code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"""configuration_blenderbot""": [
"""BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BlenderbotConfig""",
"""BlenderbotOnnxConfig""",
],
"""tokenization_blenderbot""": ["""BlenderbotTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""BlenderbotTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BlenderbotForCausalLM""",
"""BlenderbotForConditionalGeneration""",
"""BlenderbotModel""",
"""BlenderbotPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""TFBlenderbotForConditionalGeneration""",
"""TFBlenderbotModel""",
"""TFBlenderbotPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""FlaxBlenderbotForConditionalGeneration""",
"""FlaxBlenderbotModel""",
"""FlaxBlenderbotPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_blenderbot import (
BLENDERBOT_PRETRAINED_CONFIG_ARCHIVE_MAP,
BlenderbotConfig,
BlenderbotOnnxConfig,
)
from .tokenization_blenderbot import BlenderbotTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_blenderbot_fast import BlenderbotTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_blenderbot import (
BLENDERBOT_PRETRAINED_MODEL_ARCHIVE_LIST,
BlenderbotForCausalLM,
BlenderbotForConditionalGeneration,
BlenderbotModel,
BlenderbotPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_blenderbot import (
TFBlenderbotForConditionalGeneration,
TFBlenderbotModel,
TFBlenderbotPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
FlaxBlenderbotPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
lowercase__ = inspect.getmembers(_lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowercase__ = "k-diffusion"
elif backend == "invisible_watermark":
lowercase__ = "invisible-watermark"
assert backend in deps, f'''{backend} is not in the deps table!'''
| 655 | 1 |
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,
)
_snake_case = {
"""configuration_owlvit""": [
"""OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""OwlViTConfig""",
"""OwlViTOnnxConfig""",
"""OwlViTTextConfig""",
"""OwlViTVisionConfig""",
],
"""processing_owlvit""": ["""OwlViTProcessor"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""OwlViTFeatureExtractor"""]
_snake_case = ["""OwlViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""OwlViTModel""",
"""OwlViTPreTrainedModel""",
"""OwlViTTextModel""",
"""OwlViTVisionModel""",
"""OwlViTForObjectDetection""",
]
if TYPE_CHECKING:
from .configuration_owlvit import (
OWLVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OwlViTConfig,
OwlViTOnnxConfig,
OwlViTTextConfig,
OwlViTVisionConfig,
)
from .processing_owlvit import OwlViTProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_owlvit import OwlViTFeatureExtractor
from .image_processing_owlvit import OwlViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_owlvit import (
OWLVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
OwlViTForObjectDetection,
OwlViTModel,
OwlViTPreTrainedModel,
OwlViTTextModel,
OwlViTVisionModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class lowerCAmelCase :
__lowerCamelCase = 42
# setable values
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = None
@classmethod
def UpperCAmelCase ( cls :Union[str, Any] , _lowercase :CommonSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray ):
'''simple docstring'''
return cls(common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase )
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
__lowerCamelCase = 42
@property
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return True
@register_to_config
def __init__( self :str , _lowercase :int = 10_00 , _lowercase :float = 0.0001 , _lowercase :float = 0.02 , _lowercase :str = "linear" , _lowercase :Optional[jnp.ndarray] = None , _lowercase :str = "fixed_small" , _lowercase :bool = True , _lowercase :str = "epsilon" , _lowercase :jnp.dtype = jnp.floataa , ):
'''simple docstring'''
lowercase__ = dtype
def UpperCAmelCase ( self :str , _lowercase :Optional[CommonSchedulerState] = None ):
'''simple docstring'''
if common is None:
lowercase__ = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
lowercase__ = jnp.array(1.0 , dtype=self.dtype )
lowercase__ = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCAmelCase ( self :List[str] , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :Tuple = () ):
'''simple docstring'''
lowercase__ = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
lowercase__ = (jnp.arange(0 , _lowercase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Tuple , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :List[str]=None , _lowercase :Tuple=None ):
'''simple docstring'''
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowercase__ = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
lowercase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
lowercase__ = jnp.clip(_lowercase , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
lowercase__ = jnp.log(jnp.clip(_lowercase , a_min=1e-20 ) )
elif variance_type == "fixed_large":
lowercase__ = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
lowercase__ = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
lowercase__ = variance
lowercase__ = state.common.betas[t]
lowercase__ = (predicted_variance + 1) / 2
lowercase__ = frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase ( self :Optional[int] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :int , _lowercase :jnp.ndarray , _lowercase :Optional[jax.random.KeyArray] = None , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = timestep
if key is None:
lowercase__ = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
lowercase__ , lowercase__ = jnp.split(_lowercase , sample.shape[1] , axis=1 )
else:
lowercase__ = None
# 1. compute alphas, betas
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
lowercase__ = 1 - alpha_prod_t
lowercase__ = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowercase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowercase__ = model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowercase__ = jnp.clip(_lowercase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
lowercase__ = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
lowercase__ = jax.random.split(_lowercase , num=1 )
lowercase__ = jax.random.normal(_lowercase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(_lowercase , _lowercase , predicted_variance=_lowercase ) ** 0.5) * noise
lowercase__ = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
lowercase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=_lowercase , state=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return add_noise_common(state.common , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Dict , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return get_velocity_common(state.common , _lowercase , _lowercase , _lowercase )
def __len__( self :List[str] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 655 | 1 |
def _A ( __magic_name__ = 10 ):
if not isinstance(__magic_name__ , __magic_name__ ) or n < 0:
raise ValueError("Invalid input" )
lowercase__ = 10**n
lowercase__ = 2_8433 * (pow(2 , 783_0457 , __magic_name__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 655 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_snake_case = logging.get_logger(__name__)
_snake_case = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Model type selected in the list: ' + ', '.join(lowercase_ )} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
__lowerCamelCase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
__lowerCamelCase = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
__lowerCamelCase = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
__lowerCamelCase = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
__lowerCamelCase = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'train'
__lowerCamelCase = 'dev'
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
def __init__( self :Optional[Any] , _lowercase :SquadDataTrainingArguments , _lowercase :PreTrainedTokenizer , _lowercase :Optional[int] = None , _lowercase :Union[str, Split] = Split.train , _lowercase :Optional[bool] = False , _lowercase :Optional[str] = None , _lowercase :Optional[str] = "pt" , ):
'''simple docstring'''
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = "v2" if args.version_2_with_negative else "v1"
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + ".lock"
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features["features"]
lowercase__ = self.old_features.get("dataset" , _lowercase )
lowercase__ = self.old_features.get("examples" , _lowercase )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
" future run" )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
lowercase__ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self :Dict ):
'''simple docstring'''
return len(self.features )
def __getitem__( self :Any , _lowercase :Any ):
'''simple docstring'''
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 655 | 1 |
from __future__ import annotations
from typing import Any
class lowerCAmelCase :
def __init__( self :List[str] , _lowercase :int ):
'''simple docstring'''
lowercase__ = num_of_nodes
lowercase__ = []
lowercase__ = {}
def UpperCAmelCase ( self :Any , _lowercase :int , _lowercase :int , _lowercase :int ):
'''simple docstring'''
self.m_edges.append([u_node, v_node, weight] )
def UpperCAmelCase ( self :Any , _lowercase :int ):
'''simple docstring'''
if self.m_component[u_node] == u_node:
return u_node
return self.find_component(self.m_component[u_node] )
def UpperCAmelCase ( self :Optional[int] , _lowercase :int ):
'''simple docstring'''
if self.m_component[u_node] != u_node:
for k in self.m_component:
lowercase__ = self.find_component(_lowercase )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :list[int] , _lowercase :int , _lowercase :int ):
'''simple docstring'''
if component_size[u_node] <= component_size[v_node]:
lowercase__ = v_node
component_size[v_node] += component_size[u_node]
self.set_component(_lowercase )
elif component_size[u_node] >= component_size[v_node]:
lowercase__ = self.find_component(_lowercase )
component_size[u_node] += component_size[v_node]
self.set_component(_lowercase )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = []
lowercase__ = 0
lowercase__ = [-1] * self.m_num_of_nodes
# A list of components (initialized to all of the nodes)
for node in range(self.m_num_of_nodes ):
self.m_component.update({node: node} )
component_size.append(1 )
lowercase__ = self.m_num_of_nodes
while num_of_components > 1:
for edge in self.m_edges:
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = self.m_component[u]
lowercase__ = self.m_component[v]
if u_component != v_component:
for component in (u_component, v_component):
if (
minimum_weight_edge[component] == -1
or minimum_weight_edge[component][2] > w
):
lowercase__ = [u, v, w]
for edge in minimum_weight_edge:
if isinstance(_lowercase , _lowercase ):
lowercase__ , lowercase__ , lowercase__ = edge
lowercase__ = self.m_component[u]
lowercase__ = self.m_component[v]
if u_component != v_component:
mst_weight += w
self.union(_lowercase , _lowercase , _lowercase )
print(f'''Added edge [{u} - {v}]\nAdded weight: {w}\n''' )
num_of_components -= 1
lowercase__ = [-1] * self.m_num_of_nodes
print(f'''The total weight of the minimal spanning tree is: {mst_weight}''' )
def _A ( ):
pass
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
_snake_case = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
_snake_case = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
_snake_case = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
_snake_case = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ["input_ids"]
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = RESOURCE_FILES_NAMES
def __init__( self :Union[str, Any] , _lowercase :Union[str, Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=False , _lowercase :Dict="utf8" , _lowercase :Optional[Any]="[UNK]" , _lowercase :Optional[int]="[SEP]" , _lowercase :List[str]="[PAD]" , _lowercase :Dict="[CLS]" , _lowercase :Optional[Any]="[MASK]" , _lowercase :Optional[Dict[str, Any]] = None , **_lowercase :Tuple , ):
'''simple docstring'''
lowercase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , vocab_file=_lowercase , encoding=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
lowercase__ = do_lower_case
lowercase__ = sentencepiece_model_ckpt
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase__ = self.load_vocab(filepath=_lowercase )
else:
lowercase__ = {self.sp_model.id_to_piece(_lowercase ): id for id in range(self.sp_model.get_piece_size() )}
lowercase__ = {v: k for k, v in self.vocab.items()}
def UpperCAmelCase ( self :Any , _lowercase :Dict ):
'''simple docstring'''
if text is None:
return None
lowercase__ = self.tokenize(_lowercase )
lowercase__ , lowercase__ = "", []
for i, ch in enumerate(_lowercase ):
if ch in self.SP_CHAR_MAPPING:
lowercase__ = self.SP_CHAR_MAPPING.get(_lowercase )
else:
lowercase__ = unicodedata.normalize("NFKC" , _lowercase )
if self.is_whitespace(_lowercase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(_lowercase ) )
lowercase__ , lowercase__ , lowercase__ = normalized_text, [], 0
if self.do_lower_case:
lowercase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase__ = token[1:]
lowercase__ = text[offset:].index(_lowercase ) + offset
lowercase__ = start + len(_lowercase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase__ = end
return token_mapping
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self :Any ):
'''simple docstring'''
lowercase__ = self.__dict__.copy()
lowercase__ = None
return state
def __setstate__( self :Optional[Any] , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ = {}
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def UpperCAmelCase ( self :List[str] , _lowercase :Optional[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(_lowercase , _lowercase ) for c in text) )
def UpperCAmelCase ( self :str , _lowercase :int , _lowercase :Union[str, Any]=False , _lowercase :Optional[int]=64 , _lowercase :Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowercase__ = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowercase__ = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowercase__ = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowercase__ = self.sp_model.EncodeAsPieces(_lowercase )
else:
lowercase__ = self.sp_model.SampleEncodeAsPieces(_lowercase , _lowercase , _lowercase )
lowercase__ = []
for pi, piece in enumerate(_lowercase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(_lowercase ) and pi != 0:
new_pieces.append(_lowercase )
continue
else:
continue
lowercase__ = 0
for i, chunk in enumerate(_lowercase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(_lowercase ) or self.is_punct(_lowercase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(_lowercase )
lowercase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
if len(_lowercase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def UpperCAmelCase ( self :Tuple , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ = self.convert_ids_to_tokens(_lowercase )
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Optional[int] ):
'''simple docstring'''
return self.vocab.get(_lowercase , self.vocab.get(self.unk_token ) )
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(_lowercase , self.unk_token )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :Tuple=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__ = [self.cls_token_id]
lowercase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def UpperCAmelCase ( self :Dict , _lowercase :int , _lowercase :Union[str, Any]=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Dict=None , _lowercase :Optional[Any]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase ( self :int , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(_lowercase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(_lowercase ) + 1) + [1] * (len(_lowercase ) + 3)
def UpperCAmelCase ( self :str , _lowercase :Optional[int] ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Dict ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def UpperCAmelCase ( self :List[str] , _lowercase :List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(_lowercase ) == 1:
lowercase__ = unicodedata.category(_lowercase )
if cat == "Zs":
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = {}
with io.open(_lowercase , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(_lowercase ):
lowercase__ = line.rstrip("\n" )
lowercase__ = int(_lowercase )
return token_to_idx
def UpperCAmelCase ( self :List[str] , _lowercase :str , _lowercase :Optional[str] = None ):
'''simple docstring'''
lowercase__ = 0
if os.path.isdir(_lowercase ):
lowercase__ = os.path.join(
_lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowercase__ = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(_lowercase , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowercase__ = token_index
writer.write(token + "\n" )
index += 1
lowercase__ = os.path.join(_lowercase , "sentencepiece.bpe.model" )
with open(_lowercase , "wb" ) as fi:
lowercase__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (vocab_file,)
| 655 | 1 |
import numpy as np
_snake_case = [
["""a""", """b""", """c""", """d""", """e"""],
["""f""", """g""", """h""", """i""", """k"""],
["""l""", """m""", """n""", """o""", """p"""],
["""q""", """r""", """s""", """t""", """u"""],
["""v""", """w""", """x""", """y""", """z"""],
]
class lowerCAmelCase :
def __init__( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = np.array(_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ , lowercase__ = np.where(letter == self.SQUARE )
lowercase__ = np.concatenate([indexa + 1, indexa + 1] )
return indexes
def UpperCAmelCase ( self :int , _lowercase :int , _lowercase :int ):
'''simple docstring'''
lowercase__ = self.SQUARE[indexa - 1, indexa - 1]
return letter
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ = message.lower()
lowercase__ = message.replace(" " , "" )
lowercase__ = message.replace("j" , "i" )
lowercase__ = np.empty((2, len(_lowercase )) )
for letter_index in range(len(_lowercase ) ):
lowercase__ = self.letter_to_numbers(message[letter_index] )
lowercase__ = numbers[0]
lowercase__ = numbers[1]
lowercase__ = first_step.reshape(2 * len(_lowercase ) )
lowercase__ = ""
for numbers_index in range(len(_lowercase ) ):
lowercase__ = int(second_step[numbers_index * 2] )
lowercase__ = int(second_step[(numbers_index * 2) + 1] )
lowercase__ = self.numbers_to_letter(_lowercase , _lowercase )
lowercase__ = encoded_message + letter
return encoded_message
def UpperCAmelCase ( self :List[Any] , _lowercase :str ):
'''simple docstring'''
lowercase__ = message.lower()
message.replace(" " , "" )
lowercase__ = np.empty(2 * len(_lowercase ) )
for letter_index in range(len(_lowercase ) ):
lowercase__ = self.letter_to_numbers(message[letter_index] )
lowercase__ = numbers[0]
lowercase__ = numbers[1]
lowercase__ = first_step.reshape((2, len(_lowercase )) )
lowercase__ = ""
for numbers_index in range(len(_lowercase ) ):
lowercase__ = int(second_step[0, numbers_index] )
lowercase__ = int(second_step[1, numbers_index] )
lowercase__ = self.numbers_to_letter(_lowercase , _lowercase )
lowercase__ = decoded_message + letter
return decoded_message
| 655 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ = pre_numerator
lowercase__ = 2 * i // 3 if i % 3 == 0 else 1
lowercase__ = cur_numerator
lowercase__ = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 | 1 |
_snake_case = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
# Return True if there is node that has not iterated.
lowercase__ = [False] * len(__magic_name__ )
lowercase__ = [s]
lowercase__ = True
while queue:
lowercase__ = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__magic_name__ )
lowercase__ = True
lowercase__ = u
return visited[t]
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = [-1] * (len(__magic_name__ ))
lowercase__ = 0
lowercase__ = []
lowercase__ = [i[:] for i in graph] # Record original cut, copy.
