBeitPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 36
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = BartphoTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : str = ["▁This", "▁is", "▁a", "▁t", "est"]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[Any] = {"unk_token": "<unk>"}
_lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
with open(self.monolingual_vocab_file, "w", encoding="utf-8") as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n")
_lowerCAmelCase : Optional[Any] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BartphoTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "This is a là test"
_lowerCAmelCase : Optional[int] = "This is a<unk><unk> test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
_lowerCAmelCase : List[Any] = "This is a là test"
_lowerCAmelCase : str = "▁This ▁is ▁a ▁l à ▁t est".split()
_lowerCAmelCase : str = tokenizer.tokenize(__a)
self.assertListEqual(__a, __a)
_lowerCAmelCase : Tuple = tokens + [tokenizer.unk_token]
_lowerCAmelCase : List[str] = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), __a)
| 36
| 1
|
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_utils
from tqdm import auto as tqdm_lib
_snake_case = threading.Lock()
_snake_case = None
_snake_case = {
"debug": logging.DEBUG,
"info": logging.INFO,
"warning": logging.WARNING,
"error": logging.ERROR,
"critical": logging.CRITICAL,
}
_snake_case = logging.WARNING
_snake_case = True
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = os.getenv("TRANSFORMERS_VERBOSITY" , _lowerCamelCase )
if env_level_str:
if env_level_str in log_levels:
return log_levels[env_level_str]
else:
logging.getLogger().warning(
F"Unknown option TRANSFORMERS_VERBOSITY={env_level_str}, "
F"has to be one of: { ', '.join(log_levels.keys() ) }" )
return _default_log_level
def A ( ):
'''simple docstring'''
return __name__.split("." )[0]
def A ( ):
'''simple docstring'''
return logging.getLogger(_get_library_name() )
def A ( ):
'''simple docstring'''
global _default_handler
with _lock:
if _default_handler:
# This library has already configured the library root logger.
return
_lowerCAmelCase : Union[str, Any] = logging.StreamHandler() # Set sys.stderr as stream.
_lowerCAmelCase : Tuple = sys.stderr.flush
# Apply our default configuration to the library root logger.
_lowerCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.addHandler(_default_handler )
library_root_logger.setLevel(_get_default_logging_level() )
_lowerCAmelCase : List[Any] = False
def A ( ):
'''simple docstring'''
global _default_handler
with _lock:
if not _default_handler:
return
_lowerCAmelCase : Tuple = _get_library_root_logger()
library_root_logger.removeHandler(_default_handler )
library_root_logger.setLevel(logging.NOTSET )
_lowerCAmelCase : List[str] = None
def A ( ):
'''simple docstring'''
return log_levels
def A ( _lowerCamelCase = None ):
'''simple docstring'''
if name is None:
_lowerCAmelCase : Union[str, Any] = _get_library_name()
_configure_library_root_logger()
return logging.getLogger(_lowerCamelCase )
def A ( ):
'''simple docstring'''
_configure_library_root_logger()
return _get_library_root_logger().getEffectiveLevel()
def A ( _lowerCamelCase ):
'''simple docstring'''
_configure_library_root_logger()
_get_library_root_logger().setLevel(_lowerCamelCase )
def A ( ):
'''simple docstring'''
return set_verbosity(_lowerCamelCase )
def A ( ):
'''simple docstring'''
return set_verbosity(_lowerCamelCase )
def A ( ):
'''simple docstring'''
return set_verbosity(_lowerCamelCase )
def A ( ):
'''simple docstring'''
return set_verbosity(_lowerCamelCase )
def A ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().removeHandler(_default_handler )
def A ( ):
'''simple docstring'''
_configure_library_root_logger()
assert _default_handler is not None
_get_library_root_logger().addHandler(_default_handler )
def A ( _lowerCamelCase ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None
_get_library_root_logger().addHandler(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
_configure_library_root_logger()
assert handler is not None and handler not in _get_library_root_logger().handlers
_get_library_root_logger().removeHandler(_lowerCamelCase )
def A ( ):
'''simple docstring'''
_configure_library_root_logger()
_lowerCAmelCase : Union[str, Any] = False
def A ( ):
'''simple docstring'''
_configure_library_root_logger()
_lowerCAmelCase : int = True
def A ( ):
'''simple docstring'''
_lowerCAmelCase : str = _get_library_root_logger().handlers
for handler in handlers:
_lowerCAmelCase : Union[str, Any] = logging.Formatter("[%(levelname)s|%(filename)s:%(lineno)s] %(asctime)s >> %(message)s" )
handler.setFormatter(_lowerCamelCase )
def A ( ):
'''simple docstring'''
_lowerCAmelCase : int = _get_library_root_logger().handlers
for handler in handlers:
handler.setFormatter(_lowerCamelCase )
def A ( self , *_lowerCamelCase , **_lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = os.getenv("TRANSFORMERS_NO_ADVISORY_WARNINGS" , _lowerCamelCase )
if no_advisory_warnings:
return
self.warning(*_lowerCamelCase , **_lowerCamelCase )
_snake_case = warning_advice
@functools.lru_cache(_lowerCamelCase )
def A ( self , *_lowerCamelCase , **_lowerCamelCase ):
'''simple docstring'''
self.warning(*_lowerCamelCase , **_lowerCamelCase )
_snake_case = warning_once
class UpperCAmelCase_ :
def __init__( self, *__a, **__a): # pylint: disable=unused-argument
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = args[0] if args else None
def __iter__( self):
'''simple docstring'''
return iter(self._iterator)
def __getattr__( self, __a):
'''simple docstring'''
def empty_fn(*__a, **__a): # pylint: disable=unused-argument
return
return empty_fn
def __enter__( self):
'''simple docstring'''
return self
def __exit__( self, __a, __a, __a):
'''simple docstring'''
return
class UpperCAmelCase_ :
def __call__( self, *__a, **__a):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm(*__a, **__a)
else:
return EmptyTqdm(*__a, **__a)
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
_lowerCAmelCase : List[str] = None
if _tqdm_active:
return tqdm_lib.tqdm.set_lock(*__a, **__a)
def snake_case__ ( self):
'''simple docstring'''
if _tqdm_active:
return tqdm_lib.tqdm.get_lock()
_snake_case = _tqdm_cls()
def A ( ):
'''simple docstring'''
global _tqdm_active
return bool(_tqdm_active )
def A ( ):
'''simple docstring'''
global _tqdm_active
_lowerCAmelCase : int = True
hf_hub_utils.enable_progress_bars()
def A ( ):
'''simple docstring'''
global _tqdm_active
_lowerCAmelCase : Optional[Any] = False
hf_hub_utils.disable_progress_bars()
| 36
|
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
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def constraint_to_multiple_of(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0 , _lowerCamelCase=None ):
_lowerCAmelCase : Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowerCAmelCase : Optional[int] = math.floor(val / multiple ) * multiple
if x < min_val:
_lowerCAmelCase : List[str] = math.ceil(val / multiple ) * multiple
return x
_lowerCAmelCase : Union[str, Any] = (output_size, output_size) if isinstance(_lowerCamelCase , _lowerCamelCase ) else output_size
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = get_image_size(_lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = output_size
# determine new height and width
_lowerCAmelCase : List[Any] = output_height / input_height
_lowerCAmelCase : Any = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowerCAmelCase : Union[str, Any] = scale_width
else:
# fit height
_lowerCAmelCase : Union[str, Any] = scale_height
_lowerCAmelCase : List[str] = constraint_to_multiple_of(scale_height * input_height , multiple=_lowerCamelCase )
_lowerCAmelCase : Dict = constraint_to_multiple_of(scale_width * input_width , multiple=_lowerCamelCase )
return (new_height, new_width)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['pixel_values']
def __init__( self, __a = True, __a = None, __a = PILImageResampling.BILINEAR, __a = False, __a = 1, __a = True, __a = 1 / 255, __a = True, __a = None, __a = None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = size if size is not None else {"height": 384, "width": 384}
_lowerCAmelCase : Optional[int] = get_size_dict(__a)
_lowerCAmelCase : Optional[Any] = do_resize
_lowerCAmelCase : Dict = size
_lowerCAmelCase : Any = keep_aspect_ratio
_lowerCAmelCase : str = ensure_multiple_of
_lowerCAmelCase : str = resample
_lowerCAmelCase : Dict = do_rescale
_lowerCAmelCase : Optional[int] = rescale_factor
_lowerCAmelCase : Dict = do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self, __a, __a, __a = False, __a = 1, __a = PILImageResampling.BICUBIC, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : List[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()}")
_lowerCAmelCase : List[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 snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return rescale(__a, scale=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return normalize(__a, mean=__a, std=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = ChannelDimension.FIRST, **__a, ):
'''simple docstring'''
_lowerCAmelCase : int = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : List[Any] = size if size is not None else self.size
_lowerCAmelCase : str = get_size_dict(__a)
_lowerCAmelCase : Dict = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowerCAmelCase : Any = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowerCAmelCase : int = resample if resample is not None else self.resample
_lowerCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase : List[str] = image_std if image_std is not None else self.image_std
_lowerCAmelCase : Optional[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_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.
_lowerCAmelCase : List[Any] = [to_numpy_array(__a) for image in images]
if do_resize:
_lowerCAmelCase : Any = [self.resize(image=__a, size=__a, resample=__a) for image in images]
if do_rescale:
_lowerCAmelCase : List[str] = [self.rescale(image=__a, scale=__a) for image in images]
if do_normalize:
_lowerCAmelCase : Dict = [self.normalize(image=__a, mean=__a, std=__a) for image in images]
_lowerCAmelCase : List[str] = [to_channel_dimension_format(__a, __a) for image in images]
_lowerCAmelCase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=__a, tensor_type=__a)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = 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):
_lowerCAmelCase : List[Any] = target_sizes.numpy()
_lowerCAmelCase : Dict = []
for idx in range(len(__a)):
_lowerCAmelCase : int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=__a)
_lowerCAmelCase : int = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(__a)
else:
_lowerCAmelCase : Dict = logits.argmax(dim=1)
_lowerCAmelCase : str = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 36
| 1
|
from typing import List, Optional, Union
import numpy as np
from ....audio_utils import mel_filter_bank, optimal_fft_length, spectrogram, window_function
from ....feature_extraction_sequence_utils import SequenceFeatureExtractor
from ....feature_extraction_utils import BatchFeature
from ....file_utils import PaddingStrategy, TensorType
from ....utils import logging
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['input_features', 'attention_mask']
def __init__( self, __a=80, __a=1_6000, __a=0.0, __a=10, __a=25, __a="hamming_window", __a=32_768.0, __a=0.97, __a=1.0, __a=True, __a=True, __a=False, **__a, ):
'''simple docstring'''
super().__init__(feature_size=__a, sampling_rate=__a, padding_value=__a, **__a)
_lowerCAmelCase : int = feature_size
_lowerCAmelCase : Optional[Any] = sampling_rate
_lowerCAmelCase : Tuple = padding_value
_lowerCAmelCase : int = hop_length
_lowerCAmelCase : str = win_length
_lowerCAmelCase : str = frame_signal_scale
_lowerCAmelCase : Union[str, Any] = preemphasis_coeff
_lowerCAmelCase : Optional[Any] = mel_floor
_lowerCAmelCase : str = normalize_means
_lowerCAmelCase : Union[str, Any] = normalize_vars
_lowerCAmelCase : List[str] = win_function
_lowerCAmelCase : str = return_attention_mask
_lowerCAmelCase : str = win_length * sampling_rate // 1000
_lowerCAmelCase : Union[str, Any] = hop_length * sampling_rate // 1000
_lowerCAmelCase : Tuple = optimal_fft_length(self.sample_size)
_lowerCAmelCase : Tuple = (self.n_fft // 2) + 1
def snake_case__ ( self, __a):
'''simple docstring'''
if self.win_function == "hamming_window":
_lowerCAmelCase : str = window_function(window_length=self.sample_size, name=self.win_function, periodic=__a)
else:
_lowerCAmelCase : Union[str, Any] = window_function(window_length=self.sample_size, name=self.win_function)
_lowerCAmelCase : List[Any] = mel_filter_bank(
num_frequency_bins=self.n_freqs, num_mel_filters=self.feature_size, min_frequency=0.0, max_frequency=self.sampling_rate / 2.0, sampling_rate=self.sampling_rate, )
_lowerCAmelCase : Optional[Any] = spectrogram(
one_waveform * self.frame_signal_scale, window=__a, frame_length=self.sample_size, hop_length=self.sample_stride, fft_length=self.n_fft, center=__a, preemphasis=self.preemphasis_coeff, mel_filters=__a, mel_floor=self.mel_floor, log_mel="log", )
return msfc_features.T
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
if self.normalize_means:
_lowerCAmelCase : Dict = x[:input_length].mean(axis=0)
_lowerCAmelCase : Optional[Any] = np.subtract(__a, __a)
if self.normalize_vars:
_lowerCAmelCase : List[str] = x[:input_length].std(axis=0)
_lowerCAmelCase : Optional[int] = np.divide(__a, __a)
if input_length < x.shape[0]:
_lowerCAmelCase : Union[str, Any] = padding_value
# make sure array is in float32
_lowerCAmelCase : Optional[int] = x.astype(np.floataa)
return x
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : int = attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
return [self._normalize_one(__a, __a, self.padding_value) for x, n in zip(__a, __a)]
def __call__( self, __a, __a = False, __a = None, __a = False, __a = None, __a = None, __a = None, __a = None, **__a, ):
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
f" {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.")
_lowerCAmelCase : List[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}")
_lowerCAmelCase : int = is_batched_numpy or (
isinstance(__a, (list, tuple)) and (isinstance(raw_speech[0], (np.ndarray, tuple, list)))
)
if is_batched:
_lowerCAmelCase : Any = [np.asarray(__a, dtype=np.floataa) for speech in raw_speech]
elif not is_batched and not isinstance(__a, np.ndarray):
_lowerCAmelCase : Tuple = np.asarray(__a, dtype=np.floataa)
elif isinstance(__a, np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
_lowerCAmelCase : Union[str, Any] = raw_speech.astype(np.floataa)
# always return batch
if not is_batched:
_lowerCAmelCase : Optional[Any] = [raw_speech]
# extract fbank features
_lowerCAmelCase : List[str] = [self._extract_mfsc_features(__a) for one_waveform in raw_speech]
# convert into correct format for padding
_lowerCAmelCase : int = BatchFeature({"input_features": features})
_lowerCAmelCase : str = self.pad(
__a, padding=__a, max_length=__a, truncation=__a, pad_to_multiple_of=__a, return_attention_mask=__a, **__a, )
# make sure list is in array format
_lowerCAmelCase : Optional[Any] = padded_inputs.get("input_features")
if isinstance(input_features[0], __a):
_lowerCAmelCase : int = [np.asarray(__a, dtype=np.floataa) for feature in input_features]
_lowerCAmelCase : str = padded_inputs.get("attention_mask")
if attention_mask is not None:
_lowerCAmelCase : Optional[Any] = [np.asarray(__a, dtype=np.intaa) for array in attention_mask]
if self.normalize_means or self.normalize_vars:
_lowerCAmelCase : int = (
np.array(__a, dtype=np.intaa)
if self._get_padding_strategies(__a, max_length=__a) is not PaddingStrategy.DO_NOT_PAD
and padding
else None
)
_lowerCAmelCase : List[Any] = self.normalize(
padded_inputs["input_features"], attention_mask=__a)
if return_tensors is not None:
_lowerCAmelCase : Tuple = padded_inputs.convert_to_tensors(__a)
return padded_inputs
| 36
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = "huggingface/label-files"
_lowerCAmelCase : int = "imagenet-1k-id2label.json"
_lowerCAmelCase : Tuple = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
_lowerCAmelCase : Tuple = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_lowerCAmelCase : Tuple = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
_lowerCAmelCase : Optional[int] = BitConfig(
conv_layer=_lowerCamelCase , num_labels=1_000 , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase , )
return config
def A ( _lowerCamelCase ):
'''simple docstring'''
if "stem.conv" in name:
_lowerCAmelCase : List[str] = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
_lowerCAmelCase : Any = name.replace("blocks" , "layers" )
if "head.fc" in name:
_lowerCAmelCase : Optional[Any] = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
_lowerCAmelCase : Any = "bit." + name
if "bit" not in name and "classifier" not in name:
_lowerCAmelCase : Dict = "bit.encoder." + name
return name
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCAmelCase : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = get_config(_lowerCamelCase )
# load original model from timm
_lowerCAmelCase : int = create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model
_lowerCAmelCase : Any = timm_model.state_dict()
for key in state_dict.copy().keys():
_lowerCAmelCase : Dict = state_dict.pop(_lowerCamelCase )
_lowerCAmelCase : Tuple = val.squeeze() if "head" in key else val
# load HuggingFace model
_lowerCAmelCase : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
_lowerCAmelCase : Dict = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
_lowerCAmelCase : Optional[int] = transform.transforms
_lowerCAmelCase : Tuple = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
_lowerCAmelCase : Tuple = BitImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_lowerCAmelCase : Optional[int] = prepare_img()
_lowerCAmelCase : Any = transform(_lowerCamelCase ).unsqueeze(0 )
_lowerCAmelCase : Optional[int] = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
_lowerCAmelCase : Tuple = model(_lowerCamelCase )
_lowerCAmelCase : str = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
_lowerCAmelCase : Union[str, Any] = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
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 to push the model to the hub.",
)
_snake_case = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 36
|
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCAmelCase_ ( a , a):
lowerCamelCase__ = 'swin'
lowerCamelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self, __a=224, __a=4, __a=3, __a=96, __a=[2, 2, 6, 2], __a=[3, 6, 12, 24], __a=7, __a=4.0, __a=True, __a=0.0, __a=0.0, __a=0.1, __a="gelu", __a=False, __a=0.02, __a=1E-5, __a=32, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = image_size
_lowerCAmelCase : Union[str, Any] = patch_size
_lowerCAmelCase : Tuple = num_channels
_lowerCAmelCase : List[Any] = embed_dim
_lowerCAmelCase : Tuple = depths
_lowerCAmelCase : Optional[Any] = len(__a)
_lowerCAmelCase : int = num_heads
_lowerCAmelCase : int = window_size
_lowerCAmelCase : int = mlp_ratio
_lowerCAmelCase : List[Any] = qkv_bias
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = drop_path_rate
_lowerCAmelCase : int = hidden_act
_lowerCAmelCase : Tuple = use_absolute_embeddings
_lowerCAmelCase : Optional[int] = layer_norm_eps
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Tuple = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCAmelCase : List[str] = int(embed_dim * 2 ** (len(__a) - 1))
_lowerCAmelCase : List[Any] = ["stem"] + [f"stage{idx}" for idx in range(1, len(__a) + 1)]
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
| 36
| 1
|
from __future__ import annotations
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
from ..test_modeling_tf_common import floats_tensor
from .test_framework_agnostic import GenerationIntegrationTestsMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
AutoTokenizer,
TFAutoModelForCausalLM,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSpeechSeqaSeq,
TFAutoModelForVisionaSeq,
TFBartForConditionalGeneration,
TFLogitsProcessorList,
TFMinLengthLogitsProcessor,
tf_top_k_top_p_filtering,
)
if is_tensorflow_text_available():
import tensorflow_text as text
@require_tf
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = tf.convert_to_tensor(
[
[
8.2_220_991, # 3rd highest value; idx. 0
-0.5_620_044,
5.23_229_752,
4.0_386_393,
-6.8_798_378,
-0.54_785_802,
-3.2_012_153,
2.92_777_176,
1.88_171_953,
7.35_341_276, # 5th highest value; idx. 9
8.43_207_833, # 2nd highest value; idx. 10
-9.85_711_836,
-5.96_209_236,
-1.13_039_161,
-7.1_115_294,
-0.8_369_633,
-5.3_186_408,
7.06_427_407,
0.81_369_344,
-0.82_023_817,
-5.9_179_796,
0.58_813_443,
-6.99_778_438,
4.71_551_189,
-0.18_771_637,
7.44_020_759, # 4th highest value; idx. 25
9.38_450_987, # 1st highest value; idx. 26
2.12_662_941,
-9.32_562_038,
2.35_652_522,
], # cummulative prob of 5 highest values <= 0.6
[
0.58_425_518,
4.53_139_238,
-5.57_510_464,
-6.28_030_699,
-7.19_529_503,
-4.02_122_551,
1.39_337_037,
-6.06_707_057,
1.59_480_517,
-9.643_119,
0.03_907_799,
0.67_231_762,
-8.88_206_726,
6.27_115_922, # 4th highest value; idx. 13
2.28_520_723,
4.82_767_506,
4.30_421_368,
8.8_275_313, # 2nd highest value; idx. 17
5.44_029_958, # 5th highest value; idx. 18
-4.4_735_794,
7.38_579_536, # 3rd highest value; idx. 20
-2.91_051_663,
2.61_946_077,
-2.5_674_762,
-9.48_959_302,
-4.02_922_645,
-1.35_416_918,
9.67_702_323, # 1st highest value; idx. 27
-5.89_478_553,
1.85_370_467,
], # cummulative prob of 5 highest values <= 0.6
], dtype=tf.floataa, )
_lowerCAmelCase : Any = tf.convert_to_tensor(
[[0, 0], [0, 9], [0, 10], [0, 25], [0, 26], [1, 13], [1, 17], [1, 18], [1, 20], [1, 27]], dtype=tf.intaa, ) # expected non filtered idx as noted above
_lowerCAmelCase : Tuple = tf.convert_to_tensor(
[8.222_099, 7.3_534_126, 8.432_078, 7.4_402_075, 9.38_451, 6.271_159, 8.827_531, 5.4_402_995, 7.3_857_956, 9.677_023], dtype=tf.floataa, ) # expected non filtered values as noted above
_lowerCAmelCase : int = tf_top_k_top_p_filtering(__a, top_k=10, top_p=0.6, min_tokens_to_keep=4)
_lowerCAmelCase : str = output[output != -float("inf")]
_lowerCAmelCase : Any = tf.cast(
tf.where(tf.not_equal(__a, tf.constant(-float("inf"), dtype=tf.floataa))), dtype=tf.intaa, )
tf.debugging.assert_near(__a, __a, rtol=1E-12)
tf.debugging.assert_equal(__a, __a)
@require_tf
class UpperCAmelCase_ ( unittest.TestCase , a):
# setting framework_dependent_parameters needs to be gated, just like its contents' imports
if is_tf_available():
lowerCamelCase__ = {
'AutoModelForCausalLM': TFAutoModelForCausalLM,
'AutoModelForSpeechSeq2Seq': TFAutoModelForSpeechSeqaSeq,
'AutoModelForSeq2SeqLM': TFAutoModelForSeqaSeqLM,
'AutoModelForVision2Seq': TFAutoModelForVisionaSeq,
'LogitsProcessorList': TFLogitsProcessorList,
'MinLengthLogitsProcessor': TFMinLengthLogitsProcessor,
'create_tensor_fn': tf.convert_to_tensor,
'floats_tensor': floats_tensor,
'return_tensors': 'tf',
}
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_lowerCAmelCase : List[Any] = 2
_lowerCAmelCase : Optional[Any] = 2
class UpperCAmelCase_ ( tf.Module):
def __init__( self, __a):
'''simple docstring'''
super(__a, self).__init__()
_lowerCAmelCase : List[Any] = model
@tf.function(
input_signature=(
tf.TensorSpec((None, input_length), tf.intaa, name="input_ids"),
tf.TensorSpec((None, input_length), tf.intaa, name="attention_mask"),
), jit_compile=__a, )
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[str] = self.model.generate(
input_ids=__a, attention_mask=__a, max_new_tokens=__a, return_dict_in_generate=__a, )
return {"sequences": outputs["sequences"]}
_lowerCAmelCase : Optional[Any] = [[2, 0], [102, 103]]
_lowerCAmelCase : int = [[1, 0], [1, 1]]
_lowerCAmelCase : Any = DummyModel(model=__a)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__a, __a, signatures={"serving_default": dummy_model.serving})
_lowerCAmelCase : Tuple = tf.saved_model.load(__a).signatures["serving_default"]
for batch_size in range(1, len(__a) + 1):
_lowerCAmelCase : List[str] = {
"input_ids": tf.constant(dummy_input_ids[:batch_size]),
"attention_mask": tf.constant(dummy_attention_masks[:batch_size]),
}
_lowerCAmelCase : Dict = serving_func(**__a)["sequences"]
_lowerCAmelCase : Optional[int] = test_model.generate(**__a, max_new_tokens=__a)
tf.debugging.assert_equal(__a, __a)
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_lowerCAmelCase : List[Any] = 1
_lowerCAmelCase : List[str] = 2
class UpperCAmelCase_ ( tf.Module):
def __init__( self, __a):
'''simple docstring'''
super(__a, self).__init__()
_lowerCAmelCase : Optional[Any] = model
@tf.function(
input_signature=(
tf.TensorSpec((batch_size, None), tf.intaa, name="input_ids"),
tf.TensorSpec((batch_size, None), tf.intaa, name="attention_mask"),
), jit_compile=__a, )
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model.generate(
input_ids=__a, attention_mask=__a, max_new_tokens=__a, return_dict_in_generate=__a, )
return {"sequences": outputs["sequences"]}
_lowerCAmelCase : Any = [[2], [102, 103]]
_lowerCAmelCase : Optional[Any] = [[1], [1, 1]]
_lowerCAmelCase : Any = DummyModel(model=__a)
with tempfile.TemporaryDirectory() as tmp_dir:
tf.saved_model.save(__a, __a, signatures={"serving_default": dummy_model.serving})
_lowerCAmelCase : str = tf.saved_model.load(__a).signatures["serving_default"]
for input_row in range(len(__a)):
_lowerCAmelCase : Any = {
"input_ids": tf.constant([dummy_input_ids[input_row]]),
"attention_mask": tf.constant([dummy_attention_masks[input_row]]),
}
_lowerCAmelCase : List[Any] = serving_func(**__a)["sequences"]
_lowerCAmelCase : Any = test_model.generate(**__a, max_new_tokens=__a)
tf.debugging.assert_equal(__a, __a)
@slow
@require_tensorflow_text
def snake_case__ ( self):
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# file needed to load the TF tokenizer
hf_hub_download(repo_id="google/flan-t5-small", filename="spiece.model", local_dir=__a)
class UpperCAmelCase_ ( tf.keras.layers.Layer):
def __init__( self):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Optional[Any] = text.SentencepieceTokenizer(
model=tf.io.gfile.GFile(os.path.join(__a, "spiece.model"), "rb").read())
_lowerCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained("hf-internal-testing/tiny-random-t5")
def snake_case__ ( self, __a, *__a, **__a):
'''simple docstring'''
_lowerCAmelCase : Any = self.tokenizer.tokenize(__a)
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = text.pad_model_inputs(
__a, max_seq_length=64, pad_value=self.model.config.pad_token_id)
_lowerCAmelCase : Union[str, Any] = self.model.generate(input_ids=__a, attention_mask=__a)
return self.tokenizer.detokenize(__a)
_lowerCAmelCase : int = CompleteSentenceTransformer()
_lowerCAmelCase : Optional[Any] = tf.keras.layers.Input(shape=(1,), dtype=tf.string, name="inputs")
_lowerCAmelCase : Optional[int] = complete_model(__a)
_lowerCAmelCase : Union[str, Any] = tf.keras.Model(__a, __a)
keras_model.save(__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = {
"do_sample": True,
"num_beams": 1,
"top_p": 0.7,
"top_k": 10,
"temperature": 0.7,
}
_lowerCAmelCase : Optional[int] = 14
_lowerCAmelCase : List[str] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_lowerCAmelCase : List[str] = "Hello, my dog is cute and"
_lowerCAmelCase : int = tokenizer(__a, return_tensors="tf")
_lowerCAmelCase : List[str] = TFAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_lowerCAmelCase : List[str] = 638
# forces the generation to happen on CPU, to avoid GPU-related quirks
with tf.device(":/CPU:0"):
tf.random.set_seed(0)
_lowerCAmelCase : str = model.generate(**__a, eos_token_id=__a, **__a)
self.assertTrue(expectation == len(generated_tokens[0]))
_lowerCAmelCase : List[Any] = [638, 198]
with tf.device(":/CPU:0"):
tf.random.set_seed(0)
_lowerCAmelCase : Optional[Any] = model.generate(**__a, eos_token_id=__a, **__a)
self.assertTrue(expectation == len(generated_tokens[0]))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-bart")
_lowerCAmelCase : str = "Hugging Face is a technology company based in New York and Paris."
_lowerCAmelCase : Any = bart_tokenizer(__a, return_tensors="tf").input_ids
_lowerCAmelCase : Any = TFBartForConditionalGeneration.from_pretrained("hf-internal-testing/tiny-random-bart")
_lowerCAmelCase : Optional[Any] = bart_model.generate(__a).numpy()
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a, __a=None, **__a):
'''simple docstring'''
return super().call(__a, **__a)
_lowerCAmelCase : Optional[int] = FakeBart.from_pretrained("hf-internal-testing/tiny-random-bart")
_lowerCAmelCase : Dict = bart_model.generate(__a, foo="bar").numpy()
self.assertTrue(np.array_equal(__a, __a))
class UpperCAmelCase_ ( bart_model.model.encoder.__class__):
def snake_case__ ( self, __a, **__a):
'''simple docstring'''
return super().call(__a, **__a)
_lowerCAmelCase : Optional[Any] = FakeEncoder(bart_model.config, bart_model.model.shared)
_lowerCAmelCase : List[str] = fake_encoder
# Normal generation still works (the output will be different because the encoder weights are different)
_lowerCAmelCase : int = bart_model.generate(__a).numpy()
with self.assertRaises(__a):
# FakeEncoder.call() accepts **kwargs -> no filtering -> value error due to unexpected input "foo"
bart_model.generate(__a, foo="bar")
| 36
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 36
| 1
|
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
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 DetrImageProcessor
class UpperCAmelCase_ ( unittest.TestCase):
def __init__( self, __a, __a=7, __a=3, __a=30, __a=400, __a=True, __a=None, __a=True, __a=1 / 255, __a=True, __a=[0.5, 0.5, 0.5], __a=[0.5, 0.5, 0.5], __a=True, ):
'''simple docstring'''
_lowerCAmelCase : int = size if size is not None else {"shortest_edge": 18, "longest_edge": 1333}
_lowerCAmelCase : Dict = parent
_lowerCAmelCase : Optional[int] = batch_size
_lowerCAmelCase : Tuple = num_channels
_lowerCAmelCase : Any = min_resolution
_lowerCAmelCase : Tuple = max_resolution
_lowerCAmelCase : Optional[Any] = do_resize
_lowerCAmelCase : Any = size
_lowerCAmelCase : Union[str, Any] = do_rescale
_lowerCAmelCase : List[Any] = rescale_factor
_lowerCAmelCase : Tuple = do_normalize
_lowerCAmelCase : Union[str, Any] = image_mean
_lowerCAmelCase : Tuple = image_std
_lowerCAmelCase : Tuple = do_pad
def snake_case__ ( self):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def snake_case__ ( self, __a, __a=False):
'''simple docstring'''
if not batched:
_lowerCAmelCase : List[str] = image_inputs[0]
if isinstance(__a, Image.Image):
_lowerCAmelCase , _lowerCAmelCase : List[str] = image.size
else:
_lowerCAmelCase , _lowerCAmelCase : List[Any] = image.shape[1], image.shape[2]
if w < h:
_lowerCAmelCase : Optional[int] = int(self.size["shortest_edge"] * h / w)
_lowerCAmelCase : List[Any] = self.size["shortest_edge"]
elif w > h:
_lowerCAmelCase : str = self.size["shortest_edge"]
_lowerCAmelCase : Union[str, Any] = int(self.size["shortest_edge"] * w / h)
else:
_lowerCAmelCase : Any = self.size["shortest_edge"]
_lowerCAmelCase : List[str] = self.size["shortest_edge"]
else:
_lowerCAmelCase : Optional[int] = []
for image in image_inputs:
_lowerCAmelCase , _lowerCAmelCase : List[str] = self.get_expected_values([image])
expected_values.append((expected_height, expected_width))
_lowerCAmelCase : List[str] = max(__a, key=lambda __a: item[0])[0]
_lowerCAmelCase : Union[str, Any] = max(__a, key=lambda __a: item[1])[1]
return expected_height, expected_width
@require_torch
@require_vision
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = DetrImageProcessor if is_vision_available() else None
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = DetrImageProcessingTester(self)
@property
def snake_case__ ( self):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : 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_rescale"))
self.assertTrue(hasattr(__a, "rescale_factor"))
self.assertTrue(hasattr(__a, "do_resize"))
self.assertTrue(hasattr(__a, "size"))
self.assertTrue(hasattr(__a, "do_pad"))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size, {"shortest_edge": 18, "longest_edge": 1333})
self.assertEqual(image_processor.do_pad, __a)
_lowerCAmelCase : int = self.image_processing_class.from_dict(
self.image_processor_dict, size=42, max_size=84, pad_and_return_pixel_mask=__a)
self.assertEqual(image_processor.size, {"shortest_edge": 42, "longest_edge": 84})
self.assertEqual(image_processor.do_pad, __a)
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
_lowerCAmelCase : List[str] = prepare_image_inputs(self.image_processor_tester, equal_resolution=__a)
for image in image_inputs:
self.assertIsInstance(__a, Image.Image)
# Test not batched input
_lowerCAmelCase : Tuple = image_processing(image_inputs[0], return_tensors="pt").pixel_values
_lowerCAmelCase , _lowerCAmelCase : Tuple = self.image_processor_tester.get_expected_values(__a)
self.assertEqual(
encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )
# Test batched
_lowerCAmelCase , _lowerCAmelCase : Dict = self.image_processor_tester.get_expected_values(__a, batched=__a)
_lowerCAmelCase : 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,
expected_height,
expected_width,
), )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
_lowerCAmelCase : int = 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
_lowerCAmelCase : List[str] = image_processing(image_inputs[0], return_tensors="pt").pixel_values
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = self.image_processor_tester.get_expected_values(__a)
self.assertEqual(
encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )
# Test batched
_lowerCAmelCase : List[Any] = image_processing(__a, return_tensors="pt").pixel_values
_lowerCAmelCase , _lowerCAmelCase : int = self.image_processor_tester.get_expected_values(__a, batched=__a)
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
), )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
_lowerCAmelCase : 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
_lowerCAmelCase : Optional[Any] = image_processing(image_inputs[0], return_tensors="pt").pixel_values
_lowerCAmelCase , _lowerCAmelCase : Any = self.image_processor_tester.get_expected_values(__a)
self.assertEqual(
encoded_images.shape, (1, self.image_processor_tester.num_channels, expected_height, expected_width), )
# Test batched
_lowerCAmelCase : Any = image_processing(__a, return_tensors="pt").pixel_values
_lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = self.image_processor_tester.get_expected_values(__a, batched=__a)
self.assertEqual(
encoded_images.shape, (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
), )
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f:
_lowerCAmelCase : int = json.loads(f.read())
_lowerCAmelCase : Optional[int] = {"image_id": 3_9769, "annotations": target}
# encode them
_lowerCAmelCase : str = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50")
_lowerCAmelCase : str = image_processing(images=__a, annotations=__a, return_tensors="pt")
# verify pixel values
_lowerCAmelCase : Optional[Any] = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, __a)
_lowerCAmelCase : Union[str, Any] = torch.tensor([0.2_796, 0.3_138, 0.3_481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], __a, atol=1E-4))
# verify area
_lowerCAmelCase : int = torch.tensor([5_887.9_600, 11_250.2_061, 489_353.8_438, 837_122.7_500, 147_967.5_156, 165_732.3_438])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], __a))
# verify boxes
_lowerCAmelCase : Union[str, Any] = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, __a)
_lowerCAmelCase : int = torch.tensor([0.5_503, 0.2_765, 0.0_604, 0.2_215])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], __a, atol=1E-3))
# verify image_id
_lowerCAmelCase : List[str] = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], __a))
# verify is_crowd
_lowerCAmelCase : int = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], __a))
# verify class_labels
_lowerCAmelCase : List[str] = torch.tensor([75, 75, 63, 65, 17, 17])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], __a))
# verify orig_size
_lowerCAmelCase : Optional[int] = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], __a))
# verify size
_lowerCAmelCase : Any = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], __a))
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f:
_lowerCAmelCase : Union[str, Any] = json.loads(f.read())
_lowerCAmelCase : Tuple = {"file_name": "000000039769.png", "image_id": 3_9769, "segments_info": target}
_lowerCAmelCase : Optional[Any] = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic")
# encode them
_lowerCAmelCase : Optional[Any] = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic")
_lowerCAmelCase : str = image_processing(images=__a, annotations=__a, masks_path=__a, return_tensors="pt")
# verify pixel values
_lowerCAmelCase : Optional[Any] = torch.Size([1, 3, 800, 1066])
self.assertEqual(encoding["pixel_values"].shape, __a)
_lowerCAmelCase : str = torch.tensor([0.2_796, 0.3_138, 0.3_481])
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3], __a, atol=1E-4))
# verify area
_lowerCAmelCase : List[Any] = torch.tensor([147_979.6_875, 165_527.0_469, 484_638.5_938, 11_292.9_375, 5_879.6_562, 7_634.1_147])
self.assertTrue(torch.allclose(encoding["labels"][0]["area"], __a))
# verify boxes
_lowerCAmelCase : Union[str, Any] = torch.Size([6, 4])
self.assertEqual(encoding["labels"][0]["boxes"].shape, __a)
_lowerCAmelCase : List[str] = torch.tensor([0.2_625, 0.5_437, 0.4_688, 0.8_625])
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0], __a, atol=1E-3))
# verify image_id
_lowerCAmelCase : List[str] = torch.tensor([3_9769])
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"], __a))
# verify is_crowd
_lowerCAmelCase : List[Any] = torch.tensor([0, 0, 0, 0, 0, 0])
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"], __a))
# verify class_labels
_lowerCAmelCase : int = torch.tensor([17, 17, 63, 75, 75, 93])
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"], __a))
# verify masks
_lowerCAmelCase : Dict = 82_2873
self.assertEqual(encoding["labels"][0]["masks"].sum().item(), __a)
# verify orig_size
_lowerCAmelCase : Tuple = torch.tensor([480, 640])
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"], __a))
# verify size
_lowerCAmelCase : Tuple = torch.tensor([800, 1066])
self.assertTrue(torch.allclose(encoding["labels"][0]["size"], __a))
| 36
|
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_snake_case = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if got_ver is None or want_ver is None:
raise ValueError(
F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
F" reinstalling {pkg}." )
if not ops[op](version.parse(_lowerCamelCase ) , version.parse(_lowerCamelCase ) ):
raise ImportError(
F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}" )
def A ( _lowerCamelCase , _lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase : List[str] = F"\n{hint}" if hint is not None else ""
# non-versioned check
if re.match(r"^[\w_\-\d]+$" , _lowerCamelCase ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = requirement, None, None
else:
_lowerCAmelCase : Optional[int] = re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
F" got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Dict = match[0]
_lowerCAmelCase : Any = want_full.split("," ) # there could be multiple requirements
_lowerCAmelCase : Optional[int] = {}
for w in want_range:
_lowerCAmelCase : Any = re.findall(r"^([\s!=<>]{1,2})(.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
F" but got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Tuple = match[0]
_lowerCAmelCase : Union[str, Any] = want_ver
if op not in ops:
raise ValueError(F"{requirement}: need one of {list(ops.keys() )}, but got {op}" )
# special case
if pkg == "python":
_lowerCAmelCase : Tuple = ".".join([str(_lowerCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return
# check if any version is installed
try:
_lowerCAmelCase : Any = importlib.metadata.version(_lowerCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F"The '{requirement}' distribution was not found and is required by this application. {hint}" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
return require_version(_lowerCamelCase , _lowerCamelCase )
| 36
| 1
|
# 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
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_xmod": [
"XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XmodConfig",
"XmodOnnxConfig",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"XMOD_PRETRAINED_MODEL_ARCHIVE_LIST",
"XmodForCausalLM",
"XmodForMaskedLM",
"XmodForMultipleChoice",
"XmodForQuestionAnswering",
"XmodForSequenceClassification",
"XmodForTokenClassification",
"XmodModel",
"XmodPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_xmod import XMOD_PRETRAINED_CONFIG_ARCHIVE_MAP, XmodConfig, XmodOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xmod import (
XMOD_PRETRAINED_MODEL_ARCHIVE_LIST,
XmodForCausalLM,
XmodForMaskedLM,
XmodForMultipleChoice,
XmodForQuestionAnswering,
XmodForSequenceClassification,
XmodForTokenClassification,
XmodModel,
XmodPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 36
|
import argparse
from collections import defaultdict
import yaml
_snake_case = "docs/source/en/_toctree.yml"
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = defaultdict(_lowerCamelCase )
_lowerCAmelCase : Any = []
_lowerCAmelCase : List[str] = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"local": doc["local"], "title": doc["title"]} )
else:
new_doc_list.append(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = new_doc_list
_lowerCAmelCase : List[Any] = [key for key, value in counts.items() if value > 1]
_lowerCAmelCase : str = []
for duplicate_key in duplicates:
_lowerCAmelCase : List[str] = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
if len(_lowerCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
"others." )
# Only add this once
new_doc.append({"local": duplicate_key, "title": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1] )
_lowerCAmelCase : Optional[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(_lowerCamelCase ) > 1:
raise ValueError("{doc_list} has two 'overview' docs which is not allowed." )
overview_doc.extend(_lowerCamelCase )
# Sort
return overview_doc
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : int = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : List[str] = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : Union[str, Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase : Optional[Any] = api_doc[scheduler_idx]["sections"]
_lowerCAmelCase : Optional[Any] = clean_doc_toc(_lowerCamelCase )
_lowerCAmelCase : int = False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase : List[Any] = True
if overwrite:
_lowerCAmelCase : Dict = new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase : Tuple = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : int = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : List[str] = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase : Dict = False
_lowerCAmelCase : Optional[int] = api_doc[pipeline_idx]["sections"]
_lowerCAmelCase : Tuple = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase : List[Any] = pipeline_doc["section"]
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if overwrite:
_lowerCAmelCase : Optional[Any] = new_sub_pipeline_doc
new_pipeline_docs.append(_lowerCamelCase )
# sort overall pipeline doc
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase : Dict = True
if overwrite:
_lowerCAmelCase : Optional[int] = new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase : Optional[int] = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_snake_case = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 36
| 1
|
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def A ( _lowerCamelCase , _lowerCamelCase=0 ):
'''simple docstring'''
return sorted(_lowerCamelCase , key=lambda _lowerCamelCase : x[column] )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=float("inf" ) ):
'''simple docstring'''
for i in range(points_counts - 1 ):
for j in range(i + 1 , _lowerCamelCase ):
_lowerCAmelCase : int = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
_lowerCAmelCase : List[str] = current_dis
return min_dis
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=float("inf" ) ):
'''simple docstring'''
for i in range(min(6 , points_counts - 1 ) , _lowerCamelCase ):
for j in range(max(0 , i - 6 ) , _lowerCamelCase ):
_lowerCAmelCase : str = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
_lowerCAmelCase : List[Any] = current_dis
return min_dis
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if points_counts <= 3:
return dis_between_closest_pair(_lowerCamelCase , _lowerCamelCase )
# recursion
_lowerCAmelCase : Any = points_counts // 2
_lowerCAmelCase : Dict = closest_pair_of_points_sqr(
_lowerCamelCase , points_sorted_on_y[:mid] , _lowerCamelCase )
_lowerCAmelCase : str = closest_pair_of_points_sqr(
_lowerCamelCase , points_sorted_on_y[mid:] , points_counts - mid )
_lowerCAmelCase : Union[str, Any] = min(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : str = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(_lowerCamelCase )
_lowerCAmelCase : str = dis_between_closest_in_strip(
_lowerCamelCase , len(_lowerCamelCase ) , _lowerCamelCase )
return min(_lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = column_based_sort(_lowerCamelCase , column=0 )
_lowerCAmelCase : Optional[int] = column_based_sort(_lowerCamelCase , column=1 )
return (
closest_pair_of_points_sqr(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
) ** 0.5
if __name__ == "__main__":
_snake_case = [(2, 3), (12, 30), (40, 50), (5, 1), (12, 10), (3, 4)]
print("Distance:", closest_pair_of_points(points, len(points)))
| 36
|
def A ( _lowerCamelCase , _lowerCamelCase ):
'''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()
| 36
| 1
|
_snake_case = 0 # The first color of the flag.
_snake_case = 1 # The second color of the flag.
_snake_case = 2 # The third color of the flag.