while bfs(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = float("Inf" )
lowercase__ = sink
while s != source:
# Find the minimum value in select path
lowercase__ = min(__magic_name__ , graph[parent[s]][s] )
lowercase__ = parent[s]
max_flow += path_flow
lowercase__ = sink
while v != source:
lowercase__ = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowercase__ = parent[v]
for i in range(len(__magic_name__ ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 655 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'AutoTokenizer'
__lowerCamelCase = ['tokenizer']
__lowerCamelCase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self :Dict , _lowercase :List[str] , _lowercase :List[Any]=None ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = speaker_embeddings
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :int , _lowercase :str="speaker_embeddings_path.json" , **_lowercase :List[str] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
lowercase__ = get_file_from_repo(
_lowercase , _lowercase , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_lowercase , _lowercase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase__ = None
else:
with open(_lowercase ) as speaker_embeddings_json:
lowercase__ = json.load(_lowercase )
else:
lowercase__ = None
lowercase__ = AutoTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(tokenizer=_lowercase , speaker_embeddings=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :List[str]="speaker_embeddings_path.json" , _lowercase :Any="speaker_embeddings" , _lowercase :bool = False , **_lowercase :Any , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_lowercase , _lowercase , "v2" ) , exist_ok=_lowercase )
lowercase__ = {}
lowercase__ = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase__ = self._load_voice_preset(_lowercase )
lowercase__ = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , _lowercase , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_lowercase , )
lowercase__ = os.path.join(_lowercase , f'''{prompt_key}_{key}.npy''' )
lowercase__ = tmp_dict
with open(os.path.join(_lowercase , _lowercase ) , "w" ) as fp:
json.dump(_lowercase , _lowercase )
super().save_pretrained(_lowercase , _lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :str = None , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.speaker_embeddings[voice_preset]
lowercase__ = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase__ = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase__ = np.load(_lowercase )
return voice_preset_dict
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=None , _lowercase :List[str]="pt" , _lowercase :List[Any]=2_56 , _lowercase :List[str]=False , _lowercase :Union[str, Any]=True , _lowercase :Dict=False , **_lowercase :Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(_lowercase , _lowercase ):
if (
isinstance(_lowercase , _lowercase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase__ = self._load_voice_preset(_lowercase )
else:
if isinstance(_lowercase , _lowercase ) and not voice_preset.endswith(".npz" ):
lowercase__ = voice_preset + ".npz"
lowercase__ = np.load(_lowercase )
if voice_preset is not None:
self._validate_voice_preset_dict(_lowercase , **_lowercase )
lowercase__ = BatchFeature(data=_lowercase , tensor_type=_lowercase )
lowercase__ = self.tokenizer(
_lowercase , return_tensors=_lowercase , padding="max_length" , max_length=_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
if voice_preset is not None:
lowercase__ = voice_preset
return encoded_text
| 655 | 1 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__="pt" ):
lowercase__ = {"add_prefix_space": True} if isinstance(__magic_name__ , __magic_name__ ) and not line.startswith(" " ) else {}
lowercase__ = padding_side
return tokenizer(
[line] , max_length=__magic_name__ , padding="max_length" if pad_to_max_length else None , truncation=__magic_name__ , return_tensors=__magic_name__ , add_special_tokens=__magic_name__ , **__magic_name__ , )
def _A ( __magic_name__ , __magic_name__ , __magic_name__=None , ):
lowercase__ = input_ids.ne(__magic_name__ ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Union[str, Any] , _lowercase :Optional[Any] , _lowercase :List[str] , _lowercase :Any , _lowercase :List[Any] , _lowercase :Tuple="train" , _lowercase :int=None , _lowercase :List[str]=None , _lowercase :Tuple=None , _lowercase :Optional[int]="" , ):
'''simple docstring'''
super().__init__()
lowercase__ = Path(_lowercase ).joinpath(type_path + ".source" )
lowercase__ = Path(_lowercase ).joinpath(type_path + ".target" )
lowercase__ = self.get_char_lens(self.src_file )
lowercase__ = max_source_length
lowercase__ = max_target_length
assert min(self.src_lens ) > 0, f'''found empty line in {self.src_file}'''
lowercase__ = tokenizer
lowercase__ = prefix
if n_obs is not None:
lowercase__ = self.src_lens[:n_obs]
lowercase__ = src_lang
lowercase__ = tgt_lang
def __len__( self :Optional[int] ):
'''simple docstring'''
return len(self.src_lens )
def __getitem__( self :int , _lowercase :str ):
'''simple docstring'''
lowercase__ = index + 1 # linecache starts at 1
lowercase__ = self.prefix + linecache.getline(str(self.src_file ) , _lowercase ).rstrip("\n" )
lowercase__ = linecache.getline(str(self.tgt_file ) , _lowercase ).rstrip("\n" )
assert source_line, f'''empty source line for index {index}'''
assert tgt_line, f'''empty tgt line for index {index}'''
# Need to add eos token manually for T5
if isinstance(self.tokenizer , _lowercase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
lowercase__ = (
self.tokenizer.question_encoder if isinstance(self.tokenizer , _lowercase ) else self.tokenizer
)
lowercase__ = self.tokenizer.generator if isinstance(self.tokenizer , _lowercase ) else self.tokenizer
lowercase__ = encode_line(_lowercase , _lowercase , self.max_source_length , "right" )
lowercase__ = encode_line(_lowercase , _lowercase , self.max_target_length , "right" )
lowercase__ = source_inputs["input_ids"].squeeze()
lowercase__ = target_inputs["input_ids"].squeeze()
lowercase__ = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def UpperCAmelCase ( _lowercase :Optional[int] ):
'''simple docstring'''
return [len(_lowercase ) for x in Path(_lowercase ).open().readlines()]
def UpperCAmelCase ( self :Any , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = torch.stack([x["input_ids"] for x in batch] )
lowercase__ = torch.stack([x["attention_mask"] for x in batch] )
lowercase__ = torch.stack([x["decoder_input_ids"] for x in batch] )
lowercase__ = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer , _lowercase )
else self.tokenizer.pad_token_id
)
lowercase__ = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer , _lowercase )
else self.tokenizer.pad_token_id
)
lowercase__ = trim_batch(_lowercase , _lowercase )
lowercase__ , lowercase__ = trim_batch(_lowercase , _lowercase , attention_mask=_lowercase )
lowercase__ = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
_snake_case = getLogger(__name__)
def _A ( __magic_name__ ):
return list(itertools.chain.from_iterable(__magic_name__ ) )
def _A ( __magic_name__ ):
lowercase__ = get_git_info()
save_json(__magic_name__ , os.path.join(__magic_name__ , "git_log.json" ) )
def _A ( __magic_name__ , __magic_name__ , __magic_name__=4 , **__magic_name__ ):
with open(__magic_name__ , "w" ) as f:
json.dump(__magic_name__ , __magic_name__ , indent=__magic_name__ , **__magic_name__ )
def _A ( __magic_name__ ):
with open(__magic_name__ ) as f:
return json.load(__magic_name__ )
def _A ( ):
lowercase__ = git.Repo(search_parent_directories=__magic_name__ )
lowercase__ = {
"repo_id": str(__magic_name__ ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def _A ( __magic_name__ , __magic_name__ ):
return list(map(__magic_name__ , __magic_name__ ) )
def _A ( __magic_name__ , __magic_name__ ):
with open(__magic_name__ , "wb" ) as f:
return pickle.dump(__magic_name__ , __magic_name__ )
def _A ( __magic_name__ ):
def remove_articles(__magic_name__ ):
return re.sub(R"\b(a|an|the)\b" , " " , __magic_name__ )
def white_space_fix(__magic_name__ ):
return " ".join(text.split() )
def remove_punc(__magic_name__ ):
lowercase__ = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__magic_name__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__magic_name__ ) ) ) )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = normalize_answer(__magic_name__ ).split()
lowercase__ = normalize_answer(__magic_name__ ).split()
lowercase__ = Counter(__magic_name__ ) & Counter(__magic_name__ )
lowercase__ = sum(common.values() )
if num_same == 0:
return 0
lowercase__ = 1.0 * num_same / len(__magic_name__ )
lowercase__ = 1.0 * num_same / len(__magic_name__ )
lowercase__ = (2 * precision * recall) / (precision + recall)
return fa
def _A ( __magic_name__ , __magic_name__ ):
return normalize_answer(__magic_name__ ) == normalize_answer(__magic_name__ )
def _A ( __magic_name__ , __magic_name__ ):
assert len(__magic_name__ ) == len(__magic_name__ )
lowercase__ = 0
for hypo, pred in zip(__magic_name__ , __magic_name__ ):
em += exact_match_score(__magic_name__ , __magic_name__ )
if len(__magic_name__ ) > 0:
em /= len(__magic_name__ )
return {"em": em}
def _A ( __magic_name__ ):
return model_prefix.startswith("rag" )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
lowercase__ = "dropout_rate"
for p in extra_params:
if getattr(__magic_name__ , __magic_name__ , __magic_name__ ):
if not hasattr(__magic_name__ , __magic_name__ ) and not hasattr(__magic_name__ , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(__magic_name__ ) )
delattr(__magic_name__ , __magic_name__ )
continue
lowercase__ = p if hasattr(__magic_name__ , __magic_name__ ) else equivalent_param[p]
setattr(__magic_name__ , __magic_name__ , getattr(__magic_name__ , __magic_name__ ) )
delattr(__magic_name__ , __magic_name__ )
return hparams, config
| 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis_float32 (there's also the fix_lavis branch)
# also note: to convert Vicuna checkpoints, we had to include /home/niels/python_projects/checkpoints/FastChat/vicuna-7b in lavis/configs/models/blip2/blip2_instruct_vicuna7b.yaml
# same for Vicuna-13b
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipImageProcessor,
InstructBlipConfig,
InstructBlipForConditionalGeneration,
InstructBlipProcessor,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
LlamaConfig,
LlamaTokenizerFast,
TaConfig,
TaTokenizerFast,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def _A ( ):
lowercase__ = "https://raw.githubusercontent.com/salesforce/LAVIS/main/docs/_static/Confusing-Pictures.jpg"
lowercase__ = Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw ).convert("RGB" )
return image
def _A ( __magic_name__ ):
lowercase__ = []
# fmt: off
# vision encoder
rename_keys.append(("visual_encoder.cls_token", "vision_model.embeddings.class_embedding") )
rename_keys.append(("visual_encoder.pos_embed", "vision_model.embeddings.position_embedding") )
rename_keys.append(("visual_encoder.patch_embed.proj.weight", "vision_model.embeddings.patch_embedding.weight") )
rename_keys.append(("visual_encoder.patch_embed.proj.bias", "vision_model.embeddings.patch_embedding.bias") )
rename_keys.append(("ln_vision.weight", "vision_model.post_layernorm.weight") )
rename_keys.append(("ln_vision.bias", "vision_model.post_layernorm.bias") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.weight''', f'''vision_model.encoder.layers.{i}.layer_norm1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm1.bias''', f'''vision_model.encoder.layers.{i}.layer_norm1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.weight''', f'''vision_model.encoder.layers.{i}.layer_norm2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.norm2.bias''', f'''vision_model.encoder.layers.{i}.layer_norm2.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.qkv.weight''', f'''vision_model.encoder.layers.{i}.self_attn.qkv.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.weight''', f'''vision_model.encoder.layers.{i}.self_attn.projection.weight''',) )
rename_keys.append((f'''visual_encoder.blocks.{i}.attn.proj.bias''', f'''vision_model.encoder.layers.{i}.self_attn.projection.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc1.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc1.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc1.bias''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.weight''', f'''vision_model.encoder.layers.{i}.mlp.fc2.weight''') )
rename_keys.append((f'''visual_encoder.blocks.{i}.mlp.fc2.bias''', f'''vision_model.encoder.layers.{i}.mlp.fc2.bias''') )
# QFormer
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.weight", "qformer.embeddings.layernorm.weight") )
rename_keys.append(("Qformer.bert.embeddings.LayerNorm.bias", "qformer.embeddings.layernorm.bias") )
# fmt: on
return rename_keys
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = dct.pop(__magic_name__ )
lowercase__ = val
def _A ( __magic_name__ , __magic_name__ ):
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
lowercase__ = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.q_bias''' )
lowercase__ = state_dict.pop(f'''visual_encoder.blocks.{i}.attn.v_bias''' )
# next, set bias in the state dict
lowercase__ = torch.cat((q_bias, torch.zeros_like(__magic_name__ , requires_grad=__magic_name__ ), v_bias) )
lowercase__ = qkv_bias
def _A ( __magic_name__ ):
lowercase__ = 364 if "coco" in model_name else 224
lowercase__ = InstructBlipVisionConfig(image_size=__magic_name__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "t5-xl" in model_name:
lowercase__ = TaConfig.from_pretrained("google/flan-t5-xl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
lowercase__ = TaConfig.from_pretrained("google/flan-t5-xxl" , dense_act_fn="gelu" , bos_token_id=1 ).to_dict()
elif "vicuna-7b" in model_name:
lowercase__ = LlamaConfig.from_pretrained("decapoda-research/llama-7b-hf" , vocab_size=3_2001 ).to_dict()
elif "vicuna-13b" in model_name:
lowercase__ = LlamaConfig.from_pretrained("decapoda-research/llama-13b-hf" , vocab_size=3_2001 ).to_dict()
else:
raise ValueError("Model name not supported" )
# the authors add one special "[DEC]" token to the vocab of Q-Former, hence vocab size = 30522 + 1
lowercase__ = InstructBlipQFormerConfig(vocab_size=3_0523 ).to_dict()
lowercase__ = InstructBlipConfig(vision_config=__magic_name__ , text_config=__magic_name__ , qformer_config=__magic_name__ )
return config, image_size
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__=None , __magic_name__=False ):
lowercase__ = AutoTokenizer.from_pretrained("bert-base-uncased" , truncation_side="left" )
qformer_tokenizer.add_special_tokens({"bos_token": "[DEC]"} )
if "t5" in model_name:
lowercase__ = TaTokenizerFast.from_pretrained("google/flan-t5-xl" , truncation_side="left" )
elif "vicuna" in model_name:
# the following was used in the original implementation:
# tokenizer = LlamaTokenizer.from_pretrained("huggyllama/llama-7b", use_fast=False, truncation_side="left")
# tokenizer.add_special_tokens({"pad_token": "[PAD]"})
# tokenizer.add_special_tokens({"bos_token": "</s>"})
# tokenizer.add_special_tokens({"eos_token": "</s>"})
# tokenizer.add_special_tokens({"unk_token": "</s>"})
lowercase__ = LlamaTokenizerFast.from_pretrained(
"huggyllama/llama-7b" , truncation_side="left" , bos_token="</s>" , unk_token="</s>" )
tokenizer.add_special_tokens({"pad_token": "[PAD]"} )
lowercase__ , lowercase__ = get_blipa_config(__magic_name__ )
lowercase__ = InstructBlipForConditionalGeneration(__magic_name__ ).eval()
lowercase__ = {
"instructblip-vicuna-7b": ("blip2_vicuna_instruct", "vicuna7b"),
"instructblip-vicuna-13b": ("blip2_vicuna_instruct", "vicuna13b"),
"instructblip-flan-t5-xl": ("blip2_t5_instruct", "flant5xl"),
"instructblip-flan-t5-xxl": ("blip2_t5_instruct", "flant5xxl"),
}
lowercase__ , lowercase__ = model_name_to_original[model_name]
# load original model
print("Loading original model..." )
lowercase__ = "cuda:1" if torch.cuda.is_available() else "cpu"
lowercase__ = "cuda:2" if torch.cuda.is_available() else "cpu"
lowercase__ , lowercase__ , lowercase__ = load_model_and_preprocess(
name=__magic_name__ , model_type=__magic_name__ , is_eval=__magic_name__ , device=__magic_name__ )
original_model.eval()
print("Done!" )
# update state dict keys
lowercase__ = original_model.state_dict()
lowercase__ = create_rename_keys(__magic_name__ )
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
lowercase__ = state_dict.pop(__magic_name__ )
if key.startswith("Qformer.bert" ):
lowercase__ = key.replace("Qformer.bert" , "qformer" )
if "attention.self" in key:
lowercase__ = key.replace("self" , "attention" )
if "llm_proj" in key:
lowercase__ = key.replace("llm_proj" , "language_projection" )
if "t5_proj" in key:
lowercase__ = key.replace("t5_proj" , "language_projection" )
if key.startswith("llm_model" ):
lowercase__ = key.replace("llm_model" , "language_model" )
if key.startswith("t5" ):
lowercase__ = key.replace("t5" , "language" )
lowercase__ = val
# read in qv biases
read_in_q_v_bias(__magic_name__ , __magic_name__ )
# note: weights get loaded in torch.float32 by default
hf_model.load_state_dict(__magic_name__ , strict=__magic_name__ )
lowercase__ = load_demo_image()
lowercase__ = "What is unusual about this image?"
# create processor
lowercase__ = BlipImageProcessor(
size={"height": image_size, "width": image_size} , image_mean=__magic_name__ , image_std=__magic_name__ )
lowercase__ = InstructBlipProcessor(
image_processor=__magic_name__ , tokenizer=__magic_name__ , qformer_tokenizer=__magic_name__ , )
lowercase__ = processor(images=__magic_name__ , text=__magic_name__ , return_tensors="pt" ).to(__magic_name__ )
# make sure processor creates exact same pixel values
lowercase__ = vis_processors["eval"](__magic_name__ ).unsqueeze(0 ).to(__magic_name__ )
lowercase__ = inputs.pixel_values
assert torch.allclose(original_pixel_values.to(pixel_values.device ) , __magic_name__ )
original_model.to(__magic_name__ )
hf_model.to(__magic_name__ )
with torch.no_grad():
if "vicuna" in model_name:
lowercase__ = original_model({"image": original_pixel_values, "text_input": [prompt]} ).logits
lowercase__ = hf_model(**__magic_name__ ).logits
else:
lowercase__ = original_model(
{"image": original_pixel_values, "text_input": [prompt], "text_output": ["\n"]} ).logits
lowercase__ = tokenizer("\n" , return_tensors="pt" ).input_ids.to(__magic_name__ )
lowercase__ = label_input_ids.masked_fill(label_input_ids == tokenizer.pad_token_id , -100 )
lowercase__ = hf_model(**__magic_name__ , labels=__magic_name__ ).logits
print("First values of original logits:" , original_logits[0, :3, :3] )
print("First values of HF logits:" , logits[0, :3, :3] )
# assert values
assert original_logits.shape == logits.shape
lowercase__ = 1e-4 if "vicuna" in model_name else 1e-5
assert torch.allclose(original_logits.to(logits.device ) , __magic_name__ , atol=__magic_name__ )
print("Looks ok!" )
print("Generating with original model..." )
lowercase__ = original_model.generate({"image": original_pixel_values, "prompt": prompt} , num_beams=5 )
# important: we need to cast the weights of the HF model to the appropriate type
print("Generating with HF model..." )
lowercase__ = hf_model.generate(
**__magic_name__ , do_sample=__magic_name__ , num_beams=5 , max_length=256 , min_length=1 , top_p=0.9 , repetition_penalty=1.5 , length_penalty=1.0 , temperature=1 , )
if "vicuna" in model_name:
# convert output id 0 to 2 (eos_token_id)
# TODO add this in the generate method?
lowercase__ = 2
print("Original generation:" , __magic_name__ )
lowercase__ = processor.batch_decode(__magic_name__ , skip_special_tokens=__magic_name__ )
lowercase__ = [text.strip() for text in output_text]
print("HF generation:" , __magic_name__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(__magic_name__ )
hf_model.save_pretrained(__magic_name__ )
if push_to_hub:
processor.push_to_hub(f'''Salesforce/{model_name}''' )
hf_model.push_to_hub(f'''Salesforce/{model_name}''' )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
_snake_case = [
"""instructblip-vicuna-7b""",
"""instructblip-vicuna-13b""",
"""instructblip-flan-t5-xl""",
"""instructblip-flan-t5-xxl""",
]
parser.add_argument(
"""--model_name""",
default="""instructblip-flan-t5-xl""",
choices=choices,
type=str,
help="""Path to hf config.json of model to convert""",
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Whether to push the model and processor to the hub after converting""",
)
_snake_case = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'van'
def __init__( self :Optional[Any] , _lowercase :Dict=2_24 , _lowercase :Union[str, Any]=3 , _lowercase :List[Any]=[7, 3, 3, 3] , _lowercase :Any=[4, 2, 2, 2] , _lowercase :Union[str, Any]=[64, 1_28, 3_20, 5_12] , _lowercase :List[Any]=[3, 3, 12, 3] , _lowercase :Dict=[8, 8, 4, 4] , _lowercase :int="gelu" , _lowercase :List[Any]=0.02 , _lowercase :List[Any]=1e-6 , _lowercase :Any=1e-2 , _lowercase :int=0.0 , _lowercase :int=0.0 , **_lowercase :Dict , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = patch_sizes
lowercase__ = strides
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = mlp_ratios
lowercase__ = hidden_act
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = layer_scale_init_value
lowercase__ = drop_path_rate
lowercase__ = dropout_rate
| 655 | 1 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def _A ( __magic_name__ ):
lowercase__ = []
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.weight''',
f'''stage{idx}.patch_embed.proj.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.projection.bias''',
f'''stage{idx}.patch_embed.proj.bias''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.weight''',
f'''stage{idx}.patch_embed.norm.weight''',
) )
embed.append(
(
f'''cvt.encoder.stages.{idx}.embedding.convolution_embeddings.normalization.bias''',
f'''stage{idx}.patch_embed.norm.bias''',
) )
return embed
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_query.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_q.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_key.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_k.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.convolution.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.conv.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.weight''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.bias''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_mean''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_mean''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.running_var''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.running_var''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.convolution_projection_value.convolution_projection.normalization.num_batches_tracked''',
f'''stage{idx}.blocks.{cnt}.attn.conv_proj_v.bn.num_batches_tracked''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_query.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_q.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_key.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_k.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.attention.projection_value.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj_v.bias''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.weight''',
f'''stage{idx}.blocks.{cnt}.attn.proj.weight''',
) )
attention_weights.append(
(
f'''cvt.encoder.stages.{idx}.layers.{cnt}.attention.output.dense.bias''',
f'''stage{idx}.blocks.{cnt}.attn.proj.bias''',
) )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.intermediate.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.weight''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.output.dense.bias''', f'''stage{idx}.blocks.{cnt}.mlp.fc2.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.weight''', f'''stage{idx}.blocks.{cnt}.norm1.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_before.bias''', f'''stage{idx}.blocks.{cnt}.norm1.bias''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.weight''', f'''stage{idx}.blocks.{cnt}.norm2.weight''') )
attention_weights.append(
(f'''cvt.encoder.stages.{idx}.layers.{cnt}.layernorm_after.bias''', f'''stage{idx}.blocks.{cnt}.norm2.bias''') )
return attention_weights
def _A ( __magic_name__ ):
lowercase__ = []
token.append((f'''cvt.encoder.stages.{idx}.cls_token''', "stage2.cls_token") )
return token
def _A ( ):
lowercase__ = []
head.append(("layernorm.weight", "norm.weight") )
head.append(("layernorm.bias", "norm.bias") )
head.append(("classifier.weight", "head.weight") )
head.append(("classifier.bias", "head.bias") )
return head
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = "imagenet-1k-id2label.json"
lowercase__ = 1000
lowercase__ = "huggingface/label-files"
lowercase__ = num_labels
lowercase__ = json.load(open(cached_download(hf_hub_url(__magic_name__ , __magic_name__ , repo_type="dataset" ) ) , "r" ) )
lowercase__ = {int(__magic_name__ ): v for k, v in idalabel.items()}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = lowercase__ = CvtConfig(num_labels=__magic_name__ , idalabel=__magic_name__ , labelaid=__magic_name__ )
# For depth size 13 (13 = 1+2+10)
if cvt_model.rsplit("/" , 1 )[-1][4:6] == "13":
lowercase__ = [1, 2, 10]
# For depth size 21 (21 = 1+4+16)
elif cvt_model.rsplit("/" , 1 )[-1][4:6] == "21":
lowercase__ = [1, 4, 16]
# For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
else:
lowercase__ = [2, 2, 20]
lowercase__ = [3, 12, 16]
lowercase__ = [192, 768, 1024]
lowercase__ = CvtForImageClassification(__magic_name__ )
lowercase__ = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k" )
lowercase__ = image_size
lowercase__ = torch.load(__magic_name__ , map_location=torch.device("cpu" ) )
lowercase__ = OrderedDict()
lowercase__ = []
for idx in range(len(config.depth ) ):
if config.cls_token[idx]:
lowercase__ = list_of_state_dict + cls_token(__magic_name__ )
lowercase__ = list_of_state_dict + embeddings(__magic_name__ )
for cnt in range(config.depth[idx] ):
lowercase__ = list_of_state_dict + attention(__magic_name__ , __magic_name__ )
lowercase__ = list_of_state_dict + final()
for gg in list_of_state_dict:
print(__magic_name__ )
for i in range(len(__magic_name__ ) ):
lowercase__ = original_weights[list_of_state_dict[i][1]]
model.load_state_dict(__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
# Download the weights from zoo: https://1drv.ms/u/s!AhIXJn_J-blW9RzF3rMW7SsLHa8h?e=blQ0Al
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--cvt_model""",
default="""cvt-w24""",
type=str,
help="""Name of the cvt model you'd like to convert.""",
)
parser.add_argument(
"""--image_size""",
default=384,
type=int,
help="""Input Image Size""",
)
parser.add_argument(
"""--cvt_file_name""",
default=R"""cvtmodels\CvT-w24-384x384-IN-22k.pth""",
type=str,
help="""Input Image Size""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
_snake_case = parser.parse_args()
convert_cvt_checkpoint(args.cvt_model, args.image_size, args.cvt_file_name, args.pytorch_dump_folder_path)
| 655 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase ( enum.Enum ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 2
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self :Any , *_lowercase :Optional[Any] , **_lowercase :Union[str, Any] ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowercase__ = None
if self.model.config.prefix is not None:
lowercase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowercase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowercase__ , lowercase__ , lowercase__ = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
lowercase__ = {**self._preprocess_params, **preprocess_params}
lowercase__ = {**self._forward_params, **forward_params}
def UpperCAmelCase ( self :Tuple , _lowercase :Optional[Any]=None , _lowercase :List[Any]=None , _lowercase :List[str]=None , _lowercase :Optional[Any]=None , _lowercase :Optional[int]=None , _lowercase :Any=None , _lowercase :Any=None , _lowercase :Dict=None , **_lowercase :Union[str, Any] , ):
'''simple docstring'''
lowercase__ = {}
if prefix is not None:
lowercase__ = prefix
if prefix:
lowercase__ = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
lowercase__ = handle_long_generation
preprocess_params.update(_lowercase )
lowercase__ = generate_kwargs
lowercase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.TENSORS
if return_type is not None:
lowercase__ = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
lowercase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self :int , *_lowercase :Optional[int] , **_lowercase :List[str] ):
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self :Union[str, Any] , _lowercase :Dict , **_lowercase :Tuple ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple , _lowercase :Optional[int]="" , _lowercase :Tuple=None , **_lowercase :List[str] ):
'''simple docstring'''
lowercase__ = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prompt_text
if handle_long_generation == "hole":
lowercase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowercase__ = generate_kwargs["max_new_tokens"]
else:
lowercase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowercase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
lowercase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
lowercase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def UpperCAmelCase ( self :str , _lowercase :int , **_lowercase :str ):
'''simple docstring'''
lowercase__ = model_inputs["input_ids"]
lowercase__ = model_inputs.get("attention_mask" , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowercase__ = None
lowercase__ = None
lowercase__ = 1
else:
lowercase__ = input_ids.shape[0]
lowercase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowercase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
lowercase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
lowercase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowercase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowercase__ = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
lowercase__ = generated_sequence.shape[0]
if self.framework == "pt":
lowercase__ = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowercase__ = tf.reshape(_lowercase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def UpperCAmelCase ( self :Any , _lowercase :Tuple , _lowercase :str=ReturnType.FULL_TEXT , _lowercase :Dict=True ):
'''simple docstring'''
lowercase__ = model_outputs["generated_sequence"][0]
lowercase__ = model_outputs["input_ids"]
lowercase__ = model_outputs["prompt_text"]
lowercase__ = generated_sequence.numpy().tolist()
lowercase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowercase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowercase__ = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowercase__ = 0
else:
lowercase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
lowercase__ = prompt_text + text[prompt_length:]
else:
lowercase__ = text[prompt_length:]
lowercase__ = {"generated_text": all_text}
records.append(_lowercase )
return records
| 655 | 1 |
import math
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = len(__magic_name__ )
lowercase__ = int(math.floor(math.sqrt(__magic_name__ ) ) )
lowercase__ = 0
while arr[min(__magic_name__ , __magic_name__ ) - 1] < x:
lowercase__ = step
step += int(math.floor(math.sqrt(__magic_name__ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
lowercase__ = prev + 1
if prev == min(__magic_name__ , __magic_name__ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
_snake_case = input("""Enter numbers separated by a comma:\n""").strip()
_snake_case = [int(item) for item in user_input.split(""",""")]
_snake_case = int(input("""Enter the number to be searched:\n"""))
_snake_case = jump_search(arr, x)
if res == -1:
print("""Number not found!""")
else:
print(F"""Number {x} is at index {res}""")
| 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_snake_case = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _A ( __magic_name__ ):
lowercase__ = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__magic_name__ )[0]
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(rows * cols * num_images )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
lowercase__ = data.reshape(__magic_name__ , __magic_name__ , __magic_name__ , 1 )
return data
@deprecated(__magic_name__ , "Please use tf.one_hot on tensors." )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = labels_dense.shape[0]
lowercase__ = numpy.arange(__magic_name__ ) * num_classes
lowercase__ = numpy.zeros((num_labels, num_classes) )
lowercase__ = 1
return labels_one_hot
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(__magic_name__ )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__magic_name__ , __magic_name__ )
return labels
class lowerCAmelCase :
@deprecated(
_lowercase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Tuple=False , _lowercase :str=False , _lowercase :Dict=dtypes.floataa , _lowercase :Optional[Any]=True , _lowercase :Any=None , ):
'''simple docstring'''
lowercase__ , lowercase__ = random_seed.get_seed(_lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ = dtypes.as_dtype(_lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
lowercase__ = 1_00_00
lowercase__ = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
lowercase__ = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ = images.astype(numpy.floataa )
lowercase__ = numpy.multiply(_lowercase , 1.0 / 255.0 )
lowercase__ = images
lowercase__ = labels
lowercase__ = 0
lowercase__ = 0
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._images
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self._labels
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return self._num_examples
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._epochs_completed
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] , _lowercase :Any=False , _lowercase :Union[str, Any]=True ):
'''simple docstring'''
if fake_data:
lowercase__ = [1] * 7_84
lowercase__ = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_lowercase )],
[fake_label for _ in range(_lowercase )],
)
lowercase__ = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perma]
lowercase__ = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ = self._num_examples - start
lowercase__ = self._images[start : self._num_examples]
lowercase__ = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perm]
lowercase__ = self.labels[perm]
# Start next epoch
lowercase__ = 0
lowercase__ = batch_size - rest_num_examples
lowercase__ = self._index_in_epoch
lowercase__ = self._images[start:end]
lowercase__ = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__magic_name__ , "Please write your own downloading logic." )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if not gfile.Exists(__magic_name__ ):
gfile.MakeDirs(__magic_name__ )
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
if not gfile.Exists(__magic_name__ ):
urllib.request.urlretrieve(__magic_name__ , __magic_name__ ) # noqa: S310
with gfile.GFile(__magic_name__ ) as f:
lowercase__ = f.size()
print("Successfully downloaded" , __magic_name__ , __magic_name__ , "bytes." )
return filepath
@deprecated(
__magic_name__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=5000 , __magic_name__=None , __magic_name__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__magic_name__ , one_hot=__magic_name__ , dtype=__magic_name__ , seed=__magic_name__ )
lowercase__ = fake()
lowercase__ = fake()
lowercase__ = fake()
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
if not source_url: # empty string check
lowercase__ = DEFAULT_SOURCE_URL
lowercase__ = "train-images-idx3-ubyte.gz"
lowercase__ = "train-labels-idx1-ubyte.gz"
lowercase__ = "t10k-images-idx3-ubyte.gz"
lowercase__ = "t10k-labels-idx1-ubyte.gz"
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
if not 0 <= validation_size <= len(__magic_name__ ):
lowercase__ = (
"Validation size should be between 0 and "
f'''{len(__magic_name__ )}. Received: {validation_size}.'''