_snake_case = (red, white, blue)
def A ( _lowerCamelCase ):
'''simple docstring'''
if not sequence:
return []
if len(_lowerCamelCase ) == 1:
return list(_lowerCamelCase )
_lowerCAmelCase : Any = 0
_lowerCAmelCase : str = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Union[str, Any] = 0
while mid <= high:
if sequence[mid] == colors[0]:
_lowerCAmelCase , _lowerCAmelCase : str = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_lowerCAmelCase , _lowerCAmelCase : List[Any] = sequence[high], sequence[mid]
high -= 1
else:
_lowerCAmelCase : List[str] = F"The elements inside the sequence must contains only {colors} values"
raise ValueError(_lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = input("Enter numbers separated by commas:\n").strip()
_snake_case = [int(item.strip()) for item in user_input.split(",")]
print(f'''{dutch_national_flag_sort(unsorted)}''')
| 36
|
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
_snake_case = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class UpperCAmelCase_ ( a):
def __init__( self, __a = 101):
'''simple docstring'''
_lowerCAmelCase : str = length
def __len__( self):
'''simple docstring'''
return self.length
def __getitem__( self, __a):
'''simple docstring'''
return i
class UpperCAmelCase_ :
def __call__( self, __a):
'''simple docstring'''
return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}
class UpperCAmelCase_ ( nn.Module):
def __init__( self):
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
_lowerCAmelCase : str = nn.Linear(120, 80)
def snake_case__ ( self, __a, __a=None):
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
class UpperCAmelCase_ ( a):
@require_torch_neuroncore
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = f"--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : List[Any] = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class UpperCAmelCase_ ( a):
@require_torch_multi_gpu
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = f"--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Any = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : Any = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
_snake_case = HfArgumentParser((TrainingArguments,))
_snake_case = parser.parse_args_into_dataclasses()[0]
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
_snake_case = DummyDataset(dataset_length)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = list(range(len(_lowerCamelCase ) ) )
_lowerCAmelCase : Union[str, Any] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}" )
return {"success": success}
_snake_case = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = 2
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = None
| 36
| 1
|
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/beit-base-patch16-224-pt22k": (
"https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'beit'
def __init__( self, __a=8192, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.0, __a=0.0, __a=0.02, __a=1E-12, __a=224, __a=16, __a=3, __a=False, __a=False, __a=False, __a=False, __a=0.1, __a=0.1, __a=True, __a=[3, 5, 7, 11], __a=[1, 2, 3, 6], __a=True, __a=0.4, __a=256, __a=1, __a=False, __a=255, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : Optional[int] = num_hidden_layers
_lowerCAmelCase : Tuple = num_attention_heads
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : Optional[Any] = hidden_act
_lowerCAmelCase : Tuple = hidden_dropout_prob
_lowerCAmelCase : Dict = attention_probs_dropout_prob
_lowerCAmelCase : Any = initializer_range
_lowerCAmelCase : List[str] = layer_norm_eps
_lowerCAmelCase : Dict = image_size
_lowerCAmelCase : int = patch_size
_lowerCAmelCase : str = num_channels
_lowerCAmelCase : List[Any] = use_mask_token
_lowerCAmelCase : List[Any] = use_absolute_position_embeddings
_lowerCAmelCase : List[str] = use_relative_position_bias
_lowerCAmelCase : Tuple = use_shared_relative_position_bias
_lowerCAmelCase : Any = layer_scale_init_value
_lowerCAmelCase : Optional[Any] = drop_path_rate
_lowerCAmelCase : Any = use_mean_pooling
# decode head attributes (semantic segmentation)
_lowerCAmelCase : Any = out_indices
_lowerCAmelCase : List[str] = pool_scales
# auxiliary head attributes (semantic segmentation)
_lowerCAmelCase : Any = use_auxiliary_head
_lowerCAmelCase : List[Any] = auxiliary_loss_weight
_lowerCAmelCase : List[Any] = auxiliary_channels
_lowerCAmelCase : Union[str, Any] = auxiliary_num_convs
_lowerCAmelCase : Optional[Any] = auxiliary_concat_input
_lowerCAmelCase : Union[str, Any] = semantic_loss_ignore_index
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
| 36
|
from __future__ import annotations
import bisect
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : int = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Optional[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
_lowerCAmelCase : Union[str, Any] = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : str = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Tuple = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
_lowerCAmelCase : Dict = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_left(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_right(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
while left <= right:
_lowerCAmelCase : int = left + (right - left) // 2
_lowerCAmelCase : int = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
_lowerCAmelCase : str = midpoint - 1
else:
_lowerCAmelCase : Any = midpoint + 1
return None
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = bisect.bisect_left(_lowerCamelCase , _lowerCamelCase )
if index != len(_lowerCamelCase ) and sorted_collection[index] == item:
return index
return None
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if right < left:
return None
_lowerCAmelCase : Optional[int] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , midpoint + 1 , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = input("Enter numbers separated by comma:\n").strip()
_snake_case = sorted(int(item) for item in user_input.split(","))
_snake_case = int(input("Enter a single number to be found in the list:\n"))
_snake_case = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
| 36
| 1
|
from __future__ import annotations
def A ( _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = None ):
'''simple docstring'''
if start is None:
_lowerCAmelCase : Union[str, Any] = 0
if end is None:
_lowerCAmelCase : int = len(_lowerCamelCase ) - 1
if start >= end:
return
_lowerCAmelCase : Union[str, Any] = (start + end) // 2
slowsort(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
slowsort(_lowerCamelCase , mid + 1 , _lowerCamelCase )
if sequence[end] < sequence[mid]:
_lowerCAmelCase , _lowerCAmelCase : Union[str, Any] = sequence[mid], sequence[end]
slowsort(_lowerCamelCase , _lowerCamelCase , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 36
|
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_lowerCAmelCase : Optional[int] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 512
_lowerCAmelCase : Union[str, Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : int = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : str = np.abs(np.fft.fft(_lowerCamelCase ) )
_lowerCAmelCase : Union[str, Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_lowerCAmelCase : List[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_lowerCamelCase )
plt.show()
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 512
_lowerCAmelCase : Optional[Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : str = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : Optional[Any] = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 36
| 1
|
import unittest
from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaForMaskedLM,
DebertaForQuestionAnswering,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaModel,
)
from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ ( a):
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=32, __a=5, __a=4, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=False, __a=True, __a="None", __a=3, __a=4, __a=None, ):
'''simple docstring'''
_lowerCAmelCase : List[str] = parent
_lowerCAmelCase : Optional[Any] = batch_size
_lowerCAmelCase : Any = seq_length
_lowerCAmelCase : List[str] = is_training
_lowerCAmelCase : Tuple = use_input_mask
_lowerCAmelCase : Union[str, Any] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Tuple = vocab_size
_lowerCAmelCase : Dict = hidden_size
_lowerCAmelCase : Any = num_hidden_layers
_lowerCAmelCase : List[Any] = num_attention_heads
_lowerCAmelCase : Optional[Any] = intermediate_size
_lowerCAmelCase : Tuple = hidden_act
_lowerCAmelCase : int = hidden_dropout_prob
_lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCAmelCase : Union[str, Any] = max_position_embeddings
_lowerCAmelCase : str = type_vocab_size
_lowerCAmelCase : Union[str, Any] = type_sequence_label_size
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : int = num_labels
_lowerCAmelCase : Optional[Any] = num_choices
_lowerCAmelCase : List[str] = relative_attention
_lowerCAmelCase : Optional[int] = position_biased_input
_lowerCAmelCase : Union[str, Any] = pos_att_type
_lowerCAmelCase : Dict = scope
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : List[str] = None
if self.use_input_mask:
_lowerCAmelCase : List[str] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : int = None
if self.use_token_type_ids:
_lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Dict = None
_lowerCAmelCase : Tuple = None
_lowerCAmelCase : Dict = None
if self.use_labels:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : List[Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.num_choices)
_lowerCAmelCase : Any = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__ ( self):
'''simple docstring'''
return DebertaConfig(
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, initializer_range=self.initializer_range, relative_attention=self.relative_attention, position_biased_input=self.position_biased_input, pos_att_type=self.pos_att_type, )
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = self.get_config()
_lowerCAmelCase : Optional[int] = 300
return config
def snake_case__ ( self, __a):
'''simple docstring'''
self.parent.assertListEqual(list(result.loss.size()), [])
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = DebertaModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Optional[Any] = model(__a, attention_mask=__a, token_type_ids=__a)[0]
_lowerCAmelCase : Any = model(__a, token_type_ids=__a)[0]
_lowerCAmelCase : Optional[Any] = model(__a)[0]
self.parent.assertListEqual(list(sequence_output.size()), [self.batch_size, self.seq_length, self.hidden_size])
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = DebertaForMaskedLM(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Union[str, Any] = 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 snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Dict = self.num_labels
_lowerCAmelCase : str = DebertaForSequenceClassification(__a)
model.to(__a)
model.eval()
_lowerCAmelCase : List[Any] = model(__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertListEqual(list(result.logits.size()), [self.batch_size, self.num_labels])
self.check_loss_output(__a)
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : str = self.num_labels
_lowerCAmelCase : List[str] = DebertaForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : 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.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = DebertaForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : int = model(
__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Dict = config_and_inputs
_lowerCAmelCase : Union[str, Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , unittest.TestCase):
lowerCamelCase__ = (
(
DebertaModel,
DebertaForMaskedLM,
DebertaForSequenceClassification,
DebertaForTokenClassification,
DebertaForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': DebertaModel,
'fill-mask': DebertaForMaskedLM,
'question-answering': DebertaForQuestionAnswering,
'text-classification': DebertaForSequenceClassification,
'token-classification': DebertaForTokenClassification,
'zero-shot': DebertaForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = True
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = DebertaModelTester(self)
_lowerCAmelCase : str = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : List[str] = DebertaModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@require_sentencepiece
@require_tokenizers
class UpperCAmelCase_ ( unittest.TestCase):
@unittest.skip(reason="Model not available yet")
def snake_case__ ( self):
'''simple docstring'''
pass
@slow
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = DebertaModel.from_pretrained("microsoft/deberta-base")
_lowerCAmelCase : Any = torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]])
_lowerCAmelCase : str = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])
with torch.no_grad():
_lowerCAmelCase : Dict = model(__a, attention_mask=__a)[0]
# compare the actual values for a slice.
_lowerCAmelCase : Dict = torch.tensor(
[[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]])
self.assertTrue(torch.allclose(output[:, 1:4, 1:4], __a, atol=1E-4), f"{output[:, 1:4, 1:4]}")
| 36
|
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
_lowerCAmelCase : List[str] = gray_code_sequence_string(_lowerCamelCase )
#
# convert them to integers
for i in range(len(_lowerCamelCase ) ):
_lowerCAmelCase : List[str] = int(sequence[i] , 2 )
return sequence
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
_lowerCAmelCase : List[Any] = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
_lowerCAmelCase : Optional[int] = gray_code_sequence_string(bit_count - 1 )
_lowerCAmelCase : str = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
_lowerCAmelCase : Dict = "0" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
_lowerCAmelCase : Optional[Any] = "1" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
| 1
|
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = tempfile.mkdtemp()
_lowerCAmelCase : int = BlipImageProcessor()
_lowerCAmelCase : Dict = GPTaTokenizer.from_pretrained("hf-internal-testing/tiny-random-GPT2Model")
_lowerCAmelCase : Tuple = BertTokenizerFast.from_pretrained("hf-internal-testing/tiny-random-bert")
_lowerCAmelCase : Optional[Any] = InstructBlipProcessor(__a, __a, __a)
processor.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname, **__a).tokenizer
def snake_case__ ( self, **__a):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname, **__a).image_processor
def snake_case__ ( self, **__a):
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname, **__a).qformer_tokenizer
def snake_case__ ( self):
'''simple docstring'''
shutil.rmtree(self.tmpdirname)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = [np.random.randint(255, size=(3, 30, 400), dtype=np.uinta)]
_lowerCAmelCase : Any = [Image.fromarray(np.moveaxis(__a, 0, -1)) for x in image_inputs]
return image_inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[Any] = InstructBlipProcessor(
tokenizer=self.get_tokenizer(), image_processor=self.get_image_processor(), qformer_tokenizer=self.get_qformer_tokenizer(), )
processor.save_pretrained(self.tmpdirname)
_lowerCAmelCase : str = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
_lowerCAmelCase : int = self.get_image_processor(do_normalize=__a, padding_value=1.0)
_lowerCAmelCase : str = InstructBlipProcessor.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)
self.assertIsInstance(processor.qformer_tokenizer, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.get_image_processor()
_lowerCAmelCase : int = self.get_tokenizer()
_lowerCAmelCase : int = self.get_qformer_tokenizer()
_lowerCAmelCase : Dict = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : int = self.prepare_image_inputs()
_lowerCAmelCase : Union[str, Any] = image_processor(__a, return_tensors="np")
_lowerCAmelCase : Any = processor(images=__a, return_tensors="np")
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1E-2)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.get_image_processor()
_lowerCAmelCase : Optional[Any] = self.get_tokenizer()
_lowerCAmelCase : List[str] = self.get_qformer_tokenizer()
_lowerCAmelCase : Union[str, Any] = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : List[str] = "lower newer"
_lowerCAmelCase : str = processor(text=__a)
_lowerCAmelCase : Dict = tokenizer(__a, return_token_type_ids=__a)
_lowerCAmelCase : Union[str, Any] = qformer_tokenizer(__a, return_token_type_ids=__a)
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key], encoded_processor[key])
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key], encoded_processor["qformer_" + key])
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = self.get_image_processor()
_lowerCAmelCase : List[str] = self.get_tokenizer()
_lowerCAmelCase : Optional[int] = self.get_qformer_tokenizer()
_lowerCAmelCase : List[str] = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : int = "lower newer"
_lowerCAmelCase : Optional[Any] = self.prepare_image_inputs()
_lowerCAmelCase : str = processor(text=__a, images=__a)
self.assertListEqual(
list(inputs.keys()), ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"], )
# test if it raises when no input is passed
with pytest.raises(__a):
processor()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.get_image_processor()
_lowerCAmelCase : Union[str, Any] = self.get_tokenizer()
_lowerCAmelCase : Dict = self.get_qformer_tokenizer()
_lowerCAmelCase : Dict = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowerCAmelCase : str = processor.batch_decode(__a)
_lowerCAmelCase : List[str] = tokenizer.batch_decode(__a)
self.assertListEqual(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.get_image_processor()
_lowerCAmelCase : List[str] = self.get_tokenizer()
_lowerCAmelCase : Optional[int] = self.get_qformer_tokenizer()
_lowerCAmelCase : Dict = InstructBlipProcessor(
tokenizer=__a, image_processor=__a, qformer_tokenizer=__a)
_lowerCAmelCase : Optional[Any] = "lower newer"
_lowerCAmelCase : Tuple = self.prepare_image_inputs()
_lowerCAmelCase : List[str] = processor(text=__a, images=__a)
self.assertListEqual(
list(inputs.keys()), ["input_ids", "attention_mask", "qformer_input_ids", "qformer_attention_mask", "pixel_values"], )
| 36
|
from PIL import Image
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = image.size
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = image.load()
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(_lowerCamelCase ):
for i in range(_lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_snake_case = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 36
| 1
|
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['image_processor', 'tokenizer']
lowerCamelCase__ = 'AutoImageProcessor'
lowerCamelCase__ = 'AutoTokenizer'
def __init__( self, __a, __a):
'''simple docstring'''
super().__init__(__a, __a)
_lowerCAmelCase : int = self.image_processor
def __call__( self, __a=None, __a=None, __a=None, **__a):
'''simple docstring'''
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none.")
if text is not None:
_lowerCAmelCase : List[str] = self.tokenizer(__a, return_tensors=__a, **__a)
if images is not None:
_lowerCAmelCase : Tuple = self.image_processor(__a, return_tensors=__a, **__a)
if text is not None and images is not None:
_lowerCAmelCase : Optional[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__a), tensor_type=__a)
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
return self.tokenizer.batch_decode(*__a, **__a)
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
return self.tokenizer.decode(*__a, **__a)
@property
def snake_case__ ( self):
'''simple docstring'''
return ["input_ids", "attention_mask", "pixel_values"]
| 36
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'wav2vec2'
def __init__( self, __a=32, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.1, __a=0.1, __a=0.1, __a=0.0, __a=0.0, __a=0.1, __a=0.1, __a=0.02, __a=1E-5, __a="group", __a="gelu", __a=(512, 512, 512, 512, 512, 512, 512), __a=(5, 2, 2, 2, 2, 2, 2), __a=(10, 3, 3, 3, 3, 2, 2), __a=False, __a=128, __a=16, __a=False, __a=True, __a=0.05, __a=10, __a=2, __a=0.0, __a=10, __a=0, __a=320, __a=2, __a=0.1, __a=100, __a=256, __a=256, __a=0.1, __a="sum", __a=False, __a=False, __a=256, __a=(512, 512, 512, 512, 1500), __a=(5, 3, 3, 1, 1), __a=(1, 2, 3, 1, 1), __a=512, __a=0, __a=1, __a=2, __a=False, __a=3, __a=2, __a=3, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a, pad_token_id=__a, bos_token_id=__a, eos_token_id=__a)
_lowerCAmelCase : str = hidden_size
_lowerCAmelCase : Optional[int] = feat_extract_norm
_lowerCAmelCase : Union[str, Any] = feat_extract_activation
_lowerCAmelCase : Optional[Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : List[str] = conv_bias
_lowerCAmelCase : str = num_conv_pos_embeddings
_lowerCAmelCase : List[Any] = num_conv_pos_embedding_groups
_lowerCAmelCase : str = len(self.conv_dim)
_lowerCAmelCase : List[str] = num_hidden_layers
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : Any = hidden_act
_lowerCAmelCase : int = num_attention_heads
_lowerCAmelCase : Optional[Any] = hidden_dropout
_lowerCAmelCase : List[str] = attention_dropout
_lowerCAmelCase : Tuple = activation_dropout
_lowerCAmelCase : int = feat_proj_dropout
_lowerCAmelCase : List[str] = final_dropout
_lowerCAmelCase : int = layerdrop
_lowerCAmelCase : int = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Optional[Any] = do_stable_layer_norm
_lowerCAmelCase : Any = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_lowerCAmelCase : str = apply_spec_augment
_lowerCAmelCase : Optional[Any] = mask_time_prob
_lowerCAmelCase : Optional[int] = mask_time_length
_lowerCAmelCase : List[str] = mask_time_min_masks
_lowerCAmelCase : Optional[int] = mask_feature_prob
_lowerCAmelCase : Optional[int] = mask_feature_length
_lowerCAmelCase : List[str] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_lowerCAmelCase : Union[str, Any] = num_codevectors_per_group
_lowerCAmelCase : str = num_codevector_groups
_lowerCAmelCase : Optional[int] = contrastive_logits_temperature
_lowerCAmelCase : Optional[int] = feat_quantizer_dropout
_lowerCAmelCase : Optional[int] = num_negatives
_lowerCAmelCase : Union[str, Any] = codevector_dim
_lowerCAmelCase : Any = proj_codevector_dim
_lowerCAmelCase : Optional[int] = diversity_loss_weight
# ctc loss
_lowerCAmelCase : Tuple = ctc_loss_reduction
_lowerCAmelCase : Tuple = ctc_zero_infinity
# adapter
_lowerCAmelCase : List[Any] = add_adapter
_lowerCAmelCase : List[str] = adapter_kernel_size
_lowerCAmelCase : str = adapter_stride
_lowerCAmelCase : List[str] = num_adapter_layers
_lowerCAmelCase : str = output_hidden_size or hidden_size
_lowerCAmelCase : Tuple = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCAmelCase : str = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : Union[str, Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : Tuple = xvector_output_dim
@property
def snake_case__ ( self):
'''simple docstring'''
return functools.reduce(operator.mul, self.conv_stride, 1)
| 36
| 1
|
import math
from datetime import datetime, timedelta
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = year % 19
_lowerCAmelCase : Optional[int] = year % 4
_lowerCAmelCase : Optional[Any] = year % 7
_lowerCAmelCase : List[Any] = math.floor(year / 100 )
_lowerCAmelCase : Any = math.floor((13 + 8 * leap_day_inhibits) / 25 )
_lowerCAmelCase : str = leap_day_inhibits / 4
_lowerCAmelCase : Union[str, Any] = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
_lowerCAmelCase : Union[str, Any] = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
_lowerCAmelCase : str = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
_lowerCAmelCase : Any = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(_lowerCamelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(_lowerCamelCase , 4 , 18 )
else:
return datetime(_lowerCamelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
_snake_case = "will be" if year > datetime.now().year else "was"
print(f'''Easter in {year} {tense} {gauss_easter(year)}''')
| 36
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'The RoBERTa Model transformer with early exiting (DeeRoBERTa). ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[Any] = RobertaEmbeddings(__a)
self.init_weights()
@add_start_docstrings(
'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[int] = config.num_labels
_lowerCAmelCase : Optional[int] = config.num_hidden_layers
_lowerCAmelCase : Optional[int] = DeeRobertaModel(__a)
_lowerCAmelCase : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob)
_lowerCAmelCase : List[str] = nn.Linear(config.hidden_size, self.config.num_labels)
@add_start_docstrings_to_model_forward(__a)
def snake_case__ ( self, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=-1, __a=False, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.num_layers
try:
_lowerCAmelCase : List[Any] = self.roberta(
__a, attention_mask=__a, token_type_ids=__a, position_ids=__a, head_mask=__a, inputs_embeds=__a, )
_lowerCAmelCase : List[Any] = outputs[1]
_lowerCAmelCase : Dict = self.dropout(__a)
_lowerCAmelCase : Dict = self.classifier(__a)
_lowerCAmelCase : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase : Tuple = e.message
_lowerCAmelCase : Union[str, Any] = e.exit_layer
_lowerCAmelCase : List[Any] = outputs[0]
if not self.training:
_lowerCAmelCase : int = entropy(__a)
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : str = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : Optional[Any] = MSELoss()
_lowerCAmelCase : int = loss_fct(logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Optional[Any] = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
_lowerCAmelCase : Optional[int] = []
for highway_exit in outputs[-1]:
_lowerCAmelCase : Any = highway_exit[0]
if not self.training:
highway_logits_all.append(__a)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : List[str] = MSELoss()
_lowerCAmelCase : List[Any] = loss_fct(highway_logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Dict = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
highway_losses.append(__a)
if train_highway:
_lowerCAmelCase : int = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase : Any = (loss,) + outputs
if not self.training:
_lowerCAmelCase : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 36
| 1
|
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase ) as metadata_file:
_lowerCAmelCase : str = json.load(_lowerCamelCase )
_lowerCAmelCase : Tuple = LukeConfig(use_entity_aware_attention=_lowerCamelCase , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
_lowerCAmelCase : Dict = torch.load(_lowerCamelCase , map_location="cpu" )["module"]
# Load the entity vocab file
_lowerCAmelCase : List[Any] = load_original_entity_vocab(_lowerCamelCase )
# add an entry for [MASK2]
_lowerCAmelCase : Dict = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
_lowerCAmelCase : Any = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
_lowerCAmelCase : Union[str, Any] = AddedToken("<ent>" , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase )
_lowerCAmelCase : str = AddedToken("<ent2>" , lstrip=_lowerCamelCase , rstrip=_lowerCamelCase )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F"Saving tokenizer to {pytorch_dump_folder_path}" )
tokenizer.save_pretrained(_lowerCamelCase )
with open(os.path.join(_lowerCamelCase , "tokenizer_config.json" ) , "r" ) as f:
_lowerCAmelCase : str = json.load(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = "MLukeTokenizer"
with open(os.path.join(_lowerCamelCase , "tokenizer_config.json" ) , "w" ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
with open(os.path.join(_lowerCamelCase , MLukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : int = MLukeTokenizer.from_pretrained(_lowerCamelCase )
# Initialize the embeddings of the special tokens
_lowerCAmelCase : Any = tokenizer.convert_tokens_to_ids(["@"] )[0]
_lowerCAmelCase : Union[str, Any] = tokenizer.convert_tokens_to_ids(["#"] )[0]
_lowerCAmelCase : Optional[Any] = state_dict["embeddings.word_embeddings.weight"]
_lowerCAmelCase : str = word_emb[ent_init_index].unsqueeze(0 )
_lowerCAmelCase : Optional[int] = word_emb[enta_init_index].unsqueeze(0 )
_lowerCAmelCase : Optional[int] = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
_lowerCAmelCase : Optional[Any] = state_dict[bias_name]
_lowerCAmelCase : Any = decoder_bias[ent_init_index].unsqueeze(0 )
_lowerCAmelCase : Optional[Any] = decoder_bias[enta_init_index].unsqueeze(0 )
_lowerCAmelCase : str = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
_lowerCAmelCase : Optional[Any] = F"encoder.layer.{layer_index}.attention.self."
_lowerCAmelCase : Tuple = state_dict[prefix + matrix_name]
_lowerCAmelCase : int = state_dict[prefix + matrix_name]
_lowerCAmelCase : List[str] = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
_lowerCAmelCase : Dict = state_dict["entity_embeddings.entity_embeddings.weight"]
_lowerCAmelCase : List[str] = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
_lowerCAmelCase : Dict = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
_lowerCAmelCase : str = state_dict["entity_predictions.bias"]
_lowerCAmelCase : str = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
_lowerCAmelCase : List[Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
_lowerCAmelCase : Optional[Any] = LukeForMaskedLM(config=_lowerCamelCase ).eval()
state_dict.pop("entity_predictions.decoder.weight" )
state_dict.pop("lm_head.decoder.weight" )
state_dict.pop("lm_head.decoder.bias" )
_lowerCAmelCase : Any = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith("lm_head" ) or key.startswith("entity_predictions" )):
_lowerCAmelCase : Optional[Any] = state_dict[key]
else:
_lowerCAmelCase : str = state_dict[key]
_lowerCAmelCase , _lowerCAmelCase : Tuple = model.load_state_dict(_lowerCamelCase , strict=_lowerCamelCase )
if set(_lowerCamelCase ) != {"luke.embeddings.position_ids"}:
raise ValueError(F"Unexpected unexpected_keys: {unexpected_keys}" )
if set(_lowerCamelCase ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F"Unexpected missing_keys: {missing_keys}" )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
_lowerCAmelCase : str = MLukeTokenizer.from_pretrained(_lowerCamelCase , task="entity_classification" )
_lowerCAmelCase : str = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
_lowerCAmelCase : Dict = (0, 9)
_lowerCAmelCase : Union[str, Any] = tokenizer(_lowerCamelCase , entity_spans=[span] , return_tensors="pt" )
_lowerCAmelCase : Dict = model(**_lowerCamelCase )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_lowerCAmelCase : Tuple = torch.Size((1, 33, 768) )
_lowerCAmelCase : str = torch.tensor([[0.08_92, 0.05_96, -0.28_19], [0.01_34, 0.11_99, 0.05_73], [-0.01_69, 0.09_27, 0.06_44]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F"Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}" )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
_lowerCAmelCase : Optional[Any] = torch.Size((1, 1, 768) )
_lowerCAmelCase : int = torch.tensor([[-0.14_82, 0.06_09, 0.03_22]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F"Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is"
F" {expected_shape}" )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , _lowerCamelCase , atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
_lowerCAmelCase : Union[str, Any] = MLukeTokenizer.from_pretrained(_lowerCamelCase )
_lowerCAmelCase : List[str] = "Tokyo is the capital of <mask>."
_lowerCAmelCase : Any = (24, 30)
_lowerCAmelCase : str = tokenizer(_lowerCamelCase , entity_spans=[span] , return_tensors="pt" )
_lowerCAmelCase : Any = model(**_lowerCamelCase )
_lowerCAmelCase : List[Any] = encoding["input_ids"][0].tolist()
_lowerCAmelCase : List[Any] = input_ids.index(tokenizer.convert_tokens_to_ids("<mask>" ) )
_lowerCAmelCase : Any = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(_lowerCamelCase )
_lowerCAmelCase : Tuple = outputs.entity_logits[0][0].argmax().item()
_lowerCAmelCase : int = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith("en:" )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(_lowerCamelCase ) )
model.save_pretrained(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = ["[MASK]", "[PAD]", "[UNK]"]
_lowerCAmelCase : Dict = [json.loads(_lowerCamelCase ) for line in open(_lowerCamelCase )]
_lowerCAmelCase : List[Any] = {}
for entry in data:
_lowerCAmelCase : int = entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
_lowerCAmelCase : Optional[Any] = entity_id
break
_lowerCAmelCase : int = F"{language}:{entity_name}"
_lowerCAmelCase : Optional[int] = entity_id
return new_mapping
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
_snake_case = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 36
|
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'vision-encoder-decoder'
lowerCamelCase__ = True
def __init__( self, **__a):
'''simple docstring'''
super().__init__(**__a)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")
_lowerCAmelCase : str = kwargs.pop("encoder")
_lowerCAmelCase : Any = encoder_config.pop("model_type")
_lowerCAmelCase : str = kwargs.pop("decoder")
_lowerCAmelCase : List[str] = decoder_config.pop("model_type")
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[int] = True
@classmethod
def snake_case__ ( cls, __a, __a, **__a):
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : str = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = copy.deepcopy(self.__dict__)
_lowerCAmelCase : List[str] = self.encoder.to_dict()
_lowerCAmelCase : List[str] = self.decoder.to_dict()
_lowerCAmelCase : Any = self.__class__.model_type
return output
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : List[str] = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : Optional[Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def snake_case__ ( self, __a, __a = -1, __a = -1, __a = False, __a = None, ):
'''simple docstring'''
import torch
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : List[str] = super().generate_dummy_inputs(
__a, batch_size=__a, seq_length=__a, is_pair=__a, framework=__a)
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = dummy_input["input_ids"].shape
_lowerCAmelCase : str = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowerCAmelCase : List[str] = dummy_input.pop("input_ids")
_lowerCAmelCase : List[str] = dummy_input.pop("attention_mask")
_lowerCAmelCase : Optional[int] = torch.zeros(__a)
return common_inputs
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self, __a):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(__a)
def snake_case__ ( self, __a, __a, __a = "default"):
'''simple docstring'''
_lowerCAmelCase : Dict = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__a, __a)
| 36
| 1
|
import cva
import numpy as np
class UpperCAmelCase_ :
def __init__( self, __a, __a):
'''simple docstring'''
if k in (0.04, 0.06):
_lowerCAmelCase : Dict = k
_lowerCAmelCase : Any = window_size
else:
raise ValueError("invalid k value")
def __str__( self):
'''simple docstring'''
return str(self.k)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = cva.imread(__a, 0)
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = img.shape
_lowerCAmelCase : list[list[int]] = []
_lowerCAmelCase : str = img.copy()
_lowerCAmelCase : Any = cva.cvtColor(__a, cva.COLOR_GRAY2RGB)
_lowerCAmelCase , _lowerCAmelCase : List[str] = np.gradient(__a)
_lowerCAmelCase : Optional[int] = dx**2
_lowerCAmelCase : str = dy**2
_lowerCAmelCase : List[str] = dx * dy
_lowerCAmelCase : Any = 0.04
_lowerCAmelCase : Optional[int] = self.window_size // 2
for y in range(__a, h - offset):
for x in range(__a, w - offset):
_lowerCAmelCase : str = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowerCAmelCase : Optional[Any] = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowerCAmelCase : Optional[Any] = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
_lowerCAmelCase : int = (wxx * wyy) - (wxy**2)
_lowerCAmelCase : str = wxx + wyy
_lowerCAmelCase : Union[str, Any] = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r])
color_img.itemset((y, x, 0), 0)
color_img.itemset((y, x, 1), 0)
color_img.itemset((y, x, 2), 255)
return color_img, corner_list
if __name__ == "__main__":
_snake_case = HarrisCorner(0.04, 3)
_snake_case, _snake_case = edge_detect.detect("path_to_image")
cva.imwrite("detect.png", color_img)
| 36
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( a):
def __get__( self, __a, __a=None):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
_lowerCAmelCase : List[Any] = "__cached_" + self.fget.__name__
_lowerCAmelCase : Dict = getattr(__a, __a, __a)
if cached is None:
_lowerCAmelCase : str = self.fget(__a)
setattr(__a, __a, __a)
return cached
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"invalid truth value {val!r}" )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.device )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if hasattr(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase , torch.dtype )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase , tf.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase , jnp.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = fields(self)
# Safety and consistency checks
if not len(__a):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
_lowerCAmelCase : Dict = getattr(self, class_fields[0].name)
_lowerCAmelCase : str = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(__a):
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = first_field.items()
_lowerCAmelCase : Dict = True
else:
try:
_lowerCAmelCase : Dict = iter(__a)
_lowerCAmelCase : Any = True
except TypeError:
_lowerCAmelCase : Any = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a):
if (
not isinstance(__a, (list, tuple))
or not len(__a) == 2
or not isinstance(element[0], __a)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value).")
break
setattr(self, element[0], element[1])
if element[1] is not None:
_lowerCAmelCase : Any = element[1]
elif first_field is not None:
_lowerCAmelCase : Any = first_field
else:
for field in class_fields:
_lowerCAmelCase : Dict = getattr(self, field.name)
if v is not None:
_lowerCAmelCase : Union[str, Any] = v
def __delitem__( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__( self, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Optional[int] = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self, __a, __a):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a, __a)
super().__setattr__(__a, __a)
def __setitem__( self, __a, __a):
'''simple docstring'''
super().__setitem__(__a, __a)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
return tuple(self[k] for k in self.keys())
class UpperCAmelCase_ ( a , a):
@classmethod
def snake_case__ ( cls, __a):
'''simple docstring'''
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'longest'
lowerCamelCase__ = 'max_length'
lowerCamelCase__ = 'do_not_pad'
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'pt'
lowerCamelCase__ = 'tf'
lowerCamelCase__ = 'np'
lowerCamelCase__ = 'jax'
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = context_managers
_lowerCAmelCase : Dict = ExitStack()
def __enter__( self):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a)
def __exit__( self, *__a, **__a):
'''simple docstring'''
self.stack.__exit__(*__a, **__a)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : str = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = model_class.__name__
_lowerCAmelCase : Optional[Any] = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( _lowerCamelCase , _lowerCamelCase = "" , _lowerCamelCase = "." ):
'''simple docstring'''
def _flatten_dict(_lowerCamelCase , _lowerCamelCase="" , _lowerCamelCase="." ):
for k, v in d.items():
_lowerCAmelCase : Dict = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase , _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
@contextmanager
def A ( _lowerCamelCase , _lowerCamelCase = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase , axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase , _lowerCamelCase )
else:
raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase , (tuple, list) ):
_lowerCAmelCase : List[Any] = [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase : Tuple = F"{repo_id}--{value}"
return auto_map
def A ( _lowerCamelCase ):
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
_lowerCAmelCase : Tuple = base_class.__module__
_lowerCAmelCase : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"Could not infer framework from class {model_class}." )
| 36
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_nllb_moe": [
"NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP",
"NllbMoeConfig",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST",
"NllbMoeForConditionalGeneration",
"NllbMoeModel",
"NllbMoePreTrainedModel",
"NllbMoeTop2Router",
"NllbMoeSparseMLP",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 36
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = _distribute_shards(**_lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = _split_gen_kwargs(_lowerCamelCase , _lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(_lowerCamelCase ):
_number_of_shards_in_gen_kwargs(_lowerCamelCase )
else:
_lowerCAmelCase : Optional[int] = _number_of_shards_in_gen_kwargs(_lowerCamelCase )
assert out == expected
| 36
| 1
|
import argparse
import json
import subprocess
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = []
_lowerCAmelCase : Optional[int] = (
F"curl -H \"Accept: application/vnd.github+json\" -H \"Authorization: Bearer {token}\""
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
_lowerCAmelCase : Any = subprocess.run(_lowerCamelCase , shell=_lowerCamelCase , stdout=subprocess.PIPE )
_lowerCAmelCase : Tuple = output.stdout.decode("utf-8" )
_lowerCAmelCase : Dict = json.loads(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = status["runners"]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(_lowerCamelCase )
# save the result so we can report them on Slack
with open("offline_runners.txt" , "w" ) as fp:
fp.write(json.dumps(_lowerCamelCase ) )
if len(_lowerCamelCase ) > 0:
_lowerCAmelCase : Tuple = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(F"The following runners are offline:\n{failed}" )
if __name__ == "__main__":
def A ( _lowerCamelCase ):
'''simple docstring'''
return values.split("," )
_snake_case = 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."
)
_snake_case = parser.parse_args()
get_runner_status(args.target_runners, args.token)
| 36
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCAmelCase_ :
def __init__( self, __a = "cpu", __a = "openai/clip-vit-large-patch14"):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = device
_lowerCAmelCase : Optional[int] = CLIPTokenizerFast.from_pretrained(__a)
_lowerCAmelCase : Any = [0.48_145_466, 0.4_578_275, 0.40_821_073]
_lowerCAmelCase : Union[str, Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]
_lowerCAmelCase : Tuple = torchvision.transforms.Normalize(self.image_mean, self.image_std)
_lowerCAmelCase : Optional[int] = torchvision.transforms.Resize(224)
_lowerCAmelCase : Dict = torchvision.transforms.CenterCrop(224)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.resize(__a)
_lowerCAmelCase : List[str] = self.center_crop(__a)
_lowerCAmelCase : Optional[Any] = self.normalize(__a)
return images
def __call__( self, __a=None, __a=None, **__a):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(text=__a, **__a)
_lowerCAmelCase : List[str] = self.preprocess_img(__a)
_lowerCAmelCase : Tuple = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a=10, __a=0.01, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=False, __a=True, __a="image", __a=True, __a=False, __a=False, __a=False, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : List[str] = None
_lowerCAmelCase : List[str] = device if device else get_device()
if vqgan:
_lowerCAmelCase : Union[str, Any] = vqgan
else:
_lowerCAmelCase : Optional[Any] = load_vqgan(self.device, conf_path=__a, ckpt_path=__a)
self.vqgan.eval()
if clip:
_lowerCAmelCase : str = clip
else:
_lowerCAmelCase : int = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
_lowerCAmelCase : Optional[int] = ProcessorGradientFlow(device=self.device)
_lowerCAmelCase : Any = iterations
_lowerCAmelCase : List[Any] = lr
_lowerCAmelCase : Tuple = log
_lowerCAmelCase : List[str] = make_grid
_lowerCAmelCase : int = return_val
_lowerCAmelCase : Dict = quantize
_lowerCAmelCase : Any = self.vqgan.decoder.z_shape
def snake_case__ ( self, __a=None, __a=None, __a=5, __a=True):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = []
if output_path is None:
_lowerCAmelCase : List[Any] = "./animation.gif"
if input_path is None:
_lowerCAmelCase : str = self.save_path
_lowerCAmelCase : str = sorted(glob(input_path + "/*"))
if not len(__a):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__a) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
_lowerCAmelCase : Optional[int] = total_duration / len(__a)
_lowerCAmelCase : Union[str, Any] = [frame_duration] * len(__a)
if extend_frames:
_lowerCAmelCase : Any = 1.5
_lowerCAmelCase : List[str] = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__a))
imageio.mimsave(__a, __a, duration=__a)
print(f"gif saved to {output_path}")
def snake_case__ ( self, __a=None, __a=None):
'''simple docstring'''
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
_lowerCAmelCase : Dict = preprocess(Image.open(__a), target_image_size=256).to(self.device)
_lowerCAmelCase : Dict = preprocess_vqgan(__a)
_lowerCAmelCase , *_lowerCAmelCase : str = self.vqgan.encode(__a)
return z
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.latent.detach().requires_grad_()
_lowerCAmelCase : Dict = base_latent + transform_vector
if self.quantize:
_lowerCAmelCase , *_lowerCAmelCase : List[Any] = self.vqgan.quantize(__a)
else:
_lowerCAmelCase : Any = trans_latent
return self.vqgan.decode(__a)
def snake_case__ ( self, __a, __a, __a=None):
'''simple docstring'''
_lowerCAmelCase : int = self.clip_preprocessor(text=__a, images=__a, return_tensors="pt", padding=__a)
_lowerCAmelCase : Optional[int] = self.clip(**__a)
_lowerCAmelCase : Any = clip_outputs.logits_per_image
if weights is not None:
_lowerCAmelCase : Tuple = similarity_logits * weights
return similarity_logits.sum()
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self._get_clip_similarity(pos_prompts["prompts"], __a, weights=(1 / pos_prompts["weights"]))
if neg_prompts:
_lowerCAmelCase : List[Any] = self._get_clip_similarity(neg_prompts["prompts"], __a, weights=neg_prompts["weights"])
else:
_lowerCAmelCase : Union[str, Any] = torch.tensor([1], device=self.device)
_lowerCAmelCase : List[str] = -torch.log(__a) + torch.log(__a)
return loss
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.randn_like(self.latent, requires_grad=__a, device=self.device)
_lowerCAmelCase : Optional[int] = torch.optim.Adam([vector], lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
_lowerCAmelCase : Any = self._add_vector(__a)
_lowerCAmelCase : Optional[Any] = loop_post_process(__a)
_lowerCAmelCase : Optional[Any] = self._get_CLIP_loss(__a, __a, __a)
print("CLIP loss", __a)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__a)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
wandb.init(reinit=__a, project="face-editor")
wandb.config.update({"Positive Prompts": positive_prompts})
wandb.config.update({"Negative Prompts": negative_prompts})
wandb.config.update({"lr": self.lr, "iterations": self.iterations})
if image_path:
_lowerCAmelCase : str = Image.open(__a)
_lowerCAmelCase : int = image.resize((256, 256))
wandb.log("Original Image", wandb.Image(__a))
def snake_case__ ( self, __a):
'''simple docstring'''
if not prompts:
return []
_lowerCAmelCase : int = []
_lowerCAmelCase : List[str] = []
if isinstance(__a, __a):
_lowerCAmelCase : Union[str, Any] = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__a, (tuple, list)):
_lowerCAmelCase : Optional[Any] = prompt[0]
_lowerCAmelCase : Union[str, Any] = float(prompt[1])
elif ":" in prompt:
_lowerCAmelCase , _lowerCAmelCase : int = prompt.split(":")
_lowerCAmelCase : Optional[Any] = float(__a)
else:
_lowerCAmelCase : Optional[int] = prompt
_lowerCAmelCase : List[Any] = 1.0
processed_prompts.append(__a)
weights.append(__a)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a, device=self.device),
}
def snake_case__ ( self, __a, __a=None, __a=None, __a=True, __a=False, __a=True, __a=True, __a=None, ):
'''simple docstring'''
if image_path:
_lowerCAmelCase : List[Any] = self._get_latent(__a)
else:
_lowerCAmelCase : Any = torch.randn(self.latent_dim, device=self.device)
if self.log:
self._init_logging(__a, __a, __a)
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCAmelCase : int = self.process_prompts(__a)
_lowerCAmelCase : List[str] = self.process_prompts(__a)
if save_final and save_path is None:
_lowerCAmelCase : int = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
if not os.path.exists(__a):
os.makedirs(__a)
else:
_lowerCAmelCase : Tuple = save_path + "_" + get_timestamp()
os.makedirs(__a)
_lowerCAmelCase : Tuple = save_path
_lowerCAmelCase : List[Any] = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__a))
_lowerCAmelCase : int = loop_post_process(__a)
for iter, transformed_img in enumerate(self._optimize_CLIP(__a, __a, __a)):
if show_intermediate:
show_pil(__a)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
if self.log:
wandb.log({"Image": wandb.Image(__a)})
if show_final:
show_pil(__a)
if save_final:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))
| 36
| 1
|
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=1_024 , _lowerCamelCase=1_024 , _lowerCamelCase=False , **_lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = AutoTokenizer.from_pretrained(_lowerCamelCase )
_lowerCAmelCase : List[str] = SeqaSeqDataset(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , type_path="train" , **_lowerCamelCase )
_lowerCAmelCase : List[Any] = tok.pad_token_id
def get_lens(_lowerCamelCase ):
_lowerCAmelCase : List[Any] = tqdm(
DataLoader(_lowerCamelCase , batch_size=512 , num_workers=8 , shuffle=_lowerCamelCase , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
_lowerCAmelCase : str = []
for batch in dl:
_lowerCAmelCase : List[Any] = batch["input_ids"].ne(_lowerCamelCase ).sum(1 ).tolist()
_lowerCAmelCase : Union[str, Any] = batch["labels"].ne(_lowerCamelCase ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(_lowerCamelCase , _lowerCamelCase ):
max_lens.append(max(_lowerCamelCase , _lowerCamelCase ) )
else:
max_lens.extend(_lowerCamelCase )
return max_lens
_lowerCAmelCase : Optional[int] = get_lens(_lowerCamelCase )
_lowerCAmelCase : Any = SeqaSeqDataset(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , type_path="val" , **_lowerCamelCase )
_lowerCAmelCase : str = get_lens(_lowerCamelCase )
pickle_save(_lowerCamelCase , train_ds.len_file )
pickle_save(_lowerCamelCase , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 36
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_snake_case = get_tests_dir("fixtures")
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = mock.Mock()
_lowerCAmelCase : int = 500
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : str = HTTPError
_lowerCAmelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__a) as mock_head:
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def snake_case__ ( self):
'''simple docstring'''
with self.assertRaises(__a):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : int = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
_lowerCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor")
self.assertIsNotNone(__a)
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TOKEN
HfFolder.save_token(__a)
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : str = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="test-image-processor", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="valid_org/test-image-processor-org", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowerCAmelCase : List[str] = CustomImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
_lowerCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=__a)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 36
| 1
|
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPoolingAndNoAttention,
ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
add_code_sample_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
logging,
replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig
_snake_case = logging.get_logger(__name__)
# General docstring
_snake_case = "ResNetConfig"
# Base docstring
_snake_case = "microsoft/resnet-50"
_snake_case = [1, 2048, 7, 7]
# Image classification docstring
_snake_case = "microsoft/resnet-50"
_snake_case = "tiger cat"
_snake_case = [
"microsoft/resnet-50",
# See all resnet models at https://huggingface.co/models?filter=resnet
]
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a, __a, __a = 3, __a = 1, __a = "relu"):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Optional[Any] = nn.Convad(
__a, __a, kernel_size=__a, stride=__a, padding=kernel_size // 2, bias=__a)
_lowerCAmelCase : List[Any] = nn.BatchNormad(__a)
_lowerCAmelCase : Optional[Any] = ACTaFN[activation] if activation is not None else nn.Identity()
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.convolution(__a)
_lowerCAmelCase : Dict = self.normalization(__a)
_lowerCAmelCase : List[Any] = self.activation(__a)
return hidden_state
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Optional[int] = ResNetConvLayer(
config.num_channels, config.embedding_size, kernel_size=7, stride=2, activation=config.hidden_act)
_lowerCAmelCase : Dict = nn.MaxPoolad(kernel_size=3, stride=2, padding=1)
_lowerCAmelCase : Tuple = config.num_channels
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = pixel_values.shape[1]
if num_channels != self.num_channels:
raise ValueError(
"Make sure that the channel dimension of the pixel values match with the one set in the configuration.")