)
raise ValueError(__magic_name__ )
lowercase__ = train_images[:validation_size]
lowercase__ = train_labels[:validation_size]
lowercase__ = train_images[validation_size:]
lowercase__ = train_labels[validation_size:]
lowercase__ = {"dtype": dtype, "reshape": reshape, "seed": seed}
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
| 655 | 1 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FlaubertForMultipleChoice,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertModel,
FlaubertWithLMHeadModel,
)
from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST
class lowerCAmelCase ( lowercase_ ):
def __init__( self :int , _lowercase :Union[str, Any] , _lowercase :Optional[Any]=13 , _lowercase :int=7 , _lowercase :str=True , _lowercase :Tuple=True , _lowercase :List[Any]=True , _lowercase :int=True , _lowercase :Union[str, Any]=True , _lowercase :Optional[int]=False , _lowercase :int=False , _lowercase :Tuple=False , _lowercase :Tuple=2 , _lowercase :List[str]=99 , _lowercase :Optional[Any]=0 , _lowercase :str=32 , _lowercase :List[str]=5 , _lowercase :Any=4 , _lowercase :Optional[int]=0.1 , _lowercase :Any=0.1 , _lowercase :int=5_12 , _lowercase :Tuple=12 , _lowercase :Union[str, Any]=2 , _lowercase :List[str]=0.02 , _lowercase :Any=3 , _lowercase :Optional[int]=4 , _lowercase :List[str]="last" , _lowercase :Optional[int]=None , _lowercase :Optional[Any]=None , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_input_lengths
lowercase__ = use_token_type_ids
lowercase__ = use_labels
lowercase__ = gelu_activation
lowercase__ = sinusoidal_embeddings
lowercase__ = causal
lowercase__ = asm
lowercase__ = n_langs
lowercase__ = vocab_size
lowercase__ = n_special
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = type_vocab_size
lowercase__ = type_sequence_label_size
lowercase__ = initializer_range
lowercase__ = num_labels
lowercase__ = num_choices
lowercase__ = summary_type
lowercase__ = use_proj
lowercase__ = scope
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
if self.use_input_lengths:
lowercase__ = (
ids_tensor([self.batch_size] , vocab_size=2 ) + self.seq_length - 2
) # small variation of seq_length
lowercase__ = None
if self.use_token_type_ids:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.n_langs )
lowercase__ = None
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ = ids_tensor([self.batch_size] , 2 ).float()
lowercase__ = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ = self.get_config()
return (
config,
input_ids,
token_type_ids,
input_lengths,
sequence_labels,
token_labels,
is_impossible_labels,
choice_labels,
input_mask,
)
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return FlaubertConfig(
vocab_size=self.vocab_size , n_special=self.n_special , emb_dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , gelu_activation=self.gelu_activation , sinusoidal_embeddings=self.sinusoidal_embeddings , asm=self.asm , causal=self.causal , n_langs=self.n_langs , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , summary_type=self.summary_type , use_proj=self.use_proj , )
def UpperCAmelCase ( self :Tuple , _lowercase :List[Any] , _lowercase :Dict , _lowercase :Optional[Any] , _lowercase :List[Any] , _lowercase :List[str] , _lowercase :List[Any] , _lowercase :str , _lowercase :List[Any] , _lowercase :Optional[Any] , ):
'''simple docstring'''
lowercase__ = FlaubertModel(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase , lengths=_lowercase , langs=_lowercase )
lowercase__ = model(_lowercase , langs=_lowercase )
lowercase__ = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Optional[int] , _lowercase :Optional[Any] , _lowercase :Dict , _lowercase :Tuple , _lowercase :int , _lowercase :List[Any] , _lowercase :Union[str, Any] , _lowercase :Optional[Any] , _lowercase :List[str] , ):
'''simple docstring'''
lowercase__ = FlaubertWithLMHeadModel(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase , token_type_ids=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self :int , _lowercase :int , _lowercase :Optional[Any] , _lowercase :Optional[Any] , _lowercase :Optional[int] , _lowercase :Any , _lowercase :Any , _lowercase :str , _lowercase :Any , _lowercase :List[str] , ):
'''simple docstring'''
lowercase__ = FlaubertForQuestionAnsweringSimple(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
lowercase__ = model(_lowercase , start_positions=_lowercase , end_positions=_lowercase )
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 UpperCAmelCase ( self :Tuple , _lowercase :Any , _lowercase :List[str] , _lowercase :List[str] , _lowercase :List[Any] , _lowercase :str , _lowercase :List[Any] , _lowercase :int , _lowercase :Optional[int] , _lowercase :List[Any] , ):
'''simple docstring'''
lowercase__ = FlaubertForQuestionAnswering(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
lowercase__ = model(
_lowercase , start_positions=_lowercase , end_positions=_lowercase , cls_index=_lowercase , is_impossible=_lowercase , p_mask=_lowercase , )
lowercase__ = model(
_lowercase , start_positions=_lowercase , end_positions=_lowercase , cls_index=_lowercase , is_impossible=_lowercase , )
((lowercase__) , ) = result_with_labels.to_tuple()
lowercase__ = model(_lowercase , start_positions=_lowercase , end_positions=_lowercase )
((lowercase__) , ) = result_with_labels.to_tuple()
self.parent.assertEqual(result_with_labels.loss.shape , () )
self.parent.assertEqual(result.start_top_log_probs.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(result.start_top_index.shape , (self.batch_size, model.config.start_n_top) )
self.parent.assertEqual(
result.end_top_log_probs.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(
result.end_top_index.shape , (self.batch_size, model.config.start_n_top * model.config.end_n_top) )
self.parent.assertEqual(result.cls_logits.shape , (self.batch_size,) )
def UpperCAmelCase ( self :Tuple , _lowercase :str , _lowercase :Optional[int] , _lowercase :Optional[int] , _lowercase :Any , _lowercase :Optional[Any] , _lowercase :str , _lowercase :Any , _lowercase :str , _lowercase :int , ):
'''simple docstring'''
lowercase__ = FlaubertForSequenceClassification(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
lowercase__ = model(_lowercase , labels=_lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase ( self :List[str] , _lowercase :int , _lowercase :Union[str, Any] , _lowercase :Optional[int] , _lowercase :Optional[int] , _lowercase :str , _lowercase :Any , _lowercase :Optional[int] , _lowercase :Union[str, Any] , _lowercase :Optional[int] , ):
'''simple docstring'''
lowercase__ = self.num_labels
lowercase__ = FlaubertForTokenClassification(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase , attention_mask=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self :List[Any] , _lowercase :List[str] , _lowercase :str , _lowercase :Dict , _lowercase :List[Any] , _lowercase :int , _lowercase :Union[str, Any] , _lowercase :Tuple , _lowercase :int , _lowercase :Any , ):
'''simple docstring'''
lowercase__ = self.num_choices
lowercase__ = FlaubertForMultipleChoice(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
lowercase__ = model(
_lowercase , attention_mask=_lowercase , token_type_ids=_lowercase , labels=_lowercase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {
"input_ids": input_ids,
"token_type_ids": token_type_ids,
"lengths": input_lengths,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_torch
class lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
__lowerCamelCase = (
(
FlaubertModel,
FlaubertWithLMHeadModel,
FlaubertForQuestionAnswering,
FlaubertForQuestionAnsweringSimple,
FlaubertForSequenceClassification,
FlaubertForTokenClassification,
FlaubertForMultipleChoice,
)
if is_torch_available()
else ()
)
__lowerCamelCase = (
{
'feature-extraction': FlaubertModel,
'fill-mask': FlaubertWithLMHeadModel,
'question-answering': FlaubertForQuestionAnsweringSimple,
'text-classification': FlaubertForSequenceClassification,
'token-classification': FlaubertForTokenClassification,
'zero-shot': FlaubertForSequenceClassification,
}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Dict , _lowercase :Union[str, Any] , _lowercase :Union[str, Any] , _lowercase :Optional[int] , _lowercase :Dict ):
'''simple docstring'''
if (
pipeline_test_casse_name == "QAPipelineTests"
and tokenizer_name is not None
and not tokenizer_name.endswith("Fast" )
):
# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
return True
return False
def UpperCAmelCase ( self :Any , _lowercase :Union[str, Any] , _lowercase :Tuple , _lowercase :Dict=False ):
'''simple docstring'''
lowercase__ = super()._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
if return_labels:
if model_class.__name__ == "FlaubertForQuestionAnswering":
lowercase__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
lowercase__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=_lowercase )
return inputs_dict
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = FlaubertModelTester(self )
lowercase__ = ConfigTester(self , config_class=_lowercase , emb_dim=37 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_model(*_lowercase )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_lm_head(*_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_simple_qa(*_lowercase )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_qa(*_lowercase )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_sequence_classif(*_lowercase )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_token_classif(*_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_flaubert_multiple_choice(*_lowercase )
@slow
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = FlaubertModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@slow
@require_torch_gpu
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == FlaubertForMultipleChoice:
return
lowercase__ = True
lowercase__ = model_class(config=_lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase )
lowercase__ = torch.jit.trace(
_lowercase , (inputs_dict["input_ids"].to("cpu" ), inputs_dict["attention_mask"].to("cpu" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowercase , os.path.join(_lowercase , "traced_model.pt" ) )
lowercase__ = torch.jit.load(os.path.join(_lowercase , "traced_model.pt" ) , map_location=_lowercase )
loaded(inputs_dict["input_ids"].to(_lowercase ) , inputs_dict["attention_mask"].to(_lowercase ) )
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = FlaubertModel.from_pretrained("flaubert/flaubert_base_cased" )
lowercase__ = torch.tensor([[0, 3_45, 2_32, 3_28, 7_40, 1_40, 16_95, 69, 60_78, 15_88, 2]] )
with torch.no_grad():
lowercase__ = model(_lowercase )[0]
lowercase__ = torch.Size((1, 11, 7_68) )
self.assertEqual(output.shape , _lowercase )
lowercase__ = torch.tensor(
[[[-2.6251, -1.4298, -0.0227], [-2.8510, -1.6387, 0.2258], [-2.8114, -1.1832, -0.3066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowercase , atol=1e-4 ) )
| 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ):
'''simple docstring'''
lowercase__ = []
lowercase__ = self
while temp:
string_rep.append(f'''{temp.data}''' )
lowercase__ = temp.next
return "->".join(_lowercase )
def _A ( __magic_name__ ):
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase__ = lowercase__ = Node(elements_list[0] )
for i in range(1 , len(__magic_name__ ) ):
lowercase__ = Node(elements_list[i] )
lowercase__ = current.next
return head
def _A ( __magic_name__ ):
if head_node is not None and isinstance(__magic_name__ , __magic_name__ ):
print_reverse(head_node.next )
print(head_node.data )
def _A ( ):
from doctest import testmod
testmod()
lowercase__ = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(__magic_name__ )
print("Elements in Reverse:" )
print_reverse(__magic_name__ )
if __name__ == "__main__":
main()
| 655 | 1 |
_snake_case = {"""a""": ["""c""", """b"""], """b""": ["""d""", """e"""], """c""": [], """d""": [], """e""": []}
_snake_case = ["""a""", """b""", """c""", """d""", """e"""]
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = start
# add current to visited
visited.append(__magic_name__ )
lowercase__ = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
lowercase__ = topological_sort(__magic_name__ , __magic_name__ , __magic_name__ )
# if all neighbors visited add current to sort
sort.append(__magic_name__ )
# if all vertices haven't been visited select a new one to visit
if len(__magic_name__ ) != len(__magic_name__ ):
for vertice in vertices:
if vertice not in visited:
lowercase__ = topological_sort(__magic_name__ , __magic_name__ , __magic_name__ )
# return sort
return sort
if __name__ == "__main__":
_snake_case = topological_sort("""a""", [], [])
print(sort)
| 655 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowercase__ = 0
while count < prec:
lowercase__ = random.randint(2 , n - 1 )
lowercase__ = bin_exp_mod(__magic_name__ , __magic_name__ , __magic_name__ )
if b != 1:
lowercase__ = True
for _ in range(__magic_name__ ):
if b == n - 1:
lowercase__ = False
break
lowercase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
_snake_case = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 655 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'wavlm'
def __init__( self :int , _lowercase :str=32 , _lowercase :List[str]=7_68 , _lowercase :int=12 , _lowercase :str=12 , _lowercase :Any=30_72 , _lowercase :Optional[Any]="gelu" , _lowercase :List[str]=0.1 , _lowercase :List[str]=0.1 , _lowercase :Tuple=0.1 , _lowercase :List[Any]=0.0 , _lowercase :Union[str, Any]=0.1 , _lowercase :int=0.1 , _lowercase :List[str]=0.02 , _lowercase :Tuple=1e-5 , _lowercase :Union[str, Any]="group" , _lowercase :Any="gelu" , _lowercase :Dict=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , _lowercase :int=(5, 2, 2, 2, 2, 2, 2) , _lowercase :List[str]=(10, 3, 3, 3, 3, 2, 2) , _lowercase :Union[str, Any]=False , _lowercase :Optional[int]=1_28 , _lowercase :Optional[Any]=16 , _lowercase :Dict=3_20 , _lowercase :List[str]=8_00 , _lowercase :List[Any]=False , _lowercase :Dict=True , _lowercase :List[str]=0.05 , _lowercase :int=10 , _lowercase :List[str]=2 , _lowercase :Dict=0.0 , _lowercase :str=10 , _lowercase :List[str]=3_20 , _lowercase :int=2 , _lowercase :Optional[int]=0.1 , _lowercase :str=1_00 , _lowercase :int=2_56 , _lowercase :Union[str, Any]=2_56 , _lowercase :Optional[int]=0.1 , _lowercase :Union[str, Any]="mean" , _lowercase :Tuple=False , _lowercase :int=False , _lowercase :Any=2_56 , _lowercase :Optional[Any]=(5_12, 5_12, 5_12, 5_12, 15_00) , _lowercase :Dict=(5, 3, 3, 1, 1) , _lowercase :Tuple=(1, 2, 3, 1, 1) , _lowercase :Optional[Any]=5_12 , _lowercase :str=80 , _lowercase :str=0 , _lowercase :List[Any]=1 , _lowercase :List[Any]=2 , _lowercase :Dict=False , _lowercase :List[Any]=3 , _lowercase :Dict=2 , _lowercase :Tuple=3 , _lowercase :Union[str, Any]=None , **_lowercase :List[str] , ):
'''simple docstring'''
super().__init__(**_lowercase , pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase )
lowercase__ = hidden_size
lowercase__ = feat_extract_norm
lowercase__ = feat_extract_activation
lowercase__ = list(_lowercase )
lowercase__ = list(_lowercase )
lowercase__ = list(_lowercase )
lowercase__ = conv_bias
lowercase__ = num_buckets
lowercase__ = max_bucket_distance
lowercase__ = num_conv_pos_embeddings
lowercase__ = num_conv_pos_embedding_groups
lowercase__ = len(self.conv_dim )
lowercase__ = num_hidden_layers
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_attention_heads
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = feat_proj_dropout
lowercase__ = final_dropout
lowercase__ = layerdrop
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
lowercase__ = num_ctc_classes
lowercase__ = vocab_size
lowercase__ = do_stable_layer_norm
lowercase__ = use_weighted_layer_sum
lowercase__ = classifier_proj_size
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
lowercase__ = apply_spec_augment
lowercase__ = mask_time_prob
lowercase__ = mask_time_length
lowercase__ = mask_time_min_masks
lowercase__ = mask_feature_prob
lowercase__ = mask_feature_length
# parameters for pretraining with codevector quantized representations
lowercase__ = num_codevectors_per_group
lowercase__ = num_codevector_groups
lowercase__ = contrastive_logits_temperature
lowercase__ = num_negatives
lowercase__ = codevector_dim
lowercase__ = proj_codevector_dim
lowercase__ = diversity_loss_weight
# ctc loss
lowercase__ = ctc_loss_reduction
lowercase__ = ctc_zero_infinity
# adapter
lowercase__ = add_adapter
lowercase__ = adapter_kernel_size
lowercase__ = adapter_stride
lowercase__ = num_adapter_layers
lowercase__ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase__ = list(_lowercase )
lowercase__ = list(_lowercase )
lowercase__ = list(_lowercase )
lowercase__ = xvector_output_dim
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCAmelCase :
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["prompt"]
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
if "image" in inputs:
lowercase__ = inputs["image"]
else:
lowercase__ = None
if "mask_image" in inputs:
lowercase__ = inputs["mask_image"]
else:
lowercase__ = None
if "original_image" in inputs:
lowercase__ = inputs["original_image"]
else:
lowercase__ = None
lowercase__ , lowercase__ = pipe.encode_prompt(_lowercase )
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowercase , _lowercase , _lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowercase , _lowercase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
| 655 | 1 |
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
_snake_case = logging.get_logger(__name__)
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Any , *_lowercase :List[str] , **_lowercase :Dict ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
requires_backends(self , "decord" )
self.check_model_type(_lowercase )
def UpperCAmelCase ( self :str , _lowercase :Any=None , _lowercase :List[str]=None , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = {}
if frame_sampling_rate is not None:
lowercase__ = frame_sampling_rate
if num_frames is not None:
lowercase__ = num_frames
lowercase__ = {}
if top_k is not None:
lowercase__ = top_k
return preprocess_params, {}, postprocess_params
def __call__( self :Union[str, Any] , _lowercase :Union[str, List[str]] , **_lowercase :int ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :List[str] , _lowercase :List[Any] , _lowercase :List[str]=None , _lowercase :List[Any]=1 ):
'''simple docstring'''
if num_frames is None:
lowercase__ = self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
lowercase__ = BytesIO(requests.get(_lowercase ).content )
lowercase__ = VideoReader(_lowercase )
videoreader.seek(0 )
lowercase__ = 0
lowercase__ = num_frames * frame_sampling_rate - 1
lowercase__ = np.linspace(_lowercase , _lowercase , num=_lowercase , dtype=np.intaa )
lowercase__ = videoreader.get_batch(_lowercase ).asnumpy()
lowercase__ = list(_lowercase )
lowercase__ = self.image_processor(_lowercase , return_tensors=self.framework )
return model_inputs
def UpperCAmelCase ( self :List[Any] , _lowercase :str ):
'''simple docstring'''
lowercase__ = self.model(**_lowercase )
return model_outputs
def UpperCAmelCase ( self :Tuple , _lowercase :Union[str, Any] , _lowercase :str=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
lowercase__ = self.model.config.num_labels
if self.framework == "pt":
lowercase__ = model_outputs.logits.softmax(-1 )[0]
lowercase__ , lowercase__ = probs.topk(_lowercase )
else:
raise ValueError(f'''Unsupported framework: {self.framework}''' )
lowercase__ = scores.tolist()
lowercase__ = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(_lowercase , _lowercase )]
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
lowercase__ = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ = model(_lowercase )["last_hidden_state"]
lowercase__ = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
lowercase__ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 655 | 1 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class lowerCAmelCase :
@staticmethod
def UpperCAmelCase ( *_lowercase :Any , **_lowercase :Optional[int] ):
'''simple docstring'''
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = MODEL_FOR_OBJECT_DETECTION_MAPPING
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Any , _lowercase :Optional[Any] ):
'''simple docstring'''
lowercase__ = ObjectDetectionPipeline(model=_lowercase , image_processor=_lowercase )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def UpperCAmelCase ( self :List[Any] , _lowercase :int , _lowercase :Any ):
'''simple docstring'''
lowercase__ = object_detector("./tests/fixtures/tests_samples/COCO/000000039769.png" , threshold=0.0 )
self.assertGreater(len(_lowercase ) , 0 )
for detected_object in outputs:
self.assertEqual(
_lowercase , {
"score": ANY(_lowercase ),
"label": ANY(_lowercase ),
"box": {"xmin": ANY(_lowercase ), "ymin": ANY(_lowercase ), "xmax": ANY(_lowercase ), "ymax": ANY(_lowercase )},
} , )
import datasets
lowercase__ = datasets.load_dataset("hf-internal-testing/fixtures_image_utils" , "image" , split="test" )
lowercase__ = [
Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"http://images.cocodataset.org/val2017/000000039769.jpg",
# RGBA
dataset[0]["file"],
# LA
dataset[1]["file"],
# L
dataset[2]["file"],
]
lowercase__ = object_detector(_lowercase , threshold=0.0 )
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for outputs in batch_outputs:
self.assertGreater(len(_lowercase ) , 0 )
for detected_object in outputs:
self.assertEqual(
_lowercase , {
"score": ANY(_lowercase ),
"label": ANY(_lowercase ),
"box": {"xmin": ANY(_lowercase ), "ymin": ANY(_lowercase ), "xmax": ANY(_lowercase ), "ymax": ANY(_lowercase )},
} , )
@require_tf
@unittest.skip("Object detection not implemented in TF" )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
pass
@require_torch
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = "hf-internal-testing/tiny-detr-mobilenetsv3"
lowercase__ = AutoModelForObjectDetection.from_pretrained(_lowercase )
lowercase__ = AutoFeatureExtractor.from_pretrained(_lowercase )
lowercase__ = ObjectDetectionPipeline(model=_lowercase , feature_extractor=_lowercase )
lowercase__ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=0.0 )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
] , )
lowercase__ = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
],
[
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
{"score": 0.3376, "label": "LABEL_0", "box": {"xmin": 1_59, "ymin": 1_20, "xmax": 4_80, "ymax": 3_59}},
],
] , )
@require_torch
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = "facebook/detr-resnet-50"
lowercase__ = AutoModelForObjectDetection.from_pretrained(_lowercase )
lowercase__ = AutoFeatureExtractor.from_pretrained(_lowercase )
lowercase__ = ObjectDetectionPipeline(model=_lowercase , feature_extractor=_lowercase )
lowercase__ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
] , )
lowercase__ = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
] , )
@require_torch
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = "facebook/detr-resnet-50"
lowercase__ = pipeline("object-detection" , model=_lowercase )
lowercase__ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
] , )
lowercase__ = object_detector(
[
"http://images.cocodataset.org/val2017/000000039769.jpg",
"http://images.cocodataset.org/val2017/000000039769.jpg",
] )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
[
{"score": 0.9982, "label": "remote", "box": {"xmin": 40, "ymin": 70, "xmax": 1_75, "ymax": 1_17}},
{"score": 0.9960, "label": "remote", "box": {"xmin": 3_33, "ymin": 72, "xmax": 3_68, "ymax": 1_87}},
{"score": 0.9955, "label": "couch", "box": {"xmin": 0, "ymin": 1, "xmax": 6_39, "ymax": 4_73}},
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
],
] , )
@require_torch
@slow
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = 0.9985
lowercase__ = "facebook/detr-resnet-50"
lowercase__ = pipeline("object-detection" , model=_lowercase )
lowercase__ = object_detector("http://images.cocodataset.org/val2017/000000039769.jpg" , threshold=_lowercase )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"score": 0.9988, "label": "cat", "box": {"xmin": 13, "ymin": 52, "xmax": 3_14, "ymax": 4_70}},
{"score": 0.9987, "label": "cat", "box": {"xmin": 3_45, "ymin": 23, "xmax": 6_40, "ymax": 3_68}},
] , )
@require_torch
@require_pytesseract
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = "Narsil/layoutlmv3-finetuned-funsd"
lowercase__ = 0.9993
lowercase__ = pipeline("object-detection" , model=_lowercase , threshold=_lowercase )
lowercase__ = object_detector(
"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png" )
self.assertEqual(
nested_simplify(_lowercase , decimals=4 ) , [
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
{"score": 0.9993, "label": "I-ANSWER", "box": {"xmin": 2_94, "ymin": 2_54, "xmax": 3_43, "ymax": 2_64}},
] , )
| 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(__magic_name__ )
lowercase__ = "".join(bin(__magic_name__ )[2:].zfill(8 ) for byte in data )
lowercase__ = len(__magic_name__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase__ = B"=" * ((6 - len(__magic_name__ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(__magic_name__ ) % 6)
else:
lowercase__ = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(__magic_name__ ) , 6 ) ).encode()
+ padding
)
def _A ( __magic_name__ ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ) and not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = (
"argument should be a bytes-like object or ASCII string, "
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(__magic_name__ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(__magic_name__ , __magic_name__ ):
try:
lowercase__ = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
lowercase__ = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(__magic_name__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase__ = encoded_data[:-padding]
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase__ = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(__magic_name__ ) , 8 )
]
return bytes(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _A ( __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = (1 - _cos) / 2
lowercase__ = 1 - _cos
lowercase__ = 1 + alpha
lowercase__ = -2 * _cos
lowercase__ = 1 - alpha
lowercase__ = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _A ( __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = (1 + _cos) / 2
lowercase__ = -1 - _cos
lowercase__ = 1 + alpha
lowercase__ = -2 * _cos
lowercase__ = 1 - alpha
lowercase__ = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _A ( __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = _sin / 2
lowercase__ = 0
lowercase__ = -ba
lowercase__ = 1 + alpha
lowercase__ = -2 * _cos
lowercase__ = 1 - alpha
lowercase__ = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _A ( __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = 1 - alpha
lowercase__ = -2 * _cos
lowercase__ = 1 + alpha
lowercase__ = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) , ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = 10 ** (gain_db / 40)
lowercase__ = 1 + alpha * big_a
lowercase__ = -2 * _cos
lowercase__ = 1 - alpha * big_a
lowercase__ = 1 + alpha / big_a
lowercase__ = -2 * _cos
lowercase__ = 1 - alpha / big_a
lowercase__ = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) , ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = 10 ** (gain_db / 40)
lowercase__ = (big_a + 1) - (big_a - 1) * _cos
lowercase__ = (big_a + 1) + (big_a - 1) * _cos
lowercase__ = (big_a - 1) - (big_a + 1) * _cos
lowercase__ = (big_a - 1) + (big_a + 1) * _cos
lowercase__ = 2 * sqrt(__magic_name__ ) * alpha
lowercase__ = big_a * (pmc + aaa)
lowercase__ = 2 * big_a * mpc
lowercase__ = big_a * (pmc - aaa)
lowercase__ = ppmc + aaa
lowercase__ = -2 * pmpc
lowercase__ = ppmc - aaa
lowercase__ = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = 1 / sqrt(2 ) , ):
lowercase__ = tau * frequency / samplerate
lowercase__ = sin(__magic_name__ )
lowercase__ = cos(__magic_name__ )
lowercase__ = _sin / (2 * q_factor)
lowercase__ = 10 ** (gain_db / 40)
lowercase__ = (big_a + 1) - (big_a - 1) * _cos
lowercase__ = (big_a + 1) + (big_a - 1) * _cos
lowercase__ = (big_a - 1) - (big_a + 1) * _cos
lowercase__ = (big_a - 1) + (big_a + 1) * _cos
lowercase__ = 2 * sqrt(__magic_name__ ) * alpha
lowercase__ = big_a * (ppmc + aaa)
lowercase__ = -2 * big_a * pmpc
lowercase__ = big_a * (ppmc - aaa)
lowercase__ = pmc + aaa
lowercase__ = 2 * mpc
lowercase__ = pmc - aaa
lowercase__ = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 655 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Dict , _lowercase :TransformeraDModel , _lowercase :AutoencoderKL , _lowercase :KarrasDiffusionSchedulers , _lowercase :Optional[Dict[int, str]] = None , ):
'''simple docstring'''
super().__init__()
self.register_modules(transformer=_lowercase , vae=_lowercase , scheduler=_lowercase )
# create a imagenet -> id dictionary for easier use
lowercase__ = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
lowercase__ = int(_lowercase )
lowercase__ = dict(sorted(self.labels.items() ) )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Union[str, List[str]] ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
lowercase__ = list(_lowercase )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self :Optional[Any] , _lowercase :List[int] , _lowercase :float = 4.0 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :int = 50 , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = len(_lowercase )
lowercase__ = self.transformer.config.sample_size
lowercase__ = self.transformer.config.in_channels
lowercase__ = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=_lowercase , device=self.device , dtype=self.transformer.dtype , )
lowercase__ = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowercase__ = torch.tensor(_lowercase , device=self.device ).reshape(-1 )
lowercase__ = torch.tensor([10_00] * batch_size , device=self.device )
lowercase__ = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(_lowercase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowercase__ = latent_model_input[: len(_lowercase ) // 2]
lowercase__ = torch.cat([half, half] , dim=0 )
lowercase__ = self.scheduler.scale_model_input(_lowercase , _lowercase )
lowercase__ = t
if not torch.is_tensor(_lowercase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
lowercase__ = latent_model_input.device.type == "mps"
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.floataa if is_mps else torch.floataa
else:
lowercase__ = torch.intaa if is_mps else torch.intaa
lowercase__ = torch.tensor([timesteps] , dtype=_lowercase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowercase__ = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase__ = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowercase__ = self.transformer(
_lowercase , timestep=_lowercase , class_labels=_lowercase ).sample
# perform guidance
if guidance_scale > 1:
lowercase__ , lowercase__ = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowercase__ , lowercase__ = torch.split(_lowercase , len(_lowercase ) // 2 , dim=0 )
lowercase__ = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowercase__ = torch.cat([half_eps, half_eps] , dim=0 )
lowercase__ = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowercase__ , lowercase__ = torch.split(_lowercase , _lowercase , dim=1 )
else:
lowercase__ = noise_pred
# compute previous image: x_t -> x_t-1
lowercase__ = self.scheduler.step(_lowercase , _lowercase , _lowercase ).prev_sample
if guidance_scale > 1:
lowercase__ , lowercase__ = latent_model_input.chunk(2 , dim=0 )
else:
lowercase__ = latent_model_input
lowercase__ = 1 / self.vae.config.scaling_factor * latents
lowercase__ = self.vae.decode(_lowercase ).sample
lowercase__ = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase__ = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
# 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 copy
import importlib.metadata
import json
import os
from dataclasses import dataclass
from typing import Any, Dict, Union
from packaging import version
from ..utils import is_torch_available, logging
if is_torch_available():
import torch
_snake_case = logging.get_logger(__name__)
@dataclass
class lowerCAmelCase :
def __init__( self :List[str] , _lowercase :Optional[int]=False , _lowercase :Tuple=False , _lowercase :str=6.0 , _lowercase :List[Any]=None , _lowercase :Union[str, Any]=False , _lowercase :List[Any]=False , _lowercase :List[Any]=None , _lowercase :str="fp4" , _lowercase :Dict=False , **_lowercase :Dict , ):
'''simple docstring'''
lowercase__ = load_in_abit
lowercase__ = load_in_abit
lowercase__ = llm_inta_threshold
lowercase__ = llm_inta_skip_modules
lowercase__ = llm_inta_enable_fpaa_cpu_offload
lowercase__ = llm_inta_has_fpaa_weight
lowercase__ = bnb_abit_quant_type
lowercase__ = bnb_abit_use_double_quant
if bnb_abit_compute_dtype is None:
lowercase__ = torch.floataa
elif isinstance(_lowercase , _lowercase ):
lowercase__ = getattr(_lowercase , _lowercase )
elif isinstance(_lowercase , torch.dtype ):
lowercase__ = bnb_abit_compute_dtype
else:
raise ValueError("bnb_4bit_compute_dtype must be a string or a torch.dtype" )
self.post_init()
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
if not isinstance(self.llm_inta_threshold , _lowercase ):
raise ValueError("llm_int8_threshold must be a float" )
if self.llm_inta_skip_modules is not None and not isinstance(self.llm_inta_skip_modules , _lowercase ):
raise ValueError("llm_int8_skip_modules must be a list of strings" )
if not isinstance(self.llm_inta_enable_fpaa_cpu_offload , _lowercase ):
raise ValueError("llm_int8_enable_fp32_cpu_offload must be a boolean" )
if not isinstance(self.llm_inta_has_fpaa_weight , _lowercase ):
raise ValueError("llm_int8_has_fp16_weight must be a boolean" )
if self.bnb_abit_compute_dtype is not None and not isinstance(self.bnb_abit_compute_dtype , torch.dtype ):
raise ValueError("bnb_4bit_compute_dtype must be torch.dtype" )
if not isinstance(self.bnb_abit_quant_type , _lowercase ):
raise ValueError("bnb_4bit_quant_type must be a string" )
if not isinstance(self.bnb_abit_use_double_quant , _lowercase ):
raise ValueError("bnb_4bit_use_double_quant must be a boolean" )
if self.load_in_abit and not version.parse(importlib.metadata.version("bitsandbytes" ) ) >= version.parse(
"0.39.0" ):
raise ValueError(
"4 bit quantization requires bitsandbytes>=0.39.0 - please upgrade your bitsandbytes version" )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return self.load_in_abit or self.load_in_abit
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
if self.load_in_abit:
return "llm_int8"
elif self.load_in_abit and self.bnb_abit_quant_type == "fp4":
return "fp4"
elif self.load_in_abit and self.bnb_abit_quant_type == "nf4":
return "nf4"
else:
return None
@classmethod
def UpperCAmelCase ( cls :Optional[int] , _lowercase :Optional[int] , _lowercase :Dict , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = cls(**_lowercase )
lowercase__ = []
for key, value in kwargs.items():
if hasattr(_lowercase , _lowercase ):
setattr(_lowercase , _lowercase , _lowercase )
to_remove.append(_lowercase )
for key in to_remove:
kwargs.pop(_lowercase , _lowercase )
if return_unused_kwargs:
return config, kwargs
else:
return config
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, os.PathLike] ):
'''simple docstring'''
with open(_lowercase , "w" , encoding="utf-8" ) as writer:
lowercase__ = self.to_dict()
lowercase__ = json.dumps(_lowercase , indent=2 , sort_keys=_lowercase ) + "\n"
writer.write(_lowercase )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = str(output["bnb_4bit_compute_dtype"] ).split("." )[1]
return output
def __repr__( self :List[str] ):
'''simple docstring'''
return f'''{self.__class__.__name__} {self.to_json_string()}'''
def UpperCAmelCase ( self :Tuple , _lowercase :bool = True ):
'''simple docstring'''
if use_diff is True:
lowercase__ = self.to_diff_dict()
else:
lowercase__ = self.to_dict()
return json.dumps(_lowercase , indent=2 , sort_keys=_lowercase ) + "\n"
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = self.to_dict()
# get the default config dict
lowercase__ = BitsAndBytesConfig().to_dict()
lowercase__ = {}
# only serialize values that differ from the default config
for key, value in config_dict.items():
if value != default_config_dict[key]:
lowercase__ = value
return serializable_config_dict
| 655 |
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 ( lowercase_ ):
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = SMALL_MODEL_IDENTIFIER