_lowerCAmelCase : int = self.embedder(__a)
_lowerCAmelCase : Tuple = self.pooler(__a)
return embedding
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a, __a, __a = 2):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Union[str, Any] = nn.Convad(__a, __a, kernel_size=1, stride=__a, bias=__a)
_lowerCAmelCase : int = nn.BatchNormad(__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.convolution(__a)
_lowerCAmelCase : Dict = self.normalization(__a)
return hidden_state
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a, __a, __a = 1, __a = "relu"):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Tuple = in_channels != out_channels or stride != 1
_lowerCAmelCase : Tuple = (
ResNetShortCut(__a, __a, stride=__a) if should_apply_shortcut else nn.Identity()
)
_lowerCAmelCase : str = nn.Sequential(
ResNetConvLayer(__a, __a, stride=__a), ResNetConvLayer(__a, __a, activation=__a), )
_lowerCAmelCase : Union[str, Any] = ACTaFN[activation]
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : int = hidden_state
_lowerCAmelCase : Dict = self.layer(__a)
_lowerCAmelCase : Optional[int] = self.shortcut(__a)
hidden_state += residual
_lowerCAmelCase : Tuple = self.activation(__a)
return hidden_state
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a, __a, __a = 1, __a = "relu", __a = 4):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : int = in_channels != out_channels or stride != 1
_lowerCAmelCase : str = out_channels // reduction
_lowerCAmelCase : Optional[int] = (
ResNetShortCut(__a, __a, stride=__a) if should_apply_shortcut else nn.Identity()
)
_lowerCAmelCase : Tuple = nn.Sequential(
ResNetConvLayer(__a, __a, kernel_size=1), ResNetConvLayer(__a, __a, stride=__a), ResNetConvLayer(__a, __a, kernel_size=1, activation=__a), )
_lowerCAmelCase : Optional[int] = ACTaFN[activation]
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Any = hidden_state
_lowerCAmelCase : Any = self.layer(__a)
_lowerCAmelCase : str = self.shortcut(__a)
hidden_state += residual
_lowerCAmelCase : int = self.activation(__a)
return hidden_state
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a, __a, __a, __a = 2, __a = 2, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : List[str] = ResNetBottleNeckLayer if config.layer_type == "bottleneck" else ResNetBasicLayer
_lowerCAmelCase : Dict = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(__a, __a, stride=__a, activation=config.hidden_act), *[layer(__a, __a, activation=config.hidden_act) for _ in range(depth - 1)], )
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = input
for layer in self.layers:
_lowerCAmelCase : Tuple = layer(__a)
return hidden_state
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : Any = nn.ModuleList([])
# based on `downsample_in_first_stage` the first layer of the first stage may or may not downsample the input
self.stages.append(
ResNetStage(
__a, config.embedding_size, config.hidden_sizes[0], stride=2 if config.downsample_in_first_stage else 1, depth=config.depths[0], ))
_lowerCAmelCase : Union[str, Any] = zip(config.hidden_sizes, config.hidden_sizes[1:])
for (in_channels, out_channels), depth in zip(__a, config.depths[1:]):
self.stages.append(ResNetStage(__a, __a, __a, depth=__a))
def snake_case__ ( self, __a, __a = False, __a = True):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_lowerCAmelCase : Optional[Any] = hidden_states + (hidden_state,)
_lowerCAmelCase : List[str] = stage_module(__a)
if output_hidden_states:
_lowerCAmelCase : str = hidden_states + (hidden_state,)
if not return_dict:
return tuple(v for v in [hidden_state, hidden_states] if v is not None)
return BaseModelOutputWithNoAttention(
last_hidden_state=__a, hidden_states=__a, )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ResNetConfig
lowerCamelCase__ = 'resnet'
lowerCamelCase__ = 'pixel_values'
lowerCamelCase__ = True
def snake_case__ ( self, __a):
'''simple docstring'''
if isinstance(__a, nn.Convad):
nn.init.kaiming_normal_(module.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(__a, (nn.BatchNormad, nn.GroupNorm)):
nn.init.constant_(module.weight, 1)
nn.init.constant_(module.bias, 0)
def snake_case__ ( self, __a, __a=False):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = value
_snake_case = R"\n This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n behavior.\n\n Parameters:\n config ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n Initializing with a config file does not load the weights associated with the model, only the\n configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"
_snake_case = R"\n Args:\n pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n [`ConvNextImageProcessor.__call__`] for details.\n\n output_hidden_states (`bool`, *optional*):\n Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n more detail.\n return_dict (`bool`, *optional*):\n Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"
@add_start_docstrings(
'The bare ResNet model outputting raw features without any specific head on top.' , a , )
class UpperCAmelCase_ ( a):
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Tuple = config
_lowerCAmelCase : List[str] = ResNetEmbeddings(__a)
_lowerCAmelCase : List[str] = ResNetEncoder(__a)
_lowerCAmelCase : Any = nn.AdaptiveAvgPoolad((1, 1))
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC, output_type=__a, config_class=_CONFIG_FOR_DOC, modality="vision", expected_output=_EXPECTED_OUTPUT_SHAPE, )
def snake_case__ ( self, __a, __a = None, __a = None):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_lowerCAmelCase : List[str] = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase : List[str] = self.embedder(__a)
_lowerCAmelCase : int = self.encoder(
__a, output_hidden_states=__a, return_dict=__a)
_lowerCAmelCase : int = encoder_outputs[0]
_lowerCAmelCase : List[Any] = self.pooler(__a)
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=__a, pooler_output=__a, hidden_states=encoder_outputs.hidden_states, )
@add_start_docstrings(
'\n ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , a , )
class UpperCAmelCase_ ( a):
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Tuple = config.num_labels
_lowerCAmelCase : str = ResNetModel(__a)
# classification head
_lowerCAmelCase : str = nn.Sequential(
nn.Flatten(), nn.Linear(config.hidden_sizes[-1], config.num_labels) if config.num_labels > 0 else nn.Identity(), )
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT, output_type=__a, config_class=_CONFIG_FOR_DOC, expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT, )
def snake_case__ ( self, __a = None, __a = None, __a = None, __a = None, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase : List[Any] = self.resnet(__a, output_hidden_states=__a, return_dict=__a)
_lowerCAmelCase : Tuple = outputs.pooler_output if return_dict else outputs[1]
_lowerCAmelCase : int = self.classifier(__a)
_lowerCAmelCase : Optional[Any] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_lowerCAmelCase : Optional[int] = "regression"
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_lowerCAmelCase : Optional[Any] = "single_label_classification"
else:
_lowerCAmelCase : Tuple = "multi_label_classification"
if self.config.problem_type == "regression":
_lowerCAmelCase : Optional[int] = MSELoss()
if self.num_labels == 1:
_lowerCAmelCase : int = loss_fct(logits.squeeze(), labels.squeeze())
else:
_lowerCAmelCase : Optional[Any] = loss_fct(__a, __a)
elif self.config.problem_type == "single_label_classification":
_lowerCAmelCase : List[str] = CrossEntropyLoss()
_lowerCAmelCase : Any = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
_lowerCAmelCase : Any = BCEWithLogitsLoss()
_lowerCAmelCase : str = loss_fct(__a, __a)
if not return_dict:
_lowerCAmelCase : Dict = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=__a, logits=__a, hidden_states=outputs.hidden_states)
@add_start_docstrings(
'\n ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n ' , a , )
class UpperCAmelCase_ ( a , a):
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
super()._init_backbone(__a)
_lowerCAmelCase : Dict = [config.embedding_size] + config.hidden_sizes
_lowerCAmelCase : List[Any] = ResNetEmbeddings(__a)
_lowerCAmelCase : Union[str, Any] = ResNetEncoder(__a)
# initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(__a)
@replace_return_docstrings(output_type=__a, config_class=_CONFIG_FOR_DOC)
def snake_case__ ( self, __a, __a = None, __a = None):
'''simple docstring'''
_lowerCAmelCase : Tuple = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase : Optional[Any] = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_lowerCAmelCase : Union[str, Any] = self.embedder(__a)
_lowerCAmelCase : Union[str, Any] = self.encoder(__a, output_hidden_states=__a, return_dict=__a)
_lowerCAmelCase : Tuple = outputs.hidden_states
_lowerCAmelCase : Dict = ()
for idx, stage in enumerate(self.stage_names):
if stage in self.out_features:
feature_maps += (hidden_states[idx],)
if not return_dict:
_lowerCAmelCase : str = (feature_maps,)
if output_hidden_states:
output += (outputs.hidden_states,)
return output
return BackboneOutput(
feature_maps=__a, hidden_states=outputs.hidden_states if output_hidden_states else None, attentions=__a, )
| 36
|
import unittest
from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=24, __a=2, __a=6, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=3, __a=None, __a=1000, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : List[str] = batch_size
_lowerCAmelCase : int = seq_length
_lowerCAmelCase : Optional[int] = is_training
_lowerCAmelCase : Dict = use_input_mask
_lowerCAmelCase : List[str] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : List[Any] = num_hidden_layers
_lowerCAmelCase : Optional[Any] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : List[str] = hidden_act
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Any = attention_probs_dropout_prob
_lowerCAmelCase : int = max_position_embeddings
_lowerCAmelCase : Optional[int] = type_vocab_size
_lowerCAmelCase : Optional[Any] = type_sequence_label_size
_lowerCAmelCase : List[str] = initializer_range
_lowerCAmelCase : List[Any] = num_labels
_lowerCAmelCase : Tuple = scope
_lowerCAmelCase : str = range_bbox
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
_lowerCAmelCase : Dict = bbox[i, j, 3]
_lowerCAmelCase : int = bbox[i, j, 1]
_lowerCAmelCase : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_lowerCAmelCase : str = bbox[i, j, 2]
_lowerCAmelCase : List[Any] = bbox[i, j, 0]
_lowerCAmelCase : str = t
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : Dict = None
if self.use_token_type_ids:
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[int] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case__ ( self):
'''simple docstring'''
return LiltConfig(
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, initializer_range=self.initializer_range, )
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = LiltModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(__a, bbox=__a, attention_mask=__a, token_type_ids=__a)
_lowerCAmelCase : str = model(__a, bbox=__a, token_type_ids=__a)
_lowerCAmelCase : List[Any] = model(__a, bbox=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.num_labels
_lowerCAmelCase : Optional[Any] = LiltForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = LiltForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Dict = config_and_inputs
_lowerCAmelCase : List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
return True
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = LiltModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase : Any = type
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : str = LiltModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)
_lowerCAmelCase : Any = torch.tensor([[1, 2]], device=__a)
_lowerCAmelCase : str = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=__a)
# forward pass
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(input_ids=__a, bbox=__a)
_lowerCAmelCase : Optional[int] = torch.Size([1, 2, 768])
_lowerCAmelCase : List[str] = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]], device=__a, )
self.assertTrue(outputs.last_hidden_state.shape, __a)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3], __a, atol=1E-3))
| 36
| 1
|
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelForTokenClassification,
AutoModelWithLMHead,
AutoTokenizer,
PretrainedConfig,
PreTrainedTokenizer,
)
from transformers.optimization import (
Adafactor,
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.utils.versions import require_version
_snake_case = logging.getLogger(__name__)
require_version("pytorch_lightning>=1.0.4")
_snake_case = {
"base": AutoModel,
"sequence-classification": AutoModelForSequenceClassification,
"question-answering": AutoModelForQuestionAnswering,
"pretraining": AutoModelForPreTraining,
"token-classification": AutoModelForTokenClassification,
"language-modeling": AutoModelWithLMHead,
"summarization": AutoModelForSeqaSeqLM,
"translation": AutoModelForSeqaSeqLM,
}
# update this and the import above to support new schedulers from transformers.optimization
_snake_case = {
"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,
# '': get_constant_schedule, # not supported for now
# '': get_constant_schedule_with_warmup, # not supported for now
}
_snake_case = sorted(arg_to_scheduler.keys())
_snake_case = "{" + ", ".join(arg_to_scheduler_choices) + "}"
class UpperCAmelCase_ ( pl.LightningModule):
def __init__( self, __a, __a=None, __a="base", __a=None, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__()
# TODO: move to self.save_hyperparameters()
# self.save_hyperparameters()
# can also expand arguments into trainer signature for easier reading
self.save_hyperparameters(__a)
_lowerCAmelCase : str = 0
_lowerCAmelCase : Optional[int] = Path(self.hparams.output_dir)
_lowerCAmelCase : Any = self.hparams.cache_dir if self.hparams.cache_dir else None
if config is None:
_lowerCAmelCase : Optional[Any] = AutoConfig.from_pretrained(
self.hparams.config_name if self.hparams.config_name else self.hparams.model_name_or_path, **({"num_labels": num_labels} if num_labels is not None else {}), cache_dir=__a, **__a, )
else:
_lowerCAmelCase : PretrainedConfig = config
_lowerCAmelCase : Dict = ("encoder_layerdrop", "decoder_layerdrop", "dropout", "attention_dropout")
for p in extra_model_params:
if getattr(self.hparams, __a, __a):
assert hasattr(self.config, __a), f"model config doesn't have a `{p}` attribute"
setattr(self.config, __a, getattr(self.hparams, __a))
if tokenizer is None:
_lowerCAmelCase : str = AutoTokenizer.from_pretrained(
self.hparams.tokenizer_name if self.hparams.tokenizer_name else self.hparams.model_name_or_path, cache_dir=__a, )
else:
_lowerCAmelCase : PreTrainedTokenizer = tokenizer
_lowerCAmelCase : Optional[int] = MODEL_MODES[mode]
if model is None:
_lowerCAmelCase : Dict = self.model_type.from_pretrained(
self.hparams.model_name_or_path, from_tf=bool(".ckpt" in self.hparams.model_name_or_path), config=self.config, cache_dir=__a, )
else:
_lowerCAmelCase : str = model
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
_lowerCAmelCase : Any = self.model_type.from_pretrained(*__a, **__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = arg_to_scheduler[self.hparams.lr_scheduler]
_lowerCAmelCase : Any = get_schedule_func(
self.opt, num_warmup_steps=self.hparams.warmup_steps, num_training_steps=self.total_steps())
_lowerCAmelCase : str = {"scheduler": scheduler, "interval": "step", "frequency": 1}
return scheduler
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model
_lowerCAmelCase : Any = ["bias", "LayerNorm.weight"]
_lowerCAmelCase : List[str] = [
{
"params": [
p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)
], # check this named paramters
"weight_decay": self.hparams.weight_decay,
},
{
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
"weight_decay": 0.0,
},
]
if self.hparams.adafactor:
_lowerCAmelCase : Any = Adafactor(
__a, lr=self.hparams.learning_rate, scale_parameter=__a, relative_step=__a)
else:
_lowerCAmelCase : List[str] = AdamW(
__a, lr=self.hparams.learning_rate, eps=self.hparams.adam_epsilon)
_lowerCAmelCase : Optional[Any] = optimizer
_lowerCAmelCase : Optional[Any] = self.get_lr_scheduler()
return [optimizer], [scheduler]
def snake_case__ ( self, __a, __a):
'''simple docstring'''
return self.validation_step(__a, __a)
def snake_case__ ( self, __a):
'''simple docstring'''
return self.validation_end(__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = max(1, self.hparams.gpus) # TODO: consider num_tpu_cores
_lowerCAmelCase : Optional[int] = self.hparams.train_batch_size * self.hparams.accumulate_grad_batches * num_devices
return (self.dataset_size / effective_batch_size) * self.hparams.max_epochs
def snake_case__ ( self, __a):
'''simple docstring'''
if stage == "test":
_lowerCAmelCase : Dict = len(self.test_dataloader().dataset)
else:
_lowerCAmelCase : List[str] = self.get_dataloader("train", self.hparams.train_batch_size, shuffle=__a)
_lowerCAmelCase : int = len(self.train_dataloader().dataset)
def snake_case__ ( self, __a, __a, __a = False):
'''simple docstring'''
raise NotImplementedError("You must implement this for your task")
def snake_case__ ( self):
'''simple docstring'''
return self.train_loader
def snake_case__ ( self):
'''simple docstring'''
return self.get_dataloader("dev", self.hparams.eval_batch_size, shuffle=__a)
def snake_case__ ( self):
'''simple docstring'''
return self.get_dataloader("test", self.hparams.eval_batch_size, shuffle=__a)
def snake_case__ ( self, __a):
'''simple docstring'''
return os.path.join(
self.hparams.data_dir, "cached_{}_{}_{}".format(
__a, list(filter(__a, self.hparams.model_name_or_path.split("/"))).pop(), str(self.hparams.max_seq_length), ), )
@pl.utilities.rank_zero_only
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.output_dir.joinpath("best_tfmr")
_lowerCAmelCase : Optional[int] = self.step_count
self.model.save_pretrained(__a)
self.tokenizer.save_pretrained(__a)
@staticmethod
def snake_case__ ( __a, __a):
'''simple docstring'''
parser.add_argument(
"--model_name_or_path", default=__a, type=__a, required=__a, help="Path to pretrained model or model identifier from huggingface.co/models", )
parser.add_argument(
"--config_name", default="", type=__a, help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name", default=__a, type=__a, help="Pretrained tokenizer name or path if not the same as model_name", )
parser.add_argument(
"--cache_dir", default=str(Path(__a).parent / "test_run" / "cache"), type=__a, help="Where do you want to store the pre-trained models downloaded from huggingface.co", )
parser.add_argument(
"--encoder_layerdrop", type=__a, help="Encoder layer dropout probability (Optional). Goes into model.config", )
parser.add_argument(
"--decoder_layerdrop", type=__a, help="Decoder layer dropout probability (Optional). Goes into model.config", )
parser.add_argument(
"--dropout", type=__a, help="Dropout probability (Optional). Goes into model.config", )
parser.add_argument(
"--attention_dropout", type=__a, help="Attention dropout probability (Optional). Goes into model.config", )
parser.add_argument("--learning_rate", default=5E-5, type=__a, help="The initial learning rate for Adam.")
parser.add_argument(
"--lr_scheduler", default="linear", choices=__a, metavar=__a, type=__a, help="Learning rate scheduler", )
parser.add_argument("--weight_decay", default=0.0, type=__a, help="Weight decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1E-8, type=__a, help="Epsilon for Adam optimizer.")
parser.add_argument("--warmup_steps", default=0, type=__a, help="Linear warmup over warmup_steps.")
parser.add_argument("--num_workers", default=4, type=__a, help="kwarg passed to DataLoader")
parser.add_argument("--num_train_epochs", dest="max_epochs", default=3, type=__a)
parser.add_argument("--train_batch_size", default=32, type=__a)
parser.add_argument("--eval_batch_size", default=32, type=__a)
parser.add_argument("--adafactor", action="store_true")
class UpperCAmelCase_ ( pl.Callback):
def snake_case__ ( self, __a, __a):
'''simple docstring'''
if (
trainer.is_global_zero and trainer.global_rank == 0
): # we initialize the retriever only on master worker with RAY. In new pytorch-lightning accelorators are removed.
pl_module.model.rag.retriever.init_retrieval() # better to use hook functions.
class UpperCAmelCase_ ( pl.Callback):
def snake_case__ ( self, __a, __a):
'''simple docstring'''
for name, param in pl_module.model.rag.named_parameters():
if param.grad is None:
print(__a)
class UpperCAmelCase_ ( pl.Callback):
def snake_case__ ( self, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Dict = trainer.lr_schedulers[0]["scheduler"]
_lowerCAmelCase : int = {f"lr_group_{i}": lr for i, lr in enumerate(lr_scheduler.get_lr())}
pl_module.logger.log_metrics(__a)
def snake_case__ ( self, __a, __a):
'''simple docstring'''
rank_zero_info("***** Validation results *****")
_lowerCAmelCase : Dict = trainer.callback_metrics
# Log results
for key in sorted(__a):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(__a, str(metrics[key])))
def snake_case__ ( self, __a, __a):
'''simple docstring'''
rank_zero_info("***** Test results *****")
_lowerCAmelCase : Tuple = trainer.callback_metrics
# Log and save results to file
_lowerCAmelCase : Any = os.path.join(pl_module.hparams.output_dir, "test_results.txt")
with open(__a, "w") as writer:
for key in sorted(__a):
if key not in ["log", "progress_bar"]:
rank_zero_info("{} = {}\n".format(__a, str(metrics[key])))
writer.write("{} = {}\n".format(__a, str(metrics[key])))
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
parser.add_argument(
"--output_dir" , default=str(Path(_lowerCamelCase ).parent / "test_run" / "model_checkpoints" ) , type=_lowerCamelCase , help="The output directory where the model predictions and checkpoints will be written." , )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=_lowerCamelCase , default="O2" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_tpu_cores" , dest="tpu_cores" , type=_lowerCamelCase )
parser.add_argument("--max_grad_norm" , dest="gradient_clip_val" , default=1.0 , type=_lowerCamelCase , help="Max gradient norm" )
parser.add_argument("--do_train" , action="store_true" , help="Whether to run training." )
parser.add_argument("--do_predict" , action="store_true" , help="Whether to run predictions on the test set." )
parser.add_argument(
"--gradient_accumulation_steps" , dest="accumulate_grad_batches" , type=_lowerCamelCase , default=1 , help="Number of updates steps to accumulate before performing a backward/update pass." , )
parser.add_argument("--seed" , type=_lowerCamelCase , default=42 , help="random seed for initialization" )
parser.add_argument(
"--data_dir" , default=str(Path(_lowerCamelCase ).parent / "test_run" / "dummy-train-data" ) , type=_lowerCamelCase , help="The input data dir. Should contain the training files for the CoNLL-2003 NER task." , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=None , _lowerCamelCase=True , _lowerCamelCase=[] , _lowerCamelCase=None , _lowerCamelCase=None , **_lowerCamelCase , ):
'''simple docstring'''
pl.seed_everything(args.seed )
# init model
_lowerCAmelCase : List[str] = Path(model.hparams.output_dir )
odir.mkdir(exist_ok=_lowerCamelCase )
# add custom checkpoints
if checkpoint_callback is None:
_lowerCAmelCase : List[str] = pl.callbacks.ModelCheckpoint(
filepath=args.output_dir , prefix="checkpoint" , monitor="val_loss" , mode="min" , save_top_k=1 )
if early_stopping_callback:
extra_callbacks.append(_lowerCamelCase )
if logging_callback is None:
_lowerCAmelCase : int = LoggingCallback()
_lowerCAmelCase : Optional[int] = {}
if args.fpaa:
_lowerCAmelCase : str = 16
if args.gpus > 1:
_lowerCAmelCase : Any = "auto"
_lowerCAmelCase : Optional[Any] = "ddp"
_lowerCAmelCase : Optional[int] = args.accumulate_grad_batches
_lowerCAmelCase : int = None
_lowerCAmelCase : Optional[Any] = "auto"
_lowerCAmelCase : int = pl.Trainer.from_argparse_args(
_lowerCamelCase , weights_summary=_lowerCamelCase , callbacks=[logging_callback] + extra_callbacks + [InitCallback()] + [checkpoint_callback] , logger=_lowerCamelCase , val_check_interval=1 , num_sanity_val_steps=2 , **_lowerCamelCase , )
if args.do_train:
trainer.fit(_lowerCamelCase )
else:
print("RAG modeling tests with new set functions successfuly executed!" )
return trainer
| 36
|
import argparse
import copy
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = {}
with open(_lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_lowerCAmelCase : Tuple = []
_list.append([line.split()[1], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_lowerCAmelCase : str = []
_list.append([line.split()[0], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase ) as f:
_lowerCAmelCase : str = f.read(1 )
_lowerCAmelCase : str = start_node
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Any = start_node
_lowerCAmelCase : str = 0
while visiting not in first_solution:
_lowerCAmelCase : Dict = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCamelCase ) and k[0] not in first_solution:
_lowerCAmelCase : List[str] = k[1]
_lowerCAmelCase : List[Any] = k[0]
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = distance_of_first_solution + int(_lowerCamelCase )
_lowerCAmelCase : str = best_node
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_lowerCAmelCase : Tuple = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = []
for n in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
for kn in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
if n == kn:
continue
_lowerCAmelCase : Optional[int] = copy.deepcopy(_lowerCamelCase )
_lowerCAmelCase : int = kn
_lowerCAmelCase : Dict = n
_lowerCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_lowerCAmelCase : str = _tmp[_tmp.index(_lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_lowerCAmelCase : Optional[Any] = distance + int(i[1] )
_tmp.append(_lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_lowerCAmelCase : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : int = first_solution
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Tuple = distance_of_first_solution
_lowerCAmelCase : Optional[int] = solution
while count <= iters:
_lowerCAmelCase : int = find_neighborhood(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : Dict = neighborhood[index_of_best_solution]
_lowerCAmelCase : int = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Union[str, Any] = False
while not found:
_lowerCAmelCase : Tuple = 0
while i < len(_lowerCamelCase ):
if best_solution[i] != solution[i]:
_lowerCAmelCase : str = best_solution[i]
_lowerCAmelCase : Tuple = solution[i]
break
_lowerCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : Optional[Any] = best_solution[:-1]
_lowerCAmelCase : Tuple = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_lowerCAmelCase : Union[str, Any] = cost
_lowerCAmelCase : List[Any] = solution
else:
_lowerCAmelCase : Optional[Any] = index_of_best_solution + 1
_lowerCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
if len(_lowerCamelCase ) >= size:
tabu_list.pop(0 )
_lowerCAmelCase : int = count + 1
return best_solution_ever, best_cost
def A ( _lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase : int = generate_neighbours(args.File )
_lowerCAmelCase , _lowerCAmelCase : List[str] = generate_first_solution(
args.File , _lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = tabu_search(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 36
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/config.json",
"umberto-commoncrawl-cased-v1": (
"https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"
),
"umberto-wikipedia-uncased-v1": (
"https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"
),
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'camembert'
def __init__( self, __a=3_0522, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=2, __a=0.02, __a=1E-12, __a=1, __a=0, __a=2, __a="absolute", __a=True, __a=None, **__a, ):
'''simple docstring'''
super().__init__(pad_token_id=__a, bos_token_id=__a, eos_token_id=__a, **__a)
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Optional[int] = hidden_size
_lowerCAmelCase : Union[str, Any] = num_hidden_layers
_lowerCAmelCase : Optional[int] = num_attention_heads
_lowerCAmelCase : Tuple = hidden_act
_lowerCAmelCase : Tuple = intermediate_size
_lowerCAmelCase : Optional[Any] = hidden_dropout_prob
_lowerCAmelCase : Tuple = attention_probs_dropout_prob
_lowerCAmelCase : List[Any] = max_position_embeddings
_lowerCAmelCase : Optional[Any] = type_vocab_size
_lowerCAmelCase : str = initializer_range
_lowerCAmelCase : List[Any] = layer_norm_eps
_lowerCAmelCase : int = position_embedding_type
_lowerCAmelCase : Dict = use_cache
_lowerCAmelCase : Dict = classifier_dropout
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
if self.task == "multiple-choice":
_lowerCAmelCase : Optional[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
_lowerCAmelCase : Union[str, Any] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
])
| 36
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = BartphoTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : str = ["▁This", "▁is", "▁a", "▁t", "est"]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[Any] = {"unk_token": "<unk>"}
_lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
with open(self.monolingual_vocab_file, "w", encoding="utf-8") as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n")
_lowerCAmelCase : Optional[Any] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BartphoTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "This is a là test"
_lowerCAmelCase : Optional[int] = "This is a<unk><unk> test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
_lowerCAmelCase : List[Any] = "This is a là test"
_lowerCAmelCase : str = "▁This ▁is ▁a ▁l à ▁t est".split()
_lowerCAmelCase : str = tokenizer.tokenize(__a)
self.assertListEqual(__a, __a)
_lowerCAmelCase : Tuple = tokens + [tokenizer.unk_token]
_lowerCAmelCase : List[str] = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), __a)
| 36
| 1
|
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 36
|
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
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def constraint_to_multiple_of(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0 , _lowerCamelCase=None ):
_lowerCAmelCase : Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowerCAmelCase : Optional[int] = math.floor(val / multiple ) * multiple
if x < min_val:
_lowerCAmelCase : List[str] = math.ceil(val / multiple ) * multiple
return x
_lowerCAmelCase : Union[str, Any] = (output_size, output_size) if isinstance(_lowerCamelCase , _lowerCamelCase ) else output_size
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = get_image_size(_lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = output_size
# determine new height and width
_lowerCAmelCase : List[Any] = output_height / input_height
_lowerCAmelCase : Any = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowerCAmelCase : Union[str, Any] = scale_width
else:
# fit height
_lowerCAmelCase : Union[str, Any] = scale_height
_lowerCAmelCase : List[str] = constraint_to_multiple_of(scale_height * input_height , multiple=_lowerCamelCase )
_lowerCAmelCase : Dict = constraint_to_multiple_of(scale_width * input_width , multiple=_lowerCamelCase )
return (new_height, new_width)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['pixel_values']
def __init__( self, __a = True, __a = None, __a = PILImageResampling.BILINEAR, __a = False, __a = 1, __a = True, __a = 1 / 255, __a = True, __a = None, __a = None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = size if size is not None else {"height": 384, "width": 384}
_lowerCAmelCase : Optional[int] = get_size_dict(__a)
_lowerCAmelCase : Optional[Any] = do_resize
_lowerCAmelCase : Dict = size
_lowerCAmelCase : Any = keep_aspect_ratio
_lowerCAmelCase : str = ensure_multiple_of
_lowerCAmelCase : str = resample
_lowerCAmelCase : Dict = do_rescale
_lowerCAmelCase : Optional[int] = rescale_factor
_lowerCAmelCase : Dict = do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self, __a, __a, __a = False, __a = 1, __a = PILImageResampling.BICUBIC, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : List[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()}")
_lowerCAmelCase : List[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 snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return rescale(__a, scale=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return normalize(__a, mean=__a, std=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = ChannelDimension.FIRST, **__a, ):
'''simple docstring'''
_lowerCAmelCase : int = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : List[Any] = size if size is not None else self.size
_lowerCAmelCase : str = get_size_dict(__a)
_lowerCAmelCase : Dict = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowerCAmelCase : Any = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowerCAmelCase : int = resample if resample is not None else self.resample
_lowerCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase : List[str] = image_std if image_std is not None else self.image_std
_lowerCAmelCase : Optional[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_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.
_lowerCAmelCase : List[Any] = [to_numpy_array(__a) for image in images]
if do_resize:
_lowerCAmelCase : Any = [self.resize(image=__a, size=__a, resample=__a) for image in images]
if do_rescale:
_lowerCAmelCase : List[str] = [self.rescale(image=__a, scale=__a) for image in images]
if do_normalize:
_lowerCAmelCase : Dict = [self.normalize(image=__a, mean=__a, std=__a) for image in images]
_lowerCAmelCase : List[str] = [to_channel_dimension_format(__a, __a) for image in images]
_lowerCAmelCase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=__a, tensor_type=__a)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = 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):
_lowerCAmelCase : List[Any] = target_sizes.numpy()
_lowerCAmelCase : Dict = []
for idx in range(len(__a)):
_lowerCAmelCase : int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=__a)
_lowerCAmelCase : int = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(__a)
else:
_lowerCAmelCase : Dict = logits.argmax(dim=1)
_lowerCAmelCase : str = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 36
| 1
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( a):
def __get__( self, __a, __a=None):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
_lowerCAmelCase : List[Any] = "__cached_" + self.fget.__name__
_lowerCAmelCase : Dict = getattr(__a, __a, __a)
if cached is None:
_lowerCAmelCase : str = self.fget(__a)
setattr(__a, __a, __a)
return cached
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"invalid truth value {val!r}" )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.device )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if hasattr(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase , torch.dtype )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase , tf.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase , jnp.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = fields(self)
# Safety and consistency checks
if not len(__a):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
_lowerCAmelCase : Dict = getattr(self, class_fields[0].name)
_lowerCAmelCase : str = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(__a):
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = first_field.items()
_lowerCAmelCase : Dict = True
else:
try:
_lowerCAmelCase : Dict = iter(__a)
_lowerCAmelCase : Any = True
except TypeError:
_lowerCAmelCase : Any = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a):
if (
not isinstance(__a, (list, tuple))
or not len(__a) == 2
or not isinstance(element[0], __a)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value).")
break
setattr(self, element[0], element[1])
if element[1] is not None:
_lowerCAmelCase : Any = element[1]
elif first_field is not None:
_lowerCAmelCase : Any = first_field
else:
for field in class_fields:
_lowerCAmelCase : Dict = getattr(self, field.name)
if v is not None:
_lowerCAmelCase : Union[str, Any] = v
def __delitem__( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__( self, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Optional[int] = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self, __a, __a):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a, __a)
super().__setattr__(__a, __a)
def __setitem__( self, __a, __a):
'''simple docstring'''
super().__setitem__(__a, __a)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
return tuple(self[k] for k in self.keys())
class UpperCAmelCase_ ( a , a):
@classmethod
def snake_case__ ( cls, __a):
'''simple docstring'''
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'longest'
lowerCamelCase__ = 'max_length'
lowerCamelCase__ = 'do_not_pad'
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'pt'
lowerCamelCase__ = 'tf'
lowerCamelCase__ = 'np'
lowerCamelCase__ = 'jax'
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = context_managers
_lowerCAmelCase : Dict = ExitStack()
def __enter__( self):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a)
def __exit__( self, *__a, **__a):
'''simple docstring'''
self.stack.__exit__(*__a, **__a)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : str = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = model_class.__name__
_lowerCAmelCase : Optional[Any] = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( _lowerCamelCase , _lowerCamelCase = "" , _lowerCamelCase = "." ):
'''simple docstring'''
def _flatten_dict(_lowerCamelCase , _lowerCamelCase="" , _lowerCamelCase="." ):
for k, v in d.items():
_lowerCAmelCase : Dict = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase , _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
@contextmanager
def A ( _lowerCamelCase , _lowerCamelCase = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase , axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase , _lowerCamelCase )
else:
raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase , (tuple, list) ):
_lowerCAmelCase : List[Any] = [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase : Tuple = F"{repo_id}--{value}"
return auto_map
def A ( _lowerCamelCase ):
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
_lowerCAmelCase : Tuple = base_class.__module__
_lowerCAmelCase : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"Could not infer framework from class {model_class}." )
| 36
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = "huggingface/label-files"
_lowerCAmelCase : int = "imagenet-1k-id2label.json"
_lowerCAmelCase : Tuple = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
_lowerCAmelCase : Tuple = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_lowerCAmelCase : Tuple = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
_lowerCAmelCase : Optional[int] = BitConfig(
conv_layer=_lowerCamelCase , num_labels=1_000 , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase , )
return config
def A ( _lowerCamelCase ):
'''simple docstring'''
if "stem.conv" in name:
_lowerCAmelCase : List[str] = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
_lowerCAmelCase : Any = name.replace("blocks" , "layers" )
if "head.fc" in name:
_lowerCAmelCase : Optional[Any] = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
_lowerCAmelCase : Any = "bit." + name
if "bit" not in name and "classifier" not in name:
_lowerCAmelCase : Dict = "bit.encoder." + name
return name
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCAmelCase : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = get_config(_lowerCamelCase )
# load original model from timm
_lowerCAmelCase : int = create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model
_lowerCAmelCase : Any = timm_model.state_dict()
for key in state_dict.copy().keys():
_lowerCAmelCase : Dict = state_dict.pop(_lowerCamelCase )
_lowerCAmelCase : Tuple = val.squeeze() if "head" in key else val
# load HuggingFace model
_lowerCAmelCase : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
_lowerCAmelCase : Dict = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
_lowerCAmelCase : Optional[int] = transform.transforms
_lowerCAmelCase : Tuple = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
_lowerCAmelCase : Tuple = BitImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_lowerCAmelCase : Optional[int] = prepare_img()
_lowerCAmelCase : Any = transform(_lowerCamelCase ).unsqueeze(0 )
_lowerCAmelCase : Optional[int] = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
_lowerCAmelCase : Tuple = model(_lowerCamelCase )
_lowerCAmelCase : str = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
_lowerCAmelCase : Union[str, Any] = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
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 to push the model to the hub.",
)
_snake_case = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36
| 1
|
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['image_processor', 'tokenizer']
lowerCamelCase__ = 'ViltImageProcessor'
lowerCamelCase__ = ('BertTokenizer', 'BertTokenizerFast')
def __init__( self, __a=None, __a=None, **__a):
'''simple docstring'''
_lowerCAmelCase : int = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead.", __a, )
_lowerCAmelCase : int = kwargs.pop("feature_extractor")
_lowerCAmelCase : str = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`.")
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`.")
super().__init__(__a, __a)
_lowerCAmelCase : int = self.image_processor
def __call__( self, __a, __a = None, __a = True, __a = False, __a = None, __a = None, __a = 0, __a = None, __a = None, __a = None, __a = False, __a = False, __a = False, __a = False, __a = True, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : Dict = self.tokenizer(
text=__a, add_special_tokens=__a, padding=__a, truncation=__a, max_length=__a, stride=__a, pad_to_multiple_of=__a, return_token_type_ids=__a, return_attention_mask=__a, return_overflowing_tokens=__a, return_special_tokens_mask=__a, return_offsets_mapping=__a, return_length=__a, verbose=__a, return_tensors=__a, **__a, )
# add pixel_values + pixel_mask
_lowerCAmelCase : List[Any] = self.image_processor(__a, return_tensors=__a)
encoding.update(__a)
return encoding
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
return self.tokenizer.batch_decode(*__a, **__a)
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
return self.tokenizer.decode(*__a, **__a)
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.tokenizer.model_input_names
_lowerCAmelCase : int = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
@property
def snake_case__ ( self):
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.", __a, )
return self.image_processor_class
@property
def snake_case__ ( self):
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.", __a, )
return self.image_processor
| 36
|
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCAmelCase_ ( a , a):
lowerCamelCase__ = 'swin'
lowerCamelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self, __a=224, __a=4, __a=3, __a=96, __a=[2, 2, 6, 2], __a=[3, 6, 12, 24], __a=7, __a=4.0, __a=True, __a=0.0, __a=0.0, __a=0.1, __a="gelu", __a=False, __a=0.02, __a=1E-5, __a=32, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = image_size
_lowerCAmelCase : Union[str, Any] = patch_size
_lowerCAmelCase : Tuple = num_channels
_lowerCAmelCase : List[Any] = embed_dim
_lowerCAmelCase : Tuple = depths
_lowerCAmelCase : Optional[Any] = len(__a)
_lowerCAmelCase : int = num_heads
_lowerCAmelCase : int = window_size
_lowerCAmelCase : int = mlp_ratio
_lowerCAmelCase : List[Any] = qkv_bias
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = drop_path_rate
_lowerCAmelCase : int = hidden_act
_lowerCAmelCase : Tuple = use_absolute_embeddings
_lowerCAmelCase : Optional[int] = layer_norm_eps
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Tuple = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCAmelCase : List[str] = int(embed_dim * 2 ** (len(__a) - 1))
_lowerCAmelCase : List[Any] = ["stem"] + [f"stage{idx}" for idx in range(1, len(__a) + 1)]
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
| 36
| 1
|
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def A ( _lowerCamelCase ):
'''simple docstring'''
if (
(cp >= 0X4E_00 and cp <= 0X9F_FF)
or (cp >= 0X34_00 and cp <= 0X4D_BF) #
or (cp >= 0X2_00_00 and cp <= 0X2_A6_DF) #
or (cp >= 0X2_A7_00 and cp <= 0X2_B7_3F) #
or (cp >= 0X2_B7_40 and cp <= 0X2_B8_1F) #
or (cp >= 0X2_B8_20 and cp <= 0X2_CE_AF) #
or (cp >= 0XF9_00 and cp <= 0XFA_FF)
or (cp >= 0X2_F8_00 and cp <= 0X2_FA_1F) #
): #
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
for char in word:
_lowerCAmelCase : int = ord(_lowerCamelCase )
if not _is_chinese_char(_lowerCamelCase ):
return 0
return 1
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = set()
for token in tokens:
_lowerCAmelCase : Optional[Any] = len(_lowerCamelCase ) > 1 and is_chinese(_lowerCamelCase )
if chinese_word:
word_set.add(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = list(_lowerCamelCase )
return word_list
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
_lowerCAmelCase : Any = max([len(_lowerCamelCase ) for w in chinese_word_set] )
_lowerCAmelCase : Tuple = bert_tokens
_lowerCAmelCase , _lowerCAmelCase : List[str] = 0, len(_lowerCamelCase )
while start < end:
_lowerCAmelCase : List[str] = True
if is_chinese(bert_word[start] ):
_lowerCAmelCase : Optional[Any] = min(end - start , _lowerCamelCase )
for i in range(_lowerCamelCase , 1 , -1 ):
_lowerCAmelCase : Tuple = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
_lowerCAmelCase : Optional[int] = "##" + bert_word[j]
_lowerCAmelCase : Dict = start + i
_lowerCAmelCase : Optional[Any] = False
break
if single_word:
start += 1
return bert_word
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = []
for i in range(0 , len(_lowerCamelCase ) , 100 ):
_lowerCAmelCase : List[Any] = ltp_tokenizer.seg(lines[i : i + 100] )[0]
_lowerCAmelCase : Tuple = [get_chinese_word(_lowerCamelCase ) for r in res]
ltp_res.extend(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
_lowerCAmelCase : str = []
for i in range(0 , len(_lowerCamelCase ) , 100 ):
_lowerCAmelCase : List[Any] = bert_tokenizer(lines[i : i + 100] , add_special_tokens=_lowerCamelCase , truncation=_lowerCamelCase , max_length=512 )
bert_res.extend(res["input_ids"] )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
_lowerCAmelCase : Any = []
for input_ids, chinese_word in zip(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = []
for id in input_ids:
_lowerCAmelCase : List[Any] = bert_tokenizer._convert_id_to_token(_lowerCamelCase )
input_tokens.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = add_sub_symbol(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : List[str] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(_lowerCamelCase ):
if token[:2] == "##":
_lowerCAmelCase : Optional[Any] = token[2:]
# save chinese tokens' pos
if len(_lowerCamelCase ) == 1 and _is_chinese_char(ord(_lowerCamelCase ) ):
ref_id.append(_lowerCamelCase )
ref_ids.append(_lowerCamelCase )
assert len(_lowerCamelCase ) == len(_lowerCamelCase )
return ref_ids
def A ( _lowerCamelCase ):
'''simple docstring'''
with open(args.file_name , "r" , encoding="utf-8" ) as f:
_lowerCAmelCase : List[str] = f.readlines()
_lowerCAmelCase : int = [line.strip() for line in data if len(_lowerCamelCase ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
_lowerCAmelCase : Dict = LTP(args.ltp ) # faster in GPU device
_lowerCAmelCase : int = BertTokenizer.from_pretrained(args.bert )
_lowerCAmelCase : Optional[Any] = prepare_ref(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
_lowerCAmelCase : Union[str, Any] = [json.dumps(_lowerCamelCase ) + "\n" for ref in ref_ids]
f.writelines(_lowerCamelCase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
_snake_case = parser.parse_args()
main(args)
| 36
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 36
| 1
|
# Copyright (c) 2021-, NVIDIA CORPORATION. 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.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0 ):
'''simple docstring'''
if name is None:
_lowerCAmelCase : Union[str, Any] = None
else:
_lowerCAmelCase : Any = "." * max(0 , spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
_lowerCAmelCase : Tuple = fmt.format(_lowerCamelCase )
# Print and recurse (if needed).
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if msg is not None:
print(_lowerCamelCase )
for k in val.keys():
recursive_print(_lowerCamelCase , val[k] , spaces + 2 )
elif isinstance(_lowerCamelCase , torch.Tensor ):
print(_lowerCamelCase , ":" , val.size() )
else:
print(_lowerCamelCase , ":" , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
_lowerCAmelCase : Optional[int] = (num_heads, hidden_size, num_splits) + input_shape[1:]
_lowerCAmelCase : Dict = param.view(*_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = param.transpose(0 , 2 )
_lowerCAmelCase : str = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
_lowerCAmelCase : Any = (num_heads, num_splits, hidden_size) + input_shape[1:]
_lowerCAmelCase : List[Any] = param.view(*_lowerCamelCase )
_lowerCAmelCase : Tuple = param.transpose(0 , 1 ).contiguous()
_lowerCAmelCase : Optional[int] = param.view(*_lowerCamelCase )
return param
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = {}
# old versions did not store training args
_lowerCAmelCase : Optional[Any] = input_state_dict.get("args" , _lowerCamelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
_lowerCAmelCase : List[Any] = ds_args.padded_vocab_size
_lowerCAmelCase : Dict = ds_args.max_position_embeddings
_lowerCAmelCase : Any = ds_args.hidden_size
_lowerCAmelCase : List[Any] = ds_args.num_layers
_lowerCAmelCase : Any = ds_args.num_attention_heads
_lowerCAmelCase : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
_lowerCAmelCase : int = config.n_head
# The hidden_size per head.
_lowerCAmelCase : List[Any] = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
_lowerCAmelCase : Tuple = input_state_dict["checkpoint_version"]
else:
_lowerCAmelCase : List[Any] = 0.0
# The model.
_lowerCAmelCase : Tuple = input_state_dict["model"]
# The language model.
_lowerCAmelCase : List[Any] = model["language_model"]
# The embeddings.
_lowerCAmelCase : Any = lm["embedding"]
# The word embeddings.
_lowerCAmelCase : Union[str, Any] = embeddings["word_embeddings"]["weight"]
# Truncate the embedding table to vocab_size rows.
_lowerCAmelCase : Optional[int] = word_embeddings[: config.vocab_size, :]
_lowerCAmelCase : int = word_embeddings
# The position embeddings.
_lowerCAmelCase : Any = embeddings["position_embeddings"]["weight"]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
_lowerCAmelCase : List[str] = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match" )
# Store the position embeddings.
_lowerCAmelCase : int = pos_embeddings
# The transformer.
_lowerCAmelCase : int = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
# The regex to extract layer names.
_lowerCAmelCase : Optional[int] = re.compile(r"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)" )
# The simple map of names for "automated" rules.
_lowerCAmelCase : int = {
"attention.dense": ".attn.c_proj.",
"self_attention.dense": ".attn.c_proj.",
"mlp.dense_h_to_4h": ".mlp.c_fc.",
"mlp.dense_4h_to_h": ".mlp.c_proj.",
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
_lowerCAmelCase : Optional[Any] = layer_re.match(_lowerCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
_lowerCAmelCase : Optional[Any] = int(m.group(1 ) )
# The name of the operation.
_lowerCAmelCase : List[str] = m.group(2 )
# Is it a weight or a bias?
_lowerCAmelCase : List[Any] = m.group(3 )
# The name of the layer.
_lowerCAmelCase : str = F"transformer.h.{layer_idx}"
# For layernorm(s), simply store the layer norm.
if op_name.endswith("layernorm" ):
_lowerCAmelCase : Optional[Any] = "ln_1" if op_name.startswith("input" ) else "ln_2"
_lowerCAmelCase : List[Any] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
_lowerCAmelCase : Any = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , _lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Dict = causal_mask
# Insert a "dummy" tensor for masked_bias.
_lowerCAmelCase : Tuple = torch.tensor(-1e4 , dtype=torch.floataa )
_lowerCAmelCase : Tuple = masked_bias
_lowerCAmelCase : List[Any] = fix_query_key_value_ordering(_lowerCamelCase , _lowerCamelCase , 3 , _lowerCamelCase , _lowerCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
_lowerCAmelCase : Dict = out_val.transpose(0 , 1 ).contiguous()
# Store.
_lowerCAmelCase : List[Any] = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
_lowerCAmelCase : Optional[int] = fix_query_key_value_ordering(_lowerCamelCase , _lowerCamelCase , 3 , _lowerCamelCase , _lowerCamelCase )
# Store. No change of shape.
_lowerCAmelCase : Tuple = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
_lowerCAmelCase : Any = megatron_to_transformers[op_name]
_lowerCAmelCase : str = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
_lowerCAmelCase : Tuple = megatron_to_transformers[op_name]
_lowerCAmelCase : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
_lowerCAmelCase : Dict = transformer["final_layernorm.weight"]
_lowerCAmelCase : List[str] = transformer["final_layernorm.bias"]
# For LM head, transformers' wants the matrix to weight embeddings.
_lowerCAmelCase : Union[str, Any] = word_embeddings
# It should be done!
return output_state_dict
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("--print-checkpoint-structure" , action="store_true" )
parser.add_argument(
"path_to_checkpoint" , type=_lowerCamelCase , help="Path to the checkpoint file (.zip archive or direct .pt file)" , )
parser.add_argument(
"--config_file" , default="" , type=_lowerCamelCase , help="An optional config json file describing the pre-trained model." , )
_lowerCAmelCase : Union[str, Any] = parser.parse_args()
# Extract the basename.
_lowerCAmelCase : List[Any] = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F"Extracting PyTorch state dictionary from {args.path_to_checkpoint}" )
if args.path_to_checkpoint.endswith(".zip" ):
with zipfile.ZipFile(args.path_to_checkpoint , "r" ) as checkpoint:
with checkpoint.open("release/mp_rank_00/model_optim_rng.pt" ) as pytorch_dict:
_lowerCAmelCase : int = torch.load(_lowerCamelCase , map_location="cpu" )
else:
_lowerCAmelCase : List[Any] = torch.load(args.path_to_checkpoint , map_location="cpu" )
_lowerCAmelCase : Dict = input_state_dict.get("args" , _lowerCamelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
_lowerCAmelCase : Union[str, Any] = "gelu_fast"
elif ds_args.openai_gelu:
_lowerCAmelCase : int = "gelu_new"
else:
_lowerCAmelCase : Dict = "gelu"
else:
# in the very early days this used to be "gelu_new"
_lowerCAmelCase : Dict = "gelu_new"
# Spell out all parameters in case the defaults change.
_lowerCAmelCase : Union[str, Any] = GPTaConfig(
vocab_size=50_257 , n_positions=1_024 , n_embd=1_024 , n_layer=24 , n_head=16 , n_inner=4_096 , activation_function=_lowerCamelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.02 , summary_type="cls_index" , summary_use_proj=_lowerCamelCase , summary_activation=_lowerCamelCase , summary_proj_to_labels=_lowerCamelCase , summary_first_dropout=0.1 , scale_attn_weights=_lowerCamelCase , use_cache=_lowerCamelCase , bos_token_id=50_256 , eos_token_id=50_256 , )
else:
_lowerCAmelCase : str = GPTaConfig.from_json_file(args.config_file )
_lowerCAmelCase : Dict = ["GPT2LMHeadModel"]
# Convert.
print("Converting" )
_lowerCAmelCase : Tuple = convert_megatron_checkpoint(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(_lowerCamelCase , _lowerCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
_lowerCAmelCase : Optional[int] = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
_lowerCAmelCase : Optional[int] = "gpt2"
elif tokenizer_type == "PretrainedFromHF":
_lowerCAmelCase : Union[str, Any] = ds_args.tokenizer_name_or_path
else:
raise ValueError(F"Unrecognized tokenizer_type {tokenizer_type}" )
else:
_lowerCAmelCase : str = "gpt2"
_lowerCAmelCase : Union[str, Any] = AutoTokenizer.from_pretrained(_lowerCamelCase )
_lowerCAmelCase : Dict = type(_lowerCamelCase ).__name__
_lowerCAmelCase : List[str] = tokenizer_class
# Store the config to file.
print("Saving config" )
config.save_pretrained(_lowerCamelCase )
# Save tokenizer based on args
print(F"Adding {tokenizer_class} tokenizer files" )
tokenizer.save_pretrained(_lowerCamelCase )
# Store the state_dict to file.
_lowerCAmelCase : List[str] = os.path.join(_lowerCamelCase , "pytorch_model.bin" )
print(F"Saving checkpoint to \"{output_checkpoint_file}\"" )
torch.save(_lowerCamelCase , _lowerCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 36
|
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_snake_case = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if got_ver is None or want_ver is None:
raise ValueError(
F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
F" reinstalling {pkg}." )
if not ops[op](version.parse(_lowerCamelCase ) , version.parse(_lowerCamelCase ) ):
raise ImportError(
F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}" )
def A ( _lowerCamelCase , _lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase : List[str] = F"\n{hint}" if hint is not None else ""
# non-versioned check
if re.match(r"^[\w_\-\d]+$" , _lowerCamelCase ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = requirement, None, None
else:
_lowerCAmelCase : Optional[int] = re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
F" got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Dict = match[0]
_lowerCAmelCase : Any = want_full.split("," ) # there could be multiple requirements
_lowerCAmelCase : Optional[int] = {}
for w in want_range:
_lowerCAmelCase : Any = re.findall(r"^([\s!=<>]{1,2})(.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
F" but got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Tuple = match[0]
_lowerCAmelCase : Union[str, Any] = want_ver
if op not in ops:
raise ValueError(F"{requirement}: need one of {list(ops.keys() )}, but got {op}" )
# special case
if pkg == "python":
_lowerCAmelCase : Tuple = ".".join([str(_lowerCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return
# check if any version is installed
try:
_lowerCAmelCase : Any = importlib.metadata.version(_lowerCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F"The '{requirement}' distribution was not found and is required by this application. {hint}" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
return require_version(_lowerCamelCase , _lowerCamelCase )
| 36
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = StableDiffusionInpaintPipeline
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
lowerCamelCase__ = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowerCamelCase__ = frozenset(
[]) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
lowerCamelCase__ = frozenset([])
def snake_case__ ( self):
'''simple docstring'''
torch.manual_seed(0)
_lowerCAmelCase : str = UNetaDConditionModel(
block_out_channels=(32, 64), layers_per_block=2, sample_size=32, in_channels=9, out_channels=4, down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"), up_block_types=("CrossAttnUpBlock2D", "UpBlock2D"), cross_attention_dim=32, attention_head_dim=(2, 4), use_linear_projection=__a, )
_lowerCAmelCase : Optional[int] = PNDMScheduler(skip_prk_steps=__a)
torch.manual_seed(0)
_lowerCAmelCase : List[Any] = AutoencoderKL(
block_out_channels=[32, 64], in_channels=3, out_channels=3, down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"], up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"], latent_channels=4, sample_size=128, )
torch.manual_seed(0)
_lowerCAmelCase : Union[str, Any] = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=32, intermediate_size=37, layer_norm_eps=1E-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=1000, hidden_act="gelu", projection_dim=512, )
_lowerCAmelCase : List[Any] = CLIPTextModel(__a)
_lowerCAmelCase : Optional[int] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
_lowerCAmelCase : str = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def snake_case__ ( self, __a, __a=0):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = floats_tensor((1, 3, 32, 32), rng=random.Random(__a)).to(__a)
_lowerCAmelCase : Optional[int] = image.cpu().permute(0, 2, 3, 1)[0]
_lowerCAmelCase : Dict = Image.fromarray(np.uinta(__a)).convert("RGB").resize((64, 64))
_lowerCAmelCase : List[str] = Image.fromarray(np.uinta(image + 4)).convert("RGB").resize((64, 64))
if str(__a).startswith("mps"):
_lowerCAmelCase : Any = torch.manual_seed(__a)
else:
_lowerCAmelCase : int = torch.Generator(device=__a).manual_seed(__a)
_lowerCAmelCase : Optional[Any] = {
"prompt": "A painting of a squirrel eating a burger",
"image": init_image,
"mask_image": mask_image,
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : List[str] = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase : Union[str, Any] = self.get_dummy_components()
_lowerCAmelCase : List[str] = StableDiffusionInpaintPipeline(**__a)
_lowerCAmelCase : Tuple = sd_pipe.to(__a)
sd_pipe.set_progress_bar_config(disable=__a)
_lowerCAmelCase : List[Any] = self.get_dummy_inputs(__a)
_lowerCAmelCase : Dict = sd_pipe(**__a).images
_lowerCAmelCase : Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
_lowerCAmelCase : Tuple = np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
def snake_case__ ( self):
'''simple docstring'''
super().test_inference_batch_single_identical(expected_max_diff=3E-3)
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
_lowerCAmelCase : Dict = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
_lowerCAmelCase : Tuple = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench.npy")
_lowerCAmelCase : List[Any] = "stabilityai/stable-diffusion-2-inpainting"
_lowerCAmelCase : int = StableDiffusionInpaintPipeline.from_pretrained(__a, safety_checker=__a)
pipe.to(__a)
pipe.set_progress_bar_config(disable=__a)
pipe.enable_attention_slicing()
_lowerCAmelCase : Tuple = "Face of a yellow cat, high resolution, sitting on a park bench"
_lowerCAmelCase : List[str] = torch.manual_seed(0)
_lowerCAmelCase : Optional[Any] = pipe(
prompt=__a, image=__a, mask_image=__a, generator=__a, output_type="np", )
_lowerCAmelCase : Any = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 9E-3
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : str = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
_lowerCAmelCase : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
_lowerCAmelCase : str = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"
"/yellow_cat_sitting_on_a_park_bench_fp16.npy")
_lowerCAmelCase : Optional[Any] = "stabilityai/stable-diffusion-2-inpainting"
_lowerCAmelCase : Any = StableDiffusionInpaintPipeline.from_pretrained(
__a, torch_dtype=torch.floataa, safety_checker=__a, )
pipe.to(__a)
pipe.set_progress_bar_config(disable=__a)
pipe.enable_attention_slicing()
_lowerCAmelCase : Optional[Any] = "Face of a yellow cat, high resolution, sitting on a park bench"
_lowerCAmelCase : Optional[Any] = torch.manual_seed(0)
_lowerCAmelCase : Tuple = pipe(
prompt=__a, image=__a, mask_image=__a, generator=__a, output_type="np", )
_lowerCAmelCase : List[Any] = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image).max() < 5E-1
def snake_case__ ( self):
'''simple docstring'''
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCAmelCase : int = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/sd2-inpaint/init_image.png")
_lowerCAmelCase : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png")
_lowerCAmelCase : Optional[Any] = "stabilityai/stable-diffusion-2-inpainting"
_lowerCAmelCase : Tuple = PNDMScheduler.from_pretrained(__a, subfolder="scheduler")
_lowerCAmelCase : int = StableDiffusionInpaintPipeline.from_pretrained(
__a, safety_checker=__a, scheduler=__a, torch_dtype=torch.floataa, )
pipe.to(__a)
pipe.set_progress_bar_config(disable=__a)
pipe.enable_attention_slicing(1)
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase : Tuple = "Face of a yellow cat, high resolution, sitting on a park bench"
_lowerCAmelCase : int = torch.manual_seed(0)
_lowerCAmelCase : Union[str, Any] = pipe(
prompt=__a, image=__a, mask_image=__a, generator=__a, num_inference_steps=2, output_type="np", )
_lowerCAmelCase : str = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 36
|
import argparse
from collections import defaultdict
import yaml
_snake_case = "docs/source/en/_toctree.yml"
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = defaultdict(_lowerCamelCase )
_lowerCAmelCase : Any = []
_lowerCAmelCase : List[str] = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"local": doc["local"], "title": doc["title"]} )
else:
new_doc_list.append(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = new_doc_list
_lowerCAmelCase : List[Any] = [key for key, value in counts.items() if value > 1]
_lowerCAmelCase : str = []
for duplicate_key in duplicates:
_lowerCAmelCase : List[str] = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
if len(_lowerCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
"others." )
# Only add this once
new_doc.append({"local": duplicate_key, "title": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1] )
_lowerCAmelCase : Optional[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(_lowerCamelCase ) > 1:
raise ValueError("{doc_list} has two 'overview' docs which is not allowed." )
overview_doc.extend(_lowerCamelCase )
# Sort
return overview_doc
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : int = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : List[str] = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : Union[str, Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase : Optional[Any] = api_doc[scheduler_idx]["sections"]
_lowerCAmelCase : Optional[Any] = clean_doc_toc(_lowerCamelCase )
_lowerCAmelCase : int = False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase : List[Any] = True
if overwrite:
_lowerCAmelCase : Dict = new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase : Tuple = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : int = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : List[str] = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase : Dict = False
_lowerCAmelCase : Optional[int] = api_doc[pipeline_idx]["sections"]
_lowerCAmelCase : Tuple = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase : List[Any] = pipeline_doc["section"]
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if overwrite:
_lowerCAmelCase : Optional[Any] = new_sub_pipeline_doc
new_pipeline_docs.append(_lowerCamelCase )
# sort overall pipeline doc
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase : Dict = True
if overwrite:
_lowerCAmelCase : Optional[int] = new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase : Optional[int] = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_snake_case = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 36
| 1
|
UpperCAmelCase__ = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(100000)]
def _a ( a :int ) -> int:
a = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum_of_digits_squared += DIGITS_SQUARED[number % 100_000]
number //= 100_000
return sum_of_digits_squared
# There are 2 Chains made,
# One ends with 89 with the chain member 58 being the one which when declared first,
# there will be the least number of iterations for all the members to be checked.
# The other one ends with 1 and has only one element 1.
# So 58 and 1 are chosen to be declared at the starting.
# Changed dictionary to an array to quicken the solution
UpperCAmelCase__ = [None] * 10000000
UpperCAmelCase__ = True
UpperCAmelCase__ = False
def _a ( a :int ) -> bool:
if CHAINS[number - 1] is not None:
return CHAINS[number - 1] # type: ignore
a = chain(next_number(a ) )
a = number_chain
while number < 10_000_000:
a = number_chain
number *= 10
return number_chain
def _a ( a :int = 10_000_000 ) -> int:
for i in range(1 , a ):
if CHAINS[i] is None:
chain(i + 1 )
return CHAINS[:number].count(a )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{solution() = }""")
| 0
|
def A ( _lowerCamelCase , _lowerCamelCase ):
'''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()
| 36
| 0
|
'''simple docstring'''
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE_: List[Any] =logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_: Union[str, Any] ={'vocab_file': 'spiece.model'}
SCREAMING_SNAKE_CASE_: List[str] ={
'vocab_file': {
'AI-Sweden/gpt-sw3-126m': 'https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-350m': 'https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-1.6b': 'https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-6.7b': 'https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model',
'AI-Sweden/gpt-sw3-20b': 'https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model',
}
}
SCREAMING_SNAKE_CASE_: Union[str, Any] ={
'AI-Sweden/gpt-sw3-126m': 20_48,
'AI-Sweden/gpt-sw3-350m': 20_48,
'AI-Sweden/gpt-sw3-1.6b': 20_48,
'AI-Sweden/gpt-sw3-6.7b': 20_48,
'AI-Sweden/gpt-sw3-20b': 20_48,
}
class __A ( UpperCamelCase__ ):
a__ : List[str] = VOCAB_FILES_NAMES
a__ : Any = PRETRAINED_VOCAB_FILES_MAP
a__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__(self : Tuple , __a : Dict , __a : Union[str, Any]=False , __a : int=False , __a : Dict=False , __a : Any=None , __a : Tuple=None , __a : List[str]=None , __a : Optional[Any]=None , __a : Optional[Dict[str, Any]] = None , **__a : Optional[int] , ):
UpperCAmelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCAmelCase_ = kwargs.get("name_or_path" )
if name_or_path is None:
logger.warning(
"name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,"
" you are testing the model, this can safely be ignored" )
UpperCAmelCase_ = "None"
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCAmelCase_ = "<|endoftext|>" if eos_token is None else eos_token
UpperCAmelCase_ = "<unk>" if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCAmelCase_ = unk_token if pad_token is None else pad_token
UpperCAmelCase_ = eos_token if bos_token is None else bos_token
else:
UpperCAmelCase_ = "<pad>" if pad_token is None else pad_token
UpperCAmelCase_ = "<s>" if bos_token is None else bos_token
super().__init__(
do_lower_case=__a , remove_space=__a , keep_accents=__a , bos_token=__a , eos_token=__a , unk_token=__a , pad_token=__a , sp_model_kwargs=self.sp_model_kwargs , **__a , )
UpperCAmelCase_ = do_lower_case
UpperCAmelCase_ = remove_space
UpperCAmelCase_ = keep_accents
UpperCAmelCase_ = vocab_file
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(__a )
# Used for whitespace normalization in input texts
# fmt : off
UpperCAmelCase_ = {" ", " ", " ", " ", " ", " ", " ", " ", " ", " ", "", ""}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCAmelCase_ = re.compile(
f"""[{"".join(map(__a , list(range(0 , 9 ) ) + list(range(11 , 32 ) ) + list(range(127 , 160 ) ) + [160, 173, 8203] ) )}]""" )
def __getstate__(self : Optional[int] ):
UpperCAmelCase_ = self.__dict__.copy()
UpperCAmelCase_ = None
return state
def __setstate__(self : Optional[Any] , __a : int ):
UpperCAmelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def _lowercase (self : int ):
return len(self.sp_model )
def _lowercase (self : List[str] , __a : str ):
UpperCAmelCase_ = self.non_printing_characters_re.sub("" , __a )
# Normalize whitespaces
UpperCAmelCase_ = "".join([char if char not in self.whitespaces else " " for char in text] )
# NFC Unicode normalization
UpperCAmelCase_ = unicodedata.normalize("NFC" , __a )
return text
def _lowercase (self : int , __a : str , **__a : str ):
UpperCAmelCase_ = self.preprocess_text(__a )
return self.sp_model.encode(__a , out_type=__a )
def _lowercase (self : Union[str, Any] , __a : str ):
return self.sp_model.PieceToId(__a )
def _lowercase (self : List[Any] , __a : int ):
return self.sp_model.IdToPiece(__a )
@staticmethod
def _lowercase (__a : str ):
return out_string
def _lowercase (self : Dict , __a : List[str] ):
UpperCAmelCase_ = []
UpperCAmelCase_ = ""
UpperCAmelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(__a ) + token
UpperCAmelCase_ = True
UpperCAmelCase_ = []
else:
current_sub_tokens.append(__a )
UpperCAmelCase_ = False
out_string += self.sp_model.decode(__a )
return out_string
def _lowercase (self : List[str] ):
UpperCAmelCase_ = {self.convert_ids_to_tokens(__a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _lowercase (self : Optional[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_ = 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_ = self.sp_model.serialized_model_proto()
fi.write(__a )
return (out_vocab_file,)
def _lowercase (self : str , __a : Union[str, List[str]] , __a : Union[str, bool] = False ):
if isinstance(__a , __a ):
UpperCAmelCase_ = self.preprocess_text(__a )
UpperCAmelCase_ = self.sp_model.encode(__a )
else:
UpperCAmelCase_ = [self.preprocess_text(__a ) for t in text]
UpperCAmelCase_ = self.sp_model.encode(__a )
if return_tensors is True or return_tensors == "pt":
UpperCAmelCase_ = torch.tensor(__a )
return token_ids
def _lowercase (self : List[Any] , __a : Union[int, List[int]] ):
return self.sp_model.decode(__a )
def _lowercase (self : int , __a : "Conversation" ):
UpperCAmelCase_ = [f"""User: {text}""" if is_user else f"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCAmelCase_ = (
f"""{self.eos_token}{self.bos_token}""" + f"""{self.bos_token}""".join(__a ) + f"""{self.bos_token}Bot:"""
)
return self.encode(text=__a )
| 1
|
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
_snake_case = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class UpperCAmelCase_ ( a):
def __init__( self, __a = 101):
'''simple docstring'''
_lowerCAmelCase : str = length
def __len__( self):
'''simple docstring'''
return self.length
def __getitem__( self, __a):
'''simple docstring'''
return i
class UpperCAmelCase_ :
def __call__( self, __a):
'''simple docstring'''
return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}
class UpperCAmelCase_ ( nn.Module):
def __init__( self):
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
_lowerCAmelCase : str = nn.Linear(120, 80)
def snake_case__ ( self, __a, __a=None):
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
class UpperCAmelCase_ ( a):
@require_torch_neuroncore
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = f"--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : List[Any] = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class UpperCAmelCase_ ( a):
@require_torch_multi_gpu
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = f"--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Any = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : Any = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
_snake_case = HfArgumentParser((TrainingArguments,))
_snake_case = parser.parse_args_into_dataclasses()[0]
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
_snake_case = DummyDataset(dataset_length)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = list(range(len(_lowerCamelCase ) ) )
_lowerCAmelCase : Union[str, Any] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}" )
return {"success": success}
_snake_case = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = 2
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = None
| 36
| 0
|
'''simple docstring'''
class __lowerCAmelCase : # Public class to implement a graph
'''simple docstring'''
def __init__(self : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[bool]] ):
'''simple docstring'''
lowercase__ = row
lowercase__ = col
lowercase__ = graph
def UpperCamelCase__ (self : Optional[int] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[bool]] ):
'''simple docstring'''
return (
0 <= i < self.ROW
and 0 <= j < self.COL
and not visited[i][j]
and self.graph[i][j]
)
def UpperCamelCase__ (self : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : list[list[bool]] ):
'''simple docstring'''
lowercase__ = [-1, -1, -1, 0, 0, 1, 1, 1] # Coordinate order
lowercase__ = [-1, 0, 1, -1, 1, -1, 0, 1]
lowercase__ = True # Make those cells visited
for k in range(8 ):
if self.is_safe(i + row_nbr[k] , j + col_nbr[k] , UpperCamelCase ):
self.diffs(i + row_nbr[k] , j + col_nbr[k] , UpperCamelCase )
def UpperCamelCase__ (self : Dict ): # And finally, count all islands.
'''simple docstring'''
lowercase__ = [[False for j in range(self.COL )] for i in range(self.ROW )]
lowercase__ = 0
for i in range(self.ROW ):
for j in range(self.COL ):
if visited[i][j] is False and self.graph[i][j] == 1:
self.diffs(UpperCamelCase , UpperCamelCase , UpperCamelCase )
count += 1
return count
| 2
|
from __future__ import annotations
import bisect
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : int = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Optional[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
_lowerCAmelCase : Union[str, Any] = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : str = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Tuple = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
_lowerCAmelCase : Dict = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_left(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_right(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
while left <= right:
_lowerCAmelCase : int = left + (right - left) // 2
_lowerCAmelCase : int = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
_lowerCAmelCase : str = midpoint - 1
else:
_lowerCAmelCase : Any = midpoint + 1
return None
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = bisect.bisect_left(_lowerCamelCase , _lowerCamelCase )
if index != len(_lowerCamelCase ) and sorted_collection[index] == item:
return index
return None
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if right < left:
return None
_lowerCAmelCase : Optional[int] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , midpoint + 1 , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = input("Enter numbers separated by comma:\n").strip()
_snake_case = sorted(int(item) for item in user_input.split(","))
_snake_case = int(input("Enter a single number to be found in the list:\n"))
_snake_case = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
| 36
| 0
|
'''simple docstring'''
import argparse
import json
import os
import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile
from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
# expert layer
A : Union[str, Any] = flax_key_tuple[:-1] + ('''weight''',)
A : Tuple = torch.permute(snake_case__ , (0, 2, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case__ ):
# linear layer
A : Any = flax_key_tuple[:-1] + ('''weight''',)
A : Union[str, Any] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
A : int = flax_key_tuple[:-1] + ('''weight''',)
return flax_key_tuple, flax_tensor
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
if "metadata" in layer:
A : Union[str, Any] = layer.split('''metadata''' )
A : List[Any] = ''''''.join(split_layer[0] )[:-1]
A : int = [tuple(('''metadata''' + split_layer[1]).split('''/''' ) )]
elif "kvstore" in layer:
A : Any = layer.split('''kvstore''' )
A : List[Any] = ''''''.join(split_layer[0] )[:-1]
A : Optional[Any] = [tuple(('''kvstore''' + split_layer[1]).split('''/''' ) )]
else:
A : Union[str, Any] = layer.split('''/''' )
A : Optional[int] = '''/'''.join(split_layer[:-1] )
A : Optional[Any] = (split_layer[-1],)
if "kvstore/path" in layer:
A : int = F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
elif "kvstore/driver" in layer:
A : int = '''file'''
else:
A : Any = checkpoint_info[layer]
return curr_real_layer_name, split_layer, content
def lowerCAmelCase_ ( snake_case__ , snake_case__ ):
'''simple docstring'''
A : Optional[int] = rename_keys(snake_case__ )
A : Tuple = {}
for k, v in current_block.items():
A : Tuple = v
A : List[str] = new_current_block
torch.save(snake_case__ , snake_case__ )
def lowerCAmelCase_ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ = WEIGHTS_NAME ):
'''simple docstring'''
A : Dict = convert_file_size_to_int(snake_case__ )
A : str = []
A : str = {}
A : Any = 0
A : List[str] = 0
os.makedirs(snake_case__ , exist_ok=snake_case__ )
with gfile.GFile(switch_checkpoint_path + '''/checkpoint''' , '''rb''' ) as fp:
A : Tuple = serialization.msgpack_restore(fp.read() )['''optimizer''']['''target''']
A : List[Any] = flatten_dict(snake_case__ , sep='''/''' )
A : List[str] = {}
for layer in checkpoint_info.keys():
A, A, A : Tuple = get_key_and_tensorstore_dict(
snake_case__ , snake_case__ , snake_case__ )
if curr_real_layer_name in all_layers:
A : List[str] = content
else:
A : Optional[Any] = {split_layer[-1]: content}
for key in all_layers.keys():
# open tensorstore file
A : Any = ts.open(unflatten_dict(all_layers[key] ) ).result().read().result()
A : List[Any] = torch.tensor(snake_case__ )
A : int = raw_weights.numel() * dtype_byte_size(raw_weights.dtype )
# use the renaming pattern from the small conversion scripts
A, A : int = rename_base_flax_keys(tuple(key.split('''/''' ) ) , snake_case__ )
A : Union[str, Any] = '''/'''.join(snake_case__ )
# If this weight is going to tip up over the maximal size, we split.
if current_block_size + weight_size > max_shard_size:
A : List[Any] = os.path.join(
snake_case__ , weights_name.replace('''.bin''' , F'-{len(snake_case__ )+1:05d}-of-???.bin' ) )
rename_and_save_block(snake_case__ , snake_case__ )
sharded_state_dicts.append(current_block.keys() )
del current_block
A : Dict = {}
A : List[Any] = 0
A : List[Any] = raw_weights.to(getattr(snake_case__ , snake_case__ ) )
current_block_size += weight_size
total_size += weight_size
# Add the last block
A : Optional[int] = os.path.join(snake_case__ , weights_name.replace('''.bin''' , F'-{len(snake_case__ )+1:05d}-of-???.bin' ) )
rename_and_save_block(snake_case__ , snake_case__ )
sharded_state_dicts.append(current_block.keys() )
# If we only have one shard, we return it
if len(snake_case__ ) == 1:
return {weights_name: sharded_state_dicts[0]}, None
# Otherwise, let's build the index
A : Union[str, Any] = {}
A : List[str] = {}
for idx, shard in enumerate(snake_case__ ):
A : int = weights_name.replace(
'''.bin''' , F'-{idx+1:05d}-of-{len(snake_case__ ):05d}.bin' ) # len(sharded_state_dicts):05d}
A : Union[str, Any] = os.path.join(snake_case__ , weights_name.replace('''.bin''' , F'-{idx+1:05d}-of-???.bin' ) )
os.rename(snake_case__ , os.path.join(snake_case__ , snake_case__ ) )
A : str = shard
for key in shard:
A : Tuple = shard_file
# Add the metadata
A : Tuple = {'''total_size''': total_size}
A : Optional[int] = {'''metadata''': metadata, '''weight_map''': weight_map}
with open(os.path.join(snake_case__ , snake_case__ ) , '''w''' , encoding='''utf-8''' ) as f:
A : Union[str, Any] = json.dumps(snake_case__ , indent=2 , sort_keys=snake_case__ ) + '''\n'''
f.write(snake_case__ )
return metadata, index
if __name__ == "__main__":
lowercase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--switch_t5x_checkpoint_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
type=str,
required=False,
help='Path to a directory containing a folder per layer. Follows the original Google format.',
)
parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
parser.add_argument(
'--pytorch_dump_folder_path',
default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
type=str,
required=False,
help='Path to the output pytorch model.',
)
lowercase : List[str] = parser.parse_args()
shard_on_the_fly(
args.switch_tax_checkpoint_path,
args.pytorch_dump_folder_path,
args.max_shard_size,
args.dtype,
)
def lowerCAmelCase_ ( ):
'''simple docstring'''
from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer
A : Optional[Any] = SwitchTransformersConfig.from_pretrained('''google/switch-base-8''' )
config.save_pretrained('''/home/arthur_huggingface_co/transformers/switch_converted''' )
A : Any = SwitchTransformersForConditionalGeneration.from_pretrained(
'''/home/arthur_huggingface_co/transformers/switch_converted''' , device_map='''auto''' )
A : Any = TaTokenizer.from_pretrained('''t5-small''' )
A : Union[str, Any] = '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''
A : List[Any] = tokenizer(snake_case__ , return_tensors='''pt''' ).input_ids
A : Optional[Any] = model.generate(snake_case__ , decoder_start_token_id=0 )
print(tokenizer.decode(out[0] ) )
| 3
|
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_lowerCAmelCase : Optional[int] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 512
_lowerCAmelCase : Union[str, Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : int = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : str = np.abs(np.fft.fft(_lowerCamelCase ) )
_lowerCAmelCase : Union[str, Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_lowerCAmelCase : List[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_lowerCamelCase )
plt.show()
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 512
_lowerCAmelCase : Optional[Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : str = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : Optional[Any] = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 36
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
__snake_case =False
@skip_mps
class UpperCAmelCase_ ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
lowerCamelCase : List[Any] = StableDiffusionAttendAndExcitePipeline
lowerCamelCase : Any = False
lowerCamelCase : List[str] = TEXT_TO_IMAGE_PARAMS
lowerCamelCase : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS.union({'''token_indices'''} )
lowerCamelCase : Dict = TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase : Optional[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def __UpperCAmelCase ( cls : str ) -> Union[str, Any]:
super().setUpClass()
torch.use_deterministic_algorithms(UpperCAmelCase__ )
@classmethod
def __UpperCAmelCase ( cls : int ) -> List[str]:
super().tearDownClass()
torch.use_deterministic_algorithms(UpperCAmelCase__ )
def __UpperCAmelCase ( self : int ) -> int:
torch.manual_seed(0 )
lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=1 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=UpperCAmelCase__ , )
lowerCAmelCase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=UpperCAmelCase__ , set_alpha_to_one=UpperCAmelCase__ , )
torch.manual_seed(0 )
lowerCAmelCase = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act='gelu' , projection_dim=5_1_2 , )
lowerCAmelCase = CLIPTextModel(UpperCAmelCase__ )
lowerCAmelCase = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowerCAmelCase = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def __UpperCAmelCase ( self : Any , UpperCAmelCase__ : int , UpperCAmelCase__ : Union[str, Any]=0 ) -> Tuple:
if str(UpperCAmelCase__ ).startswith('mps' ):
lowerCAmelCase = torch.manual_seed(UpperCAmelCase__ )
else:
lowerCAmelCase = torch.Generator(device=UpperCAmelCase__ ).manual_seed(UpperCAmelCase__ )
lowerCAmelCase = lowerCAmelCase = {
'prompt': 'a cat and a frog',
'token_indices': [2, 5],
'generator': generator,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
'max_iter_to_alter': 2,
'thresholds': {0: 0.7},
}
return inputs
def __UpperCAmelCase ( self : Optional[int] ) -> List[Any]:
lowerCAmelCase = 'cpu'
lowerCAmelCase = self.get_dummy_components()
lowerCAmelCase = self.pipeline_class(**UpperCAmelCase__ )
pipe.to(UpperCAmelCase__ )
pipe.set_progress_bar_config(disable=UpperCAmelCase__ )
lowerCAmelCase = self.get_dummy_inputs(UpperCAmelCase__ )
lowerCAmelCase = pipe(**UpperCAmelCase__ ).images
lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 6_4, 6_4, 3) )
lowerCAmelCase = np.array(
[0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496] )
lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(UpperCAmelCase__ , 1E-3 )
def __UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
super().test_cpu_offload_forward_pass(expected_max_diff=5E-4 )
def __UpperCAmelCase ( self : str ) -> int:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def __UpperCAmelCase ( self : Optional[int] ) -> Optional[int]:
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7E-4 )
def __UpperCAmelCase ( self : List[Any] ) -> int:
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
def __UpperCAmelCase ( self : Union[str, Any] ) -> Tuple:
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5E-4 )
def __UpperCAmelCase ( self : Optional[int] ) -> Any:
super().test_save_load_local(expected_max_difference=5E-4 )
def __UpperCAmelCase ( self : List[Any] ) -> List[str]:
super().test_save_load_optional_components(expected_max_difference=4E-4 )
@require_torch_gpu
@slow
class UpperCAmelCase_ ( unittest.TestCase ):
@classmethod
def __UpperCAmelCase ( cls : List[Any] ) -> List[Any]:
super().setUpClass()
torch.use_deterministic_algorithms(UpperCAmelCase__ )
@classmethod
def __UpperCAmelCase ( cls : str ) -> Optional[Any]:
super().tearDownClass()
torch.use_deterministic_algorithms(UpperCAmelCase__ )
def __UpperCAmelCase ( self : Tuple ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCAmelCase ( self : List[str] ) -> Optional[Any]:
lowerCAmelCase = torch.manual_seed(5_1 )
lowerCAmelCase = StableDiffusionAttendAndExcitePipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , safety_checker=UpperCAmelCase__ , torch_dtype=torch.floataa )
pipe.to('cuda' )
lowerCAmelCase = 'a painting of an elephant with glasses'
lowerCAmelCase = [5, 7]
lowerCAmelCase = pipe(
prompt=UpperCAmelCase__ , token_indices=UpperCAmelCase__ , guidance_scale=7.5 , generator=UpperCAmelCase__ , num_inference_steps=5 , max_iter_to_alter=5 , output_type='numpy' , ).images[0]
lowerCAmelCase = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy' )
assert np.abs((expected_image - image).max() ) < 5E-1
| 4
|
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
_lowerCAmelCase : List[str] = gray_code_sequence_string(_lowerCamelCase )
#
# convert them to integers
for i in range(len(_lowerCamelCase ) ):
_lowerCAmelCase : List[str] = int(sequence[i] , 2 )
return sequence
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
_lowerCAmelCase : List[Any] = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
_lowerCAmelCase : Optional[int] = gray_code_sequence_string(bit_count - 1 )
_lowerCAmelCase : str = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
_lowerCAmelCase : Dict = "0" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
_lowerCAmelCase : Optional[Any] = "1" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
| 0
|
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
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
'''google/vit-base-patch16-224''': '''https://huggingface.co/vit-base-patch16-224/resolve/main/config.json''',
# See all ViT models at https://huggingface.co/models?filter=vit
}
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = '''vit'''
def __init__(self , UpperCAmelCase=7_6_8 , UpperCAmelCase=1_2 , UpperCAmelCase=1_2 , UpperCAmelCase=3_0_7_2 , UpperCAmelCase="gelu" , UpperCAmelCase=0.0 , UpperCAmelCase=0.0 , UpperCAmelCase=0.02 , UpperCAmelCase=1e-12 , UpperCAmelCase=2_2_4 , UpperCAmelCase=1_6 , UpperCAmelCase=3 , UpperCAmelCase=True , UpperCAmelCase=1_6 , **UpperCAmelCase , ) -> List[str]:
super().__init__(**UpperCAmelCase )
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =initializer_range
_lowercase =layer_norm_eps
_lowercase =image_size
_lowercase =patch_size
_lowercase =num_channels
_lowercase =qkv_bias
_lowercase =encoder_stride
class lowerCamelCase__ ( lowerCAmelCase):
SCREAMING_SNAKE_CASE__ = version.parse('''1.11''')
@property
def __A (self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __A (self ) -> float:
return 1e-4
| 5
|
from PIL import Image
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = image.size
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = image.load()
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(_lowerCamelCase ):
for i in range(_lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_snake_case = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 36
| 0
|
def __lowerCAmelCase ( a__ ) -> str:
return "".join([hex(a__ )[2:].zfill(2 ).upper() for byte in list(a__ )] )
def __lowerCAmelCase ( a__ ) -> bytes:
# Check data validity, following RFC3548
# https://www.ietf.org/rfc/rfc3548.txt
if (len(a__ ) % 2) != 0:
raise ValueError(
'''Base16 encoded data is invalid:
Data 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(a__ ) <= set('''0123456789ABCDEF''' ):
raise ValueError(
'''Base16 encoded data is invalid:
Data 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(a__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 6
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'wav2vec2'
def __init__( self, __a=32, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.1, __a=0.1, __a=0.1, __a=0.0, __a=0.0, __a=0.1, __a=0.1, __a=0.02, __a=1E-5, __a="group", __a="gelu", __a=(512, 512, 512, 512, 512, 512, 512), __a=(5, 2, 2, 2, 2, 2, 2), __a=(10, 3, 3, 3, 3, 2, 2), __a=False, __a=128, __a=16, __a=False, __a=True, __a=0.05, __a=10, __a=2, __a=0.0, __a=10, __a=0, __a=320, __a=2, __a=0.1, __a=100, __a=256, __a=256, __a=0.1, __a="sum", __a=False, __a=False, __a=256, __a=(512, 512, 512, 512, 1500), __a=(5, 3, 3, 1, 1), __a=(1, 2, 3, 1, 1), __a=512, __a=0, __a=1, __a=2, __a=False, __a=3, __a=2, __a=3, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a, pad_token_id=__a, bos_token_id=__a, eos_token_id=__a)
_lowerCAmelCase : str = hidden_size
_lowerCAmelCase : Optional[int] = feat_extract_norm
_lowerCAmelCase : Union[str, Any] = feat_extract_activation
_lowerCAmelCase : Optional[Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : List[str] = conv_bias
_lowerCAmelCase : str = num_conv_pos_embeddings
_lowerCAmelCase : List[Any] = num_conv_pos_embedding_groups
_lowerCAmelCase : str = len(self.conv_dim)
_lowerCAmelCase : List[str] = num_hidden_layers
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : Any = hidden_act
_lowerCAmelCase : int = num_attention_heads
_lowerCAmelCase : Optional[Any] = hidden_dropout
_lowerCAmelCase : List[str] = attention_dropout
_lowerCAmelCase : Tuple = activation_dropout
_lowerCAmelCase : int = feat_proj_dropout
_lowerCAmelCase : List[str] = final_dropout
_lowerCAmelCase : int = layerdrop
_lowerCAmelCase : int = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Optional[Any] = do_stable_layer_norm
_lowerCAmelCase : Any = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_lowerCAmelCase : str = apply_spec_augment
_lowerCAmelCase : Optional[Any] = mask_time_prob
_lowerCAmelCase : Optional[int] = mask_time_length
_lowerCAmelCase : List[str] = mask_time_min_masks
_lowerCAmelCase : Optional[int] = mask_feature_prob
_lowerCAmelCase : Optional[int] = mask_feature_length
_lowerCAmelCase : List[str] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_lowerCAmelCase : Union[str, Any] = num_codevectors_per_group
_lowerCAmelCase : str = num_codevector_groups
_lowerCAmelCase : Optional[int] = contrastive_logits_temperature
_lowerCAmelCase : Optional[int] = feat_quantizer_dropout
_lowerCAmelCase : Optional[int] = num_negatives
_lowerCAmelCase : Union[str, Any] = codevector_dim
_lowerCAmelCase : Any = proj_codevector_dim
_lowerCAmelCase : Optional[int] = diversity_loss_weight
# ctc loss
_lowerCAmelCase : Tuple = ctc_loss_reduction
_lowerCAmelCase : Tuple = ctc_zero_infinity
# adapter
_lowerCAmelCase : List[Any] = add_adapter
_lowerCAmelCase : List[str] = adapter_kernel_size
_lowerCAmelCase : str = adapter_stride
_lowerCAmelCase : List[str] = num_adapter_layers
_lowerCAmelCase : str = output_hidden_size or hidden_size
_lowerCAmelCase : Tuple = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCAmelCase : str = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : Union[str, Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : Tuple = xvector_output_dim
@property
def snake_case__ ( self):
'''simple docstring'''
return functools.reduce(operator.mul, self.conv_stride, 1)
| 36
| 0
|
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
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
"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 A ( _UpperCAmelCase ):
"""simple docstring"""
lowerCamelCase = 'deberta-v2'
def __init__( self : int,lowercase_ : List[str]=1_2_8_1_0_0,lowercase_ : Union[str, Any]=1_5_3_6,lowercase_ : Any=2_4,lowercase_ : Optional[int]=2_4,lowercase_ : Tuple=6_1_4_4,lowercase_ : Dict="gelu",lowercase_ : str=0.1,lowercase_ : List[Any]=0.1,lowercase_ : int=5_1_2,lowercase_ : Any=0,lowercase_ : Optional[int]=0.02,lowercase_ : List[str]=1E-7,lowercase_ : int=False,lowercase_ : int=-1,lowercase_ : str=0,lowercase_ : Tuple=True,lowercase_ : Dict=None,lowercase_ : int=0,lowercase_ : Tuple="gelu",**lowercase_ : List[Any],)-> Union[str, Any]:
'''simple docstring'''
super().__init__(**lowercase_ )
A__ = hidden_size
A__ = num_hidden_layers
A__ = num_attention_heads
A__ = intermediate_size
A__ = hidden_act
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = max_position_embeddings
A__ = type_vocab_size
A__ = initializer_range
A__ = relative_attention
A__ = max_relative_positions
A__ = pad_token_id
A__ = position_biased_input
# Backwards compatibility
if type(lowercase_ ) == str:
A__ = [x.strip() for x in pos_att_type.lower().split('|' )]
A__ = pos_att_type
A__ = vocab_size
A__ = layer_norm_eps
A__ = kwargs.get('pooler_hidden_size',lowercase_ )
A__ = pooler_dropout
A__ = pooler_hidden_act
class A ( _UpperCAmelCase ):
"""simple docstring"""
@property
def snake_case__ ( self : int )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
A__ = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
A__ = {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 snake_case__ ( self : List[Any] )-> int:
'''simple docstring'''
return 1_2
def snake_case__ ( self : Dict,lowercase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"],lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : int = -1,lowercase_ : bool = False,lowercase_ : Optional["TensorType"] = None,lowercase_ : int = 3,lowercase_ : int = 4_0,lowercase_ : int = 4_0,lowercase_ : "PreTrainedTokenizerBase" = None,)-> Mapping[str, Any]:
'''simple docstring'''
A__ = super().generate_dummy_inputs(preprocessor=lowercase_,framework=lowercase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 7
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'The RoBERTa Model transformer with early exiting (DeeRoBERTa). ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[Any] = RobertaEmbeddings(__a)
self.init_weights()
@add_start_docstrings(
'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[int] = config.num_labels
_lowerCAmelCase : Optional[int] = config.num_hidden_layers
_lowerCAmelCase : Optional[int] = DeeRobertaModel(__a)
_lowerCAmelCase : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob)
_lowerCAmelCase : List[str] = nn.Linear(config.hidden_size, self.config.num_labels)
@add_start_docstrings_to_model_forward(__a)
def snake_case__ ( self, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=-1, __a=False, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.num_layers
try:
_lowerCAmelCase : List[Any] = self.roberta(
__a, attention_mask=__a, token_type_ids=__a, position_ids=__a, head_mask=__a, inputs_embeds=__a, )
_lowerCAmelCase : List[Any] = outputs[1]
_lowerCAmelCase : Dict = self.dropout(__a)
_lowerCAmelCase : Dict = self.classifier(__a)
_lowerCAmelCase : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase : Tuple = e.message
_lowerCAmelCase : Union[str, Any] = e.exit_layer
_lowerCAmelCase : List[Any] = outputs[0]
if not self.training:
_lowerCAmelCase : int = entropy(__a)
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : str = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : Optional[Any] = MSELoss()
_lowerCAmelCase : int = loss_fct(logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Optional[Any] = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
_lowerCAmelCase : Optional[int] = []
for highway_exit in outputs[-1]:
_lowerCAmelCase : Any = highway_exit[0]
if not self.training:
highway_logits_all.append(__a)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : List[str] = MSELoss()
_lowerCAmelCase : List[Any] = loss_fct(highway_logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Dict = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
highway_losses.append(__a)
if train_highway:
_lowerCAmelCase : int = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase : Any = (loss,) + outputs
if not self.training:
_lowerCAmelCase : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 36
| 0
|
from __future__ import annotations
lowerCAmelCase_ = list[list[int]]
# assigning initial values to the grid
lowerCAmelCase_ = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
lowerCAmelCase_ = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
if location := find_empty_location(SCREAMING_SNAKE_CASE__ ):
snake_case_, snake_case_ = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case_ = digit
if sudoku(SCREAMING_SNAKE_CASE__ ) is not None:
return grid
snake_case_ = 0
return None
def __SCREAMING_SNAKE_CASE (SCREAMING_SNAKE_CASE__ ):
for row in grid:
for cell in row:
print(SCREAMING_SNAKE_CASE__ , end=''' ''' )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print('''\nExample grid:\n''' + '''=''' * 20)
print_solution(example_grid)
print('''\nExample grid solution:''')
lowerCAmelCase_ = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print('''Cannot find a solution.''')
| 8
|
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'vision-encoder-decoder'
lowerCamelCase__ = True
def __init__( self, **__a):
'''simple docstring'''
super().__init__(**__a)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")
_lowerCAmelCase : str = kwargs.pop("encoder")
_lowerCAmelCase : Any = encoder_config.pop("model_type")
_lowerCAmelCase : str = kwargs.pop("decoder")
_lowerCAmelCase : List[str] = decoder_config.pop("model_type")
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[int] = True
@classmethod
def snake_case__ ( cls, __a, __a, **__a):
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : str = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = copy.deepcopy(self.__dict__)
_lowerCAmelCase : List[str] = self.encoder.to_dict()
_lowerCAmelCase : List[str] = self.decoder.to_dict()
_lowerCAmelCase : Any = self.__class__.model_type
return output
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : List[str] = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : Optional[Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def snake_case__ ( self, __a, __a = -1, __a = -1, __a = False, __a = None, ):
'''simple docstring'''
import torch
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : List[str] = super().generate_dummy_inputs(
__a, batch_size=__a, seq_length=__a, is_pair=__a, framework=__a)
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = dummy_input["input_ids"].shape
_lowerCAmelCase : str = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowerCAmelCase : List[str] = dummy_input.pop("input_ids")
_lowerCAmelCase : List[str] = dummy_input.pop("attention_mask")
_lowerCAmelCase : Optional[int] = torch.zeros(__a)
return common_inputs
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self, __a):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(__a)
def snake_case__ ( self, __a, __a, __a = "default"):
'''simple docstring'''
_lowerCAmelCase : Dict = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__a, __a)
| 36
| 0
|
from __future__ import annotations
def _UpperCamelCase ( lowercase__ , lowercase__ ):
# Checks if the entire collection has been sorted
if len(lowercase__ ) <= 1 or n <= 1:
return
insert_next(lowercase__ , n - 1 )
rec_insertion_sort(lowercase__ , n - 1 )
def _UpperCamelCase ( lowercase__ , lowercase__ ):
# Checks order between adjacent elements
if index >= len(lowercase__ ) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = (
collection[index],
collection[index - 1],
)
insert_next(lowercase__ , index + 1 )
if __name__ == "__main__":
__lowerCAmelCase : Any =input('Enter integers separated by spaces: ')
__lowerCAmelCase : list[int] =[int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list)
| 9
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( a):
def __get__( self, __a, __a=None):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
_lowerCAmelCase : List[Any] = "__cached_" + self.fget.__name__
_lowerCAmelCase : Dict = getattr(__a, __a, __a)
if cached is None:
_lowerCAmelCase : str = self.fget(__a)
setattr(__a, __a, __a)
return cached
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"invalid truth value {val!r}" )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.device )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if hasattr(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase , torch.dtype )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase , tf.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase , jnp.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = fields(self)
# Safety and consistency checks
if not len(__a):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
_lowerCAmelCase : Dict = getattr(self, class_fields[0].name)
_lowerCAmelCase : str = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(__a):
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = first_field.items()
_lowerCAmelCase : Dict = True
else:
try:
_lowerCAmelCase : Dict = iter(__a)
_lowerCAmelCase : Any = True
except TypeError:
_lowerCAmelCase : Any = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a):
if (
not isinstance(__a, (list, tuple))
or not len(__a) == 2
or not isinstance(element[0], __a)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value).")
break
setattr(self, element[0], element[1])
if element[1] is not None:
_lowerCAmelCase : Any = element[1]
elif first_field is not None:
_lowerCAmelCase : Any = first_field
else:
for field in class_fields:
_lowerCAmelCase : Dict = getattr(self, field.name)
if v is not None:
_lowerCAmelCase : Union[str, Any] = v
def __delitem__( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__( self, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Optional[int] = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self, __a, __a):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a, __a)
super().__setattr__(__a, __a)
def __setitem__( self, __a, __a):
'''simple docstring'''
super().__setitem__(__a, __a)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
return tuple(self[k] for k in self.keys())
class UpperCAmelCase_ ( a , a):
@classmethod
def snake_case__ ( cls, __a):
'''simple docstring'''
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'longest'
lowerCamelCase__ = 'max_length'
lowerCamelCase__ = 'do_not_pad'
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'pt'
lowerCamelCase__ = 'tf'
lowerCamelCase__ = 'np'
lowerCamelCase__ = 'jax'
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = context_managers
_lowerCAmelCase : Dict = ExitStack()
def __enter__( self):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a)
def __exit__( self, *__a, **__a):
'''simple docstring'''
self.stack.__exit__(*__a, **__a)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : str = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = model_class.__name__
_lowerCAmelCase : Optional[Any] = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( _lowerCamelCase , _lowerCamelCase = "" , _lowerCamelCase = "." ):
'''simple docstring'''
def _flatten_dict(_lowerCamelCase , _lowerCamelCase="" , _lowerCamelCase="." ):
for k, v in d.items():
_lowerCAmelCase : Dict = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase , _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
@contextmanager
def A ( _lowerCamelCase , _lowerCamelCase = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase , axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase , _lowerCamelCase )
else:
raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase , (tuple, list) ):
_lowerCAmelCase : List[Any] = [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase : Tuple = F"{repo_id}--{value}"
return auto_map
def A ( _lowerCamelCase ):
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
_lowerCAmelCase : Tuple = base_class.__module__
_lowerCAmelCase : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"Could not infer framework from class {model_class}." )
| 36
| 0
|
def lowerCAmelCase_ ( __a ) -> List[Any]:
"""simple docstring"""
if collection == []:
return []
# get some information about the collection
lowerCamelCase__: List[Any] =len(__a )
lowerCamelCase__: List[str] =max(__a )
lowerCamelCase__: Dict =min(__a )
# create the counting array
lowerCamelCase__: Tuple =coll_max + 1 - coll_min
lowerCamelCase__: Optional[int] =[0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , __a ):
lowerCamelCase__: int =counting_arr[i] + counting_arr[i - 1]
# create the output collection
lowerCamelCase__: Dict =[0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , __a ) ):
lowerCamelCase__: int =collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def lowerCAmelCase_ ( __a ) -> str:
"""simple docstring"""
return "".join([chr(__a ) for i in counting_sort([ord(__a ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("thisisthestring") == "eghhiiinrsssttt"
__A = input("Enter numbers separated by a comma:\n").strip()
__A = [int(item) for item in user_input.split(",")]
print(counting_sort(unsorted))
| 10
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = _distribute_shards(**_lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = _split_gen_kwargs(_lowerCamelCase , _lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(_lowerCamelCase ):
_number_of_shards_in_gen_kwargs(_lowerCamelCase )
else:
_lowerCAmelCase : Optional[int] = _number_of_shards_in_gen_kwargs(_lowerCamelCase )
assert out == expected
| 36
| 0
|
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
lowerCAmelCase__ = logging.getLogger(__name__)
lowerCAmelCase__ = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
lowerCAmelCase__ = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(a)} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "Pretrained config name or path if not the same as model_name"})
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"})
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."} , )
__SCREAMING_SNAKE_CASE = field(
default="main" , metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
} , )
def _lowerCamelCase ( self) -> Optional[Any]:
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path")
@dataclass
class lowerCAmelCase__ :
'''simple docstring'''
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "The name of the dataset to use (via the datasets library)."})
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "The configuration name of the dataset to use (via the datasets library)."})
__SCREAMING_SNAKE_CASE = field(default=a , metadata={"help": "The input training data file (a text file)."})
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "An optional input train ref data file for whole word masking in Chinese."} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "Overwrite the cached training and evaluation sets"})
__SCREAMING_SNAKE_CASE = field(
default=5 , metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={
"help": (
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated. Default to the max input length of the model."