lowercase__ = "pt"
lowercase__ = "tf"
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowercase )
def UpperCAmelCase ( self :Tuple , _lowercase :int ):
'''simple docstring'''
lowercase__ = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowercase )
model_tf.save_pretrained(_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = "mock_framework"
# Framework provided - return whatever the user provides
lowercase__ = FeaturesManager.determine_framework(self.test_model , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_tf )
# Both in environment -> use PyTorch
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# Both not in environment -> raise error
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
| 655 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def _A ( __magic_name__ , __magic_name__=7 ):
lowercase__ = None
if token is not None:
lowercase__ = {"Accept": "application/vnd.github+json", "Authorization": f'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
lowercase__ = "636036"
lowercase__ = 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}'''
lowercase__ = requests.get(__magic_name__ , headers=__magic_name__ ).json()
return result["workflow_runs"]
def _A ( __magic_name__ ):
lowercase__ = get_daily_ci_runs(__magic_name__ )
lowercase__ = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
lowercase__ = workflow_run["id"]
break
return workflow_run_id
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = get_last_daily_ci_runs(__magic_name__ )
if workflow_run_id is not None:
lowercase__ = get_artifacts_links(worflow_run_id=__magic_name__ , token=__magic_name__ )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
lowercase__ = artifacts_links[artifact_name]
download_artifact(
artifact_name=__magic_name__ , artifact_url=__magic_name__ , output_dir=__magic_name__ , token=__magic_name__ )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
get_last_daily_ci_artifacts(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = {}
for artifact_name in artifact_names:
lowercase__ = os.path.join(__magic_name__ , f'''{artifact_name}.zip''' )
if os.path.isfile(__magic_name__ ):
lowercase__ = {}
with zipfile.ZipFile(__magic_name__ ) as z:
for filename in z.namelist():
if not os.path.isdir(__magic_name__ ):
# read the file
with z.open(__magic_name__ ) as f:
lowercase__ = f.read().decode("UTF-8" )
return results
| 655 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git_vision_model'
def __init__( self :Dict , _lowercase :Dict=7_68 , _lowercase :Dict=30_72 , _lowercase :Tuple=12 , _lowercase :List[str]=12 , _lowercase :Tuple=3 , _lowercase :Dict=2_24 , _lowercase :Tuple=16 , _lowercase :Optional[int]="quick_gelu" , _lowercase :Union[str, Any]=1e-5 , _lowercase :Tuple=0.0 , _lowercase :Tuple=0.02 , **_lowercase :Optional[Any] , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_channels
lowercase__ = patch_size
lowercase__ = image_size
lowercase__ = initializer_range
lowercase__ = attention_dropout
lowercase__ = layer_norm_eps
lowercase__ = hidden_act
@classmethod
def UpperCAmelCase ( cls :List[str] , _lowercase :Union[str, os.PathLike] , **_lowercase :Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
lowercase__ , lowercase__ = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
lowercase__ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowercase , **_lowercase )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git'
def __init__( self :Union[str, Any] , _lowercase :Dict=None , _lowercase :List[str]=3_05_22 , _lowercase :Tuple=7_68 , _lowercase :Any=6 , _lowercase :Dict=12 , _lowercase :Any=30_72 , _lowercase :List[Any]="gelu" , _lowercase :Tuple=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :Optional[Any]=10_24 , _lowercase :Any=0.02 , _lowercase :int=1e-12 , _lowercase :List[Any]=0 , _lowercase :int="absolute" , _lowercase :List[str]=True , _lowercase :Any=False , _lowercase :int=1_01 , _lowercase :str=1_02 , _lowercase :Dict=None , **_lowercase :List[str] , ):
'''simple docstring'''
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , pad_token_id=_lowercase , **_lowercase )
if vision_config is None:
lowercase__ = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
lowercase__ = GitVisionConfig(**_lowercase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
lowercase__ = tie_word_embeddings
lowercase__ = num_image_with_embedding
lowercase__ = bos_token_id
lowercase__ = eos_token_id
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.vision_config.to_dict()
lowercase__ = self.__class__.model_type
return output
| 655 | 1 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
lowercase__ = inspect.getmembers(_lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowercase__ = "k-diffusion"
elif backend == "invisible_watermark":
lowercase__ = "invisible-watermark"
assert backend in deps, f'''{backend} is not in the deps table!'''
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModel.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModel.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForPreTraining.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForCausalLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForMaskedLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
| 655 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
_snake_case = False
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return 12
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return 12
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return 32
@property
def UpperCAmelCase ( self :int ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , num_vq_embeddings=self.num_embed , vq_embed_dim=3 , )
return model
@property
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def UpperCAmelCase ( self :int ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
return CLIPTextModel(_lowercase )
@property
def UpperCAmelCase ( self :str ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = 12
lowercase__ = 12
lowercase__ = {
"attention_bias": True,
"cross_attention_dim": 32,
"attention_head_dim": height * width,
"num_attention_heads": 1,
"num_vector_embeds": self.num_embed,
"num_embeds_ada_norm": self.num_embeds_ada_norm,
"norm_num_groups": 32,
"sample_size": width,
"activation_fn": "geglu-approximate",
}
lowercase__ = TransformeraDModel(**_lowercase )
return model
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = "cpu"
lowercase__ = self.dummy_vqvae
lowercase__ = self.dummy_text_encoder
lowercase__ = self.dummy_tokenizer
lowercase__ = self.dummy_transformer
lowercase__ = VQDiffusionScheduler(self.num_embed )
lowercase__ = LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase )
lowercase__ = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
lowercase__ = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = "teddy bear playing in the pool"
lowercase__ = torch.Generator(device=_lowercase ).manual_seed(0 )
lowercase__ = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="np" )
lowercase__ = output.images
lowercase__ = torch.Generator(device=_lowercase ).manual_seed(0 )
lowercase__ = pipe(
[prompt] , generator=_lowercase , output_type="np" , return_dict=_lowercase , num_inference_steps=2 )[0]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
lowercase__ = np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = "cpu"
lowercase__ = self.dummy_vqvae
lowercase__ = self.dummy_text_encoder
lowercase__ = self.dummy_tokenizer
lowercase__ = self.dummy_transformer
lowercase__ = VQDiffusionScheduler(self.num_embed )
lowercase__ = LearnedClassifierFreeSamplingEmbeddings(
learnable=_lowercase , hidden_size=self.text_embedder_hidden_size , length=tokenizer.model_max_length )
lowercase__ = VQDiffusionPipeline(
vqvae=_lowercase , text_encoder=_lowercase , tokenizer=_lowercase , transformer=_lowercase , scheduler=_lowercase , learned_classifier_free_sampling_embeddings=_lowercase , )
lowercase__ = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = "teddy bear playing in the pool"
lowercase__ = torch.Generator(device=_lowercase ).manual_seed(0 )
lowercase__ = pipe([prompt] , generator=_lowercase , num_inference_steps=2 , output_type="np" )
lowercase__ = output.images
lowercase__ = torch.Generator(device=_lowercase ).manual_seed(0 )
lowercase__ = pipe(
[prompt] , generator=_lowercase , output_type="np" , return_dict=_lowercase , num_inference_steps=2 )[0]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 24, 24, 3)
lowercase__ = np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2.0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch_gpu
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy" )
lowercase__ = VQDiffusionPipeline.from_pretrained("microsoft/vq-diffusion-ithq" )
lowercase__ = pipeline.to(_lowercase )
pipeline.set_progress_bar_config(disable=_lowercase )
# requires GPU generator for gumbel softmax
# don't use GPU generator in tests though
lowercase__ = torch.Generator(device=_lowercase ).manual_seed(0 )
lowercase__ = pipeline(
"teddy bear playing in the pool" , num_images_per_prompt=1 , generator=_lowercase , output_type="np" , )
lowercase__ = output.images[0]
assert image.shape == (2_56, 2_56, 3)
assert np.abs(expected_image - image ).max() < 2.0
| 655 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
def _A ( __magic_name__ ):
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
lowercase__ = value
else:
lowercase__ = value
return new_state_dict
def _A ( __magic_name__ ):
lowercase__ = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ = in_proj_weight_cross_attn[:256, :]
lowercase__ = in_proj_bias_cross_attn[:256]
lowercase__ = in_proj_weight_cross_attn[256:512, :]
lowercase__ = in_proj_bias_cross_attn[256:512]
lowercase__ = in_proj_weight_cross_attn[-256:, :]
lowercase__ = in_proj_bias_cross_attn[-256:]
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = max(__magic_name__ , __magic_name__ )
lowercase__ = 800 if "detection" in checkpoint_url else 1000
lowercase__ = target_max_size / current_max_size
lowercase__ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def _A ( __magic_name__ ):
lowercase__ = F.to_tensor(__magic_name__ )
lowercase__ = F.normalize(__magic_name__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
logger.info("Converting model..." )
# load original state dict
lowercase__ = torch.hub.load_state_dict_from_url(__magic_name__ , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = rename_backbone_keys(__magic_name__ )
# query, key and value matrices need special treatment
read_in_q_k_v(__magic_name__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
# create HuggingFace model and load state dict
lowercase__ = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase__ = 15
lowercase__ = 2
lowercase__ = {0: "table", 1: "table rotated"}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
else:
lowercase__ = 125
lowercase__ = 6
lowercase__ = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1000 )
lowercase__ = TableTransformerForObjectDetection(__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
# verify our conversion
lowercase__ = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
lowercase__ = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=__magic_name__ )
lowercase__ = Image.open(__magic_name__ ).convert("RGB" )
lowercase__ = normalize(resize(__magic_name__ , __magic_name__ ) ).unsqueeze(0 )
lowercase__ = model(__magic_name__ )
if "detection" in checkpoint_url:
lowercase__ = (1, 15, 3)
lowercase__ = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
lowercase__ = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
lowercase__ = (1, 125, 7)
lowercase__ = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
lowercase__ = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __magic_name__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __magic_name__ , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
lowercase__ = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(__magic_name__ )
image_processor.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_snake_case = {
"""configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""AlbertTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""AlbertTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AlbertForMaskedLM""",
"""AlbertForMultipleChoice""",
"""AlbertForPreTraining""",
"""AlbertForQuestionAnswering""",
"""AlbertForSequenceClassification""",
"""AlbertForTokenClassification""",
"""AlbertModel""",
"""AlbertPreTrainedModel""",
"""load_tf_weights_in_albert""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFAlbertForMaskedLM""",
"""TFAlbertForMultipleChoice""",
"""TFAlbertForPreTraining""",
"""TFAlbertForQuestionAnswering""",
"""TFAlbertForSequenceClassification""",
"""TFAlbertForTokenClassification""",
"""TFAlbertMainLayer""",
"""TFAlbertModel""",
"""TFAlbertPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""FlaxAlbertForMaskedLM""",
"""FlaxAlbertForMultipleChoice""",
"""FlaxAlbertForPreTraining""",
"""FlaxAlbertForQuestionAnswering""",
"""FlaxAlbertForSequenceClassification""",
"""FlaxAlbertForTokenClassification""",
"""FlaxAlbertModel""",
"""FlaxAlbertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
from torch import nn
class lowerCAmelCase ( nn.Module ):
def __init__( self :Tuple , _lowercase :Optional[Any] , _lowercase :Dict ):
'''simple docstring'''
super().__init__()
lowercase__ = class_size
lowercase__ = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
lowercase__ = nn.Linear(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.mlp(_lowercase )
return logits
| 655 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case = """
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def _A ( __magic_name__ , __magic_name__ , __magic_name__=8 ):
lowercase__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase ( lowercase_ ):
def __init__( self :List[str] , _lowercase :UNetaDConditionModel , _lowercase :DDPMScheduler , _lowercase :VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_lowercase , scheduler=_lowercase , movq=_lowercase , )
lowercase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Tuple , _lowercase :List[str] , _lowercase :Tuple , _lowercase :Optional[Any] , _lowercase :int , _lowercase :str ):
'''simple docstring'''
if latents is None:
lowercase__ = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase__ = latents.to(_lowercase )
lowercase__ = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase ( self :int , _lowercase :int=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
lowercase__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=_lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase__ , lowercase__ = cpu_offload_with_hook(_lowercase , _lowercase , prev_module_hook=_lowercase )
# We'll offload the last model manually.
lowercase__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowercase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowercase )
def __call__( self :int , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :int = 5_12 , _lowercase :int = 5_12 , _lowercase :int = 1_00 , _lowercase :float = 4.0 , _lowercase :int = 1 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :Optional[torch.FloatTensor] = None , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = self._execution_device
lowercase__ = guidance_scale > 1.0
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
lowercase__ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
if do_classifier_free_guidance:
lowercase__ = image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = negative_image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowercase )
self.scheduler.set_timesteps(_lowercase , device=_lowercase )
lowercase__ = self.scheduler.timesteps
lowercase__ = self.unet.config.in_channels
lowercase__ , lowercase__ = downscale_height_and_width(_lowercase , _lowercase , self.movq_scale_factor )
# create initial latent
lowercase__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowercase , _lowercase , _lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowercase ) ):
# expand the latents if we are doing classifier free guidance
lowercase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase__ = {"image_embeds": image_embeds}
lowercase__ = self.unet(
sample=_lowercase , timestep=_lowercase , encoder_hidden_states=_lowercase , added_cond_kwargs=_lowercase , return_dict=_lowercase , )[0]
if do_classifier_free_guidance:
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
lowercase__ , lowercase__ = noise_pred.chunk(2 )
lowercase__ , lowercase__ = variance_pred.chunk(2 )
lowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase__ = self.scheduler.step(
_lowercase , _lowercase , _lowercase , generator=_lowercase , )[0]
# post-processing
lowercase__ = self.movq.decode(_lowercase , force_not_quantize=_lowercase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase__ = image * 0.5 + 0.5
lowercase__ = image.clamp(0 , 1 )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
from unittest.mock import patch
import pyspark
from datasets.packaged_modules.spark.spark import (
Spark,
SparkExamplesIterable,
_generate_iterable_examples,
)
from ..utils import (
require_dill_gt_0_3_2,
require_not_windows,
)
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
for part_id in partition_order:
lowercase__ = df.where(f'''SPARK_PARTITION_ID() = {part_id}''' ).collect()
for row_idx, row in enumerate(__magic_name__ ):
expected_row_ids_and_row_dicts.append((f'''{part_id}_{row_idx}''', row.asDict()) )
return expected_row_ids_and_row_dicts
@require_not_windows
@require_dill_gt_0_3_2
def _A ( ):
lowercase__ = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowercase__ = spark.range(100 ).repartition(1 )
lowercase__ = Spark(__magic_name__ )
# The id ints will be converted to Pyarrow int64s, so each row will be 8 bytes. Setting a max_shard_size of 16 means
# that each partition can hold 2 rows.
spark_builder._repartition_df_if_needed(max_shard_size=16 )
# Given that the dataframe has 100 rows and each partition has 2 rows, we expect 50 partitions.
assert spark_builder.df.rdd.getNumPartitions() == 50
@require_not_windows
@require_dill_gt_0_3_2
def _A ( ):
lowercase__ = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowercase__ = spark.range(10 ).repartition(2 )
lowercase__ = [1, 0]
lowercase__ = _generate_iterable_examples(__magic_name__ , __magic_name__ ) # Reverse the partitions.