)
} , )
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={"help": "The number of processes to use for the preprocessing."} , )
__SCREAMING_SNAKE_CASE = field(
default=0.15 , metadata={"help": "Ratio of tokens to mask for masked language modeling loss"})
__SCREAMING_SNAKE_CASE = field(
default=a , metadata={
"help": (
"Whether to pad all samples to `max_seq_length`. "
"If False, will pad the samples dynamically when batching to the maximum length in the batch."
)
} , )
def _lowerCamelCase ( self) -> Union[str, Any]:
if self.train_file is not None:
_A : Any = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
_A : int = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def _UpperCAmelCase (UpperCamelCase__ : str , UpperCamelCase__ : Dict ):
with open(UpperCamelCase__ , "r" , encoding="utf-8" ) as f:
_A : Union[str, Any] = [json.loads(UpperCamelCase__ ) for line in f.read().splitlines() if (len(UpperCamelCase__ ) > 0 and not line.isspace())]
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ )
_A : Dict = {c: dataset[c] for c in dataset.column_names}
_A : str = refs
return Dataset.from_dict(UpperCamelCase__ )
def _UpperCAmelCase ():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_A : Union[str, Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(".json" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_A , _A , _A : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_A , _A , _A : Optional[int] = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
_A : Any = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_A : int = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , handlers=[logging.StreamHandler(sys.stdout )] , )
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}" )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("Training/evaluation parameters %s" , UpperCamelCase__ )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
#
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
#
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.dataset_name is not None:
# Downloading and loading a dataset from the hub.
_A : List[Any] = load_dataset(data_args.dataset_name , data_args.dataset_config_name )
if "validation" not in datasets.keys():
_A : Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"train[:{data_args.validation_split_percentage}%]" , )
_A : Optional[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=f"train[{data_args.validation_split_percentage}%:]" , )
else:
_A : int = {}
if data_args.train_file is not None:
_A : Any = data_args.train_file
if data_args.validation_file is not None:
_A : List[str] = data_args.validation_file
_A : int = data_args.train_file.split("." )[-1]
if extension == "txt":
_A : Tuple = "text"
_A : Dict = load_dataset(UpperCamelCase__ , data_files=UpperCamelCase__ )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_A : Dict = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
_A : Optional[Any] = AutoConfig.from_pretrained(model_args.config_name , **UpperCamelCase__ )
elif model_args.model_name_or_path:
_A : List[str] = AutoConfig.from_pretrained(model_args.model_name_or_path , **UpperCamelCase__ )
else:
_A : Optional[Any] = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}" )
config.update_from_string(model_args.config_overrides )
logger.info(f"New config: {config}" )
_A : Optional[int] = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
_A : List[Any] = AutoTokenizer.from_pretrained(model_args.tokenizer_name , **UpperCamelCase__ )
elif model_args.model_name_or_path:
_A : Any = AutoTokenizer.from_pretrained(model_args.model_name_or_path , **UpperCamelCase__ )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name." )
if model_args.model_name_or_path:
_A : Dict = AutoModelForMaskedLM.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=UpperCamelCase__ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
else:
logger.info("Training new model from scratch" )
_A : Tuple = AutoModelForMaskedLM.from_config(UpperCamelCase__ )
model.resize_token_embeddings(len(UpperCamelCase__ ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
_A : str = datasets["train"].column_names
else:
_A : Tuple = datasets["validation"].column_names
_A : Union[str, Any] = "text" if "text" in column_names else column_names[0]
_A : List[Any] = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(UpperCamelCase__ : Dict ):
# Remove empty lines
_A : Union[str, Any] = [line for line in examples["text"] if len(UpperCamelCase__ ) > 0 and not line.isspace()]
return tokenizer(examples["text"] , padding=UpperCamelCase__ , truncation=UpperCamelCase__ , max_length=data_args.max_seq_length )
_A : Optional[int] = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , num_proc=data_args.preprocessing_num_workers , remove_columns=[text_column_name] , load_from_cache_file=not data_args.overwrite_cache , )
# Add the chinese references if provided
if data_args.train_ref_file is not None:
_A : str = add_chinese_references(tokenized_datasets["train"] , data_args.train_ref_file )
if data_args.validation_ref_file is not None:
_A : Tuple = add_chinese_references(
tokenized_datasets["validation"] , data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
_A : Union[str, Any] = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
_A : Any = False
# Data collator
# This one will take care of randomly masking the tokens.
_A : Union[str, Any] = DataCollatorForWholeWordMask(tokenizer=UpperCamelCase__ , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
_A : Dict = Trainer(
model=UpperCamelCase__ , args=UpperCamelCase__ , train_dataset=tokenized_datasets["train"] if training_args.do_train else None , eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None , tokenizer=UpperCamelCase__ , data_collator=UpperCamelCase__ , )
# Training
if training_args.do_train:
if last_checkpoint is not None:
_A : Dict = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
_A : Tuple = model_args.model_name_or_path
else:
_A : Tuple = None
_A : str = trainer.train(resume_from_checkpoint=UpperCamelCase__ )
trainer.save_model() # Saves the tokenizer too for easy upload
_A : Optional[Any] = os.path.join(training_args.output_dir , "train_results.txt" )
if trainer.is_world_process_zero():
with open(UpperCamelCase__ , "w" ) as writer:
logger.info("***** Train results *****" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(f" {key} = {value}" )
writer.write(f"{key} = {value}\n" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , "trainer_state.json" ) )
# Evaluation
_A : str = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
_A : List[Any] = trainer.evaluate()
_A : Optional[Any] = math.exp(eval_output["eval_loss"] )
_A : Dict = perplexity
_A : Dict = os.path.join(training_args.output_dir , "eval_results_mlm_wwm.txt" )
if trainer.is_world_process_zero():
with open(UpperCamelCase__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in sorted(results.items() ):
logger.info(f" {key} = {value}" )
writer.write(f"{key} = {value}\n" )
return results
def _UpperCAmelCase (UpperCamelCase__ : Tuple ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 11
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCAmelCase_ :
def __init__( self, __a = "cpu", __a = "openai/clip-vit-large-patch14"):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = device
_lowerCAmelCase : Optional[int] = CLIPTokenizerFast.from_pretrained(__a)
_lowerCAmelCase : Any = [0.48_145_466, 0.4_578_275, 0.40_821_073]
_lowerCAmelCase : Union[str, Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]
_lowerCAmelCase : Tuple = torchvision.transforms.Normalize(self.image_mean, self.image_std)
_lowerCAmelCase : Optional[int] = torchvision.transforms.Resize(224)
_lowerCAmelCase : Dict = torchvision.transforms.CenterCrop(224)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.resize(__a)
_lowerCAmelCase : List[str] = self.center_crop(__a)
_lowerCAmelCase : Optional[Any] = self.normalize(__a)
return images
def __call__( self, __a=None, __a=None, **__a):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(text=__a, **__a)
_lowerCAmelCase : List[str] = self.preprocess_img(__a)
_lowerCAmelCase : Tuple = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a=10, __a=0.01, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=False, __a=True, __a="image", __a=True, __a=False, __a=False, __a=False, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : List[str] = None
_lowerCAmelCase : List[str] = device if device else get_device()
if vqgan:
_lowerCAmelCase : Union[str, Any] = vqgan
else:
_lowerCAmelCase : Optional[Any] = load_vqgan(self.device, conf_path=__a, ckpt_path=__a)
self.vqgan.eval()
if clip:
_lowerCAmelCase : str = clip
else:
_lowerCAmelCase : int = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
_lowerCAmelCase : Optional[int] = ProcessorGradientFlow(device=self.device)
_lowerCAmelCase : Any = iterations
_lowerCAmelCase : List[Any] = lr
_lowerCAmelCase : Tuple = log
_lowerCAmelCase : List[str] = make_grid
_lowerCAmelCase : int = return_val
_lowerCAmelCase : Dict = quantize
_lowerCAmelCase : Any = self.vqgan.decoder.z_shape
def snake_case__ ( self, __a=None, __a=None, __a=5, __a=True):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = []
if output_path is None:
_lowerCAmelCase : List[Any] = "./animation.gif"
if input_path is None:
_lowerCAmelCase : str = self.save_path
_lowerCAmelCase : str = sorted(glob(input_path + "/*"))
if not len(__a):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__a) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
_lowerCAmelCase : Optional[int] = total_duration / len(__a)
_lowerCAmelCase : Union[str, Any] = [frame_duration] * len(__a)
if extend_frames:
_lowerCAmelCase : Any = 1.5
_lowerCAmelCase : List[str] = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__a))
imageio.mimsave(__a, __a, duration=__a)
print(f"gif saved to {output_path}")
def snake_case__ ( self, __a=None, __a=None):
'''simple docstring'''
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
_lowerCAmelCase : Dict = preprocess(Image.open(__a), target_image_size=256).to(self.device)
_lowerCAmelCase : Dict = preprocess_vqgan(__a)
_lowerCAmelCase , *_lowerCAmelCase : str = self.vqgan.encode(__a)
return z
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.latent.detach().requires_grad_()
_lowerCAmelCase : Dict = base_latent + transform_vector
if self.quantize:
_lowerCAmelCase , *_lowerCAmelCase : List[Any] = self.vqgan.quantize(__a)
else:
_lowerCAmelCase : Any = trans_latent
return self.vqgan.decode(__a)
def snake_case__ ( self, __a, __a, __a=None):
'''simple docstring'''
_lowerCAmelCase : int = self.clip_preprocessor(text=__a, images=__a, return_tensors="pt", padding=__a)
_lowerCAmelCase : Optional[int] = self.clip(**__a)
_lowerCAmelCase : Any = clip_outputs.logits_per_image
if weights is not None:
_lowerCAmelCase : Tuple = similarity_logits * weights
return similarity_logits.sum()
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self._get_clip_similarity(pos_prompts["prompts"], __a, weights=(1 / pos_prompts["weights"]))
if neg_prompts:
_lowerCAmelCase : List[Any] = self._get_clip_similarity(neg_prompts["prompts"], __a, weights=neg_prompts["weights"])
else:
_lowerCAmelCase : Union[str, Any] = torch.tensor([1], device=self.device)
_lowerCAmelCase : List[str] = -torch.log(__a) + torch.log(__a)
return loss
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.randn_like(self.latent, requires_grad=__a, device=self.device)
_lowerCAmelCase : Optional[int] = torch.optim.Adam([vector], lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
_lowerCAmelCase : Any = self._add_vector(__a)
_lowerCAmelCase : Optional[Any] = loop_post_process(__a)
_lowerCAmelCase : Optional[Any] = self._get_CLIP_loss(__a, __a, __a)
print("CLIP loss", __a)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__a)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
wandb.init(reinit=__a, project="face-editor")
wandb.config.update({"Positive Prompts": positive_prompts})
wandb.config.update({"Negative Prompts": negative_prompts})
wandb.config.update({"lr": self.lr, "iterations": self.iterations})
if image_path:
_lowerCAmelCase : str = Image.open(__a)
_lowerCAmelCase : int = image.resize((256, 256))
wandb.log("Original Image", wandb.Image(__a))
def snake_case__ ( self, __a):
'''simple docstring'''
if not prompts:
return []
_lowerCAmelCase : int = []
_lowerCAmelCase : List[str] = []
if isinstance(__a, __a):
_lowerCAmelCase : Union[str, Any] = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__a, (tuple, list)):
_lowerCAmelCase : Optional[Any] = prompt[0]
_lowerCAmelCase : Union[str, Any] = float(prompt[1])
elif ":" in prompt:
_lowerCAmelCase , _lowerCAmelCase : int = prompt.split(":")
_lowerCAmelCase : Optional[Any] = float(__a)
else:
_lowerCAmelCase : Optional[int] = prompt
_lowerCAmelCase : List[Any] = 1.0
processed_prompts.append(__a)
weights.append(__a)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a, device=self.device),
}
def snake_case__ ( self, __a, __a=None, __a=None, __a=True, __a=False, __a=True, __a=True, __a=None, ):
'''simple docstring'''
if image_path:
_lowerCAmelCase : List[Any] = self._get_latent(__a)
else:
_lowerCAmelCase : Any = torch.randn(self.latent_dim, device=self.device)
if self.log:
self._init_logging(__a, __a, __a)
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCAmelCase : int = self.process_prompts(__a)
_lowerCAmelCase : List[str] = self.process_prompts(__a)
if save_final and save_path is None:
_lowerCAmelCase : int = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
if not os.path.exists(__a):
os.makedirs(__a)
else:
_lowerCAmelCase : Tuple = save_path + "_" + get_timestamp()
os.makedirs(__a)
_lowerCAmelCase : Tuple = save_path
_lowerCAmelCase : List[Any] = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__a))
_lowerCAmelCase : int = loop_post_process(__a)
for iter, transformed_img in enumerate(self._optimize_CLIP(__a, __a, __a)):
if show_intermediate:
show_pil(__a)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
if self.log:
wandb.log({"Image": wandb.Image(__a)})
if show_final:
show_pil(__a)
if save_final:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))
| 36
| 0
|
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
UpperCAmelCase_ = logging.get_logger(__name__)
@add_end_docstrings(__lowerCamelCase)
class lowerCamelCase__( __lowerCamelCase):
def __init__( self: List[str] , *UpperCamelCase_: Any , **UpperCamelCase_: Union[str, Any] ):
super().__init__(*UpperCamelCase_ , **UpperCamelCase_ )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def lowerCAmelCase__ ( self: Dict , UpperCamelCase_: Tuple=None ):
__lowerCamelCase = {}
if top_k is not None:
__lowerCamelCase = top_k
return {}, {}, postprocess_params
def __call__( self: Tuple , UpperCamelCase_: Union[str, List[str], "Image.Image", List["Image.Image"]] , **UpperCamelCase_: Optional[int] ):
return super().__call__(UpperCamelCase_ , **UpperCamelCase_ )
def lowerCAmelCase__ ( self: List[str] , UpperCamelCase_: Union[str, Any] ):
__lowerCamelCase = load_image(UpperCamelCase_ )
__lowerCamelCase = self.image_processor(images=UpperCamelCase_ , return_tensors=self.framework )
return model_inputs
def lowerCAmelCase__ ( self: Dict , UpperCamelCase_: Any ):
__lowerCamelCase = self.model(**UpperCamelCase_ )
return model_outputs
def lowerCAmelCase__ ( self: Dict , UpperCamelCase_: List[str] , UpperCamelCase_: Union[str, Any]=5 ):
if top_k > self.model.config.num_labels:
__lowerCamelCase = self.model.config.num_labels
if self.framework == "pt":
__lowerCamelCase = model_outputs.logits.softmax(-1 )[0]
__lowerCamelCase, __lowerCamelCase = probs.topk(UpperCamelCase_ )
elif self.framework == "tf":
__lowerCamelCase = stable_softmax(model_outputs.logits , axis=-1 )[0]
__lowerCamelCase = tf.math.top_k(UpperCamelCase_ , k=UpperCamelCase_ )
__lowerCamelCase, __lowerCamelCase = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(F'Unsupported framework: {self.framework}' )
__lowerCamelCase = scores.tolist()
__lowerCamelCase = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCamelCase_ , UpperCamelCase_ )]
| 12
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_snake_case = get_tests_dir("fixtures")
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = mock.Mock()
_lowerCAmelCase : int = 500
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : str = HTTPError
_lowerCAmelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__a) as mock_head:
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def snake_case__ ( self):
'''simple docstring'''
with self.assertRaises(__a):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : int = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
_lowerCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor")
self.assertIsNotNone(__a)
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TOKEN
HfFolder.save_token(__a)
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : str = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="test-image-processor", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="valid_org/test-image-processor-org", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowerCAmelCase : List[str] = CustomImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
_lowerCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=__a)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 36
| 0
|
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {"""vocab_file""": """spiece.model"""}
lowerCAmelCase : Optional[Any] = {
"""vocab_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
}
}
lowerCAmelCase : Any = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
# Segments (not really needed)
lowerCAmelCase : Any = 0
lowerCAmelCase : List[Any] = 1
lowerCAmelCase : Union[str, Any] = 2
lowerCAmelCase : Dict = 3
lowerCAmelCase : List[Any] = 4
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : List[Any] = VOCAB_FILES_NAMES
_UpperCAmelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase : List[Any] = '''left'''
def __init__( self : List[str] , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Union[str, Any]=False , lowerCAmelCase__ : Dict=True , lowerCAmelCase__ : str=False , lowerCAmelCase__ : Any="<s>" , lowerCAmelCase__ : Optional[int]="</s>" , lowerCAmelCase__ : Optional[int]="<unk>" , lowerCAmelCase__ : List[str]="<sep>" , lowerCAmelCase__ : Tuple="<pad>" , lowerCAmelCase__ : int="<cls>" , lowerCAmelCase__ : List[str]="<mask>" , lowerCAmelCase__ : List[Any]=["<eop>", "<eod>"] , lowerCAmelCase__ : Optional[Dict[str, Any]] = None , **lowerCAmelCase__ : int , ):
# Mask token behave like a normal word, i.e. include the space before it
SCREAMING_SNAKE_CASE_: Union[str, Any] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__) if isinstance(lowerCAmelCase__ , lowerCAmelCase__) else mask_token
SCREAMING_SNAKE_CASE_: Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCAmelCase__ , remove_space=lowerCAmelCase__ , keep_accents=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , sp_model_kwargs=self.sp_model_kwargs , **lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE_: Dict = 3
SCREAMING_SNAKE_CASE_: List[str] = do_lower_case
SCREAMING_SNAKE_CASE_: List[Any] = remove_space
SCREAMING_SNAKE_CASE_: int = keep_accents
SCREAMING_SNAKE_CASE_: Tuple = vocab_file
SCREAMING_SNAKE_CASE_: Any = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(lowerCAmelCase__)
@property
def _SCREAMING_SNAKE_CASE ( self : List[str]):
return len(self.sp_model)
def _SCREAMING_SNAKE_CASE ( self : List[str]):
SCREAMING_SNAKE_CASE_: Optional[Any] = {self.convert_ids_to_tokens(lowerCAmelCase__): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def __getstate__( self : Optional[int]):
SCREAMING_SNAKE_CASE_: Optional[Any] = self.__dict__.copy()
SCREAMING_SNAKE_CASE_: Optional[int] = None
return state
def __setstate__( self : Tuple , lowerCAmelCase__ : Any):
SCREAMING_SNAKE_CASE_: Any = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
SCREAMING_SNAKE_CASE_: List[Any] = {}
SCREAMING_SNAKE_CASE_: int = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(self.vocab_file)
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase__ : Any):
if self.remove_space:
SCREAMING_SNAKE_CASE_: int = " ".join(inputs.strip().split())
else:
SCREAMING_SNAKE_CASE_: int = inputs
SCREAMING_SNAKE_CASE_: Tuple = outputs.replace("``" , "\"").replace("''" , "\"")
if not self.keep_accents:
SCREAMING_SNAKE_CASE_: List[str] = unicodedata.normalize("NFKD" , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = "".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase__)])
if self.do_lower_case:
SCREAMING_SNAKE_CASE_: Union[str, Any] = outputs.lower()
return outputs
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : str):
SCREAMING_SNAKE_CASE_: str = self.preprocess_text(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Optional[Any] = self.sp_model.encode(lowerCAmelCase__ , out_type=lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Tuple = []
for piece in pieces:
if len(lowerCAmelCase__) > 1 and piece[-1] == str(",") and piece[-2].isdigit():
SCREAMING_SNAKE_CASE_: List[str] = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase__ , ""))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
SCREAMING_SNAKE_CASE_: List[str] = cur_pieces[1:]
else:
SCREAMING_SNAKE_CASE_: Union[str, Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(lowerCAmelCase__)
else:
new_pieces.append(lowerCAmelCase__)
return new_pieces
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ : Dict):
return self.sp_model.PieceToId(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : Dict):
return self.sp_model.IdToPiece(lowerCAmelCase__)
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : Union[str, Any]):
SCREAMING_SNAKE_CASE_: List[str] = "".join(lowerCAmelCase__).replace(lowerCAmelCase__ , " ").strip()
return out_string
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : bool = False , lowerCAmelCase__ : bool = None , lowerCAmelCase__ : bool = True , **lowerCAmelCase__ : Optional[int] , ):
SCREAMING_SNAKE_CASE_: List[Any] = kwargs.pop("use_source_tokenizer" , lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: int = self.convert_ids_to_tokens(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__)
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
SCREAMING_SNAKE_CASE_: Optional[int] = []
SCREAMING_SNAKE_CASE_: Optional[Any] = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__))
SCREAMING_SNAKE_CASE_: Dict = []
sub_texts.append(lowerCAmelCase__)
else:
current_sub_text.append(lowerCAmelCase__)
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase__))
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
SCREAMING_SNAKE_CASE_: Union[str, Any] = "".join(lowerCAmelCase__)
SCREAMING_SNAKE_CASE_: Union[str, Any] = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
SCREAMING_SNAKE_CASE_: Dict = self.clean_up_tokenization(lowerCAmelCase__)
return clean_text
else:
return text
def _SCREAMING_SNAKE_CASE ( self : int , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
SCREAMING_SNAKE_CASE_: Optional[int] = [self.sep_token_id]
SCREAMING_SNAKE_CASE_: Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None , lowerCAmelCase__ : bool = False):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase__ , token_ids_a=lowerCAmelCase__ , already_has_special_tokens=lowerCAmelCase__)
if token_ids_a is not None:
return ([0] * len(lowerCAmelCase__)) + [1] + ([0] * len(lowerCAmelCase__)) + [1, 1]
return ([0] * len(lowerCAmelCase__)) + [1, 1]
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
SCREAMING_SNAKE_CASE_: Tuple = [self.sep_token_id]
SCREAMING_SNAKE_CASE_: List[str] = [2]
if token_ids_a is None:
return len(token_ids_a + sep) * [0] + cls_segment_id
return len(token_ids_a + sep) * [0] + len(token_ids_a + sep) * [1] + cls_segment_id
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None):
if not os.path.isdir(lowerCAmelCase__):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
SCREAMING_SNAKE_CASE_: int = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowerCAmelCase__)
elif not os.path.isfile(self.vocab_file):
with open(lowerCAmelCase__ , "wb") as fi:
SCREAMING_SNAKE_CASE_: Dict = self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase__)
return (out_vocab_file,)
| 13
|
import unittest
from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=24, __a=2, __a=6, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=3, __a=None, __a=1000, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : List[str] = batch_size
_lowerCAmelCase : int = seq_length
_lowerCAmelCase : Optional[int] = is_training
_lowerCAmelCase : Dict = use_input_mask
_lowerCAmelCase : List[str] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : List[Any] = num_hidden_layers
_lowerCAmelCase : Optional[Any] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : List[str] = hidden_act
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Any = attention_probs_dropout_prob
_lowerCAmelCase : int = max_position_embeddings
_lowerCAmelCase : Optional[int] = type_vocab_size
_lowerCAmelCase : Optional[Any] = type_sequence_label_size
_lowerCAmelCase : List[str] = initializer_range
_lowerCAmelCase : List[Any] = num_labels
_lowerCAmelCase : Tuple = scope
_lowerCAmelCase : str = range_bbox
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
_lowerCAmelCase : Dict = bbox[i, j, 3]
_lowerCAmelCase : int = bbox[i, j, 1]
_lowerCAmelCase : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_lowerCAmelCase : str = bbox[i, j, 2]
_lowerCAmelCase : List[Any] = bbox[i, j, 0]
_lowerCAmelCase : str = t
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : Dict = None
if self.use_token_type_ids:
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[int] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case__ ( self):
'''simple docstring'''
return LiltConfig(
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, initializer_range=self.initializer_range, )
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = LiltModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(__a, bbox=__a, attention_mask=__a, token_type_ids=__a)
_lowerCAmelCase : str = model(__a, bbox=__a, token_type_ids=__a)
_lowerCAmelCase : List[Any] = model(__a, bbox=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.num_labels
_lowerCAmelCase : Optional[Any] = LiltForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = LiltForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Dict = config_and_inputs
_lowerCAmelCase : List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
return True
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = LiltModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase : Any = type
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : str = LiltModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)
_lowerCAmelCase : Any = torch.tensor([[1, 2]], device=__a)
_lowerCAmelCase : str = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=__a)
# forward pass
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(input_ids=__a, bbox=__a)
_lowerCAmelCase : Optional[int] = torch.Size([1, 2, 768])
_lowerCAmelCase : List[str] = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]], device=__a, )
self.assertTrue(outputs.last_hidden_state.shape, __a)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3], __a, atol=1E-3))
| 36
| 0
|
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,
)
| 14
|
import argparse
import copy
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = {}
with open(_lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_lowerCAmelCase : Tuple = []
_list.append([line.split()[1], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_lowerCAmelCase : str = []
_list.append([line.split()[0], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase ) as f:
_lowerCAmelCase : str = f.read(1 )
_lowerCAmelCase : str = start_node
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Any = start_node
_lowerCAmelCase : str = 0
while visiting not in first_solution:
_lowerCAmelCase : Dict = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCamelCase ) and k[0] not in first_solution:
_lowerCAmelCase : List[str] = k[1]
_lowerCAmelCase : List[Any] = k[0]
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = distance_of_first_solution + int(_lowerCamelCase )
_lowerCAmelCase : str = best_node
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_lowerCAmelCase : Tuple = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = []
for n in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
for kn in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
if n == kn:
continue
_lowerCAmelCase : Optional[int] = copy.deepcopy(_lowerCamelCase )
_lowerCAmelCase : int = kn
_lowerCAmelCase : Dict = n
_lowerCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_lowerCAmelCase : str = _tmp[_tmp.index(_lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_lowerCAmelCase : Optional[Any] = distance + int(i[1] )
_tmp.append(_lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_lowerCAmelCase : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : int = first_solution
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Tuple = distance_of_first_solution
_lowerCAmelCase : Optional[int] = solution
while count <= iters:
_lowerCAmelCase : int = find_neighborhood(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : Dict = neighborhood[index_of_best_solution]
_lowerCAmelCase : int = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Union[str, Any] = False
while not found:
_lowerCAmelCase : Tuple = 0
while i < len(_lowerCamelCase ):
if best_solution[i] != solution[i]:
_lowerCAmelCase : str = best_solution[i]
_lowerCAmelCase : Tuple = solution[i]
break
_lowerCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : Optional[Any] = best_solution[:-1]
_lowerCAmelCase : Tuple = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_lowerCAmelCase : Union[str, Any] = cost
_lowerCAmelCase : List[Any] = solution
else:
_lowerCAmelCase : Optional[Any] = index_of_best_solution + 1
_lowerCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
if len(_lowerCamelCase ) >= size:
tabu_list.pop(0 )
_lowerCAmelCase : int = count + 1
return best_solution_ever, best_cost
def A ( _lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase : int = generate_neighbours(args.File )
_lowerCAmelCase , _lowerCAmelCase : List[str] = generate_first_solution(
args.File , _lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = tabu_search(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 36
| 0
|
from __future__ import annotations
from collections import deque
class UpperCAmelCase :
'''simple docstring'''
def __init__( self : Optional[Any] ,A : list[str] ):
__A = []
self.adlist.append(
{"value": "", "next_states": [], "fail_state": 0, "output": []} )
for keyword in keywords:
self.add_keyword(A )
self.set_fail_transitions()
def UpperCamelCase_ ( self : Dict ,A : int ,A : str ):
for state in self.adlist[current_state]["next_states"]:
if char == self.adlist[state]["value"]:
return state
return None
def UpperCamelCase_ ( self : str ,A : str ):
__A = 0
for character in keyword:
__A = self.find_next_state(A ,A )
if next_state is None:
self.adlist.append(
{
"value": character,
"next_states": [],
"fail_state": 0,
"output": [],
} )
self.adlist[current_state]["next_states"].append(len(self.adlist ) - 1 )
__A = len(self.adlist ) - 1
else:
__A = next_state
self.adlist[current_state]["output"].append(A )
def UpperCamelCase_ ( self : str ):
__A = deque()
for node in self.adlist[0]["next_states"]:
q.append(A )
__A = 0
while q:
__A = q.popleft()
for child in self.adlist[r]["next_states"]:
q.append(A )
__A = self.adlist[r]["fail_state"]
while (
self.find_next_state(A ,self.adlist[child]["value"] ) is None
and state != 0
):
__A = self.adlist[state]["fail_state"]
__A = self.find_next_state(
A ,self.adlist[child]["value"] )
if self.adlist[child]["fail_state"] is None:
__A = 0
__A = (
self.adlist[child]["output"]
+ self.adlist[self.adlist[child]["fail_state"]]["output"]
)
def UpperCamelCase_ ( self : Optional[int] ,A : str ):
__A = {} # returns a dict with keywords and list of its occurrences
__A = 0
for i in range(len(A ) ):
while (
self.find_next_state(A ,string[i] ) is None
and current_state != 0
):
__A = self.adlist[current_state]["fail_state"]
__A = self.find_next_state(A ,string[i] )
if next_state is None:
__A = 0
else:
__A = next_state
for key in self.adlist[current_state]["output"]:
if key not in result:
__A = []
result[key].append(i - len(A ) + 1 )
return result
if __name__ == "__main__":
import doctest
doctest.testmod()
| 15
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = BartphoTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : str = ["▁This", "▁is", "▁a", "▁t", "est"]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[Any] = {"unk_token": "<unk>"}
_lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
with open(self.monolingual_vocab_file, "w", encoding="utf-8") as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n")
_lowerCAmelCase : Optional[Any] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BartphoTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "This is a là test"
_lowerCAmelCase : Optional[int] = "This is a<unk><unk> test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
_lowerCAmelCase : List[Any] = "This is a là test"
_lowerCAmelCase : str = "▁This ▁is ▁a ▁l à ▁t est".split()
_lowerCAmelCase : str = tokenizer.tokenize(__a)
self.assertListEqual(__a, __a)
_lowerCAmelCase : Tuple = tokens + [tokenizer.unk_token]
_lowerCAmelCase : List[str] = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), __a)
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"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
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,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
lowerCAmelCase_ = logging.get_logger(__name__)
def __UpperCAmelCase ( __lowerCamelCase ) -> List[List[ImageInput]]:
if isinstance(__lowerCamelCase , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__lowerCamelCase , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__lowerCamelCase ):
return [[videos]]
raise ValueError(f"""Could not make batched video from {videos}""" )
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : str = ["pixel_values"]
def __init__( self : List[Any] ,_snake_case : bool = True ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : bool = True ,_snake_case : Dict[str, int] = None ,_snake_case : bool = True ,_snake_case : Union[int, float] = 1 / 255 ,_snake_case : bool = True ,_snake_case : bool = True ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,**_snake_case : List[str] ,) -> None:
"""simple docstring"""
super().__init__(**_snake_case )
lowercase__ : int = size if size is not None else {'''shortest_edge''': 256}
lowercase__ : Union[str, Any] = get_size_dict(_snake_case ,default_to_square=_snake_case )
lowercase__ : List[str] = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
lowercase__ : Optional[Any] = get_size_dict(_snake_case ,param_name='''crop_size''' )
lowercase__ : List[Any] = do_resize
lowercase__ : Optional[int] = size
lowercase__ : Union[str, Any] = do_center_crop
lowercase__ : int = crop_size
lowercase__ : List[str] = resample
lowercase__ : int = do_rescale
lowercase__ : Tuple = rescale_factor
lowercase__ : List[Any] = offset
lowercase__ : Optional[int] = do_normalize
lowercase__ : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase__ : Union[str, Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def UpperCAmelCase ( self : Optional[int] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : PILImageResampling = PILImageResampling.BILINEAR ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : List[str] ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Optional[int] = get_size_dict(_snake_case ,default_to_square=_snake_case )
if "shortest_edge" in size:
lowercase__ : Optional[int] = get_resize_output_image_size(_snake_case ,size['''shortest_edge'''] ,default_to_square=_snake_case )
elif "height" in size and "width" in size:
lowercase__ : Optional[Any] = (size['''height'''], size['''width'''])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(_snake_case ,size=_snake_case ,resample=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[Any] ,_snake_case : np.ndarray ,_snake_case : Dict[str, int] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Union[str, Any] ,) -> np.ndarray:
"""simple docstring"""
lowercase__ : Dict = get_size_dict(_snake_case )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(_snake_case ,size=(size['''height'''], size['''width''']) ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : np.ndarray ,_snake_case : Union[int, float] ,_snake_case : bool = True ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> Any:
"""simple docstring"""
lowercase__ : List[Any] = image.astype(np.floataa )
if offset:
lowercase__ : List[str] = image - (scale / 2)
return rescale(_snake_case ,scale=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : List[str] ,_snake_case : np.ndarray ,_snake_case : Union[float, List[float]] ,_snake_case : Union[float, List[float]] ,_snake_case : Optional[Union[str, ChannelDimension]] = None ,**_snake_case : Tuple ,) -> np.ndarray:
"""simple docstring"""
return normalize(_snake_case ,mean=_snake_case ,std=_snake_case ,data_format=_snake_case ,**_snake_case )
def UpperCAmelCase ( self : Union[str, Any] ,_snake_case : ImageInput ,_snake_case : bool = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : Dict[str, int] = None ,_snake_case : bool = None ,_snake_case : float = None ,_snake_case : bool = None ,_snake_case : bool = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[ChannelDimension] = ChannelDimension.FIRST ,) -> np.ndarray:
"""simple docstring"""
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_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop 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.''' )
if offset and not do_rescale:
raise ValueError('''For offset, do_rescale must also be set to True.''' )
# All transformations expect numpy arrays.
lowercase__ : Dict = to_numpy_array(_snake_case )
if do_resize:
lowercase__ : Union[str, Any] = self.resize(image=_snake_case ,size=_snake_case ,resample=_snake_case )
if do_center_crop:
lowercase__ : Optional[Any] = self.center_crop(_snake_case ,size=_snake_case )
if do_rescale:
lowercase__ : List[Any] = self.rescale(image=_snake_case ,scale=_snake_case ,offset=_snake_case )
if do_normalize:
lowercase__ : List[Any] = self.normalize(image=_snake_case ,mean=_snake_case ,std=_snake_case )
lowercase__ : List[str] = to_channel_dimension_format(_snake_case ,_snake_case )
return image
def UpperCAmelCase ( self : List[Any] ,_snake_case : ImageInput ,_snake_case : bool = None ,_snake_case : Dict[str, int] = None ,_snake_case : PILImageResampling = None ,_snake_case : bool = None ,_snake_case : Dict[str, int] = None ,_snake_case : bool = None ,_snake_case : float = None ,_snake_case : bool = None ,_snake_case : bool = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[float, List[float]]] = None ,_snake_case : Optional[Union[str, TensorType]] = None ,_snake_case : ChannelDimension = ChannelDimension.FIRST ,**_snake_case : Dict ,) -> PIL.Image.Image:
"""simple docstring"""
lowercase__ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
lowercase__ : int = resample if resample is not None else self.resample
lowercase__ : List[Any] = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase__ : str = do_rescale if do_rescale is not None else self.do_rescale
lowercase__ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase__ : Optional[Any] = offset if offset is not None else self.offset
lowercase__ : Optional[Any] = do_normalize if do_normalize is not None else self.do_normalize
lowercase__ : Any = image_mean if image_mean is not None else self.image_mean
lowercase__ : List[str] = image_std if image_std is not None else self.image_std
lowercase__ : Union[str, Any] = size if size is not None else self.size
lowercase__ : Union[str, Any] = get_size_dict(_snake_case ,default_to_square=_snake_case )
lowercase__ : Tuple = crop_size if crop_size is not None else self.crop_size
lowercase__ : Union[str, Any] = get_size_dict(_snake_case ,param_name='''crop_size''' )
if not valid_images(_snake_case ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
lowercase__ : Any = make_batched(_snake_case )
lowercase__ : Optional[int] = [
[
self._preprocess_image(
image=_snake_case ,do_resize=_snake_case ,size=_snake_case ,resample=_snake_case ,do_center_crop=_snake_case ,crop_size=_snake_case ,do_rescale=_snake_case ,rescale_factor=_snake_case ,offset=_snake_case ,do_normalize=_snake_case ,image_mean=_snake_case ,image_std=_snake_case ,data_format=_snake_case ,)
for img in video
]
for video in videos
]
lowercase__ : Dict = {'''pixel_values''': videos}
return BatchFeature(data=_snake_case ,tensor_type=_snake_case )
| 16
|
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
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def constraint_to_multiple_of(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0 , _lowerCamelCase=None ):
_lowerCAmelCase : Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowerCAmelCase : Optional[int] = math.floor(val / multiple ) * multiple
if x < min_val:
_lowerCAmelCase : List[str] = math.ceil(val / multiple ) * multiple
return x
_lowerCAmelCase : Union[str, Any] = (output_size, output_size) if isinstance(_lowerCamelCase , _lowerCamelCase ) else output_size
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = get_image_size(_lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = output_size
# determine new height and width
_lowerCAmelCase : List[Any] = output_height / input_height
_lowerCAmelCase : Any = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowerCAmelCase : Union[str, Any] = scale_width
else:
# fit height
_lowerCAmelCase : Union[str, Any] = scale_height
_lowerCAmelCase : List[str] = constraint_to_multiple_of(scale_height * input_height , multiple=_lowerCamelCase )
_lowerCAmelCase : Dict = constraint_to_multiple_of(scale_width * input_width , multiple=_lowerCamelCase )
return (new_height, new_width)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['pixel_values']
def __init__( self, __a = True, __a = None, __a = PILImageResampling.BILINEAR, __a = False, __a = 1, __a = True, __a = 1 / 255, __a = True, __a = None, __a = None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = size if size is not None else {"height": 384, "width": 384}
_lowerCAmelCase : Optional[int] = get_size_dict(__a)
_lowerCAmelCase : Optional[Any] = do_resize
_lowerCAmelCase : Dict = size
_lowerCAmelCase : Any = keep_aspect_ratio
_lowerCAmelCase : str = ensure_multiple_of
_lowerCAmelCase : str = resample
_lowerCAmelCase : Dict = do_rescale
_lowerCAmelCase : Optional[int] = rescale_factor
_lowerCAmelCase : Dict = do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self, __a, __a, __a = False, __a = 1, __a = PILImageResampling.BICUBIC, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : List[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()}")
_lowerCAmelCase : List[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 snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return rescale(__a, scale=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return normalize(__a, mean=__a, std=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = ChannelDimension.FIRST, **__a, ):
'''simple docstring'''
_lowerCAmelCase : int = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : List[Any] = size if size is not None else self.size
_lowerCAmelCase : str = get_size_dict(__a)
_lowerCAmelCase : Dict = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowerCAmelCase : Any = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowerCAmelCase : int = resample if resample is not None else self.resample
_lowerCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase : List[str] = image_std if image_std is not None else self.image_std
_lowerCAmelCase : Optional[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_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.
_lowerCAmelCase : List[Any] = [to_numpy_array(__a) for image in images]
if do_resize:
_lowerCAmelCase : Any = [self.resize(image=__a, size=__a, resample=__a) for image in images]
if do_rescale:
_lowerCAmelCase : List[str] = [self.rescale(image=__a, scale=__a) for image in images]
if do_normalize:
_lowerCAmelCase : Dict = [self.normalize(image=__a, mean=__a, std=__a) for image in images]
_lowerCAmelCase : List[str] = [to_channel_dimension_format(__a, __a) for image in images]
_lowerCAmelCase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=__a, tensor_type=__a)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = 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):
_lowerCAmelCase : List[Any] = target_sizes.numpy()
_lowerCAmelCase : Dict = []
for idx in range(len(__a)):
_lowerCAmelCase : int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=__a)
_lowerCAmelCase : int = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(__a)
else:
_lowerCAmelCase : Dict = logits.argmax(dim=1)
_lowerCAmelCase : str = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 36
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"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
_a = '__DUMMY_TRANSFORMERS_USER__'
_a = 'Dummy User'
_a = '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'
_a = 'https://hub-ci.huggingface.co'
_a = CI_HUB_ENDPOINT + '/datasets/{repo_id}/resolve/{revision}/{path}'
_a = CI_HUB_ENDPOINT + '/{repo_id}/resolve/{revision}/{filename}'
_a = Path('~/.huggingface/hub_ci_token').expanduser()
@pytest.fixture
def _A ( UpperCamelCase_ : List[Any]) -> Tuple:
'''simple docstring'''
monkeypatch.setattr(
"huggingface_hub.file_download.HUGGINGFACE_CO_URL_TEMPLATE", UpperCamelCase_)
@pytest.fixture
def _A ( UpperCamelCase_ : int) -> List[Any]:
'''simple docstring'''
monkeypatch.setattr("datasets.config.HF_ENDPOINT", UpperCamelCase_)
monkeypatch.setattr("datasets.config.HUB_DATASETS_URL", UpperCamelCase_)
@pytest.fixture
def _A ( UpperCamelCase_ : str) -> Dict:
'''simple docstring'''
monkeypatch.setattr("huggingface_hub.hf_api.HfFolder.path_token", UpperCamelCase_)
@pytest.fixture
def _A ( UpperCamelCase_ : Optional[Any], UpperCamelCase_ : List[Any]) -> List[str]:
'''simple docstring'''
HfFolder.save_token(UpperCamelCase_)
yield
HfFolder.delete_token()
@pytest.fixture(scope="session")
def _A ( ) -> List[Any]:
'''simple docstring'''
return HfApi(endpoint=UpperCamelCase_)
@pytest.fixture(scope="session")
def _A ( UpperCamelCase_ : HfApi) -> List[Any]:
'''simple docstring'''
__lowercase = HfFolder.get_token()
HfFolder.save_token(UpperCamelCase_)
yield CI_HUB_USER_TOKEN
if previous_token is not None:
HfFolder.save_token(UpperCamelCase_)
@pytest.fixture
def _A ( UpperCamelCase_ : Dict) -> int:
'''simple docstring'''
def _cleanup_repo(UpperCamelCase_ : Optional[int]):
hf_api.delete_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset")
return _cleanup_repo
@pytest.fixture
def _A ( UpperCamelCase_ : str) -> Any:
'''simple docstring'''
@contextmanager
def _temporary_repo(UpperCamelCase_ : Any):
try:
yield repo_id
finally:
cleanup_repo(UpperCamelCase_)
return _temporary_repo
@pytest.fixture(scope="session")
def _A ( UpperCamelCase_ : HfApi, UpperCamelCase_ : str, UpperCamelCase_ : Optional[int]) -> List[Any]:
'''simple docstring'''
__lowercase = F"""repo_txt_data-{int(time.time() * 10E3)}"""
__lowercase = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset", private=UpperCamelCase_)
hf_api.upload_file(
token=UpperCamelCase_, path_or_fileobj=str(UpperCamelCase_), path_in_repo="data/text_data.txt", repo_id=UpperCamelCase_, repo_type="dataset", )
yield repo_id
try:
hf_api.delete_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset")
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _A ( UpperCamelCase_ : Tuple, UpperCamelCase_ : Any, UpperCamelCase_ : Dict) -> Optional[int]:
'''simple docstring'''
return hf_private_dataset_repo_txt_data_
@pytest.fixture(scope="session")
def _A ( UpperCamelCase_ : HfApi, UpperCamelCase_ : int, UpperCamelCase_ : Optional[int]) -> int:
'''simple docstring'''
__lowercase = F"""repo_zipped_txt_data-{int(time.time() * 10E3)}"""
__lowercase = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset", private=UpperCamelCase_)
hf_api.upload_file(
token=UpperCamelCase_, path_or_fileobj=str(UpperCamelCase_), path_in_repo="data.zip", repo_id=UpperCamelCase_, repo_type="dataset", )
yield repo_id
try:
hf_api.delete_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset")
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _A ( UpperCamelCase_ : List[str], UpperCamelCase_ : Dict, UpperCamelCase_ : Any) -> int:
'''simple docstring'''
return hf_private_dataset_repo_zipped_txt_data_
@pytest.fixture(scope="session")
def _A ( UpperCamelCase_ : HfApi, UpperCamelCase_ : List[str], UpperCamelCase_ : List[str]) -> List[Any]:
'''simple docstring'''
__lowercase = F"""repo_zipped_img_data-{int(time.time() * 10E3)}"""
__lowercase = F"""{CI_HUB_USER}/{repo_name}"""
hf_api.create_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset", private=UpperCamelCase_)
hf_api.upload_file(
token=UpperCamelCase_, path_or_fileobj=str(UpperCamelCase_), path_in_repo="data.zip", repo_id=UpperCamelCase_, repo_type="dataset", )
yield repo_id
try:
hf_api.delete_repo(UpperCamelCase_, token=UpperCamelCase_, repo_type="dataset")
except (requests.exceptions.HTTPError, ValueError): # catch http error and token invalid error
pass
@pytest.fixture()
def _A ( UpperCamelCase_ : Union[str, Any], UpperCamelCase_ : List[str], UpperCamelCase_ : List[str]) -> str:
'''simple docstring'''
return hf_private_dataset_repo_zipped_img_data_
| 17
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = "huggingface/label-files"
_lowerCAmelCase : int = "imagenet-1k-id2label.json"
_lowerCAmelCase : Tuple = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
_lowerCAmelCase : Tuple = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_lowerCAmelCase : Tuple = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
_lowerCAmelCase : Optional[int] = BitConfig(
conv_layer=_lowerCamelCase , num_labels=1_000 , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase , )
return config
def A ( _lowerCamelCase ):
'''simple docstring'''
if "stem.conv" in name:
_lowerCAmelCase : List[str] = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
_lowerCAmelCase : Any = name.replace("blocks" , "layers" )
if "head.fc" in name:
_lowerCAmelCase : Optional[Any] = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
_lowerCAmelCase : Any = "bit." + name
if "bit" not in name and "classifier" not in name:
_lowerCAmelCase : Dict = "bit.encoder." + name
return name
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCAmelCase : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = get_config(_lowerCamelCase )
# load original model from timm
_lowerCAmelCase : int = create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model
_lowerCAmelCase : Any = timm_model.state_dict()
for key in state_dict.copy().keys():
_lowerCAmelCase : Dict = state_dict.pop(_lowerCamelCase )
_lowerCAmelCase : Tuple = val.squeeze() if "head" in key else val
# load HuggingFace model
_lowerCAmelCase : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
_lowerCAmelCase : Dict = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
_lowerCAmelCase : Optional[int] = transform.transforms
_lowerCAmelCase : Tuple = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
_lowerCAmelCase : Tuple = BitImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_lowerCAmelCase : Optional[int] = prepare_img()
_lowerCAmelCase : Any = transform(_lowerCamelCase ).unsqueeze(0 )
_lowerCAmelCase : Optional[int] = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
_lowerCAmelCase : Tuple = model(_lowerCamelCase )
_lowerCAmelCase : str = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
_lowerCAmelCase : Union[str, Any] = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
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 to push the model to the hub.",
)
_snake_case = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36
| 0
|
def _snake_case ( lowerCAmelCase : Tuple ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Dict = 1
SCREAMING_SNAKE_CASE_ : Optional[Any] = 2
while i * i <= n:
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
while n % i == 0:
n //= i
multiplicity += 1
n_divisors *= multiplicity + 1
i += 1
if n > 1:
n_divisors *= 2
return n_divisors
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = 1
SCREAMING_SNAKE_CASE_ : List[str] = 1
while True:
i += 1
t_num += i
if count_divisors(lowerCAmelCase ) > 5_0_0:
break
return t_num
if __name__ == "__main__":
print(solution())
| 18
|
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
_snake_case = logging.get_logger(__name__)
_snake_case = {
"microsoft/swin-tiny-patch4-window7-224": (
"https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
),
# See all Swin models at https://huggingface.co/models?filter=swin
}
class UpperCAmelCase_ ( a , a):
lowerCamelCase__ = 'swin'
lowerCamelCase__ = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self, __a=224, __a=4, __a=3, __a=96, __a=[2, 2, 6, 2], __a=[3, 6, 12, 24], __a=7, __a=4.0, __a=True, __a=0.0, __a=0.0, __a=0.1, __a="gelu", __a=False, __a=0.02, __a=1E-5, __a=32, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = image_size
_lowerCAmelCase : Union[str, Any] = patch_size
_lowerCAmelCase : Tuple = num_channels
_lowerCAmelCase : List[Any] = embed_dim
_lowerCAmelCase : Tuple = depths
_lowerCAmelCase : Optional[Any] = len(__a)
_lowerCAmelCase : int = num_heads
_lowerCAmelCase : int = window_size
_lowerCAmelCase : int = mlp_ratio
_lowerCAmelCase : List[Any] = qkv_bias
_lowerCAmelCase : str = hidden_dropout_prob
_lowerCAmelCase : Union[str, Any] = attention_probs_dropout_prob
_lowerCAmelCase : Any = drop_path_rate
_lowerCAmelCase : int = hidden_act
_lowerCAmelCase : Tuple = use_absolute_embeddings
_lowerCAmelCase : Optional[int] = layer_norm_eps
_lowerCAmelCase : Tuple = initializer_range
_lowerCAmelCase : Tuple = encoder_stride
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
_lowerCAmelCase : List[str] = int(embed_dim * 2 ** (len(__a) - 1))
_lowerCAmelCase : List[Any] = ["stem"] + [f"stage{idx}" for idx in range(1, len(__a) + 1)]
_lowerCAmelCase , _lowerCAmelCase : Optional[int] = get_aligned_output_features_output_indices(
out_features=__a, out_indices=__a, stage_names=self.stage_names)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
| 36
| 0
|
import os
import re
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
__A =logging.get_logger(__name__)
__A ={'''vocab_file''': '''spiece.model'''}
__A ={
'''vocab_file''': {
'''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
'''google/bigbird-roberta-large''': (
'''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
),
'''google/bigbird-base-trivia-itc''': (
'''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
),
}
}
__A ={
'''google/bigbird-roberta-base''': 4_0_9_6,
'''google/bigbird-roberta-large''': 4_0_9_6,
'''google/bigbird-base-trivia-itc''': 4_0_9_6,
}
class _SCREAMING_SNAKE_CASE ( snake_case_ ):
lowerCAmelCase__ = VOCAB_FILES_NAMES
lowerCAmelCase__ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCAmelCase__ = ['input_ids', 'attention_mask']
lowerCAmelCase__ = []
def __init__( self , lowercase , lowercase="<unk>" , lowercase="<s>" , lowercase="</s>" , lowercase="<pad>" , lowercase="[SEP]" , lowercase="[MASK]" , lowercase="[CLS]" , lowercase = None , **lowercase , ) -> None:
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else bos_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else eos_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else unk_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else pad_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else cls_token
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else sep_token
# Mask token behave like a normal word, i.e. include the space before it
lowerCamelCase_ = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
lowerCamelCase_ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowercase , eos_token=lowercase , unk_token=lowercase , pad_token=lowercase , sep_token=lowercase , mask_token=lowercase , cls_token=lowercase , sp_model_kwargs=self.sp_model_kwargs , **lowercase , )
lowerCamelCase_ = vocab_file
lowerCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowercase )
@property
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
return self.sp_model.get_piece_size()
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
lowerCamelCase_ = {self.convert_ids_to_tokens(lowercase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Tuple:
lowerCamelCase_ = self.__dict__.copy()
lowerCamelCase_ = None
return state
def __setstate__( self , lowercase ) -> Tuple:
lowerCamelCase_ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowerCamelCase_ = {}
lowerCamelCase_ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> List[str]:
return self.sp_model.encode(lowercase , out_type=lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Union[str, Any]:
return self.sp_model.piece_to_id(lowercase )
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Union[str, Any]:
lowerCamelCase_ = self.sp_model.IdToPiece(lowercase )
return token
def SCREAMING_SNAKE_CASE_( self , lowercase ) -> Any:
lowerCamelCase_ = []
lowerCamelCase_ = ""
lowerCamelCase_ = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(lowercase ) + token
lowerCamelCase_ = True
lowerCamelCase_ = []
else:
current_sub_tokens.append(lowercase )
lowerCamelCase_ = False
out_string += self.sp_model.decode(lowercase )
return out_string.strip()
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = False , lowercase = None , lowercase = True , **lowercase , ) -> str:
lowerCamelCase_ = kwargs.pop("use_source_tokenizer" , lowercase )
lowerCamelCase_ = self.convert_ids_to_tokens(lowercase , skip_special_tokens=lowercase )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
lowerCamelCase_ = []
lowerCamelCase_ = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase ) )
lowerCamelCase_ = []
sub_texts.append(lowercase )
else:
current_sub_text.append(lowercase )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowercase ) )
# Mimic the behavior of the Rust tokenizer:
# No space before [MASK] and [SEP]
if spaces_between_special_tokens:
lowerCamelCase_ = re.sub(R" (\[(MASK|SEP)\])" , R"\1" , " ".join(lowercase ) )
else:
lowerCamelCase_ = "".join(lowercase )
lowerCamelCase_ = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
lowerCamelCase_ = self.clean_up_tokenization(lowercase )
return clean_text
else:
return text
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> Tuple[str]:
if not os.path.isdir(lowercase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
lowerCamelCase_ = os.path.join(
lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowercase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , lowercase )
elif not os.path.isfile(self.vocab_file ):
with open(lowercase , "wb" ) as fi:
lowerCamelCase_ = self.sp_model.serialized_model_proto()
fi.write(lowercase )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
lowerCamelCase_ = [self.sep_token_id]
return cls + token_ids_a + sep + token_ids_a + sep
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None , lowercase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
if token_ids_a is None:
return [1] + ([0] * len(lowercase )) + [1]
return [1] + ([0] * len(lowercase )) + [1] + ([0] * len(lowercase )) + [1]
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase = None ) -> List[int]:
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [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]
| 19
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
)
else:
from .modeling_text_unet import UNetFlatConditionModel
from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline
| 36
| 0
|
def _snake_case( SCREAMING_SNAKE_CASE__ = 1_000 ) -> int:
lowercase , lowercase : int = 1, 1
lowercase : Tuple = []
for i in range(1 , n + 1 ):
lowercase : Union[str, Any] = prev_numerator + 2 * prev_denominator
lowercase : List[Any] = prev_numerator + prev_denominator
if len(str(SCREAMING_SNAKE_CASE__ ) ) > len(str(SCREAMING_SNAKE_CASE__ ) ):
result.append(SCREAMING_SNAKE_CASE__ )
lowercase : Any = numerator
lowercase : List[str] = denominator
return len(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 20
|
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
_snake_case = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operator.gt,
}
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if got_ver is None or want_ver is None:
raise ValueError(
F"Unable to compare versions for {requirement}: need={want_ver} found={got_ver}. This is unusual. Consider"
F" reinstalling {pkg}." )
if not ops[op](version.parse(_lowerCamelCase ) , version.parse(_lowerCamelCase ) ):
raise ImportError(
F"{requirement} is required for a normal functioning of this module, but found {pkg}=={got_ver}.{hint}" )
def A ( _lowerCamelCase , _lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase : List[str] = F"\n{hint}" if hint is not None else ""
# non-versioned check
if re.match(r"^[\w_\-\d]+$" , _lowerCamelCase ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : List[str] = requirement, None, None
else:
_lowerCAmelCase : Optional[int] = re.findall(r"^([^!=<>\s]+)([\s!=<>]{1,2}.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23, but"
F" got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Dict = match[0]
_lowerCAmelCase : Any = want_full.split("," ) # there could be multiple requirements
_lowerCAmelCase : Optional[int] = {}
for w in want_range:
_lowerCAmelCase : Any = re.findall(r"^([\s!=<>]{1,2})(.+)" , _lowerCamelCase )
if not match:
raise ValueError(
"requirement needs to be in the pip package format, .e.g., package_a==1.23, or package_b>=1.23,"
F" but got {requirement}" )
_lowerCAmelCase , _lowerCAmelCase : Tuple = match[0]
_lowerCAmelCase : Union[str, Any] = want_ver
if op not in ops:
raise ValueError(F"{requirement}: need one of {list(ops.keys() )}, but got {op}" )
# special case
if pkg == "python":
_lowerCAmelCase : Tuple = ".".join([str(_lowerCamelCase ) for x in sys.version_info[:3]] )
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
return
# check if any version is installed
try:
_lowerCAmelCase : Any = importlib.metadata.version(_lowerCamelCase )
except importlib.metadata.PackageNotFoundError:
raise importlib.metadata.PackageNotFoundError(
F"The '{requirement}' distribution was not found and is required by this application. {hint}" )
# check that the right version is installed if version number or a range was provided
if want_ver is not None:
for op, want_ver in wanted.items():
_compare_versions(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = "Try: pip install transformers -U or pip install -e '.[dev]' if you're working with git main"
return require_version(_lowerCamelCase , _lowerCamelCase )
| 36
| 0
|
import os
import numpy
import onnx
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Any:
_lowercase : Optional[Any] = a.name
_lowercase : Dict = b.name
_lowercase : List[str] = ''
_lowercase : int = ''
_lowercase : Optional[int] = a == b
_lowercase : str = name_a
_lowercase : Optional[int] = name_b
return res
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Union[str, Any]:
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(lowerCamelCase_ , lowerCamelCase_ )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g , lowerCamelCase_ , lowerCamelCase_ )
_graph_replace_input_with(node_proto.attribute[1].g , lowerCamelCase_ , lowerCamelCase_ )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g , lowerCamelCase_ , lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
for n in graph_proto.node:
_node_replace_input_with(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Optional[int]:
_lowercase : List[str] = list(model.graph.initializer )
_lowercase : str = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
_lowercase : List[Any] = inits[i].name
_lowercase : Optional[int] = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph , lowerCamelCase_ , lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[int]:
_lowercase : str = os.path.dirname(lowerCamelCase_ )
_lowercase : Tuple = os.path.basename(lowerCamelCase_ )
_lowercase : Optional[int] = onnx.load(os.path.join(lowerCamelCase_ , lowerCamelCase_ ) )
_lowercase : Optional[int] = list(model.graph.initializer )
_lowercase : Dict = set()
_lowercase : Optional[int] = {}
_lowercase : Union[str, Any] = []
_lowercase : Optional[int] = 0
for i in range(len(lowerCamelCase_ ) ):
if i in dup_set:
continue
for j in range(i + 1 , len(lowerCamelCase_ ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i] , inits[j] ):
dup_set.add(lowerCamelCase_ )
dup_set.add(lowerCamelCase_ )
_lowercase : Optional[int] = inits[j].data_type
_lowercase : Optional[int] = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print('unexpected data type: ' , lowerCamelCase_ )
total_reduced_size += mem_size
_lowercase : str = inits[i].name
_lowercase : List[str] = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(lowerCamelCase_ )
else:
_lowercase : Tuple = [name_j]
ind_to_replace.append((j, i) )
print('total reduced size: ' , total_reduced_size / 1024 / 1024 / 1024 , 'GB' )
_lowercase : int = sorted(lowerCamelCase_ )
_remove_dup_initializers_from_model(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
_lowercase : Tuple = 'optimized_' + model_file_name
_lowercase : int = os.path.join(lowerCamelCase_ , lowerCamelCase_ )
onnx.save(lowerCamelCase_ , lowerCamelCase_ )
return new_model
| 21
|
import argparse
from collections import defaultdict
import yaml
_snake_case = "docs/source/en/_toctree.yml"
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = defaultdict(_lowerCamelCase )
_lowerCAmelCase : Any = []
_lowerCAmelCase : List[str] = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({"local": doc["local"], "title": doc["title"]} )
else:
new_doc_list.append(_lowerCamelCase )
_lowerCAmelCase : Optional[Any] = new_doc_list
_lowerCAmelCase : List[Any] = [key for key, value in counts.items() if value > 1]
_lowerCAmelCase : str = []
for duplicate_key in duplicates:
_lowerCAmelCase : List[str] = list({doc["title"] for doc in doc_list if doc["local"] == duplicate_key} )
if len(_lowerCamelCase ) > 1:
raise ValueError(
F"{duplicate_key} is present several times in the documentation table of content at "
"`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the "
"others." )
# Only add this once
new_doc.append({"local": duplicate_key, "title": titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if "local" not in counts or counts[doc["local"]] == 1] )
_lowerCAmelCase : Optional[Any] = sorted(_lowerCamelCase , key=lambda _lowerCamelCase : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(_lowerCamelCase ) > 1:
raise ValueError("{doc_list} has two 'overview' docs which is not allowed." )
overview_doc.extend(_lowerCamelCase )
# Sort
return overview_doc
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : int = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[Any] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : List[str] = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : Union[str, Any] = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
_lowerCAmelCase : Optional[Any] = api_doc[scheduler_idx]["sections"]
_lowerCAmelCase : Optional[Any] = clean_doc_toc(_lowerCamelCase )
_lowerCAmelCase : int = False
if new_scheduler_doc != scheduler_doc:
_lowerCAmelCase : List[Any] = True
if overwrite:
_lowerCAmelCase : Dict = new_scheduler_doc
if diff:
if overwrite:
_lowerCAmelCase : Tuple = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
def A ( _lowerCamelCase=False ):
'''simple docstring'''
with open(_lowerCamelCase , encoding="utf-8" ) as f:
_lowerCAmelCase : Tuple = yaml.safe_load(f.read() )
# Get to the API doc
_lowerCAmelCase : Optional[int] = 0
while content[api_idx]["title"] != "API":
api_idx += 1
_lowerCAmelCase : int = content[api_idx]["sections"]
# Then to the model doc
_lowerCAmelCase : List[str] = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
_lowerCAmelCase : Dict = False
_lowerCAmelCase : Optional[int] = api_doc[pipeline_idx]["sections"]
_lowerCAmelCase : Tuple = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
_lowerCAmelCase : List[Any] = pipeline_doc["section"]
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if overwrite:
_lowerCAmelCase : Optional[Any] = new_sub_pipeline_doc
new_pipeline_docs.append(_lowerCamelCase )
# sort overall pipeline doc
_lowerCAmelCase : Union[str, Any] = clean_doc_toc(_lowerCamelCase )
if new_pipeline_docs != pipeline_docs:
_lowerCAmelCase : Dict = True
if overwrite:
_lowerCAmelCase : Optional[int] = new_pipeline_docs
if diff:
if overwrite:
_lowerCAmelCase : Optional[int] = api_doc
with open(_lowerCamelCase , "w" , encoding="utf-8" ) as f:
f.write(yaml.dump(_lowerCamelCase , allow_unicode=_lowerCamelCase ) )
else:
raise ValueError(
"The model doc part of the table of content is not properly sorted, run `make style` to fix this." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
_snake_case = parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 36
| 0
|
'''simple docstring'''
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class A_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_lowerCamelCase : List[str] = StableDiffusionDiffEditPipeline
_lowerCamelCase : Any = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
_lowerCamelCase : str = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
_lowerCamelCase : List[Any] = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
_lowerCamelCase : Optional[Any] = frozenset([] )
def lowercase ( self : Optional[int] ):
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=snake_case_ , )
_UpperCAmelCase = DDIMScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , clip_sample=snake_case_ , set_alpha_to_one=snake_case_ , )
_UpperCAmelCase = DDIMInverseScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule="scaled_linear" , clip_sample=snake_case_ , set_alpha_to_zero=snake_case_ , )
torch.manual_seed(0 )
_UpperCAmelCase = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="gelu" , projection_dim=5_1_2 , )
_UpperCAmelCase = CLIPTextModel(snake_case_ )
_UpperCAmelCase = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
_UpperCAmelCase = {
"unet": unet,
"scheduler": scheduler,
"inverse_scheduler": inverse_scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowercase ( self : Optional[Any] , snake_case_ : str , snake_case_ : List[Any]=0 ):
_UpperCAmelCase = floats_tensor((1, 1_6, 1_6) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
_UpperCAmelCase = floats_tensor((1, 2, 4, 1_6, 1_6) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
if str(snake_case_ ).startswith("mps" ):
_UpperCAmelCase = torch.manual_seed(snake_case_ )
else:
_UpperCAmelCase = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_UpperCAmelCase = {
"prompt": "a dog and a newt",
"mask_image": mask,
"image_latents": latents,
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowercase ( self : Dict , snake_case_ : Union[str, Any] , snake_case_ : Union[str, Any]=0 ):
_UpperCAmelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(snake_case_ ) ).convert("RGB" )
if str(snake_case_ ).startswith("mps" ):
_UpperCAmelCase = torch.manual_seed(snake_case_ )
else:
_UpperCAmelCase = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_UpperCAmelCase = {
"image": image,
"source_prompt": "a cat and a frog",
"target_prompt": "a dog and a newt",
"generator": generator,
"num_inference_steps": 2,
"num_maps_per_mask": 2,
"mask_encode_strength": 1.0,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def lowercase ( self : List[Any] , snake_case_ : List[str] , snake_case_ : Any=0 ):
_UpperCAmelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
_UpperCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_UpperCAmelCase = Image.fromarray(np.uinta(snake_case_ ) ).convert("RGB" )
if str(snake_case_ ).startswith("mps" ):
_UpperCAmelCase = torch.manual_seed(snake_case_ )
else:
_UpperCAmelCase = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_UpperCAmelCase = {
"image": image,
"prompt": "a cat and a frog",
"generator": generator,
"num_inference_steps": 2,
"inpaint_strength": 1.0,
"guidance_scale": 6.0,
"decode_latents": True,
"output_type": "numpy",
}
return inputs
def lowercase ( self : Tuple ):
if not hasattr(self.pipeline_class , "_optional_components" ):
return
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(snake_case_ , snake_case_ , snake_case_ )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
_UpperCAmelCase = self.get_dummy_inputs(snake_case_ )
_UpperCAmelCase = pipe(**snake_case_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(snake_case_ )
_UpperCAmelCase = self.pipeline_class.from_pretrained(snake_case_ )
pipe_loaded.to(snake_case_ )
pipe_loaded.set_progress_bar_config(disable=snake_case_ )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(snake_case_ , snake_case_ ) is None , f'`{optional_component}` did not stay set to None after loading.' , )
_UpperCAmelCase = self.get_dummy_inputs(snake_case_ )
_UpperCAmelCase = pipe_loaded(**snake_case_ )[0]
_UpperCAmelCase = np.abs(output - output_loaded ).max()
self.assertLess(snake_case_ , 1e-4 )
def lowercase ( self : Any ):
_UpperCAmelCase = "cpu"
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_mask_inputs(snake_case_ )
_UpperCAmelCase = pipe.generate_mask(**snake_case_ )
_UpperCAmelCase = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 1_6, 1_6) )
_UpperCAmelCase = np.array([0] * 9 )
_UpperCAmelCase = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case_ , 1e-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = "cpu"
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inversion_inputs(snake_case_ )
_UpperCAmelCase = pipe.invert(**snake_case_ ).images
_UpperCAmelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 3_2, 3_2, 3) )
_UpperCAmelCase = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case_ , 1e-3 )
def lowercase ( self : List[Any] ):
super().test_inference_batch_single_identical(expected_max_diff=5e-3 )
def lowercase ( self : List[Any] ):
_UpperCAmelCase = "cpu"
_UpperCAmelCase = self.get_dummy_components()
_UpperCAmelCase = {"beta_start": 0.0_0_0_8_5, "beta_end": 0.0_1_2, "beta_schedule": "scaled_linear"}
_UpperCAmelCase = DPMSolverMultistepScheduler(**snake_case_ )
_UpperCAmelCase = DPMSolverMultistepInverseScheduler(**snake_case_ )
_UpperCAmelCase = self.pipeline_class(**snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = self.get_dummy_inversion_inputs(snake_case_ )
_UpperCAmelCase = pipe.invert(**snake_case_ ).images
_UpperCAmelCase = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 3_2, 3_2, 3) )
_UpperCAmelCase = np.array(
[0.5_1_5_0, 0.5_1_3_4, 0.5_0_4_3, 0.5_3_7_6, 0.4_6_9_4, 0.5_1_0_5_0, 0.5_0_1_5, 0.4_4_0_7, 0.4_7_9_9] , )
_UpperCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(snake_case_ , 1e-3 )
@require_torch_gpu
@slow
class A_ ( unittest.TestCase ):
def lowercase ( self : Any ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def lowercase ( cls : Dict ):
_UpperCAmelCase = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png" )
_UpperCAmelCase = raw_image.convert("RGB" ).resize((7_6_8, 7_6_8) )
_UpperCAmelCase = raw_image
def lowercase ( self : Union[str, Any] ):
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=snake_case_ , torch_dtype=torch.floataa )
_UpperCAmelCase = DDIMScheduler.from_config(pipe.scheduler.config )
_UpperCAmelCase = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = "a bowl of fruit"
_UpperCAmelCase = "a bowl of pears"
_UpperCAmelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=snake_case_ , target_prompt=snake_case_ , generator=snake_case_ , )
_UpperCAmelCase = pipe.invert(
prompt=snake_case_ , image=self.raw_image , inpaint_strength=0.7 , generator=snake_case_ ).latents
_UpperCAmelCase = pipe(
prompt=snake_case_ , mask_image=snake_case_ , image_latents=snake_case_ , generator=snake_case_ , negative_prompt=snake_case_ , inpaint_strength=0.7 , output_type="numpy" , ).images[0]
_UpperCAmelCase = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_6_8, 7_6_8) ) )
/ 2_5_5
)
assert np.abs((expected_image - image).max() ) < 5e-1
def lowercase ( self : Optional[int] ):
_UpperCAmelCase = torch.manual_seed(0 )
_UpperCAmelCase = StableDiffusionDiffEditPipeline.from_pretrained(
"stabilityai/stable-diffusion-2-1" , safety_checker=snake_case_ , torch_dtype=torch.floataa )
_UpperCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
_UpperCAmelCase = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=snake_case_ )
_UpperCAmelCase = "a bowl of fruit"
_UpperCAmelCase = "a bowl of pears"
_UpperCAmelCase = pipe.generate_mask(
image=self.raw_image , source_prompt=snake_case_ , target_prompt=snake_case_ , generator=snake_case_ , )
_UpperCAmelCase = pipe.invert(
prompt=snake_case_ , image=self.raw_image , inpaint_strength=0.7 , generator=snake_case_ , num_inference_steps=2_5 , ).latents
_UpperCAmelCase = pipe(
prompt=snake_case_ , mask_image=snake_case_ , image_latents=snake_case_ , generator=snake_case_ , negative_prompt=snake_case_ , inpaint_strength=0.7 , num_inference_steps=2_5 , output_type="numpy" , ).images[0]
_UpperCAmelCase = (
np.array(
load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/diffedit/pears.png" ).resize((7_6_8, 7_6_8) ) )
/ 2_5_5
)
assert np.abs((expected_image - image).max() ) < 5e-1
| 22
|
def A ( _lowerCamelCase , _lowerCamelCase ):
'''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()
| 36
| 0
|
'''simple docstring'''
import os
from pickle import UnpicklingError
from typing import Dict, Tuple
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict
import transformers
from .utils import logging
UpperCamelCase__: Tuple = logging.get_logger(__name__)
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str] , _lowerCAmelCase : List[str]=False ) -> List[str]:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
if not is_sharded:
UpperCAmelCase : Optional[Any] = os.path.abspath(_lowerCAmelCase )
logger.info(f"""Loading PyTorch weights from {pt_path}""" )
UpperCAmelCase : List[str] = torch.load(_lowerCAmelCase , map_location='''cpu''' )
logger.info(f"""PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values() ):,} parameters.""" )
UpperCAmelCase : Any = convert_pytorch_state_dict_to_flax(_lowerCAmelCase , _lowerCAmelCase )
else:
# model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
UpperCAmelCase : List[Any] = convert_pytorch_sharded_state_dict_to_flax(_lowerCAmelCase , _lowerCAmelCase )
return flax_state_dict
def snake_case_ ( _lowerCAmelCase : Tuple[str] , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : Dict[str, jnp.ndarray] , _lowerCAmelCase : str , ) -> (Tuple[str], np.ndarray):
def is_key_or_prefix_key_in_dict(_lowerCAmelCase : Tuple[str] ) -> bool:
return len(set(_lowerCAmelCase ) & {key, (model_prefix,) + key} ) > 0
# layer norm
UpperCAmelCase : Dict = pt_tuple_key[:-1] + ('''scale''',)
if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(_lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer mean
UpperCAmelCase : List[Any] = pt_tuple_key[:-1] + ('''mean''',)
if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# batch norm layer var
UpperCAmelCase : str = pt_tuple_key[:-1] + ('''var''',)
if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# embedding
UpperCAmelCase : Dict = pt_tuple_key[:-1] + ('''embedding''',)
if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(_lowerCAmelCase ):
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCAmelCase : List[str] = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ):
UpperCAmelCase : Any = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCAmelCase : Dict = pt_tuple_key[:-1] + ('''kernel''',)
if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(_lowerCAmelCase ):
UpperCAmelCase : Dict = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCAmelCase : List[str] = pt_tuple_key[:-1] + ('''weight''',)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCAmelCase : List[Any] = pt_tuple_key[:-1] + ('''bias''',)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
UpperCAmelCase : str = None
if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
UpperCAmelCase : Optional[int] = pt_tuple_key[-2] + '''_g'''
elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
UpperCAmelCase : Union[str, Any] = pt_tuple_key[-2] + '''_v'''
if name is not None:
UpperCAmelCase : List[Any] = pt_tuple_key[:-3] + (name,)
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def snake_case_ ( _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
# convert pytorch tensor to numpy
UpperCAmelCase : Dict = {k: v.numpy() for k, v in pt_state_dict.items()}
UpperCAmelCase : Any = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers
if "params" in flax_model.params:
UpperCAmelCase : Optional[Any] = flax_model.params['''params''']
else:
UpperCAmelCase : Any = flax_model.params
UpperCAmelCase : str = flatten_dict(_lowerCAmelCase )
# add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
UpperCAmelCase : List[str] = flatten_dict(flax_model.params['''batch_stats'''] )
random_flax_state_dict.update(_lowerCAmelCase )
UpperCAmelCase : str = {}
UpperCAmelCase : Union[str, Any] = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
UpperCAmelCase : Optional[Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase : Optional[Any] = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
UpperCAmelCase : Dict = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCAmelCase : Dict = pt_tuple_key[1:]
# Correctly rename weight parameters
UpperCAmelCase , UpperCAmelCase : List[Any] = rename_key_and_reshape_tensor(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# add model prefix if necessary
UpperCAmelCase : Optional[int] = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
UpperCAmelCase : Any = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1] or "var" in flax_key[-1]:
UpperCAmelCase : Dict = jnp.asarray(_lowerCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
continue
# also add unexpected weight so that warning is thrown
UpperCAmelCase : List[str] = jnp.asarray(_lowerCAmelCase )
else:
# also add unexpected weight so that warning is thrown
UpperCAmelCase : str = jnp.asarray(_lowerCAmelCase )
return unflatten_dict(_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any ) -> str:
import torch
# Load the index
UpperCAmelCase : int = {}
for shard_file in shard_filenames:
# load using msgpack utils
UpperCAmelCase : str = torch.load(_lowerCAmelCase )
UpperCAmelCase : int = {k: v.numpy() for k, v in pt_state_dict.items()}
UpperCAmelCase : Optional[int] = flax_model.base_model_prefix
# use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
if "batch_stats" in flax_model.params:
UpperCAmelCase : str = flax_model.params['''params''']
UpperCAmelCase : int = flatten_dict(_lowerCAmelCase )
random_flax_state_dict.update(flatten_dict(flax_model.params['''batch_stats'''] ) )
else:
UpperCAmelCase : Any = flax_model.params
UpperCAmelCase : List[Any] = flatten_dict(_lowerCAmelCase )
UpperCAmelCase : str = (model_prefix not in flax_model_params) and (
model_prefix in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
UpperCAmelCase : Optional[Any] = (model_prefix in flax_model_params) and (
model_prefix not in {k.split('''.''' )[0] for k in pt_state_dict.keys()}
)
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCAmelCase : Any = tuple(pt_key.split('''.''' ) )
# remove base model prefix if necessary
UpperCAmelCase : str = pt_tuple_key[0] == model_prefix
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCAmelCase : str = pt_tuple_key[1:]
# Correctly rename weight parameters
UpperCAmelCase , UpperCAmelCase : str = rename_key_and_reshape_tensor(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# add model prefix if necessary
UpperCAmelCase : int = (model_prefix,) + flax_key in random_flax_state_dict
if load_base_model_into_model_with_head and require_base_model_prefix:
UpperCAmelCase : int = (model_prefix,) + flax_key
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# add batch stats if the model contains batchnorm layers
if "batch_stats" in flax_model.params:
if "mean" in flax_key[-1]:
UpperCAmelCase : Tuple = jnp.asarray(_lowerCAmelCase )
continue
if "var" in flax_key[-1]:
UpperCAmelCase : Tuple = jnp.asarray(_lowerCAmelCase )
continue
# remove num_batches_tracked key
if "num_batches_tracked" in flax_key[-1]:
flax_state_dict.pop(_lowerCAmelCase , _lowerCAmelCase )
continue
# also add unexpected weight so that warning is thrown
UpperCAmelCase : int = jnp.asarray(_lowerCAmelCase )
else:
# also add unexpected weight so that warning is thrown
UpperCAmelCase : Any = jnp.asarray(_lowerCAmelCase )
return unflatten_dict(_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : List[Any] ) -> Tuple:
UpperCAmelCase : Any = os.path.abspath(_lowerCAmelCase )
logger.info(f"""Loading Flax weights from {flax_checkpoint_path}""" )
# import correct flax class
UpperCAmelCase : Dict = getattr(_lowerCAmelCase , '''Flax''' + model.__class__.__name__ )
# load flax weight dict
with open(_lowerCAmelCase , '''rb''' ) as state_f:
try:
UpperCAmelCase : Tuple = from_bytes(_lowerCAmelCase , state_f.read() )
except UnpicklingError:
raise EnvironmentError(f"""Unable to convert {flax_checkpoint_path} to Flax deserializable object. """ )
return load_flax_weights_in_pytorch_model(_lowerCAmelCase , _lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str ) -> Any:
try:
import torch # noqa: F401
except ImportError:
logger.error(
'''Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see'''
''' https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation'''
''' instructions.''' )
raise
# check if we have bf16 weights
UpperCAmelCase : Tuple = flatten_dict(jax.tree_util.tree_map(lambda _lowerCAmelCase : x.dtype == jnp.bfloataa , _lowerCAmelCase ) ).values()
if any(_lowerCAmelCase ):
# convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
# and bf16 is not fully supported in PT yet.
logger.warning(
'''Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` '''
'''before loading those in PyTorch model.''' )
UpperCAmelCase : Any = jax.tree_util.tree_map(
lambda _lowerCAmelCase : params.astype(np.floataa ) if params.dtype == jnp.bfloataa else params , _lowerCAmelCase )
UpperCAmelCase : Optional[int] = flatten_dict(_lowerCAmelCase )
UpperCAmelCase : str = pt_model.state_dict()
UpperCAmelCase : List[str] = (pt_model.base_model_prefix in flax_state) and (
pt_model.base_model_prefix not in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
UpperCAmelCase : Any = (pt_model.base_model_prefix not in flax_state) and (
pt_model.base_model_prefix in {k.split('''.''' )[0] for k in pt_model_dict.keys()}
)
# keep track of unexpected & missing keys
UpperCAmelCase : Dict = []
UpperCAmelCase : Union[str, Any] = set(pt_model_dict.keys() )
for flax_key_tuple, flax_tensor in flax_state_dict.items():
UpperCAmelCase : Any = flax_key_tuple[0] == pt_model.base_model_prefix
UpperCAmelCase : Optional[Any] = '''.'''.join((pt_model.base_model_prefix,) + flax_key_tuple ) in pt_model_dict
# adapt flax_key to prepare for loading from/to base model only
if load_model_with_head_into_base_model and has_base_model_prefix:
UpperCAmelCase : Tuple = flax_key_tuple[1:]
elif load_base_model_into_model_with_head and require_base_model_prefix:
UpperCAmelCase : str = (pt_model.base_model_prefix,) + flax_key_tuple
# rename flax weights to PyTorch format
if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(_lowerCAmelCase ) not in pt_model_dict:
# conv layer
UpperCAmelCase : str = flax_key_tuple[:-1] + ('''weight''',)
UpperCAmelCase : List[str] = jnp.transpose(_lowerCAmelCase , (3, 2, 0, 1) )
elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase ) not in pt_model_dict:
# linear layer
UpperCAmelCase : Any = flax_key_tuple[:-1] + ('''weight''',)
UpperCAmelCase : List[Any] = flax_tensor.T
elif flax_key_tuple[-1] in ["scale", "embedding"]:
UpperCAmelCase : Optional[int] = flax_key_tuple[:-1] + ('''weight''',)
# adding batch stats from flax batch norm to pt
elif "mean" in flax_key_tuple[-1]:
UpperCAmelCase : Dict = flax_key_tuple[:-1] + ('''running_mean''',)
elif "var" in flax_key_tuple[-1]:
UpperCAmelCase : str = flax_key_tuple[:-1] + ('''running_var''',)
if "batch_stats" in flax_state:
UpperCAmelCase : Union[str, Any] = '''.'''.join(flax_key_tuple[1:] ) # Remove the params/batch_stats header
else:
UpperCAmelCase : Any = '''.'''.join(_lowerCAmelCase )
# We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
UpperCAmelCase : Union[str, Any] = {}
# New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
for key in pt_model_dict:
UpperCAmelCase : str = key.split('''.''' )
UpperCAmelCase : Dict = None
if key_components[-3::2] == ["parametrizations", "original0"]:
UpperCAmelCase : Dict = key_components[-2] + '''_g'''
elif key_components[-3::2] == ["parametrizations", "original1"]:
UpperCAmelCase : str = key_components[-2] + '''_v'''
if name is not None:
UpperCAmelCase : Any = key_components[:-3] + [name]
UpperCAmelCase : Union[str, Any] = '''.'''.join(_lowerCAmelCase )
UpperCAmelCase : Dict = key
if flax_key in special_pt_names:
UpperCAmelCase : Optional[Any] = special_pt_names[flax_key]
if flax_key in pt_model_dict:
if flax_tensor.shape != pt_model_dict[flax_key].shape:
raise ValueError(
f"""Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected """
f"""to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
else:
# add weight to pytorch dict
UpperCAmelCase : Optional[int] = np.asarray(_lowerCAmelCase ) if not isinstance(_lowerCAmelCase , np.ndarray ) else flax_tensor
UpperCAmelCase : Optional[int] = torch.from_numpy(_lowerCAmelCase )
# remove from missing keys
missing_keys.remove(_lowerCAmelCase )
else:
# weight is not expected by PyTorch model
unexpected_keys.append(_lowerCAmelCase )
pt_model.load_state_dict(_lowerCAmelCase )
# re-transform missing_keys to list
UpperCAmelCase : Any = list(_lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
logger.warning(
'''Some weights of the Flax model were not used when initializing the PyTorch model'''
f""" {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing"""
f""" {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture"""
''' (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This'''
f""" IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect"""
''' to be exactly identical (e.g. initializing a BertForSequenceClassification model from a'''
''' FlaxBertForSequenceClassification model).''' )
else:
logger.warning(f"""All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n""" )
if len(_lowerCAmelCase ) > 0:
logger.warning(
f"""Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly"""
f""" initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to"""
''' use it for predictions and inference.''' )
else:
logger.warning(
f"""All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n"""
'''If your task is similar to the task the model of the checkpoint was trained on, '''
f"""you can already use {pt_model.__class__.__name__} for predictions without further training.""" )
return pt_model
| 23
|
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
_snake_case = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class UpperCAmelCase_ ( a):
def __init__( self, __a = 101):
'''simple docstring'''
_lowerCAmelCase : str = length
def __len__( self):
'''simple docstring'''
return self.length
def __getitem__( self, __a):
'''simple docstring'''
return i
class UpperCAmelCase_ :
def __call__( self, __a):
'''simple docstring'''
return {"input_ids": torch.tensor(__a), "labels": torch.tensor(__a)}
class UpperCAmelCase_ ( nn.Module):
def __init__( self):
'''simple docstring'''
super().__init__()
# Add some (unused) params otherwise DDP will complain.
_lowerCAmelCase : str = nn.Linear(120, 80)
def snake_case__ ( self, __a, __a=None):
'''simple docstring'''
if labels is not None:
return torch.tensor(0.0, device=input_ids.device), input_ids
else:
return input_ids
class UpperCAmelCase_ ( a):
@require_torch_neuroncore
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = f"--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Tuple = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : List[Any] = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
class UpperCAmelCase_ ( a):
@require_torch_multi_gpu
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = f"--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n ".split()
_lowerCAmelCase : Any = self.get_auto_remove_tmp_dir()
_lowerCAmelCase : Optional[int] = f"--output_dir {output_dir}".split()
_lowerCAmelCase : Any = ["torchrun"] + distributed_args + args
execute_subprocess_async(__a, env=self.get_env())
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
_snake_case = HfArgumentParser((TrainingArguments,))
_snake_case = parser.parse_args_into_dataclasses()[0]
logger.warning(
f'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
f'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
_snake_case = DummyDataset(dataset_length)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = list(range(len(_lowerCamelCase ) ) )
_lowerCAmelCase : Union[str, Any] = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"Predictions and/or labels do not match expected results:\n - predictions: "
F"{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}" )
return {"success": success}
_snake_case = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = 2
_snake_case = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
_snake_case = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
_snake_case = None
| 36
| 0
|
import unittest
import numpy as np
from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
)
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__(self : List[Any] , a__ : Optional[int] , a__ : Optional[int]=13 , a__ : List[Any]=7 , a__ : Dict=True , a__ : Optional[Any]=True , a__ : List[Any]=True , a__ : Optional[Any]=True , a__ : Optional[int]=99 , a__ : Optional[Any]=32 , a__ : List[str]=5 , a__ : Any=4 , a__ : str=37 , a__ : Optional[int]="gelu" , a__ : Optional[Any]=0.1 , a__ : Dict=0.1 , a__ : Any=512 , a__ : Union[str, Any]=16 , a__ : Any=2 , a__ : Optional[int]=0.0_2 , a__ : Optional[int]=4 , ):
"""simple docstring"""
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_attention_mask
__snake_case = use_token_type_ids
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = type_vocab_size
__snake_case = type_sequence_label_size
__snake_case = initializer_range
__snake_case = num_choices
def a (self : Union[str, Any] ):
"""simple docstring"""
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_attention_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case = None
if self.use_token_type_ids:
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case = RobertaPreLayerNormConfig(
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 , )
return config, input_ids, token_type_ids, attention_mask
def a (self : List[Any] ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
def a (self : Dict ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = True
__snake_case = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
__snake_case = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase , unittest.TestCase ):
A_ : Any = True
A_ : Optional[Any] = (
(
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
)
if is_flax_available()
else ()
)
def a (self : Dict ):
"""simple docstring"""
__snake_case = FlaxRobertaPreLayerNormModelTester(self )
@slow
def a (self : List[Any] ):
"""simple docstring"""
for model_class_name in self.all_model_classes:
__snake_case = model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=a__ )
__snake_case = model(np.ones((1, 1) ) )
self.assertIsNotNone(a__ )
@require_flax
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
@slow
def a (self : str ):
"""simple docstring"""
__snake_case = FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=a__ )
__snake_case = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
__snake_case = model(a__ )[0]
__snake_case = [1, 11, 5_0265]
self.assertEqual(list(output.shape ) , a__ )
# compare the actual values for a slice.
__snake_case = np.array(
[[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , a__ , atol=1E-4 ) )
@slow
def a (self : Any ):
"""simple docstring"""
__snake_case = FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40''' , from_pt=a__ )
__snake_case = np.array([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]] , dtype=jnp.intaa )
__snake_case = model(a__ )[0]
# compare the actual values for a slice.
__snake_case = np.array(
[[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]] , dtype=np.floataa )
self.assertTrue(np.allclose(output[:, :3, :3] , a__ , atol=1E-4 ) )
| 24
|
from __future__ import annotations
import bisect
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : int = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Optional[Any] = lo + (hi - lo) // 2
if sorted_collection[mid] < item:
_lowerCAmelCase : Union[str, Any] = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
if hi < 0:
_lowerCAmelCase : str = len(_lowerCamelCase )
while lo < hi:
_lowerCAmelCase : Tuple = lo + (hi - lo) // 2
if sorted_collection[mid] <= item:
_lowerCAmelCase : Dict = mid + 1
else:
_lowerCAmelCase : str = mid
return lo
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_left(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = 0 , _lowerCamelCase = -1 ):
'''simple docstring'''
sorted_collection.insert(bisect_right(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) , _lowerCamelCase )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 0
_lowerCAmelCase : Union[str, Any] = len(_lowerCamelCase ) - 1
while left <= right:
_lowerCAmelCase : int = left + (right - left) // 2
_lowerCAmelCase : int = sorted_collection[midpoint]
if current_item == item:
return midpoint
elif item < current_item:
_lowerCAmelCase : str = midpoint - 1
else:
_lowerCAmelCase : Any = midpoint + 1
return None
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = bisect.bisect_left(_lowerCamelCase , _lowerCamelCase )
if index != len(_lowerCamelCase ) and sorted_collection[index] == item:
return index
return None
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if right < left:
return None
_lowerCAmelCase : Optional[int] = left + (right - left) // 2
if sorted_collection[midpoint] == item:
return midpoint
elif sorted_collection[midpoint] > item:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , midpoint - 1 )
else:
return binary_search_by_recursion(_lowerCamelCase , _lowerCamelCase , midpoint + 1 , _lowerCamelCase )
if __name__ == "__main__":
_snake_case = input("Enter numbers separated by comma:\n").strip()
_snake_case = sorted(int(item) for item in user_input.split(","))
_snake_case = int(input("Enter a single number to be found in the list:\n"))
_snake_case = binary_search(collection, target)
if result is None:
print(f'''{target} was not found in {collection}.''')
else:
print(f'''{target} was found at position {result} in {collection}.''')