lowercase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__magic_name__ , __magic_name__ )
for i, (row_id, row_dict) in enumerate(generate_fn() ):
lowercase__ , lowercase__ = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def _A ( ):
lowercase__ = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowercase__ = spark.range(10 ).repartition(1 )
lowercase__ = SparkExamplesIterable(__magic_name__ )
assert it.n_shards == 1
for i, (row_id, row_dict) in enumerate(__magic_name__ ):
assert row_id == f'''0_{i}'''
assert row_dict == {"id": i}
@require_not_windows
@require_dill_gt_0_3_2
def _A ( ):
lowercase__ = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowercase__ = spark.range(30 ).repartition(3 )
# Mock the generator so that shuffle reverses the partition indices.
with patch("numpy.random.Generator" ) as generator_mock:
lowercase__ = lambda __magic_name__ : x.reverse()
lowercase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__magic_name__ , [2, 1, 0] )
lowercase__ = SparkExamplesIterable(__magic_name__ ).shuffle_data_sources(__magic_name__ )
assert shuffled_it.n_shards == 3
for i, (row_id, row_dict) in enumerate(__magic_name__ ):
lowercase__ , lowercase__ = expected_row_ids_and_row_dicts[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def _A ( ):
lowercase__ = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowercase__ = spark.range(20 ).repartition(4 )
# Partitions 0 and 2
lowercase__ = SparkExamplesIterable(__magic_name__ ).shard_data_sources(worker_id=0 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowercase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__magic_name__ , [0, 2] )
for i, (row_id, row_dict) in enumerate(__magic_name__ ):
lowercase__ , lowercase__ = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
# Partitions 1 and 3
lowercase__ = SparkExamplesIterable(__magic_name__ ).shard_data_sources(worker_id=1 , num_workers=2 )
assert shard_it_a.n_shards == 2
lowercase__ = _get_expected_row_ids_and_row_dicts_for_partition_order(__magic_name__ , [1, 3] )
for i, (row_id, row_dict) in enumerate(__magic_name__ ):
lowercase__ , lowercase__ = expected_row_ids_and_row_dicts_a[i]
assert row_id == expected_row_id
assert row_dict == expected_row_dict
@require_not_windows
@require_dill_gt_0_3_2
def _A ( ):
lowercase__ = pyspark.sql.SparkSession.builder.master("local[*]" ).appName("pyspark" ).getOrCreate()
lowercase__ = spark.range(100 ).repartition(1 )
lowercase__ = Spark(__magic_name__ )
# Choose a small max_shard_size for maximum partitioning.
spark_builder._repartition_df_if_needed(max_shard_size=1 )
# The new number of partitions should not be greater than the number of rows.
assert spark_builder.df.rdd.getNumPartitions() == 100
| 655 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
lowercase__ = inspect.getmembers(_lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowercase__ = "k-diffusion"
elif backend == "invisible_watermark":
lowercase__ = "invisible-watermark"
assert backend in deps, f'''{backend} is not in the deps table!'''
| 655 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = ViTImageProcessor if is_vision_available() else None
@property
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = (3, 32, 1_28)
lowercase__ = tempfile.mkdtemp()
# fmt: off
lowercase__ = ["[GO]", "[s]", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "a", "b", "c", "d", "e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", "q", "r", "s", "t", "u", "v", "w", "x", "y", "z"]
# fmt: on
lowercase__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
lowercase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
with open(self.vocab_file , "w" , encoding="utf-8" ) as fp:
fp.write(json.dumps(_lowercase ) + "\n" )
lowercase__ = {
"do_normalize": False,
"do_resize": True,
"image_processor_type": "ViTImageProcessor",
"resample": 3,
"size": {"height": 32, "width": 1_28},
}
lowercase__ = os.path.join(self.tmpdirname , _lowercase )
with open(self.image_processor_file , "w" , encoding="utf-8" ) as fp:
json.dump(_lowercase , _lowercase )
def UpperCAmelCase ( self :Tuple , **_lowercase :List[Any] ):
'''simple docstring'''
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase ( self :Any , **_lowercase :Optional[Any] ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )
lowercase__ = Image.fromarray(np.moveaxis(_lowercase , 0 , -1 ) )
return image_input
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
processor.save_pretrained(self.tmpdirname )
lowercase__ = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_lowercase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowercase )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_image_processor()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
processor.save_pretrained(self.tmpdirname )
lowercase__ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
lowercase__ = self.get_image_processor(do_normalize=_lowercase , padding_value=1.0 )
lowercase__ = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="(BOS)" , eos_token="(EOS)" , do_normalize=_lowercase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = self.prepare_image_inputs()
lowercase__ = image_processor(_lowercase , return_tensors="np" )
lowercase__ = processor(images=_lowercase , return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = "test"
lowercase__ = processor(text=_lowercase )
lowercase__ = tokenizer(_lowercase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = "test"
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , ["pixel_values", "labels"] )
# test if it raises when no input is passed
with pytest.raises(_lowercase ):
processor()
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
lowercase__ = processor.char_decode(_lowercase )
lowercase__ = tokenizer.batch_decode(_lowercase )
lowercase__ = [seq.replace(" " , "" ) for seq in decoded_tok]
self.assertListEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = None
lowercase__ = self.prepare_image_inputs()
lowercase__ = processor(text=_lowercase , images=_lowercase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = self.get_image_processor()
lowercase__ = self.get_tokenizer()
lowercase__ = MgpstrProcessor(tokenizer=_lowercase , image_processor=_lowercase )
lowercase__ = torch.randn(1 , 27 , 38 )
lowercase__ = torch.randn(1 , 27 , 5_02_57 )
lowercase__ = torch.randn(1 , 27 , 3_05_22 )
lowercase__ = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["generated_text", "scores", "char_preds", "bpe_preds", "wp_preds"] )
| 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class lowerCAmelCase :
__lowerCamelCase = 42
# setable values
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = None
@classmethod
def UpperCAmelCase ( cls :Union[str, Any] , _lowercase :CommonSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray ):
'''simple docstring'''
return cls(common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase )
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
__lowerCamelCase = 42
@property
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return True
@register_to_config
def __init__( self :str , _lowercase :int = 10_00 , _lowercase :float = 0.0001 , _lowercase :float = 0.02 , _lowercase :str = "linear" , _lowercase :Optional[jnp.ndarray] = None , _lowercase :str = "fixed_small" , _lowercase :bool = True , _lowercase :str = "epsilon" , _lowercase :jnp.dtype = jnp.floataa , ):
'''simple docstring'''
lowercase__ = dtype
def UpperCAmelCase ( self :str , _lowercase :Optional[CommonSchedulerState] = None ):
'''simple docstring'''
if common is None:
lowercase__ = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
lowercase__ = jnp.array(1.0 , dtype=self.dtype )
lowercase__ = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCAmelCase ( self :List[str] , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :Tuple = () ):
'''simple docstring'''
lowercase__ = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
lowercase__ = (jnp.arange(0 , _lowercase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Tuple , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :List[str]=None , _lowercase :Tuple=None ):
'''simple docstring'''
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowercase__ = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
lowercase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
lowercase__ = jnp.clip(_lowercase , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
lowercase__ = jnp.log(jnp.clip(_lowercase , a_min=1e-20 ) )
elif variance_type == "fixed_large":
lowercase__ = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
lowercase__ = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
lowercase__ = variance
lowercase__ = state.common.betas[t]
lowercase__ = (predicted_variance + 1) / 2
lowercase__ = frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase ( self :Optional[int] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :int , _lowercase :jnp.ndarray , _lowercase :Optional[jax.random.KeyArray] = None , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = timestep
if key is None:
lowercase__ = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
lowercase__ , lowercase__ = jnp.split(_lowercase , sample.shape[1] , axis=1 )
else:
lowercase__ = None
# 1. compute alphas, betas
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
lowercase__ = 1 - alpha_prod_t
lowercase__ = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowercase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowercase__ = model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowercase__ = jnp.clip(_lowercase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
lowercase__ = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
lowercase__ = jax.random.split(_lowercase , num=1 )
lowercase__ = jax.random.normal(_lowercase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(_lowercase , _lowercase , predicted_variance=_lowercase ) ** 0.5) * noise
lowercase__ = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
lowercase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=_lowercase , state=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return add_noise_common(state.common , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Dict , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return get_velocity_common(state.common , _lowercase , _lowercase , _lowercase )
def __len__( self :List[str] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 655 | 1 |
import argparse
import json
import subprocess
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
lowercase__ = (
f'''curl -H "Accept: application/vnd.github+json" -H "Authorization: Bearer {token}"'''
" https://api.github.com/repos/huggingface/transformers/actions/runners"
)
lowercase__ = subprocess.run(__magic_name__ , shell=__magic_name__ , stdout=subprocess.PIPE )
lowercase__ = output.stdout.decode("utf-8" )
lowercase__ = json.loads(__magic_name__ )
lowercase__ = status["runners"]
for runner in runners:
if runner["name"] in target_runners:
if runner["status"] == "offline":
offline_runners.append(__magic_name__ )
# save the result so we can report them on Slack
with open("offline_runners.txt" , "w" ) as fp:
fp.write(json.dumps(__magic_name__ ) )
if len(__magic_name__ ) > 0:
lowercase__ = "\n".join([x["name"] for x in offline_runners] )
raise ValueError(f'''The following runners are offline:\n{failed}''' )
if __name__ == "__main__":
def _A ( __magic_name__ ):
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)
| 655 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_snake_case = logging.get_logger(__name__)
_snake_case = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Model type selected in the list: ' + ', '.join(lowercase_ )} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
__lowerCamelCase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
__lowerCamelCase = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
__lowerCamelCase = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
__lowerCamelCase = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
__lowerCamelCase = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'train'
__lowerCamelCase = 'dev'
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
def __init__( self :Optional[Any] , _lowercase :SquadDataTrainingArguments , _lowercase :PreTrainedTokenizer , _lowercase :Optional[int] = None , _lowercase :Union[str, Split] = Split.train , _lowercase :Optional[bool] = False , _lowercase :Optional[str] = None , _lowercase :Optional[str] = "pt" , ):
'''simple docstring'''
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = "v2" if args.version_2_with_negative else "v1"
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + ".lock"
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features["features"]
lowercase__ = self.old_features.get("dataset" , _lowercase )
lowercase__ = self.old_features.get("examples" , _lowercase )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
" future run" )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
lowercase__ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self :Dict ):
'''simple docstring'''
return len(self.features )
def __getitem__( self :Any , _lowercase :Any ):
'''simple docstring'''
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 655 | 1 |
def _A ( __magic_name__ ):
lowercase__ , lowercase__ = [], []
while len(__magic_name__ ) > 1:
lowercase__ , lowercase__ = min(__magic_name__ ), max(__magic_name__ )
start.append(__magic_name__ )
end.append(__magic_name__ )
collection.remove(__magic_name__ )
collection.remove(__magic_name__ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
_snake_case = input("""Enter numbers separated by a comma:\n""").strip()
_snake_case = [int(item) for item in user_input.split(""",""")]
print(*merge_sort(unsorted), sep=""",""")
| 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
_snake_case = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
_snake_case = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
_snake_case = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
_snake_case = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ["input_ids"]
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = RESOURCE_FILES_NAMES
def __init__( self :Union[str, Any] , _lowercase :Union[str, Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=False , _lowercase :Dict="utf8" , _lowercase :Optional[Any]="[UNK]" , _lowercase :Optional[int]="[SEP]" , _lowercase :List[str]="[PAD]" , _lowercase :Dict="[CLS]" , _lowercase :Optional[Any]="[MASK]" , _lowercase :Optional[Dict[str, Any]] = None , **_lowercase :Tuple , ):
'''simple docstring'''
lowercase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , vocab_file=_lowercase , encoding=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
lowercase__ = do_lower_case
lowercase__ = sentencepiece_model_ckpt
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase__ = self.load_vocab(filepath=_lowercase )
else:
lowercase__ = {self.sp_model.id_to_piece(_lowercase ): id for id in range(self.sp_model.get_piece_size() )}
lowercase__ = {v: k for k, v in self.vocab.items()}
def UpperCAmelCase ( self :Any , _lowercase :Dict ):
'''simple docstring'''
if text is None:
return None
lowercase__ = self.tokenize(_lowercase )
lowercase__ , lowercase__ = "", []
for i, ch in enumerate(_lowercase ):
if ch in self.SP_CHAR_MAPPING:
lowercase__ = self.SP_CHAR_MAPPING.get(_lowercase )
else:
lowercase__ = unicodedata.normalize("NFKC" , _lowercase )
if self.is_whitespace(_lowercase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(_lowercase ) )
lowercase__ , lowercase__ , lowercase__ = normalized_text, [], 0
if self.do_lower_case:
lowercase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase__ = token[1:]
lowercase__ = text[offset:].index(_lowercase ) + offset
lowercase__ = start + len(_lowercase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase__ = end
return token_mapping
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self :Any ):
'''simple docstring'''
lowercase__ = self.__dict__.copy()
lowercase__ = None
return state
def __setstate__( self :Optional[Any] , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ = {}
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def UpperCAmelCase ( self :List[str] , _lowercase :Optional[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(_lowercase , _lowercase ) for c in text) )
def UpperCAmelCase ( self :str , _lowercase :int , _lowercase :Union[str, Any]=False , _lowercase :Optional[int]=64 , _lowercase :Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowercase__ = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowercase__ = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowercase__ = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowercase__ = self.sp_model.EncodeAsPieces(_lowercase )
else:
lowercase__ = self.sp_model.SampleEncodeAsPieces(_lowercase , _lowercase , _lowercase )
lowercase__ = []
for pi, piece in enumerate(_lowercase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(_lowercase ) and pi != 0:
new_pieces.append(_lowercase )
continue
else:
continue
lowercase__ = 0
for i, chunk in enumerate(_lowercase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(_lowercase ) or self.is_punct(_lowercase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(_lowercase )
lowercase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
if len(_lowercase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def UpperCAmelCase ( self :Tuple , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ = self.convert_ids_to_tokens(_lowercase )
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Optional[int] ):
'''simple docstring'''
return self.vocab.get(_lowercase , self.vocab.get(self.unk_token ) )
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(_lowercase , self.unk_token )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :Tuple=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__ = [self.cls_token_id]
lowercase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def UpperCAmelCase ( self :Dict , _lowercase :int , _lowercase :Union[str, Any]=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Dict=None , _lowercase :Optional[Any]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase ( self :int , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(_lowercase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(_lowercase ) + 1) + [1] * (len(_lowercase ) + 3)
def UpperCAmelCase ( self :str , _lowercase :Optional[int] ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Dict ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def UpperCAmelCase ( self :List[str] , _lowercase :List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(_lowercase ) == 1:
lowercase__ = unicodedata.category(_lowercase )
if cat == "Zs":
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = {}
with io.open(_lowercase , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(_lowercase ):
lowercase__ = line.rstrip("\n" )
lowercase__ = int(_lowercase )
return token_to_idx
def UpperCAmelCase ( self :List[str] , _lowercase :str , _lowercase :Optional[str] = None ):
'''simple docstring'''
lowercase__ = 0
if os.path.isdir(_lowercase ):
lowercase__ = os.path.join(
_lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowercase__ = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(_lowercase , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowercase__ = token_index
writer.write(token + "\n" )
index += 1
lowercase__ = os.path.join(_lowercase , "sentencepiece.bpe.model" )
with open(_lowercase , "wb" ) as fi:
lowercase__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (vocab_file,)
| 655 | 1 |
from ...processing_utils import ProcessorMixin
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'WhisperFeatureExtractor'
__lowerCamelCase = 'WhisperTokenizer'
def __init__( self :Tuple , _lowercase :Tuple , _lowercase :List[str] ):
'''simple docstring'''
super().__init__(_lowercase , _lowercase )
lowercase__ = self.feature_extractor
lowercase__ = False
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Any=None , _lowercase :int=None , _lowercase :Optional[int]=True ):
'''simple docstring'''
return self.tokenizer.get_decoder_prompt_ids(task=_lowercase , language=_lowercase , no_timestamps=_lowercase )
def __call__( self :Tuple , *_lowercase :Optional[Any] , **_lowercase :str ):
'''simple docstring'''
if self._in_target_context_manager:
return self.current_processor(*_lowercase , **_lowercase )
lowercase__ = kwargs.pop("audio" , _lowercase )
lowercase__ = kwargs.pop("sampling_rate" , _lowercase )
lowercase__ = kwargs.pop("text" , _lowercase )
if len(_lowercase ) > 0:
lowercase__ = args[0]
lowercase__ = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if audio is not None:
lowercase__ = self.feature_extractor(_lowercase , *_lowercase , sampling_rate=_lowercase , **_lowercase )