| 36
| 0
|
"""simple docstring"""
from tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require_tf, require_torch
@require_torch
@require_tf
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
def __magic_name__ (self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = SMALL_MODEL_IDENTIFIER
SCREAMING_SNAKE_CASE__ : str = """pt"""
SCREAMING_SNAKE_CASE__ : Dict = """tf"""
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = TFAutoModel.from_pretrained(self.test_model , from_pt=SCREAMING_SNAKE_CASE__ )
model_tf.save_pretrained(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = """mock_framework"""
# Framework provided - return whatever the user provides
SCREAMING_SNAKE_CASE__ : Tuple = FeaturesManager.determine_framework(self.test_model , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[int] = FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__ )
self.assertEqual(SCREAMING_SNAKE_CASE__ , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Dict = FeaturesManager.determine_framework(SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Union[str, Any] = MagicMock(return_value=SCREAMING_SNAKE_CASE__ )
with patch("""transformers.onnx.features.is_tf_available""" , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(SCREAMING_SNAKE_CASE__ , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
SCREAMING_SNAKE_CASE__ : Optional[int] = MagicMock(return_value=SCREAMING_SNAKE_CASE__ )
with patch("""transformers.onnx.features.is_torch_available""" , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Dict = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(SCREAMING_SNAKE_CASE__ , self.framework_tf )
# Both in environment -> use PyTorch
SCREAMING_SNAKE_CASE__ : List[str] = MagicMock(return_value=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[Any] = MagicMock(return_value=SCREAMING_SNAKE_CASE__ )
with patch("""transformers.onnx.features.is_tf_available""" , SCREAMING_SNAKE_CASE__ ), patch(
"""transformers.onnx.features.is_torch_available""" , SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Optional[int] = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(SCREAMING_SNAKE_CASE__ , self.framework_pt )
# Both not in environment -> raise error
SCREAMING_SNAKE_CASE__ : Any = MagicMock(return_value=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : List[str] = MagicMock(return_value=SCREAMING_SNAKE_CASE__ )
with patch("""transformers.onnx.features.is_tf_available""" , SCREAMING_SNAKE_CASE__ ), patch(
"""transformers.onnx.features.is_torch_available""" , SCREAMING_SNAKE_CASE__ ):
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
SCREAMING_SNAKE_CASE__ : Any = FeaturesManager.determine_framework(self.test_model )
| 25
|
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class UpperCAmelCase_ ( a):
def snake_case__ ( self, __a):
'''simple docstring'''
return 0.0
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = min([-20, np.min(fft_results[1 : samplerate // 2 - 1] )] )
_lowerCAmelCase : Optional[int] = max([20, np.max(fft_results[1 : samplerate // 2 - 1] )] )
return lowest, highest
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = 512
_lowerCAmelCase : Union[str, Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : Optional[Any] = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : int = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : str = np.abs(np.fft.fft(_lowerCamelCase ) )
_lowerCAmelCase : Union[str, Any] = 20 * np.logaa(_lowerCamelCase )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
# Display within reasonable bounds
_lowerCAmelCase : List[Any] = get_bounds(_lowerCamelCase , _lowerCamelCase )
plt.ylim(max([-80, bounds[0]] ) , min([80, bounds[1]] ) )
plt.ylabel("Gain (dB)" )
plt.plot(_lowerCamelCase )
plt.show()
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = 512
_lowerCAmelCase : Optional[Any] = [1] + [0] * (size - 1)
_lowerCAmelCase : str = [filter_type.process(_lowerCamelCase ) for item in inputs]
_lowerCAmelCase : Optional[Any] = [0] * (samplerate - size) # zero-padding
outputs += filler
_lowerCAmelCase : Optional[Any] = np.angle(np.fft.fft(_lowerCamelCase ) )
# Frequencies on log scale from 24 to nyquist frequency
plt.xlim(24 , samplerate / 2 - 1 )
plt.xlabel("Frequency (Hz)" )
plt.xscale("log" )
plt.ylim(-2 * pi , 2 * pi )
plt.ylabel("Phase shift (Radians)" )
plt.plot(np.unwrap(_lowerCamelCase , -2 * pi ) )
plt.show()
| 36
| 0
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
"The RoBERTa Model transformer with early exiting (DeeRoBERTa). ",UpperCamelCase__,)
class lowercase ( UpperCamelCase__ ):
_a = RobertaConfig
_a = "roberta"
def __init__( self , _a ) -> Optional[int]:
super().__init__(_a )
_A : Union[str, Any] = RobertaEmbeddings(_a )
self.init_weights()
@add_start_docstrings(
"RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ",UpperCamelCase__,)
class lowercase ( UpperCamelCase__ ):
_a = RobertaConfig
_a = "roberta"
def __init__( self , _a ) -> str:
super().__init__(_a )
_A : Any = config.num_labels
_A : Dict = config.num_hidden_layers
_A : List[str] = DeeRobertaModel(_a )
_A : int = nn.Dropout(config.hidden_dropout_prob )
_A : int = nn.Linear(config.hidden_size , self.config.num_labels )
@add_start_docstrings_to_model_forward(_a )
def a__ ( self , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , _a=None , _a=-1 , _a=False , ) -> Any:
_A : Optional[int] = self.num_layers
try:
_A : List[str] = self.roberta(
_a , attention_mask=_a , token_type_ids=_a , position_ids=_a , head_mask=_a , inputs_embeds=_a , )
_A : List[str] = outputs[1]
_A : List[str] = self.dropout(_a )
_A : Optional[Any] = self.classifier(_a )
_A : List[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_A : List[Any] = e.message
_A : Optional[int] = e.exit_layer
_A : Optional[int] = outputs[0]
if not self.training:
_A : int = entropy(_a )
_A : int = []
_A : int = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_A : Union[str, Any] = MSELoss()
_A : Tuple = loss_fct(logits.view(-1 ) , labels.view(-1 ) )
else:
_A : List[Any] = CrossEntropyLoss()
_A : Dict = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
# work with highway exits
_A : Optional[Any] = []
for highway_exit in outputs[-1]:
_A : Tuple = highway_exit[0]
if not self.training:
highway_logits_all.append(_a )
highway_entropy.append(highway_exit[2] )
if self.num_labels == 1:
# We are doing regression
_A : List[str] = MSELoss()
_A : Optional[int] = loss_fct(highway_logits.view(-1 ) , labels.view(-1 ) )
else:
_A : List[Any] = CrossEntropyLoss()
_A : Tuple = loss_fct(highway_logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
highway_losses.append(_a )
if train_highway:
_A : Dict = (sum(highway_losses[:-1] ),) + outputs
# exclude the final highway, of course
else:
_A : int = (loss,) + outputs
if not self.training:
_A : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_A : Union[str, Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 26
|
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count < 0:
raise ValueError("The given input must be positive" )
# get the generated string sequence
_lowerCAmelCase : List[str] = gray_code_sequence_string(_lowerCamelCase )
#
# convert them to integers
for i in range(len(_lowerCamelCase ) ):
_lowerCAmelCase : List[str] = int(sequence[i] , 2 )
return sequence
def A ( _lowerCamelCase ):
'''simple docstring'''
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
_lowerCAmelCase : List[Any] = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
_lowerCAmelCase : Optional[int] = gray_code_sequence_string(bit_count - 1 )
_lowerCAmelCase : str = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
_lowerCAmelCase : Dict = "0" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
_lowerCAmelCase : Optional[Any] = "1" + smaller_sequence[i]
sequence.append(_lowerCamelCase )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 36
| 0
|
'''simple docstring'''
import random
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
__a : Optional[Any] = num - 1
__a : List[str] = 0
while s % 2 == 0:
__a : Any = s // 2
t += 1
for _ in range(5 ):
__a : Tuple = random.randrange(2 , num - 1 )
__a : int = pow(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if v != 1:
__a : List[str] = 0
while v != (num - 1):
if i == t - 1:
return False
else:
__a : Union[str, Any] = i + 1
__a : Union[str, Any] = (v**2) % num
return True
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int ):
if num < 2:
return False
__a : str = [
2,
3,
5,
7,
11,
13,
17,
19,
23,
29,
31,
37,
41,
43,
47,
53,
59,
61,
67,
71,
73,
79,
83,
89,
97,
101,
103,
107,
109,
113,
127,
131,
137,
139,
149,
151,
157,
163,
167,
173,
179,
181,
191,
193,
197,
199,
211,
223,
227,
229,
233,
239,
241,
251,
257,
263,
269,
271,
277,
281,
283,
293,
307,
311,
313,
317,
331,
337,
347,
349,
353,
359,
367,
373,
379,
383,
389,
397,
401,
409,
419,
421,
431,
433,
439,
443,
449,
457,
461,
463,
467,
479,
487,
491,
499,
503,
509,
521,
523,
541,
547,
557,
563,
569,
571,
577,
587,
593,
599,
601,
607,
613,
617,
619,
631,
641,
643,
647,
653,
659,
661,
673,
677,
683,
691,
701,
709,
719,
727,
733,
739,
743,
751,
757,
761,
769,
773,
787,
797,
809,
811,
821,
823,
827,
829,
839,
853,
857,
859,
863,
877,
881,
883,
887,
907,
911,
919,
929,
937,
941,
947,
953,
967,
971,
977,
983,
991,
997,
]
if num in low_primes:
return True
for prime in low_primes:
if (num % prime) == 0:
return False
return rabin_miller(_SCREAMING_SNAKE_CASE )
def lowerCamelCase (_SCREAMING_SNAKE_CASE : int = 1_024 ):
while True:
__a : Any = random.randrange(2 ** (keysize - 1) , 2 ** (keysize) )
if is_prime_low_num(_SCREAMING_SNAKE_CASE ):
return num
if __name__ == "__main__":
__lowercase : List[str] = generate_large_prime()
print(('Prime number:', num))
print(('is_prime_low_num:', is_prime_low_num(num)))
| 27
|
from PIL import Image
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase : int = image.size
_lowerCAmelCase : Any = 0
_lowerCAmelCase : Tuple = image.load()
for i in range(_lowerCamelCase ):
for j in range(_lowerCamelCase ):
_lowerCAmelCase : Union[str, Any] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(_lowerCamelCase ):
for i in range(_lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = 255 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
_snake_case = mean_threshold(Image.open("path_to_image").convert("L"))
image.save("output_image_path")
| 36
| 0
|
'''simple docstring'''
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError("To use the rich extension, install rich with `pip install rich`")
| 28
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'wav2vec2'
def __init__( self, __a=32, __a=768, __a=12, __a=12, __a=3072, __a="gelu", __a=0.1, __a=0.1, __a=0.1, __a=0.0, __a=0.0, __a=0.1, __a=0.1, __a=0.02, __a=1E-5, __a="group", __a="gelu", __a=(512, 512, 512, 512, 512, 512, 512), __a=(5, 2, 2, 2, 2, 2, 2), __a=(10, 3, 3, 3, 3, 2, 2), __a=False, __a=128, __a=16, __a=False, __a=True, __a=0.05, __a=10, __a=2, __a=0.0, __a=10, __a=0, __a=320, __a=2, __a=0.1, __a=100, __a=256, __a=256, __a=0.1, __a="sum", __a=False, __a=False, __a=256, __a=(512, 512, 512, 512, 1500), __a=(5, 3, 3, 1, 1), __a=(1, 2, 3, 1, 1), __a=512, __a=0, __a=1, __a=2, __a=False, __a=3, __a=2, __a=3, __a=None, __a=None, **__a, ):
'''simple docstring'''
super().__init__(**__a, pad_token_id=__a, bos_token_id=__a, eos_token_id=__a)
_lowerCAmelCase : str = hidden_size
_lowerCAmelCase : Optional[int] = feat_extract_norm
_lowerCAmelCase : Union[str, Any] = feat_extract_activation
_lowerCAmelCase : Optional[Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : List[str] = conv_bias
_lowerCAmelCase : str = num_conv_pos_embeddings
_lowerCAmelCase : List[Any] = num_conv_pos_embedding_groups
_lowerCAmelCase : str = len(self.conv_dim)
_lowerCAmelCase : List[str] = num_hidden_layers
_lowerCAmelCase : str = intermediate_size
_lowerCAmelCase : Any = hidden_act
_lowerCAmelCase : int = num_attention_heads
_lowerCAmelCase : Optional[Any] = hidden_dropout
_lowerCAmelCase : List[str] = attention_dropout
_lowerCAmelCase : Tuple = activation_dropout
_lowerCAmelCase : int = feat_proj_dropout
_lowerCAmelCase : List[str] = final_dropout
_lowerCAmelCase : int = layerdrop
_lowerCAmelCase : int = layer_norm_eps
_lowerCAmelCase : Union[str, Any] = initializer_range
_lowerCAmelCase : str = vocab_size
_lowerCAmelCase : Optional[Any] = do_stable_layer_norm
_lowerCAmelCase : Any = use_weighted_layer_sum
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="
" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="
f" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"
f" `len(config.conv_kernel) = {len(self.conv_kernel)}`.")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
_lowerCAmelCase : str = apply_spec_augment
_lowerCAmelCase : Optional[Any] = mask_time_prob
_lowerCAmelCase : Optional[int] = mask_time_length
_lowerCAmelCase : List[str] = mask_time_min_masks
_lowerCAmelCase : Optional[int] = mask_feature_prob
_lowerCAmelCase : Optional[int] = mask_feature_length
_lowerCAmelCase : List[str] = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
_lowerCAmelCase : Union[str, Any] = num_codevectors_per_group
_lowerCAmelCase : str = num_codevector_groups
_lowerCAmelCase : Optional[int] = contrastive_logits_temperature
_lowerCAmelCase : Optional[int] = feat_quantizer_dropout
_lowerCAmelCase : Optional[int] = num_negatives
_lowerCAmelCase : Union[str, Any] = codevector_dim
_lowerCAmelCase : Any = proj_codevector_dim
_lowerCAmelCase : Optional[int] = diversity_loss_weight
# ctc loss
_lowerCAmelCase : Tuple = ctc_loss_reduction
_lowerCAmelCase : Tuple = ctc_zero_infinity
# adapter
_lowerCAmelCase : List[Any] = add_adapter
_lowerCAmelCase : List[str] = adapter_kernel_size
_lowerCAmelCase : str = adapter_stride
_lowerCAmelCase : List[str] = num_adapter_layers
_lowerCAmelCase : str = output_hidden_size or hidden_size
_lowerCAmelCase : Tuple = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
_lowerCAmelCase : str = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
_lowerCAmelCase : str = list(__a)
_lowerCAmelCase : Union[str, Any] = list(__a)
_lowerCAmelCase : List[str] = list(__a)
_lowerCAmelCase : Tuple = xvector_output_dim
@property
def snake_case__ ( self):
'''simple docstring'''
return functools.reduce(operator.mul, self.conv_stride, 1)
| 36
| 0
|
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__UpperCAmelCase = namedtuple(
'_TestCommandArgs',
[
'dataset',
'name',
'cache_dir',
'data_dir',
'all_configs',
'save_infos',
'ignore_verifications',
'force_redownload',
'clear_cache',
],
defaults=[None, None, None, False, False, False, False, False],
)
def lowercase__ ( __snake_case : int , __snake_case : Union[str, Any] ):
'''simple docstring'''
return (abs(source - target ) / target) < 0.01
@pytest.mark.integration
def lowercase__ ( __snake_case : Dict ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = _TestCommandArgs(dataset=__snake_case , all_configs=__snake_case , save_infos=__snake_case )
UpperCAmelCase_ : Optional[int] = TestCommand(*__snake_case )
test_command.run()
UpperCAmelCase_ : Optional[int] = os.path.join(__snake_case , 'README.md' )
assert os.path.exists(__snake_case )
UpperCAmelCase_ : str = DatasetInfosDict.from_directory(__snake_case )
UpperCAmelCase_ : List[str] = DatasetInfosDict(
{
'default': DatasetInfo(
features=Features(
{
'tokens': Sequence(Value('string' ) ),
'ner_tags': Sequence(
ClassLabel(names=['O', 'B-PER', 'I-PER', 'B-ORG', 'I-ORG', 'B-LOC', 'I-LOC'] ) ),
'langs': Sequence(Value('string' ) ),
'spans': Sequence(Value('string' ) ),
} ) , splits=[
{
'name': 'train',
'num_bytes': 2_351_563,
'num_examples': 10_000,
},
{
'name': 'validation',
'num_bytes': 238_418,
'num_examples': 1_000,
},
] , download_size=3_940_680 , dataset_size=2_589_981 , )
} )
assert dataset_infos.keys() == expected_dataset_infos.keys()
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
UpperCAmelCase_ , UpperCAmelCase_ : Optional[Any] = getattr(dataset_infos['default'] , __snake_case ), getattr(expected_dataset_infos['default'] , __snake_case )
if key == "num_bytes":
assert is_apercent_close(__snake_case , __snake_case )
elif key == "splits":
assert list(__snake_case ) == list(__snake_case )
for split in result:
assert result[split].name == expected[split].name
assert result[split].num_examples == expected[split].num_examples
assert is_apercent_close(result[split].num_bytes , expected[split].num_bytes )
else:
result == expected
| 29
|
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.roberta.modeling_roberta import (
ROBERTA_INPUTS_DOCSTRING,
ROBERTA_START_DOCSTRING,
RobertaEmbeddings,
)
from .modeling_highway_bert import BertPreTrainedModel, DeeBertModel, HighwayException, entropy
@add_start_docstrings(
'The RoBERTa Model transformer with early exiting (DeeRoBERTa). ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[Any] = RobertaEmbeddings(__a)
self.init_weights()
@add_start_docstrings(
'RoBERTa Model (with early exiting - DeeRoBERTa) with a classifier on top,\n also takes care of multi-layer training. ' , a , )
class UpperCAmelCase_ ( a):
lowerCamelCase__ = RobertaConfig
lowerCamelCase__ = 'roberta'
def __init__( self, __a):
'''simple docstring'''
super().__init__(__a)
_lowerCAmelCase : Optional[int] = config.num_labels
_lowerCAmelCase : Optional[int] = config.num_hidden_layers
_lowerCAmelCase : Optional[int] = DeeRobertaModel(__a)
_lowerCAmelCase : Union[str, Any] = nn.Dropout(config.hidden_dropout_prob)
_lowerCAmelCase : List[str] = nn.Linear(config.hidden_size, self.config.num_labels)
@add_start_docstrings_to_model_forward(__a)
def snake_case__ ( self, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=-1, __a=False, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = self.num_layers
try:
_lowerCAmelCase : List[Any] = self.roberta(
__a, attention_mask=__a, token_type_ids=__a, position_ids=__a, head_mask=__a, inputs_embeds=__a, )
_lowerCAmelCase : List[Any] = outputs[1]
_lowerCAmelCase : Dict = self.dropout(__a)
_lowerCAmelCase : Dict = self.classifier(__a)
_lowerCAmelCase : Optional[Any] = (logits,) + outputs[2:] # add hidden states and attention if they are here
except HighwayException as e:
_lowerCAmelCase : Tuple = e.message
_lowerCAmelCase : Union[str, Any] = e.exit_layer
_lowerCAmelCase : List[Any] = outputs[0]
if not self.training:
_lowerCAmelCase : int = entropy(__a)
_lowerCAmelCase : List[Any] = []
_lowerCAmelCase : str = []
if labels is not None:
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : Optional[Any] = MSELoss()
_lowerCAmelCase : int = loss_fct(logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Optional[Any] = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
# work with highway exits
_lowerCAmelCase : Optional[int] = []
for highway_exit in outputs[-1]:
_lowerCAmelCase : Any = highway_exit[0]
if not self.training:
highway_logits_all.append(__a)
highway_entropy.append(highway_exit[2])
if self.num_labels == 1:
# We are doing regression
_lowerCAmelCase : List[str] = MSELoss()
_lowerCAmelCase : List[Any] = loss_fct(highway_logits.view(-1), labels.view(-1))
else:
_lowerCAmelCase : Dict = CrossEntropyLoss()
_lowerCAmelCase : Optional[Any] = loss_fct(highway_logits.view(-1, self.num_labels), labels.view(-1))
highway_losses.append(__a)
if train_highway:
_lowerCAmelCase : int = (sum(highway_losses[:-1]),) + outputs
# exclude the final highway, of course
else:
_lowerCAmelCase : Any = (loss,) + outputs
if not self.training:
_lowerCAmelCase : Optional[Any] = outputs + ((original_entropy, highway_entropy), exit_layer)
if output_layer >= 0:
_lowerCAmelCase : Optional[Any] = (
(outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
) # use the highway of the last layer
return outputs # (loss), logits, (hidden_states), (attentions), entropy
| 36
| 0
|
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class lowercase__:
"""simple docstring"""
def _lowercase ( self : List[Any] , SCREAMING_SNAKE_CASE_ : List[Any] ) -> Tuple:
raise NotImplementedError()
def _lowercase ( self : Dict ) -> str:
raise NotImplementedError()
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Any , SCREAMING_SNAKE_CASE_ : "AutoTokenizer" , SCREAMING_SNAKE_CASE_ : bool = False , **SCREAMING_SNAKE_CASE_ : Optional[Any] ) -> int:
lowercase_ = tokenizer
lowercase_ = skip_prompt
lowercase_ = decode_kwargs
# variables used in the streaming process
lowercase_ = []
lowercase_ = 0
lowercase_ = True
def _lowercase ( self : Optional[int] , SCREAMING_SNAKE_CASE_ : str ) -> Optional[Any]:
if len(value.shape ) > 1 and value.shape[0] > 1:
raise ValueError('''TextStreamer only supports batch size 1''' )
elif len(value.shape ) > 1:
lowercase_ = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
lowercase_ = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist() )
lowercase_ = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
# After the symbol for a new line, we flush the cache.
if text.endswith('''\n''' ):
lowercase_ = text[self.print_len :]
lowercase_ = []
lowercase_ = 0
# If the last token is a CJK character, we print the characters.
elif len(SCREAMING_SNAKE_CASE_ ) > 0 and self._is_chinese_char(ord(text[-1] ) ):
lowercase_ = text[self.print_len :]
self.print_len += len(SCREAMING_SNAKE_CASE_ )
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
lowercase_ = text[self.print_len : text.rfind(''' ''' ) + 1]
self.print_len += len(SCREAMING_SNAKE_CASE_ )
self.on_finalized_text(SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : Union[str, Any] ) -> Tuple:
# Flush the cache, if it exists
if len(self.token_cache ) > 0:
lowercase_ = self.tokenizer.decode(self.token_cache , **self.decode_kwargs )
lowercase_ = text[self.print_len :]
lowercase_ = []
lowercase_ = 0
else:
lowercase_ = ''''''
lowercase_ = True
self.on_finalized_text(SCREAMING_SNAKE_CASE_ , stream_end=SCREAMING_SNAKE_CASE_ )
def _lowercase ( self : int , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : bool = False ) -> int:
print(SCREAMING_SNAKE_CASE_ , flush=SCREAMING_SNAKE_CASE_ , end='''''' if not stream_end else None )
def _lowercase ( self : List[str] , SCREAMING_SNAKE_CASE_ : int ) -> Optional[Any]:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4_e_0_0 and cp <= 0x9_f_f_f)
or (cp >= 0x3_4_0_0 and cp <= 0x4_d_b_f) #
or (cp >= 0x2_0_0_0_0 and cp <= 0x2_a_6_d_f) #
or (cp >= 0x2_a_7_0_0 and cp <= 0x2_b_7_3_f) #
or (cp >= 0x2_b_7_4_0 and cp <= 0x2_b_8_1_f) #
or (cp >= 0x2_b_8_2_0 and cp <= 0x2_c_e_a_f) #
or (cp >= 0xf_9_0_0 and cp <= 0xf_a_f_f)
or (cp >= 0x2_f_8_0_0 and cp <= 0x2_f_a_1_f) #
): #
return True
return False
class lowercase__( UpperCAmelCase ):
"""simple docstring"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : "AutoTokenizer" , SCREAMING_SNAKE_CASE_ : bool = False , SCREAMING_SNAKE_CASE_ : Optional[float] = None , **SCREAMING_SNAKE_CASE_ : List[str] ) -> Any:
super().__init__(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ )
lowercase_ = Queue()
lowercase_ = None
lowercase_ = timeout
def _lowercase ( self : Dict , SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : bool = False ) -> Tuple:
self.text_queue.put(SCREAMING_SNAKE_CASE_ , timeout=self.timeout )
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout )
def __iter__( self : Tuple ) -> Union[str, Any]:
return self
def _lowercase ( self : List[Any] ) -> List[str]:
lowercase_ = self.text_queue.get(timeout=self.timeout )
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 30
|
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
_snake_case = logging.get_logger(__name__)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'vision-encoder-decoder'
lowerCamelCase__ = True
def __init__( self, **__a):
'''simple docstring'''
super().__init__(**__a)
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f"A configuraton of type {self.model_type} cannot be instantiated because "
f"not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}")
_lowerCAmelCase : str = kwargs.pop("encoder")
_lowerCAmelCase : Any = encoder_config.pop("model_type")
_lowerCAmelCase : str = kwargs.pop("decoder")
_lowerCAmelCase : List[str] = decoder_config.pop("model_type")
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[Any] = AutoConfig.for_model(__a, **__a)
_lowerCAmelCase : Optional[int] = True
@classmethod
def snake_case__ ( cls, __a, __a, **__a):
'''simple docstring'''
logger.info("Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config")
_lowerCAmelCase : Optional[Any] = True
_lowerCAmelCase : str = True
return cls(encoder=encoder_config.to_dict(), decoder=decoder_config.to_dict(), **__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = copy.deepcopy(self.__dict__)
_lowerCAmelCase : List[str] = self.encoder.to_dict()
_lowerCAmelCase : List[str] = self.decoder.to_dict()
_lowerCAmelCase : Any = self.__class__.model_type
return output
class UpperCAmelCase_ ( a):
lowerCamelCase__ = version.parse('1.11')
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
])
@property
def snake_case__ ( self):
'''simple docstring'''
return 1E-4
@property
def snake_case__ ( self):
'''simple docstring'''
return OrderedDict({"last_hidden_state": {0: "batch", 1: "encoder_sequence"}})
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : List[str] = {0: "batch", 1: "past_decoder_sequence + sequence"}
_lowerCAmelCase : Optional[Any] = {0: "batch", 1: "encoder_sequence"}
return common_inputs
def snake_case__ ( self, __a, __a = -1, __a = -1, __a = False, __a = None, ):
'''simple docstring'''
import torch
_lowerCAmelCase : Optional[Any] = OrderedDict()
_lowerCAmelCase : List[str] = super().generate_dummy_inputs(
__a, batch_size=__a, seq_length=__a, is_pair=__a, framework=__a)
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = dummy_input["input_ids"].shape
_lowerCAmelCase : str = (batch, encoder_sequence, self._config.encoder_hidden_size)
_lowerCAmelCase : List[str] = dummy_input.pop("input_ids")
_lowerCAmelCase : List[str] = dummy_input.pop("attention_mask")
_lowerCAmelCase : Optional[int] = torch.zeros(__a)
return common_inputs
class UpperCAmelCase_ ( a):
@property
def snake_case__ ( self):
'''simple docstring'''
pass
def snake_case__ ( self, __a):
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(__a)
def snake_case__ ( self, __a, __a, __a = "default"):
'''simple docstring'''
_lowerCAmelCase : Dict = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__a, __a)
| 36
| 0
|
'''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
|
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class UpperCAmelCase_ ( a):
def __get__( self, __a, __a=None):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute")
_lowerCAmelCase : List[Any] = "__cached_" + self.fget.__name__
_lowerCAmelCase : Dict = getattr(__a, __a, __a)
if cached is None:
_lowerCAmelCase : str = self.fget(__a)
setattr(__a, __a, __a)
return cached
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F"invalid truth value {val!r}" )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_torch_fx_proxy(_lowerCamelCase ):
return True
if is_torch_available():
import torch
if isinstance(_lowerCamelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(_lowerCamelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(_lowerCamelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return isinstance(_lowerCamelCase , np.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return _is_numpy(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
return isinstance(_lowerCamelCase , torch.device )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_device(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import torch
if isinstance(_lowerCamelCase , _lowerCamelCase ):
if hasattr(_lowerCamelCase , _lowerCamelCase ):
_lowerCAmelCase : Optional[Any] = getattr(_lowerCamelCase , _lowerCamelCase )
else:
return False
return isinstance(_lowerCamelCase , torch.dtype )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_torch_available() else _is_torch_dtype(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
return isinstance(_lowerCamelCase , tf.Tensor )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tensorflow(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(_lowerCamelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(_lowerCamelCase )
return type(_lowerCamelCase ) == tf.Tensor
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_tf_available() else _is_tf_symbolic_tensor(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
import jax.numpy as jnp # noqa: F811
return isinstance(_lowerCamelCase , jnp.ndarray )
def A ( _lowerCamelCase ):
'''simple docstring'''
return False if not is_flax_available() else _is_jax(_lowerCamelCase )
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_py_obj(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return [to_py_obj(_lowerCamelCase ) for o in obj]
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase ).tolist()
elif isinstance(_lowerCamelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def A ( _lowerCamelCase ):
'''simple docstring'''
if isinstance(_lowerCamelCase , (dict, UserDict) ):
return {k: to_numpy(_lowerCamelCase ) for k, v in obj.items()}
elif isinstance(_lowerCamelCase , (list, tuple) ):
return np.array(_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
return obj.numpy()
elif is_torch_tensor(_lowerCamelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(_lowerCamelCase ):
return np.asarray(_lowerCamelCase )
else:
return obj
class UpperCAmelCase_ ( a):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = fields(self)
# Safety and consistency checks
if not len(__a):
raise ValueError(f"{self.__class__.__name__} has no fields.")
if not all(field.default is None for field in class_fields[1:]):
raise ValueError(f"{self.__class__.__name__} should not have more than one required field.")
_lowerCAmelCase : Dict = getattr(self, class_fields[0].name)
_lowerCAmelCase : str = all(getattr(self, field.name) is None for field in class_fields[1:])
if other_fields_are_none and not is_tensor(__a):
if isinstance(__a, __a):
_lowerCAmelCase : Tuple = first_field.items()
_lowerCAmelCase : Dict = True
else:
try:
_lowerCAmelCase : Dict = iter(__a)
_lowerCAmelCase : Any = True
except TypeError:
_lowerCAmelCase : Any = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(__a):
if (
not isinstance(__a, (list, tuple))
or not len(__a) == 2
or not isinstance(element[0], __a)
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_lowerCAmelCase : Any = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f"Cannot set key/value for {element}. It needs to be a tuple (key, value).")
break
setattr(self, element[0], element[1])
if element[1] is not None:
_lowerCAmelCase : Any = element[1]
elif first_field is not None:
_lowerCAmelCase : Any = first_field
else:
for field in class_fields:
_lowerCAmelCase : Dict = getattr(self, field.name)
if v is not None:
_lowerCAmelCase : Union[str, Any] = v
def __delitem__( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance.")
def snake_case__ ( self, *__a, **__a):
'''simple docstring'''
raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance.")
def __getitem__( self, __a):
'''simple docstring'''
if isinstance(__a, __a):
_lowerCAmelCase : Optional[int] = dict(self.items())
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self, __a, __a):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(__a, __a)
super().__setattr__(__a, __a)
def __setitem__( self, __a, __a):
'''simple docstring'''
super().__setitem__(__a, __a)
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(__a, __a)
def snake_case__ ( self):
'''simple docstring'''
return tuple(self[k] for k in self.keys())
class UpperCAmelCase_ ( a , a):
@classmethod
def snake_case__ ( cls, __a):
'''simple docstring'''
raise ValueError(
f"{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys())}")
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'longest'
lowerCamelCase__ = 'max_length'
lowerCamelCase__ = 'do_not_pad'
class UpperCAmelCase_ ( a):
lowerCamelCase__ = 'pt'
lowerCamelCase__ = 'tf'
lowerCamelCase__ = 'np'
lowerCamelCase__ = 'jax'
class UpperCAmelCase_ :
def __init__( self, __a):
'''simple docstring'''
_lowerCAmelCase : Tuple = context_managers
_lowerCAmelCase : Dict = ExitStack()
def __enter__( self):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(__a)
def __exit__( self, *__a, **__a):
'''simple docstring'''
self.stack.__exit__(*__a, **__a)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Tuple = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : str = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : str = model_class.__name__
_lowerCAmelCase : Optional[Any] = infer_framework(_lowerCamelCase )
if framework == "tf":
_lowerCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_lowerCAmelCase : List[Any] = inspect.signature(model_class.forward ) # PyTorch models
else:
_lowerCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def A ( _lowerCamelCase , _lowerCamelCase = "" , _lowerCamelCase = "." ):
'''simple docstring'''
def _flatten_dict(_lowerCamelCase , _lowerCamelCase="" , _lowerCamelCase="." ):
for k, v in d.items():
_lowerCAmelCase : Dict = str(_lowerCamelCase ) + delimiter + str(_lowerCamelCase ) if parent_key else k
if v and isinstance(_lowerCamelCase , _lowerCamelCase ):
yield from flatten_dict(_lowerCamelCase , _lowerCamelCase , delimiter=_lowerCamelCase ).items()
else:
yield key, v
return dict(_flatten_dict(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
@contextmanager
def A ( _lowerCamelCase , _lowerCamelCase = False ):
'''simple docstring'''
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.transpose(_lowerCamelCase , axes=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.T if axes is None else array.permute(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.transpose(_lowerCamelCase , perm=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.transpose(_lowerCamelCase , axes=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for transpose: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.reshape(*_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.reshape(_lowerCamelCase , _lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.reshape(_lowerCamelCase , _lowerCamelCase )
else:
raise ValueError(F"Type not supported for reshape: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase=None ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.squeeze(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for squeeze: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.expand_dims(_lowerCamelCase , _lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.unsqueeze(dim=_lowerCamelCase )
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return jnp.expand_dims(_lowerCamelCase , axis=_lowerCamelCase )
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase ):
'''simple docstring'''
if is_numpy_array(_lowerCamelCase ):
return np.size(_lowerCamelCase )
elif is_torch_tensor(_lowerCamelCase ):
return array.numel()
elif is_tf_tensor(_lowerCamelCase ):
import tensorflow as tf
return tf.size(_lowerCamelCase )
elif is_jax_tensor(_lowerCamelCase ):
return array.size
else:
raise ValueError(F"Type not supported for expand_dims: {type(_lowerCamelCase )}." )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
for key, value in auto_map.items():
if isinstance(_lowerCamelCase , (tuple, list) ):
_lowerCAmelCase : List[Any] = [F"{repo_id}--{v}" if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_lowerCAmelCase : Tuple = F"{repo_id}--{value}"
return auto_map
def A ( _lowerCamelCase ):
'''simple docstring'''
for base_class in inspect.getmro(_lowerCamelCase ):
_lowerCAmelCase : Tuple = base_class.__module__
_lowerCAmelCase : int = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F"Could not infer framework from class {model_class}." )
| 36
| 0
|
from manim import *
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
def SCREAMING_SNAKE_CASE ( self : Any ) -> Optional[int]:
a_ : Optional[int] = Rectangle(height=0.5 , width=0.5 )
a_ : List[Any] = Rectangle(height=0.25 , width=0.25 )
a_ : Optional[Any] = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
a_ : str = [mem.copy() for i in range(6 )]
a_ : Tuple = [mem.copy() for i in range(6 )]
a_ : Any = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : int = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Optional[Any] = VGroup(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Optional[Any] = Text('CPU' , font_size=2_4 )
a_ : Any = Group(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(SCREAMING_SNAKE_CASE__ )
a_ : Union[str, Any] = [mem.copy() for i in range(4 )]
a_ : List[Any] = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Any = Text('GPU' , font_size=2_4 )
a_ : Optional[Any] = Group(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE__ )
gpu.move_to([-1, -1, 0] )
self.add(SCREAMING_SNAKE_CASE__ )
a_ : Optional[Any] = [mem.copy() for i in range(6 )]
a_ : List[Any] = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : List[str] = Text('Model' , font_size=2_4 )
a_ : int = Group(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE__ )
model.move_to([3, -1.0, 0] )
self.add(SCREAMING_SNAKE_CASE__ )
a_ : Dict = []
a_ : str = []
a_ : int = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE__ ):
rect.set_stroke(SCREAMING_SNAKE_CASE__ )
a_ : int = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(SCREAMING_SNAKE_CASE__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=SCREAMING_SNAKE_CASE__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(model_cpu_arr[0] , direction=SCREAMING_SNAKE_CASE__ , buff=0.0 )
else:
cpu_target.next_to(model_cpu_arr[i - 1] , direction=SCREAMING_SNAKE_CASE__ , buff=0.0 )
self.add(SCREAMING_SNAKE_CASE__ )
model_cpu_arr.append(SCREAMING_SNAKE_CASE__ )
self.add(*SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ )
a_ : Tuple = [mem.copy() for i in range(6 )]
a_ : Union[str, Any] = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Dict = Text('Loaded Checkpoint' , font_size=2_4 )
a_ : str = Group(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE__ )
checkpoint.move_to([3, 0.5, 0] )
self.add(SCREAMING_SNAKE_CASE__ )
a_ : Dict = []
a_ : Optional[int] = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE__ ):
a_ : Union[str, Any] = fill.copy().set_fill(SCREAMING_SNAKE_CASE__ , opacity=0.7 )
target.move_to(SCREAMING_SNAKE_CASE__ )
ckpt_arr.append(SCREAMING_SNAKE_CASE__ )
a_ : List[str] = target.copy()
if i < 5:
cpu_target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.move_to(cpu_right_col_base[i - 5] )
ckpt_cpu_arr.append(SCREAMING_SNAKE_CASE__ )
self.add(*SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ )
a_ : List[str] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
a_ : Optional[Any] = MarkupText(
F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=1_8 , )
key_text.move_to([-5, 2.4, 0] )
self.add(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
a_ : Optional[int] = MarkupText(
F"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=1_8 , )
blue_text.next_to(SCREAMING_SNAKE_CASE__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
self.add(SCREAMING_SNAKE_CASE__ )
a_ : str = MarkupText(
F"""Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.""" , font_size=2_4 , )
step_a.move_to([2, 2, 0] )
a_ : List[Any] = [meta_mem.copy() for i in range(6 )]
a_ : Optional[Any] = [meta_mem.copy() for i in range(6 )]
a_ : int = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Optional[int] = VGroup(*SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Tuple = VGroup(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0 )
a_ : Dict = Text('Disk' , font_size=2_4 )
a_ : Optional[Any] = Group(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ).arrange(SCREAMING_SNAKE_CASE__ , buff=0.5 , aligned_edge=SCREAMING_SNAKE_CASE__ )
disk.move_to([-4.0, -1.25, 0] )
self.play(Write(SCREAMING_SNAKE_CASE__ , run_time=3 ) , Write(SCREAMING_SNAKE_CASE__ , run_time=1 ) , Create(SCREAMING_SNAKE_CASE__ , run_time=1 ) )
a_ : List[Any] = []
for i, rect in enumerate(SCREAMING_SNAKE_CASE__ ):
a_ : List[str] = rect.copy()
target.generate_target()
target.target.move_to(disk_left_col_base[i] ).scale(0.5 )
animations.append(MoveToTarget(SCREAMING_SNAKE_CASE__ , run_time=1.5 ) )
self.play(*SCREAMING_SNAKE_CASE__ )
self.play(FadeOut(SCREAMING_SNAKE_CASE__ ) )
a_ : Optional[Any] = MarkupText(F"""Then, the checkpoint is removed from memory\nthrough garbage collection.""" , font_size=2_4 )
step_a.move_to([2, 2, 0] )
self.play(Write(SCREAMING_SNAKE_CASE__ , run_time=3 ) )
self.play(
FadeOut(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ ) , )
self.wait()
| 32
|
import pytest
from datasets.utils.sharding import _distribute_shards, _number_of_shards_in_gen_kwargs, _split_gen_kwargs
@pytest.mark.parametrize(
"kwargs, expected" , [
({"num_shards": 0, "max_num_jobs": 1}, []),
({"num_shards": 10, "max_num_jobs": 1}, [range(10 )]),
({"num_shards": 10, "max_num_jobs": 10}, [range(_lowerCamelCase , i + 1 ) for i in range(10 )]),
({"num_shards": 1, "max_num_jobs": 10}, [range(1 )]),
({"num_shards": 10, "max_num_jobs": 3}, [range(0 , 4 ), range(4 , 7 ), range(7 , 10 )]),
({"num_shards": 3, "max_num_jobs": 10}, [range(0 , 1 ), range(1 , 2 ), range(2 , 3 )]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = _distribute_shards(**_lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, max_num_jobs, expected" , [
({"foo": 0}, 10, [{"foo": 0}]),
({"shards": [0, 1, 2, 3]}, 1, [{"shards": [0, 1, 2, 3]}]),
({"shards": [0, 1, 2, 3]}, 4, [{"shards": [0]}, {"shards": [1]}, {"shards": [2]}, {"shards": [3]}]),
({"shards": [0, 1]}, 4, [{"shards": [0]}, {"shards": [1]}]),
({"shards": [0, 1, 2, 3]}, 2, [{"shards": [0, 1]}, {"shards": [2, 3]}]),
] , )
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = _split_gen_kwargs(_lowerCamelCase , _lowerCamelCase )
assert out == expected
@pytest.mark.parametrize(
"gen_kwargs, expected" , [
({"foo": 0}, 1),
({"shards": [0]}, 1),
({"shards": [0, 1, 2, 3]}, 4),
({"shards": [0, 1, 2, 3], "foo": 0}, 4),
({"shards": [0, 1, 2, 3], "other": (0, 1)}, 4),
({"shards": [0, 1, 2, 3], "shards2": [0, 1]}, RuntimeError),
] , )
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
if expected is RuntimeError:
with pytest.raises(_lowerCamelCase ):
_number_of_shards_in_gen_kwargs(_lowerCamelCase )
else:
_lowerCAmelCase : Optional[int] = _number_of_shards_in_gen_kwargs(_lowerCamelCase )
assert out == expected
| 36
| 0
|
"""simple docstring"""
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
SCREAMING_SNAKE_CASE_ : int
SCREAMING_SNAKE_CASE_ : TreeNode | None = None
SCREAMING_SNAKE_CASE_ : TreeNode | None = None
__A : List[str] = namedtuple('''CoinsDistribResult''', '''moves excess''')
def lowercase ( __snake_case : TreeNode | None ):
if root is None:
return 0
# Validation
def count_nodes(__snake_case : TreeNode | None ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__snake_case : TreeNode | None ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(__snake_case ) != count_coins(__snake_case ):
raise ValueError('''The nodes number should be same as the number of coins''' )
# Main calculation
def get_distrib(__snake_case : TreeNode | None ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase_ , lowercase_ : List[Any] = get_distrib(node.left )
lowercase_ , lowercase_ : Optional[Any] = get_distrib(node.right )
lowercase_ : Any = 1 - left_distrib_excess
lowercase_ : List[Any] = 1 - right_distrib_excess
lowercase_ : Optional[int] = (
left_distrib_moves
+ right_distrib_moves
+ abs(__snake_case )
+ abs(__snake_case )
)
lowercase_ : Any = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(__snake_case , __snake_case )
return get_distrib(__snake_case )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 33
|
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenizerFast
from utils import get_device, get_timestamp, show_pil
class UpperCAmelCase_ :
def __init__( self, __a = "cpu", __a = "openai/clip-vit-large-patch14"):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = device
_lowerCAmelCase : Optional[int] = CLIPTokenizerFast.from_pretrained(__a)
_lowerCAmelCase : Any = [0.48_145_466, 0.4_578_275, 0.40_821_073]
_lowerCAmelCase : Union[str, Any] = [0.26_862_954, 0.26_130_258, 0.27_577_711]
_lowerCAmelCase : Tuple = torchvision.transforms.Normalize(self.image_mean, self.image_std)
_lowerCAmelCase : Optional[int] = torchvision.transforms.Resize(224)
_lowerCAmelCase : Dict = torchvision.transforms.CenterCrop(224)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.resize(__a)
_lowerCAmelCase : List[str] = self.center_crop(__a)
_lowerCAmelCase : Optional[Any] = self.normalize(__a)
return images
def __call__( self, __a=None, __a=None, **__a):
'''simple docstring'''
_lowerCAmelCase : str = self.tokenizer(text=__a, **__a)
_lowerCAmelCase : List[str] = self.preprocess_img(__a)
_lowerCAmelCase : Tuple = {key: value.to(self.device) for (key, value) in encoding.items()}
return encoding
class UpperCAmelCase_ ( nn.Module):
def __init__( self, __a=10, __a=0.01, __a=None, __a=None, __a=None, __a=None, __a=None, __a=None, __a=False, __a=True, __a="image", __a=True, __a=False, __a=False, __a=False, ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase : List[str] = None
_lowerCAmelCase : List[str] = device if device else get_device()
if vqgan:
_lowerCAmelCase : Union[str, Any] = vqgan
else:
_lowerCAmelCase : Optional[Any] = load_vqgan(self.device, conf_path=__a, ckpt_path=__a)
self.vqgan.eval()
if clip:
_lowerCAmelCase : str = clip
else:
_lowerCAmelCase : int = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
self.clip.to(self.device)
_lowerCAmelCase : Optional[int] = ProcessorGradientFlow(device=self.device)
_lowerCAmelCase : Any = iterations
_lowerCAmelCase : List[Any] = lr
_lowerCAmelCase : Tuple = log
_lowerCAmelCase : List[str] = make_grid
_lowerCAmelCase : int = return_val
_lowerCAmelCase : Dict = quantize
_lowerCAmelCase : Any = self.vqgan.decoder.z_shape
def snake_case__ ( self, __a=None, __a=None, __a=5, __a=True):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = []
if output_path is None:
_lowerCAmelCase : List[Any] = "./animation.gif"
if input_path is None:
_lowerCAmelCase : str = self.save_path
_lowerCAmelCase : str = sorted(glob(input_path + "/*"))
if not len(__a):
raise ValueError(
"No images found in save path, aborting (did you pass save_intermediate=True to the generate"
" function?)")
if len(__a) == 1:
print("Only one image found in save path, (did you pass save_intermediate=True to the generate function?)")
_lowerCAmelCase : Optional[int] = total_duration / len(__a)
_lowerCAmelCase : Union[str, Any] = [frame_duration] * len(__a)
if extend_frames:
_lowerCAmelCase : Any = 1.5
_lowerCAmelCase : List[str] = 3
for file_name in paths:
if file_name.endswith(".png"):
images.append(imageio.imread(__a))
imageio.mimsave(__a, __a, duration=__a)
print(f"gif saved to {output_path}")
def snake_case__ ( self, __a=None, __a=None):
'''simple docstring'''
if not (path or img):
raise ValueError("Input either path or tensor")
if img is not None:
raise NotImplementedError
_lowerCAmelCase : Dict = preprocess(Image.open(__a), target_image_size=256).to(self.device)
_lowerCAmelCase : Dict = preprocess_vqgan(__a)
_lowerCAmelCase , *_lowerCAmelCase : str = self.vqgan.encode(__a)
return z
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.latent.detach().requires_grad_()
_lowerCAmelCase : Dict = base_latent + transform_vector
if self.quantize:
_lowerCAmelCase , *_lowerCAmelCase : List[Any] = self.vqgan.quantize(__a)
else:
_lowerCAmelCase : Any = trans_latent
return self.vqgan.decode(__a)
def snake_case__ ( self, __a, __a, __a=None):
'''simple docstring'''
_lowerCAmelCase : int = self.clip_preprocessor(text=__a, images=__a, return_tensors="pt", padding=__a)
_lowerCAmelCase : Optional[int] = self.clip(**__a)
_lowerCAmelCase : Any = clip_outputs.logits_per_image
if weights is not None:
_lowerCAmelCase : Tuple = similarity_logits * weights
return similarity_logits.sum()
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self._get_clip_similarity(pos_prompts["prompts"], __a, weights=(1 / pos_prompts["weights"]))
if neg_prompts:
_lowerCAmelCase : List[Any] = self._get_clip_similarity(neg_prompts["prompts"], __a, weights=neg_prompts["weights"])
else:
_lowerCAmelCase : Union[str, Any] = torch.tensor([1], device=self.device)
_lowerCAmelCase : List[str] = -torch.log(__a) + torch.log(__a)
return loss
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = torch.randn_like(self.latent, requires_grad=__a, device=self.device)
_lowerCAmelCase : Optional[int] = torch.optim.Adam([vector], lr=self.lr)
for i in range(self.iterations):
optim.zero_grad()
_lowerCAmelCase : Any = self._add_vector(__a)
_lowerCAmelCase : Optional[Any] = loop_post_process(__a)
_lowerCAmelCase : Optional[Any] = self._get_CLIP_loss(__a, __a, __a)
print("CLIP loss", __a)
if self.log:
wandb.log({"CLIP Loss": clip_loss})
clip_loss.backward(retain_graph=__a)
optim.step()
if self.return_val == "image":
yield custom_to_pil(transformed_img[0])
else:
yield vector
def snake_case__ ( self, __a, __a, __a):
'''simple docstring'''
wandb.init(reinit=__a, project="face-editor")
wandb.config.update({"Positive Prompts": positive_prompts})
wandb.config.update({"Negative Prompts": negative_prompts})
wandb.config.update({"lr": self.lr, "iterations": self.iterations})
if image_path:
_lowerCAmelCase : str = Image.open(__a)
_lowerCAmelCase : int = image.resize((256, 256))
wandb.log("Original Image", wandb.Image(__a))
def snake_case__ ( self, __a):
'''simple docstring'''
if not prompts:
return []
_lowerCAmelCase : int = []
_lowerCAmelCase : List[str] = []
if isinstance(__a, __a):
_lowerCAmelCase : Union[str, Any] = [prompt.strip() for prompt in prompts.split("|")]
for prompt in prompts:
if isinstance(__a, (tuple, list)):
_lowerCAmelCase : Optional[Any] = prompt[0]
_lowerCAmelCase : Union[str, Any] = float(prompt[1])
elif ":" in prompt:
_lowerCAmelCase , _lowerCAmelCase : int = prompt.split(":")
_lowerCAmelCase : Optional[Any] = float(__a)
else:
_lowerCAmelCase : Optional[int] = prompt
_lowerCAmelCase : List[Any] = 1.0
processed_prompts.append(__a)
weights.append(__a)
return {
"prompts": processed_prompts,
"weights": torch.tensor(__a, device=self.device),
}
def snake_case__ ( self, __a, __a=None, __a=None, __a=True, __a=False, __a=True, __a=True, __a=None, ):
'''simple docstring'''
if image_path:
_lowerCAmelCase : List[Any] = self._get_latent(__a)
else:
_lowerCAmelCase : Any = torch.randn(self.latent_dim, device=self.device)
if self.log:
self._init_logging(__a, __a, __a)
assert pos_prompts, "You must provide at least one positive prompt."
_lowerCAmelCase : int = self.process_prompts(__a)
_lowerCAmelCase : List[str] = self.process_prompts(__a)
if save_final and save_path is None:
_lowerCAmelCase : int = os.path.join("./outputs/", "_".join(pos_prompts["prompts"]))
if not os.path.exists(__a):
os.makedirs(__a)
else:
_lowerCAmelCase : Tuple = save_path + "_" + get_timestamp()
os.makedirs(__a)
_lowerCAmelCase : Tuple = save_path
_lowerCAmelCase : List[Any] = self.vqgan.decode(self.latent)[0]
if show_intermediate:
print("Original Image")
show_pil(custom_to_pil(__a))
_lowerCAmelCase : int = loop_post_process(__a)
for iter, transformed_img in enumerate(self._optimize_CLIP(__a, __a, __a)):
if show_intermediate:
show_pil(__a)
if save_intermediate:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}.png"))
if self.log:
wandb.log({"Image": wandb.Image(__a)})
if show_final:
show_pil(__a)
if save_final:
transformed_img.save(os.path.join(self.save_path, f"iter_{iter:03d}_final.png"))
| 36
| 0
|
'''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
A =logging.get_logger(__name__)
A ={
'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 _a ( __a ):
def __init__( self : List[Any] , lowercase : int=None , lowercase : Any=None , *lowercase : int , **lowercase : str ):
'''simple docstring'''
super().__init__(*lowercase , **lowercase )
if config is None:
assert isinstance(self.model , lowercase ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f" {self.model.__class__}"
)
UpperCAmelCase = self.model.config
else:
UpperCAmelCase = config
UpperCAmelCase = data_args
UpperCAmelCase = self.config.tgt_vocab_size if isinstance(self.config , lowercase ) 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 = 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 = label_smoothed_nll_loss
def A ( self : Optional[Any] , lowercase : int ):
'''simple docstring'''
if self.optimizer is None:
UpperCAmelCase = ['''bias''', '''LayerNorm.weight''']
UpperCAmelCase = [
{
'''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 = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
UpperCAmelCase = Adafactor
UpperCAmelCase = {'''scale_parameter''': False, '''relative_step''': False}
else:
UpperCAmelCase = AdamW
UpperCAmelCase = {
'''betas''': (self.args.adam_betaa, self.args.adam_betaa),
'''eps''': self.args.adam_epsilon,
}
UpperCAmelCase = self.args.learning_rate
if self.sharded_ddp:
UpperCAmelCase = OSS(
params=lowercase , optim=lowercase , **lowercase , )
else:
UpperCAmelCase = optimizer_cls(lowercase , **lowercase )
if self.lr_scheduler is None:
UpperCAmelCase = self._get_lr_scheduler(lowercase )
else: # ignoring --lr_scheduler
logger.warning('''scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.''' )
def A ( self : Optional[Any] , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
UpperCAmelCase = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
UpperCAmelCase = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
UpperCAmelCase = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=lowercase )
return scheduler
def A ( self : Tuple ):
'''simple docstring'''
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 : int , lowercase : str , lowercase : Any , lowercase : Any ):
'''simple docstring'''
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 = model(**lowercase , use_cache=lowercase )[0]
UpperCAmelCase = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
UpperCAmelCase , UpperCAmelCase = model(**lowercase , labels=lowercase , use_cache=lowercase )[:2]
else:
# compute label smoothed loss
UpperCAmelCase = model(**lowercase , use_cache=lowercase )[0]
UpperCAmelCase = torch.nn.functional.log_softmax(lowercase , dim=-1 )
UpperCAmelCase , UpperCAmelCase = self.loss_fn(lowercase , lowercase , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def A ( self : Tuple , lowercase : Any , lowercase : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase = inputs.pop('''labels''' )
UpperCAmelCase , UpperCAmelCase = self._compute_loss(lowercase , lowercase , lowercase )
return loss
def A ( self : str , lowercase : nn.Module , lowercase : Dict[str, Union[torch.Tensor, Any]] , lowercase : bool , lowercase : Optional[List[str]] = None , ):
'''simple docstring'''
UpperCAmelCase = self._prepare_inputs(lowercase )
UpperCAmelCase = {
'''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 = self.model.generate(
inputs['''input_ids'''] , attention_mask=inputs['''attention_mask'''] , **lowercase , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
UpperCAmelCase = self._pad_tensors_to_max_len(lowercase , gen_kwargs['''max_length'''] )
UpperCAmelCase = inputs.pop('''labels''' )
with torch.no_grad():
# compute loss on predict data
UpperCAmelCase , UpperCAmelCase = self._compute_loss(lowercase , lowercase , lowercase )
UpperCAmelCase = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
UpperCAmelCase = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
UpperCAmelCase = self._pad_tensors_to_max_len(lowercase , gen_kwargs['''max_length'''] )
return (loss, logits, labels)
def A ( self : List[str] , lowercase : List[Any] , lowercase : str ):
'''simple docstring'''
UpperCAmelCase = 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 = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
UpperCAmelCase = tensor
return padded_tensor
| 34
|
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
_snake_case = get_tests_dir("fixtures")
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = mock.Mock()
_lowerCAmelCase : int = 500
_lowerCAmelCase : Tuple = {}
_lowerCAmelCase : str = HTTPError
_lowerCAmelCase : Union[str, Any] = {}
# Download this model to make sure it's in the cache.
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request", return_value=__a) as mock_head:
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("hf-internal-testing/tiny-random-vit")
# This check we did call the fake head request
mock_head.assert_called()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json")
def snake_case__ ( self):
'''simple docstring'''
with self.assertRaises(__a):
# config is in subfolder, the following should not work without specifying the subfolder
_lowerCAmelCase : int = AutoImageProcessor.from_pretrained("hf-internal-testing/stable-diffusion-all-variants")
_lowerCAmelCase : Optional[Any] = AutoImageProcessor.from_pretrained(
"hf-internal-testing/stable-diffusion-all-variants", subfolder="feature_extractor")
self.assertIsNotNone(__a)
@is_staging_test
class UpperCAmelCase_ ( unittest.TestCase):
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = TOKEN
HfFolder.save_token(__a)
@classmethod
def snake_case__ ( cls):
'''simple docstring'''
try:
delete_repo(token=cls._token, repo_id="test-image-processor")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="valid_org/test-image-processor-org")
except HTTPError:
pass
try:
delete_repo(token=cls._token, repo_id="test-dynamic-image-processor")
except HTTPError:
pass
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : str = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="test-image-processor", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained(f"{USER}/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Any = ViTImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("valid_org/test-image-processor", use_auth_token=self._token)
_lowerCAmelCase : Tuple = ViTImageProcessor.from_pretrained("valid_org/test-image-processor")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
# Reset repo
delete_repo(token=self._token, repo_id="valid_org/test-image-processor")
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
__a, repo_id="valid_org/test-image-processor-org", push_to_hub=__a, use_auth_token=self._token)
_lowerCAmelCase : Optional[int] = ViTImageProcessor.from_pretrained("valid_org/test-image-processor-org")
for k, v in image_processor.__dict__.items():
self.assertEqual(__a, getattr(__a, __a))
def snake_case__ ( self):
'''simple docstring'''
CustomImageProcessor.register_for_auto_class()
_lowerCAmelCase : List[str] = CustomImageProcessor.from_pretrained(__a)
image_processor.push_to_hub("test-dynamic-image-processor", use_auth_token=self._token)
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map, {"AutoImageProcessor": "custom_image_processing.CustomImageProcessor"}, )
_lowerCAmelCase : Tuple = AutoImageProcessor.from_pretrained(
f"{USER}/test-dynamic-image-processor", trust_remote_code=__a)
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__, "CustomImageProcessor")
| 36
| 0
|
'''simple docstring'''
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
__a = TypeVar("KT")
__a = TypeVar("VT")
class UpperCAmelCase_ ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : Optional[Any] , snake_case_ : KT | str = "root" , snake_case_ : VT | None = None ):
snake_case__ : Union[str, Any] = key
snake_case__ : str = value
snake_case__ : list[Node[KT, VT]] = []
def __repr__( self : Tuple ):
return f"Node({self.key}: {self.value})"
@property
def lowerCamelCase ( self : Optional[Any] ):
return len(self.forward )
class UpperCAmelCase_ ( Generic[KT, VT] ):
"""simple docstring"""
def __init__( self : List[Any] , snake_case_ : float = 0.5 , snake_case_ : int = 16 ):
snake_case__ : Node[KT, VT] = Node[KT, VT]()
snake_case__ : Optional[Any] = 0
snake_case__ : Union[str, Any] = p
snake_case__ : int = max_level
def __str__( self : Optional[Any] ):
snake_case__ : str = list(self )
if len(snake_case_ ) == 0:
return f"SkipList(level={self.level})"
snake_case__ : Optional[Any] = max((len(str(snake_case_ ) ) for item in items) , default=4 )
snake_case__ : Optional[Any] = max(snake_case_ , 4 ) + 4
snake_case__ : Optional[Any] = self.head
snake_case__ : Dict = []
snake_case__ : Tuple = node.forward.copy()
lines.append(f"[{node.key}]".ljust(snake_case_ , """-""" ) + """* """ * len(snake_case_ ) )
lines.append(""" """ * label_size + """| """ * len(snake_case_ ) )
while len(node.forward ) != 0:
snake_case__ : Tuple = node.forward[0]
lines.append(
f"[{node.key}]".ljust(snake_case_ , """-""" )
+ """ """.join(str(n.key ) if n.key == node.key else """|""" for n in forwards ) )
lines.append(""" """ * label_size + """| """ * len(snake_case_ ) )
snake_case__ : List[str] = node.forward
lines.append("""None""".ljust(snake_case_ ) + """* """ * len(snake_case_ ) )
return f"SkipList(level={self.level})\n" + "\n".join(snake_case_ )
def __iter__( self : Tuple ):
snake_case__ : int = self.head
while len(node.forward ) != 0:
yield node.forward[0].key
snake_case__ : Dict = node.forward[0]
def lowerCamelCase ( self : Optional[Any] ):
snake_case__ : Union[str, Any] = 1
while random() < self.p and level < self.max_level:
level += 1
return level
def lowerCamelCase ( self : List[Any] , snake_case_ : List[str] ):
snake_case__ : Optional[Any] = []
snake_case__ : Tuple = self.head
for i in reversed(range(self.level ) ):
# i < node.level - When node level is lesser than `i` decrement `i`.
# node.forward[i].key < key - Jumping to node with key value higher
# or equal to searched key would result
# in skipping searched key.
while i < node.level and node.forward[i].key < key:
snake_case__ : Dict = node.forward[i]
# Each leftmost node (relative to searched node) will potentially have to
# be updated.
update_vector.append(snake_case_ )
update_vector.reverse() # Note that we were inserting values in reverse order.
# len(node.forward) != 0 - If current node doesn't contain any further
# references then searched key is not present.
# node.forward[0].key == key - Next node key should be equal to search key
# if key is present.
if len(node.forward ) != 0 and node.forward[0].key == key:
return node.forward[0], update_vector
else:
return None, update_vector
def lowerCamelCase ( self : Tuple , snake_case_ : KT ):
snake_case__ , snake_case__ : List[str] = self._locate_node(snake_case_ )
if node is not None:
for i, update_node in enumerate(snake_case_ ):
# Remove or replace all references to removed node.
if update_node.level > i and update_node.forward[i].key == key:
if node.level > i:
snake_case__ : Union[str, Any] = node.forward[i]
else:
snake_case__ : Any = update_node.forward[:i]
def lowerCamelCase ( self : List[Any] , snake_case_ : KT , snake_case_ : VT ):
snake_case__ , snake_case__ : Optional[int] = self._locate_node(snake_case_ )
if node is not None:
snake_case__ : str = value
else:
snake_case__ : Tuple = self.random_level()
if level > self.level:
# After level increase we have to add additional nodes to head.
for _ in range(self.level - 1 , snake_case_ ):
update_vector.append(self.head )
snake_case__ : int = level
snake_case__ : str = Node(snake_case_ , snake_case_ )
for i, update_node in enumerate(update_vector[:level] ):
# Change references to pass through new node.
if update_node.level > i:
new_node.forward.append(update_node.forward[i] )
if update_node.level < i + 1:
update_node.forward.append(snake_case_ )
else:
snake_case__ : Union[str, Any] = new_node
def lowerCamelCase ( self : Any , snake_case_ : VT ):
snake_case__ , snake_case__ : Optional[Any] = self._locate_node(snake_case_ )
if node is not None:
return node.value
return None
def __snake_case( ) -> str:
snake_case__ : str = SkipList()
skip_list.insert("""Key1""" , 3 )
skip_list.insert("""Key2""" , 12 )
skip_list.insert("""Key3""" , 41 )
skip_list.insert("""Key4""" , -19 )
snake_case__ : Dict = skip_list.head
snake_case__ : Dict = {}
while node.level != 0:
snake_case__ : Dict = node.forward[0]
snake_case__ : List[Any] = node.value
assert len(_lowerCAmelCase ) == 4
assert all_values["Key1"] == 3
assert all_values["Key2"] == 12
assert all_values["Key3"] == 41
assert all_values["Key4"] == -19
def __snake_case( ) -> str:
snake_case__ : Union[str, Any] = SkipList()
skip_list.insert("""Key1""" , 10 )
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""Key5""" , 7 )
skip_list.insert("""Key7""" , 10 )
skip_list.insert("""Key10""" , 5 )
skip_list.insert("""Key7""" , 7 )
skip_list.insert("""Key5""" , 5 )
skip_list.insert("""Key10""" , 10 )
snake_case__ : Tuple = skip_list.head
snake_case__ : int = {}
while node.level != 0:
snake_case__ : Any = node.forward[0]
snake_case__ : Optional[int] = node.value
if len(_lowerCAmelCase ) != 4:
print()
assert len(_lowerCAmelCase ) == 4
assert all_values["Key1"] == 12
assert all_values["Key7"] == 7
assert all_values["Key5"] == 5
assert all_values["Key10"] == 10
def __snake_case( ) -> Any:
snake_case__ : str = SkipList()
assert skip_list.find("""Some key""" ) is None
def __snake_case( ) -> Optional[Any]:
snake_case__ : Dict = SkipList()
skip_list.insert("""Key2""" , 20 )
assert skip_list.find("""Key2""" ) == 20
skip_list.insert("""Some Key""" , 10 )
skip_list.insert("""Key2""" , 8 )
skip_list.insert("""V""" , 13 )
assert skip_list.find("""Y""" ) is None
assert skip_list.find("""Key2""" ) == 8
assert skip_list.find("""Some Key""" ) == 10
assert skip_list.find("""V""" ) == 13
def __snake_case( ) -> Union[str, Any]:
snake_case__ : int = SkipList()
skip_list.delete("""Some key""" )
assert len(skip_list.head.forward ) == 0
def __snake_case( ) -> Any:
snake_case__ : Tuple = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""Key2""" ) is None
def __snake_case( ) -> int:
snake_case__ : Dict = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 14 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""V""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) == 14
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""X""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) == 12
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key1""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) == 15
skip_list.delete("""Key2""" )
assert skip_list.find("""V""" ) is None
assert skip_list.find("""X""" ) is None
assert skip_list.find("""Key1""" ) is None
assert skip_list.find("""Key2""" ) is None
def __snake_case( ) -> Optional[int]:
snake_case__ : Dict = SkipList()
skip_list.insert("""Key1""" , 12 )
skip_list.insert("""V""" , 13 )
skip_list.insert("""X""" , 142 )
skip_list.insert("""Key2""" , 15 )
skip_list.delete("""X""" )
def traverse_keys(_lowerCAmelCase ):
yield node.key
for forward_node in node.forward:
yield from traverse_keys(_lowerCAmelCase )
assert len(set(traverse_keys(skip_list.head ) ) ) == 4
def __snake_case( ) -> Tuple:
def is_sorted(_lowerCAmelCase ):
return all(next_item >= item for item, next_item in zip(_lowerCAmelCase , lst[1:] ) )
snake_case__ : Optional[Any] = SkipList()
for i in range(10 ):
skip_list.insert(_lowerCAmelCase , _lowerCAmelCase )
assert is_sorted(list(_lowerCAmelCase ) )
skip_list.delete(5 )
skip_list.delete(8 )
skip_list.delete(2 )
assert is_sorted(list(_lowerCAmelCase ) )
skip_list.insert(-12 , -12 )
skip_list.insert(77 , 77 )
assert is_sorted(list(_lowerCAmelCase ) )
def __snake_case( ) -> List[str]:
for _ in range(100 ):
# Repeat test 100 times due to the probabilistic nature of skip list
# random values == random bugs
test_insert()
test_insert_overrides_existing_value()
test_searching_empty_list_returns_none()
test_search()
test_deleting_item_from_empty_list_do_nothing()
test_deleted_items_are_not_founded_by_find_method()
test_delete_removes_only_given_key()
test_delete_doesnt_leave_dead_nodes()
test_iter_always_yields_sorted_values()
def __snake_case( ) -> Dict:
snake_case__ : Any = SkipList()
skip_list.insert(2 , """2""" )
skip_list.insert(4 , """4""" )
skip_list.insert(6 , """4""" )
skip_list.insert(4 , """5""" )
skip_list.insert(8 , """4""" )
skip_list.insert(9 , """4""" )
skip_list.delete(4 )
print(_lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 35
|
import unittest
from transformers import LiltConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
)
from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST
class UpperCAmelCase_ :
def __init__( self, __a, __a=13, __a=7, __a=True, __a=True, __a=True, __a=True, __a=99, __a=24, __a=2, __a=6, __a=37, __a="gelu", __a=0.1, __a=0.1, __a=512, __a=16, __a=2, __a=0.02, __a=3, __a=None, __a=1000, ):
'''simple docstring'''
_lowerCAmelCase : Tuple = parent
_lowerCAmelCase : List[str] = batch_size
_lowerCAmelCase : int = seq_length
_lowerCAmelCase : Optional[int] = is_training
_lowerCAmelCase : Dict = use_input_mask
_lowerCAmelCase : List[str] = use_token_type_ids
_lowerCAmelCase : str = use_labels
_lowerCAmelCase : Optional[Any] = vocab_size
_lowerCAmelCase : Tuple = hidden_size
_lowerCAmelCase : List[Any] = num_hidden_layers
_lowerCAmelCase : Optional[Any] = num_attention_heads
_lowerCAmelCase : Any = intermediate_size
_lowerCAmelCase : List[str] = hidden_act
_lowerCAmelCase : Union[str, Any] = hidden_dropout_prob
_lowerCAmelCase : Any = attention_probs_dropout_prob
_lowerCAmelCase : int = max_position_embeddings
_lowerCAmelCase : Optional[int] = type_vocab_size
_lowerCAmelCase : Optional[Any] = type_sequence_label_size
_lowerCAmelCase : List[str] = initializer_range
_lowerCAmelCase : List[Any] = num_labels
_lowerCAmelCase : Tuple = scope
_lowerCAmelCase : str = range_bbox
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
# Ensure that bbox is legal
for i in range(bbox.shape[0]):
for j in range(bbox.shape[1]):
if bbox[i, j, 3] < bbox[i, j, 1]:
_lowerCAmelCase : Dict = bbox[i, j, 3]
_lowerCAmelCase : int = bbox[i, j, 1]
_lowerCAmelCase : Tuple = t
if bbox[i, j, 2] < bbox[i, j, 0]:
_lowerCAmelCase : str = bbox[i, j, 2]
_lowerCAmelCase : List[Any] = bbox[i, j, 0]
_lowerCAmelCase : str = t
_lowerCAmelCase : Optional[Any] = None
if self.use_input_mask:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
_lowerCAmelCase : Dict = None
if self.use_token_type_ids:
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
_lowerCAmelCase : Optional[int] = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : Optional[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
_lowerCAmelCase : Optional[int] = self.get_config()
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case__ ( self):
'''simple docstring'''
return LiltConfig(
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, initializer_range=self.initializer_range, )
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = LiltModel(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(__a, bbox=__a, attention_mask=__a, token_type_ids=__a)
_lowerCAmelCase : str = model(__a, bbox=__a, token_type_ids=__a)
_lowerCAmelCase : List[Any] = model(__a, bbox=__a)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = self.num_labels
_lowerCAmelCase : Optional[Any] = LiltForTokenClassification(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Dict = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, labels=__a)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
def snake_case__ ( self, __a, __a, __a, __a, __a, __a, __a, ):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = LiltForQuestionAnswering(config=__a)
model.to(__a)
model.eval()
_lowerCAmelCase : Tuple = model(
__a, bbox=__a, attention_mask=__a, token_type_ids=__a, start_positions=__a, end_positions=__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 snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = self.prepare_config_and_inputs()
(
(
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) , (
_lowerCAmelCase
) ,
) : Dict = config_and_inputs
_lowerCAmelCase : List[Any] = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class UpperCAmelCase_ ( a , a , a , unittest.TestCase):
lowerCamelCase__ = (
(
LiltModel,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltForQuestionAnswering,
)
if is_torch_available()
else ()
)
lowerCamelCase__ = (
{
'feature-extraction': LiltModel,
'question-answering': LiltForQuestionAnswering,
'text-classification': LiltForSequenceClassification,
'token-classification': LiltForTokenClassification,
'zero-shot': LiltForSequenceClassification,
}
if is_torch_available()
else {}
)
lowerCamelCase__ = False
lowerCamelCase__ = False
def snake_case__ ( self, __a, __a, __a, __a, __a):
'''simple docstring'''
return True
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = LiltModelTester(self)
_lowerCAmelCase : Union[str, Any] = ConfigTester(self, config_class=__a, hidden_size=37)
def snake_case__ ( self):
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowerCAmelCase : Any = type
self.model_tester.create_and_check_model(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a)
@slow
def snake_case__ ( self):
'''simple docstring'''
for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : str = LiltModel.from_pretrained(__a)
self.assertIsNotNone(__a)
@require_torch
@slow
class UpperCAmelCase_ ( unittest.TestCase):
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Dict = LiltModel.from_pretrained("SCUT-DLVCLab/lilt-roberta-en-base").to(__a)
_lowerCAmelCase : Any = torch.tensor([[1, 2]], device=__a)
_lowerCAmelCase : str = torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]], device=__a)
# forward pass
with torch.no_grad():
_lowerCAmelCase : Optional[Any] = model(input_ids=__a, bbox=__a)
_lowerCAmelCase : Optional[int] = torch.Size([1, 2, 768])
_lowerCAmelCase : List[str] = torch.tensor(
[[-0.0_653, 0.0_950, -0.0_061], [-0.0_545, 0.0_926, -0.0_324]], device=__a, )
self.assertTrue(outputs.last_hidden_state.shape, __a)
self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3], __a, atol=1E-3))
| 36
| 0
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
__lowercase : Tuple = ['''keras_nlp''']
def __init__( self ,*__UpperCAmelCase ,**__UpperCAmelCase ) -> Union[str, Any]:
requires_backends(self ,["""keras_nlp"""] )
| 37
|
import argparse
import copy
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : int = {}
with open(_lowerCamelCase ) as f:
for line in f:
if line.split()[0] not in dict_of_neighbours:
_lowerCAmelCase : Tuple = []
_list.append([line.split()[1], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[0]].append(
[line.split()[1], line.split()[2]] )
if line.split()[1] not in dict_of_neighbours:
_lowerCAmelCase : str = []
_list.append([line.split()[0], line.split()[2]] )
_lowerCAmelCase : Any = _list
else:
dict_of_neighbours[line.split()[1]].append(
[line.split()[0], line.split()[2]] )
return dict_of_neighbours
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
with open(_lowerCamelCase ) as f:
_lowerCAmelCase : str = f.read(1 )
_lowerCAmelCase : str = start_node
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : Any = start_node
_lowerCAmelCase : str = 0
while visiting not in first_solution:
_lowerCAmelCase : Dict = 10_000
for k in dict_of_neighbours[visiting]:
if int(k[1] ) < int(_lowerCamelCase ) and k[0] not in first_solution:
_lowerCAmelCase : List[str] = k[1]
_lowerCAmelCase : List[Any] = k[0]
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Optional[int] = distance_of_first_solution + int(_lowerCamelCase )
_lowerCAmelCase : str = best_node
first_solution.append(_lowerCamelCase )
_lowerCAmelCase : Union[str, Any] = 0
for k in dict_of_neighbours[first_solution[-2]]:
if k[0] == start_node:
break
position += 1
_lowerCAmelCase : Tuple = (
distance_of_first_solution
+ int(dict_of_neighbours[first_solution[-2]][position][1] )
- 10_000
)
return first_solution, distance_of_first_solution
def A ( _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = []
for n in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
for kn in solution[1:-1]:
_lowerCAmelCase : Dict = solution.index(_lowerCamelCase )
if n == kn:
continue
_lowerCAmelCase : Optional[int] = copy.deepcopy(_lowerCamelCase )
_lowerCAmelCase : int = kn
_lowerCAmelCase : Dict = n
_lowerCAmelCase : Optional[int] = 0
for k in _tmp[:-1]:
_lowerCAmelCase : str = _tmp[_tmp.index(_lowerCamelCase ) + 1]
for i in dict_of_neighbours[k]:
if i[0] == next_node:
_lowerCAmelCase : Optional[Any] = distance + int(i[1] )
_tmp.append(_lowerCamelCase )
if _tmp not in neighborhood_of_solution:
neighborhood_of_solution.append(_tmp )
_lowerCAmelCase : List[Any] = len(neighborhood_of_solution[0] ) - 1
neighborhood_of_solution.sort(key=lambda _lowerCamelCase : x[index_of_last_item_in_the_list] )
return neighborhood_of_solution
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = 1
_lowerCAmelCase : int = first_solution
_lowerCAmelCase : Tuple = []
_lowerCAmelCase : Tuple = distance_of_first_solution
_lowerCAmelCase : Optional[int] = solution
while count <= iters:
_lowerCAmelCase : int = find_neighborhood(_lowerCamelCase , _lowerCamelCase )
_lowerCAmelCase : Tuple = 0
_lowerCAmelCase : Dict = neighborhood[index_of_best_solution]
_lowerCAmelCase : int = len(_lowerCamelCase ) - 1
_lowerCAmelCase : Union[str, Any] = False
while not found:
_lowerCAmelCase : Tuple = 0
while i < len(_lowerCamelCase ):
if best_solution[i] != solution[i]:
_lowerCAmelCase : str = best_solution[i]
_lowerCAmelCase : Tuple = solution[i]
break
_lowerCAmelCase : int = i + 1
if [first_exchange_node, second_exchange_node] not in tabu_list and [
second_exchange_node,
first_exchange_node,
] not in tabu_list:
tabu_list.append([first_exchange_node, second_exchange_node] )
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : Optional[Any] = best_solution[:-1]
_lowerCAmelCase : Tuple = neighborhood[index_of_best_solution][best_cost_index]
if cost < best_cost:
_lowerCAmelCase : Union[str, Any] = cost
_lowerCAmelCase : List[Any] = solution
else:
_lowerCAmelCase : Optional[Any] = index_of_best_solution + 1
_lowerCAmelCase : Optional[Any] = neighborhood[index_of_best_solution]
if len(_lowerCamelCase ) >= size:
tabu_list.pop(0 )
_lowerCAmelCase : int = count + 1
return best_solution_ever, best_cost
def A ( _lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase : int = generate_neighbours(args.File )
_lowerCAmelCase , _lowerCAmelCase : List[str] = generate_first_solution(
args.File , _lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = tabu_search(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , args.Iterations , args.Size , )
print(F"Best solution: {best_sol}, with total distance: {best_cost}." )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser(description="Tabu Search")
parser.add_argument(
"-f",
"--File",
type=str,
help="Path to the file containing the data",
required=True,
)
parser.add_argument(
"-i",
"--Iterations",
type=int,
help="How many iterations the algorithm should perform",
required=True,
)
parser.add_argument(
"-s", "--Size", type=int, help="Size of the tabu list", required=True
)
# Pass the arguments to main method
main(parser.parse_args())
| 36
| 0
|
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,
)
UpperCAmelCase_ : Union[str, Any] = {
'''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:
UpperCAmelCase_ : Any = ['''CLIPTokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Union[str, Any] = ['''CLIPFeatureExtractor''']
UpperCAmelCase_ : Dict = ['''CLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = [
'''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPModel''',
'''CLIPPreTrainedModel''',
'''CLIPTextModel''',
'''CLIPTextModelWithProjection''',
'''CLIPVisionModel''',
'''CLIPVisionModelWithProjection''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : str = [
'''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCLIPModel''',
'''TFCLIPPreTrainedModel''',
'''TFCLIPTextModel''',
'''TFCLIPVisionModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Any = [
'''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
UpperCAmelCase_ : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 38
|
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = get_tests_dir("fixtures/test_sentencepiece_bpe.model")
class UpperCAmelCase_ ( a , unittest.TestCase):
lowerCamelCase__ = BartphoTokenizer
lowerCamelCase__ = False
lowerCamelCase__ = True
def snake_case__ ( self):
'''simple docstring'''
super().setUp()
_lowerCAmelCase : str = ["▁This", "▁is", "▁a", "▁t", "est"]
_lowerCAmelCase : List[str] = dict(zip(__a, range(len(__a))))
_lowerCAmelCase : Optional[Any] = {"unk_token": "<unk>"}
_lowerCAmelCase : Optional[int] = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["monolingual_vocab_file"])
with open(self.monolingual_vocab_file, "w", encoding="utf-8") as fp:
for token in vocab_tokens:
fp.write(f"{token} {vocab_tokens[token]}\n")
_lowerCAmelCase : Optional[Any] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
tokenizer.save_pretrained(self.tmpdirname)
def snake_case__ ( self, **__a):
'''simple docstring'''
kwargs.update(self.special_tokens_map)
return BartphoTokenizer.from_pretrained(self.tmpdirname, **__a)
def snake_case__ ( self, __a):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = "This is a là test"
_lowerCAmelCase : Optional[int] = "This is a<unk><unk> test"
return input_text, output_text
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = BartphoTokenizer(__a, self.monolingual_vocab_file, **self.special_tokens_map)
_lowerCAmelCase : List[Any] = "This is a là test"
_lowerCAmelCase : str = "▁This ▁is ▁a ▁l à ▁t est".split()
_lowerCAmelCase : str = tokenizer.tokenize(__a)
self.assertListEqual(__a, __a)
_lowerCAmelCase : Tuple = tokens + [tokenizer.unk_token]
_lowerCAmelCase : List[str] = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a), __a)
| 36
| 0
|
from __future__ import annotations
from typing import Any
def __A ( __lowerCAmelCase )-> int:
"""simple docstring"""
if not postfix_notation:
return 0
_UpperCAmelCase = {'+', '-', '*', '/'}
_UpperCAmelCase = []
for token in postfix_notation:
if token in operations:
_UpperCAmelCase , _UpperCAmelCase = stack.pop(), stack.pop()
if token == "+":
stack.append(a + b )
elif token == "-":
stack.append(a - b )
elif token == "*":
stack.append(a * b )
else:
if a * b < 0 and a % b != 0:
stack.append(a // b + 1 )
else:
stack.append(a // b )
else:
stack.append(int(__lowerCAmelCase ) )
return stack.pop()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 39
|
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
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
def constraint_to_multiple_of(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase=0 , _lowerCamelCase=None ):
_lowerCAmelCase : Tuple = round(val / multiple ) * multiple
if max_val is not None and x > max_val:
_lowerCAmelCase : Optional[int] = math.floor(val / multiple ) * multiple
if x < min_val:
_lowerCAmelCase : List[str] = math.ceil(val / multiple ) * multiple
return x
_lowerCAmelCase : Union[str, Any] = (output_size, output_size) if isinstance(_lowerCamelCase , _lowerCamelCase ) else output_size
_lowerCAmelCase , _lowerCAmelCase : Optional[Any] = get_image_size(_lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase : Any = output_size
# determine new height and width
_lowerCAmelCase : List[Any] = output_height / input_height
_lowerCAmelCase : Any = output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
_lowerCAmelCase : Union[str, Any] = scale_width
else:
# fit height
_lowerCAmelCase : Union[str, Any] = scale_height
_lowerCAmelCase : List[str] = constraint_to_multiple_of(scale_height * input_height , multiple=_lowerCamelCase )
_lowerCAmelCase : Dict = constraint_to_multiple_of(scale_width * input_width , multiple=_lowerCamelCase )
return (new_height, new_width)
class UpperCAmelCase_ ( a):
lowerCamelCase__ = ['pixel_values']
def __init__( self, __a = True, __a = None, __a = PILImageResampling.BILINEAR, __a = False, __a = 1, __a = True, __a = 1 / 255, __a = True, __a = None, __a = None, **__a, ):
'''simple docstring'''
super().__init__(**__a)
_lowerCAmelCase : Any = size if size is not None else {"height": 384, "width": 384}
_lowerCAmelCase : Optional[int] = get_size_dict(__a)
_lowerCAmelCase : Optional[Any] = do_resize
_lowerCAmelCase : Dict = size
_lowerCAmelCase : Any = keep_aspect_ratio
_lowerCAmelCase : str = ensure_multiple_of
_lowerCAmelCase : str = resample
_lowerCAmelCase : Dict = do_rescale
_lowerCAmelCase : Optional[int] = rescale_factor
_lowerCAmelCase : Dict = do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
_lowerCAmelCase : int = image_std if image_std is not None else IMAGENET_STANDARD_STD
def snake_case__ ( self, __a, __a, __a = False, __a = 1, __a = PILImageResampling.BICUBIC, __a = None, **__a, ):
'''simple docstring'''
_lowerCAmelCase : List[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()}")
_lowerCAmelCase : List[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 snake_case__ ( self, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return rescale(__a, scale=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a, __a, __a = None, **__a, ):
'''simple docstring'''
return normalize(__a, mean=__a, std=__a, data_format=__a, **__a)
def snake_case__ ( self, __a, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = None, __a = ChannelDimension.FIRST, **__a, ):
'''simple docstring'''
_lowerCAmelCase : int = do_resize if do_resize is not None else self.do_resize
_lowerCAmelCase : List[Any] = size if size is not None else self.size
_lowerCAmelCase : str = get_size_dict(__a)
_lowerCAmelCase : Dict = keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
_lowerCAmelCase : Any = ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
_lowerCAmelCase : int = resample if resample is not None else self.resample
_lowerCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
_lowerCAmelCase : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
_lowerCAmelCase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
_lowerCAmelCase : Dict = image_mean if image_mean is not None else self.image_mean
_lowerCAmelCase : List[str] = image_std if image_std is not None else self.image_std
_lowerCAmelCase : Optional[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_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.
_lowerCAmelCase : List[Any] = [to_numpy_array(__a) for image in images]
if do_resize:
_lowerCAmelCase : Any = [self.resize(image=__a, size=__a, resample=__a) for image in images]
if do_rescale:
_lowerCAmelCase : List[str] = [self.rescale(image=__a, scale=__a) for image in images]
if do_normalize:
_lowerCAmelCase : Dict = [self.normalize(image=__a, mean=__a, std=__a) for image in images]
_lowerCAmelCase : List[str] = [to_channel_dimension_format(__a, __a) for image in images]
_lowerCAmelCase : Optional[Any] = {"pixel_values": images}
return BatchFeature(data=__a, tensor_type=__a)
def snake_case__ ( self, __a, __a = None):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = 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):
_lowerCAmelCase : List[Any] = target_sizes.numpy()
_lowerCAmelCase : Dict = []
for idx in range(len(__a)):
_lowerCAmelCase : int = torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0), size=target_sizes[idx], mode="bilinear", align_corners=__a)
_lowerCAmelCase : int = resized_logits[0].argmax(dim=0)
semantic_segmentation.append(__a)
else:
_lowerCAmelCase : Dict = logits.argmax(dim=1)
_lowerCAmelCase : str = [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 36
| 0
|
"""simple docstring"""
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
__lowercase = """true"""
def lowercase ( A_ , A_=82 , A_=16 )-> Tuple:
'''simple docstring'''
set_seed(42 )
a : Dict = RegressionModel()
a : Tuple = deepcopy(A_ )
a : List[str] = RegressionDataset(length=A_ )
a : List[Any] = DataLoader(A_ , batch_size=A_ )
model.to(accelerator.device )
a , a : int = accelerator.prepare(A_ , A_ )
return model, ddp_model, dataloader
def lowercase ( A_ , A_=False )-> List[Any]:
'''simple docstring'''
a : Tuple = AutoTokenizer.from_pretrained("hf-internal-testing/mrpc-bert-base-cased" )
a : List[str] = load_dataset("glue" , "mrpc" , split="validation" )
def tokenize_function(A_ ):
a : Union[str, Any] = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=A_ , max_length=A_ )
return outputs
with accelerator.main_process_first():
a : Union[str, Any] = dataset.map(
A_ , batched=A_ , remove_columns=["idx", "sentence1", "sentence2"] , )
a : Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(A_ ):
if use_longest:
return tokenizer.pad(A_ , padding="longest" , return_tensors="pt" )
return tokenizer.pad(A_ , padding="max_length" , max_length=128 , return_tensors="pt" )
return DataLoader(A_ , shuffle=A_ , collate_fn=A_ , batch_size=16 )
def lowercase ( A_ , A_ )-> Tuple:
'''simple docstring'''
a : Tuple = Accelerator(dispatch_batches=A_ , split_batches=A_ )
a : List[str] = get_dataloader(A_ , not dispatch_batches )
a : List[Any] = AutoModelForSequenceClassification.from_pretrained(
"hf-internal-testing/mrpc-bert-base-cased" , return_dict=A_ )
a , a : List[str] = accelerator.prepare(A_ , A_ )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowercase ( A_ , A_ , A_ )-> Tuple:
'''simple docstring'''
a : Dict = []
for batch in dataloader:
a , a : Optional[int] = batch.values()
with torch.no_grad():
a : Union[str, Any] = model(A_ )
a , a : str = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
a , a : Any = [], []
for logit, targ in logits_and_targets:
logits.append(A_ )
targs.append(A_ )
a , a : List[str] = torch.cat(A_ ), torch.cat(A_ )
return logits, targs
def lowercase ( A_ , A_=82 , A_=False , A_=False , A_=16 )-> str:
'''simple docstring'''
a , a , a : Tuple = get_basic_setup(A_ , A_ , A_ )
a , a : Dict = generate_predictions(A_ , A_ , A_ )
assert (
len(A_ ) == num_samples
), F'''Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(A_ )}'''
def lowercase ( A_ = False , A_ = False )-> Union[str, Any]:
'''simple docstring'''
a : Any = evaluate.load("glue" , "mrpc" )
a , a : List[str] = get_mrpc_setup(A_ , A_ )
# First do baseline
a , a , a : Any = setup["no"]
model.to(A_ )
model.eval()
for batch in dataloader:
batch.to(A_ )
with torch.inference_mode():
a : Optional[int] = model(**A_ )
a : Dict = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=A_ , references=batch["labels"] )
a : Optional[int] = metric.compute()
# Then do distributed
a , a , a : List[str] = setup["ddp"]
model.eval()
for batch in dataloader:
with torch.inference_mode():
a : Tuple = model(**A_ )
a : Dict = outputs.logits.argmax(dim=-1 )
a : List[str] = batch["labels"]
a , a : Tuple = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=A_ , references=A_ )
a : List[Any] = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] , distributed[key] ), F'''Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n'''
def lowercase ( )-> List[str]:
'''simple docstring'''
a : int = Accelerator(split_batches=A_ , dispatch_batches=A_ )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print("**Testing gather_for_metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`''' )
test_mrpc(A_ , A_ )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test torch metrics**" )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
a : Optional[Any] = Accelerator(split_batches=A_ , dispatch_batches=A_ )
if accelerator.is_local_main_process:
print(F'''With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99''' )
test_torch_metrics(A_ , 99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print("**Test last batch is not dropped when perfectly divisible**" )
a : Dict = Accelerator()
test_torch_metrics(A_ , 512 )
accelerator.state._reset_state()
def lowercase ( A_ )-> Tuple:
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 40
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[Any] = "huggingface/label-files"
_lowerCAmelCase : int = "imagenet-1k-id2label.json"
_lowerCAmelCase : Tuple = json.load(open(hf_hub_download(_lowerCamelCase , _lowerCamelCase , repo_type="dataset" ) , "r" ) )
_lowerCAmelCase : Tuple = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
_lowerCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
_lowerCAmelCase : Tuple = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
_lowerCAmelCase : Optional[int] = BitConfig(
conv_layer=_lowerCamelCase , num_labels=1_000 , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase , )
return config
def A ( _lowerCamelCase ):
'''simple docstring'''
if "stem.conv" in name:
_lowerCAmelCase : List[str] = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
_lowerCAmelCase : Any = name.replace("blocks" , "layers" )
if "head.fc" in name:
_lowerCAmelCase : Optional[Any] = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
_lowerCAmelCase : Any = "bit." + name
if "bit" not in name and "classifier" not in name:
_lowerCAmelCase : Dict = "bit.encoder." + name
return name
def A ( ):
'''simple docstring'''
_lowerCAmelCase : Tuple = "http://images.cocodataset.org/val2017/000000039769.jpg"
_lowerCAmelCase : Optional[int] = Image.open(requests.get(_lowerCamelCase , stream=_lowerCamelCase ).raw )
return im
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase : Dict = get_config(_lowerCamelCase )
# load original model from timm
_lowerCAmelCase : int = create_model(_lowerCamelCase , pretrained=_lowerCamelCase )
timm_model.eval()
# load state_dict of original model
_lowerCAmelCase : Any = timm_model.state_dict()
for key in state_dict.copy().keys():
_lowerCAmelCase : Dict = state_dict.pop(_lowerCamelCase )
_lowerCAmelCase : Tuple = val.squeeze() if "head" in key else val
# load HuggingFace model
_lowerCAmelCase : Optional[Any] = BitForImageClassification(_lowerCamelCase )
model.eval()
model.load_state_dict(_lowerCamelCase )
# create image processor
_lowerCAmelCase : Dict = create_transform(**resolve_data_config({} , model=_lowerCamelCase ) )
_lowerCAmelCase : Optional[int] = transform.transforms
_lowerCAmelCase : Tuple = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
_lowerCAmelCase : Tuple = BitImageProcessor(
do_resize=_lowerCamelCase , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_lowerCamelCase , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_lowerCamelCase , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
_lowerCAmelCase : Optional[int] = prepare_img()
_lowerCAmelCase : Any = transform(_lowerCamelCase ).unsqueeze(0 )
_lowerCAmelCase : Optional[int] = processor(_lowerCamelCase , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_lowerCamelCase , _lowerCamelCase )
# verify logits
with torch.no_grad():
_lowerCAmelCase : Tuple = model(_lowerCamelCase )
_lowerCAmelCase : str = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
_lowerCAmelCase : Union[str, Any] = timm_model(_lowerCamelCase )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_lowerCamelCase , outputs.logits , atol=1e-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
print(F"Saving model {model_name} and processor to {pytorch_dump_folder_path}" )
model.save_pretrained(_lowerCamelCase )
processor.save_pretrained(_lowerCamelCase )
if push_to_hub:
print(F"Pushing model {model_name} and processor to the hub" )
model.push_to_hub(F"ybelkada/{model_name}" )
processor.push_to_hub(F"ybelkada/{model_name}" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--model_name",
default="resnetv2_50x1_bitm",
type=str,
help="Name of the BiT timm model you'd like to convert.",
)
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 to push the model to the hub.",
)
_snake_case = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 36
| 0
|
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