if text is not None:
lowercase__ = self.tokenizer(_lowercase , **_lowercase )
if text is None:
return inputs
elif audio is None:
return encodings
else:
lowercase__ = encodings["input_ids"]
return inputs
def UpperCAmelCase ( self :Optional[int] , *_lowercase :str , **_lowercase :Union[str, Any] ):
'''simple docstring'''
return self.tokenizer.batch_decode(*_lowercase , **_lowercase )
def UpperCAmelCase ( self :List[Any] , *_lowercase :int , **_lowercase :List[str] ):
'''simple docstring'''
return self.tokenizer.decode(*_lowercase , **_lowercase )
def UpperCAmelCase ( self :Tuple , _lowercase :str , _lowercase :List[Any]="np" ):
'''simple docstring'''
return self.tokenizer.get_prompt_ids(_lowercase , return_tensors=_lowercase )
| 655 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ = pre_numerator
lowercase__ = 2 * i // 3 if i % 3 == 0 else 1
lowercase__ = cur_numerator
lowercase__ = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def _A ( ):
raise RuntimeError("CUDA out of memory." )
class lowerCAmelCase ( nn.Module ):
def __init__( self :Tuple ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Linear(3 , 4 )
lowercase__ = nn.BatchNormad(4 )
lowercase__ = nn.Linear(4 , 5 )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Union[str, Any] ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(_lowercase ) ) )
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = []
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(_lowercase :Dict ):
nonlocal batch_sizes
batch_sizes.append(_lowercase )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(_lowercase , [1_28, 64, 32, 16, 8] )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = []
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(_lowercase :int , _lowercase :Any ):
nonlocal batch_sizes
batch_sizes.append(_lowercase )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
lowercase__ , lowercase__ = mock_training_loop_function("hello" )
self.assertListEqual(_lowercase , [1_28, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, "hello"] )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(_lowercase :Optional[Any] ):
pass
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function()
self.assertIn("No executable batch size found, reached zero." , cm.exception.args[0] )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(_lowercase :List[str] ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function()
self.assertIn("No executable batch size found, reached zero." , cm.exception.args[0] )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=1_28 )
def mock_training_loop_function(_lowercase :Optional[Any] , _lowercase :Tuple , _lowercase :List[Any] ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function(1_28 , "hello" , "world" )
self.assertIn("Batch size was passed into `f`" , cm.exception.args[0] )
self.assertIn("`f(arg1='hello', arg2='world')" , cm.exception.args[0] )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(_lowercase :Optional[int] ):
raise ValueError("Oops, we had an error!" )
with self.assertRaises(_lowercase ) as cm:
mock_training_loop_function()
self.assertIn("Oops, we had an error!" , cm.exception.args[0] )
@require_cuda
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = torch.cuda.memory_allocated()
lowercase__ = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , _lowercase )
lowercase__ = release_memory(_lowercase )
self.assertEqual(torch.cuda.memory_allocated() , _lowercase )
| 655 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'AutoTokenizer'
__lowerCamelCase = ['tokenizer']
__lowerCamelCase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self :Dict , _lowercase :List[str] , _lowercase :List[Any]=None ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = speaker_embeddings
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :int , _lowercase :str="speaker_embeddings_path.json" , **_lowercase :List[str] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
lowercase__ = get_file_from_repo(
_lowercase , _lowercase , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_lowercase , _lowercase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase__ = None
else:
with open(_lowercase ) as speaker_embeddings_json:
lowercase__ = json.load(_lowercase )
else:
lowercase__ = None
lowercase__ = AutoTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(tokenizer=_lowercase , speaker_embeddings=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :List[str]="speaker_embeddings_path.json" , _lowercase :Any="speaker_embeddings" , _lowercase :bool = False , **_lowercase :Any , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_lowercase , _lowercase , "v2" ) , exist_ok=_lowercase )
lowercase__ = {}
lowercase__ = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase__ = self._load_voice_preset(_lowercase )
lowercase__ = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , _lowercase , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_lowercase , )
lowercase__ = os.path.join(_lowercase , f'''{prompt_key}_{key}.npy''' )
lowercase__ = tmp_dict
with open(os.path.join(_lowercase , _lowercase ) , "w" ) as fp:
json.dump(_lowercase , _lowercase )
super().save_pretrained(_lowercase , _lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :str = None , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.speaker_embeddings[voice_preset]
lowercase__ = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase__ = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase__ = np.load(_lowercase )
return voice_preset_dict
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=None , _lowercase :List[str]="pt" , _lowercase :List[Any]=2_56 , _lowercase :List[str]=False , _lowercase :Union[str, Any]=True , _lowercase :Dict=False , **_lowercase :Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(_lowercase , _lowercase ):
if (
isinstance(_lowercase , _lowercase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase__ = self._load_voice_preset(_lowercase )
else:
if isinstance(_lowercase , _lowercase ) and not voice_preset.endswith(".npz" ):
lowercase__ = voice_preset + ".npz"
lowercase__ = np.load(_lowercase )
if voice_preset is not None:
self._validate_voice_preset_dict(_lowercase , **_lowercase )
lowercase__ = BatchFeature(data=_lowercase , tensor_type=_lowercase )
lowercase__ = self.tokenizer(
_lowercase , return_tensors=_lowercase , padding="max_length" , max_length=_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
if voice_preset is not None:
lowercase__ = voice_preset
return encoded_text
| 655 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 | 1 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
def _A ( __magic_name__ ):
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
lowercase__ = value
else:
lowercase__ = value
return new_state_dict
def _A ( __magic_name__ ):
lowercase__ = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ = in_proj_weight_cross_attn[:256, :]
lowercase__ = in_proj_bias_cross_attn[:256]
lowercase__ = in_proj_weight_cross_attn[256:512, :]
lowercase__ = in_proj_bias_cross_attn[256:512]
lowercase__ = in_proj_weight_cross_attn[-256:, :]
lowercase__ = in_proj_bias_cross_attn[-256:]
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = max(__magic_name__ , __magic_name__ )
lowercase__ = 800 if "detection" in checkpoint_url else 1000
lowercase__ = target_max_size / current_max_size
lowercase__ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def _A ( __magic_name__ ):
lowercase__ = F.to_tensor(__magic_name__ )
lowercase__ = F.normalize(__magic_name__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
logger.info("Converting model..." )
# load original state dict
lowercase__ = torch.hub.load_state_dict_from_url(__magic_name__ , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = rename_backbone_keys(__magic_name__ )
# query, key and value matrices need special treatment
read_in_q_k_v(__magic_name__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
# create HuggingFace model and load state dict
lowercase__ = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase__ = 15
lowercase__ = 2
lowercase__ = {0: "table", 1: "table rotated"}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
else:
lowercase__ = 125
lowercase__ = 6
lowercase__ = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1000 )
lowercase__ = TableTransformerForObjectDetection(__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
# verify our conversion
lowercase__ = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
lowercase__ = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=__magic_name__ )
lowercase__ = Image.open(__magic_name__ ).convert("RGB" )
lowercase__ = normalize(resize(__magic_name__ , __magic_name__ ) ).unsqueeze(0 )
lowercase__ = model(__magic_name__ )
if "detection" in checkpoint_url:
lowercase__ = (1, 15, 3)
lowercase__ = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
lowercase__ = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
lowercase__ = (1, 125, 7)
lowercase__ = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
lowercase__ = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __magic_name__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __magic_name__ , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
lowercase__ = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(__magic_name__ )
image_processor.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'van'
def __init__( self :Optional[Any] , _lowercase :Dict=2_24 , _lowercase :Union[str, Any]=3 , _lowercase :List[Any]=[7, 3, 3, 3] , _lowercase :Any=[4, 2, 2, 2] , _lowercase :Union[str, Any]=[64, 1_28, 3_20, 5_12] , _lowercase :List[Any]=[3, 3, 12, 3] , _lowercase :Dict=[8, 8, 4, 4] , _lowercase :int="gelu" , _lowercase :List[Any]=0.02 , _lowercase :List[Any]=1e-6 , _lowercase :Any=1e-2 , _lowercase :int=0.0 , _lowercase :int=0.0 , **_lowercase :Dict , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = patch_sizes
lowercase__ = strides
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = mlp_ratios
lowercase__ = hidden_act
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = layer_scale_init_value
lowercase__ = drop_path_rate
lowercase__ = dropout_rate
| 655 | 1 |
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode
import transformers
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
ViTImageProcessor,
ViTMAEConfig,
ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
_snake_case = logging.getLogger(__name__)
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt""")
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default='cifar10' , metadata={'help': 'Name of a dataset from the datasets package'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The column name of the images in the files.'} )
__lowerCamelCase = field(default=lowercase_ , metadata={'help': 'A folder containing the training data.'} )
__lowerCamelCase = field(default=lowercase_ , metadata={'help': 'A folder containing the validation data.'} )
__lowerCamelCase = field(
default=0.15 , metadata={'help': 'Percent to split off of train for validation.'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = {}
if self.train_dir is not None:
lowercase__ = self.train_dir
if self.validation_dir is not None:
lowercase__ = self.validation_dir
lowercase__ = data_files if data_files else None
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'} )
__lowerCamelCase = field(
default=lowercase_ , 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'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'} )
__lowerCamelCase = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
__lowerCamelCase = field(default=lowercase_ , metadata={'help': 'Name or path of preprocessor config.'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
__lowerCamelCase = field(
default=0.75 , metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Whether or not to train with normalized pixel values as target.'} )
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = field(
default=1e-3 , metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'} )
def _A ( __magic_name__ ):
lowercase__ = torch.stack([example["pixel_values"] for example in examples] )
return {"pixel_values": pixel_values}
def _A ( ):
# 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.
lowercase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments) )
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.
lowercase__ , lowercase__ , lowercase__ = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
lowercase__ , lowercase__ , lowercase__ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_mae" , __magic_name__ , __magic_name__ )
# 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 )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowercase__ = training_args.get_process_log_level()
logger.setLevel(__magic_name__ )
transformers.utils.logging.set_verbosity(__magic_name__ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# 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}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowercase__ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowercase__ = 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 and training_args.resume_from_checkpoint is 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." )
# Initialize our dataset.
lowercase__ = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , data_files=data_args.data_files , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# If we don't have a validation split, split off a percentage of train as validation.
lowercase__ = None if "validation" in ds.keys() else data_args.train_val_split
if isinstance(data_args.train_val_split , __magic_name__ ) and data_args.train_val_split > 0.0:
lowercase__ = ds["train"].train_test_split(data_args.train_val_split )
lowercase__ = split["train"]
lowercase__ = split["test"]
# Load pretrained model and image processor
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowercase__ = {
"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:
lowercase__ = ViTMAEConfig.from_pretrained(model_args.config_name , **__magic_name__ )
elif model_args.model_name_or_path:
lowercase__ = ViTMAEConfig.from_pretrained(model_args.model_name_or_path , **__magic_name__ )
else:
lowercase__ = ViTMAEConfig()
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}''' )
# adapt config
config.update(
{
"mask_ratio": model_args.mask_ratio,
"norm_pix_loss": model_args.norm_pix_loss,
} )
# create image processor
if model_args.image_processor_name:
lowercase__ = ViTImageProcessor.from_pretrained(model_args.image_processor_name , **__magic_name__ )
elif model_args.model_name_or_path:
lowercase__ = ViTImageProcessor.from_pretrained(model_args.model_name_or_path , **__magic_name__ )
else:
lowercase__ = ViTImageProcessor()
# create model
if model_args.model_name_or_path:
lowercase__ = ViTMAEForPreTraining.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__magic_name__ , 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" )
lowercase__ = ViTMAEForPreTraining(__magic_name__ )
if training_args.do_train:
lowercase__ = ds["train"].column_names
else:
lowercase__ = ds["validation"].column_names
if data_args.image_column_name is not None:
lowercase__ = data_args.image_column_name
elif "image" in column_names:
lowercase__ = "image"
elif "img" in column_names:
lowercase__ = "img"
else:
lowercase__ = column_names[0]
# transformations as done in original MAE paper
# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
if "shortest_edge" in image_processor.size:
lowercase__ = image_processor.size["shortest_edge"]
else:
lowercase__ = (image_processor.size["height"], image_processor.size["width"])
lowercase__ = Compose(
[
Lambda(lambda __magic_name__ : img.convert("RGB" ) if img.mode != "RGB" else img ),
RandomResizedCrop(__magic_name__ , scale=(0.2, 1.0) , interpolation=InterpolationMode.BICUBIC ),
RandomHorizontalFlip(),
ToTensor(),
Normalize(mean=image_processor.image_mean , std=image_processor.image_std ),
] )
def preprocess_images(__magic_name__ ):
lowercase__ = [transforms(__magic_name__ ) for image in examples[image_column_name]]
return examples
if training_args.do_train:
if "train" not in ds:
raise ValueError("--do_train requires a train dataset" )
if data_args.max_train_samples is not None:
lowercase__ = ds["train"].shuffle(seed=training_args.seed ).select(range(data_args.max_train_samples ) )
# Set the training transforms
ds["train"].set_transform(__magic_name__ )
if training_args.do_eval:
if "validation" not in ds:
raise ValueError("--do_eval requires a validation dataset" )
if data_args.max_eval_samples is not None:
lowercase__ = (
ds["validation"].shuffle(seed=training_args.seed ).select(range(data_args.max_eval_samples ) )
)
# Set the validation transforms
ds["validation"].set_transform(__magic_name__ )
# Compute absolute learning rate
lowercase__ = (
training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
)
if training_args.base_learning_rate is not None:
lowercase__ = training_args.base_learning_rate * total_train_batch_size / 256
# Initialize our trainer
lowercase__ = Trainer(
model=__magic_name__ , args=__magic_name__ , train_dataset=ds["train"] if training_args.do_train else None , eval_dataset=ds["validation"] if training_args.do_eval else None , tokenizer=__magic_name__ , data_collator=__magic_name__ , )
# Training
if training_args.do_train:
lowercase__ = None
if training_args.resume_from_checkpoint is not None:
lowercase__ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowercase__ = last_checkpoint
lowercase__ = trainer.train(resume_from_checkpoint=__magic_name__ )
trainer.save_model()
trainer.log_metrics("train" , train_result.metrics )
trainer.save_metrics("train" , train_result.metrics )
trainer.save_state()
# Evaluation
if training_args.do_eval:
lowercase__ = trainer.evaluate()
trainer.log_metrics("eval" , __magic_name__ )
trainer.save_metrics("eval" , __magic_name__ )
# Write model card and (optionally) push to hub
lowercase__ = {
"tasks": "masked-auto-encoding",
"dataset": data_args.dataset_name,
"tags": ["masked-auto-encoding"],
}
if training_args.push_to_hub:
trainer.push_to_hub(**__magic_name__ )
else:
trainer.create_model_card(**__magic_name__ )
def _A ( __magic_name__ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase ( enum.Enum ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 2
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self :Any , *_lowercase :Optional[Any] , **_lowercase :Union[str, Any] ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowercase__ = None
if self.model.config.prefix is not None:
lowercase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowercase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowercase__ , lowercase__ , lowercase__ = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
lowercase__ = {**self._preprocess_params, **preprocess_params}
lowercase__ = {**self._forward_params, **forward_params}
def UpperCAmelCase ( self :Tuple , _lowercase :Optional[Any]=None , _lowercase :List[Any]=None , _lowercase :List[str]=None , _lowercase :Optional[Any]=None , _lowercase :Optional[int]=None , _lowercase :Any=None , _lowercase :Any=None , _lowercase :Dict=None , **_lowercase :Union[str, Any] , ):
'''simple docstring'''
lowercase__ = {}
if prefix is not None:
lowercase__ = prefix
if prefix:
lowercase__ = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
lowercase__ = handle_long_generation
preprocess_params.update(_lowercase )
lowercase__ = generate_kwargs
lowercase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.TENSORS
if return_type is not None:
lowercase__ = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
lowercase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self :int , *_lowercase :Optional[int] , **_lowercase :List[str] ):
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self :Union[str, Any] , _lowercase :Dict , **_lowercase :Tuple ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple , _lowercase :Optional[int]="" , _lowercase :Tuple=None , **_lowercase :List[str] ):
'''simple docstring'''
lowercase__ = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prompt_text
if handle_long_generation == "hole":
lowercase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowercase__ = generate_kwargs["max_new_tokens"]
else:
lowercase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowercase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
lowercase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
lowercase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def UpperCAmelCase ( self :str , _lowercase :int , **_lowercase :str ):
'''simple docstring'''
lowercase__ = model_inputs["input_ids"]
lowercase__ = model_inputs.get("attention_mask" , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowercase__ = None
lowercase__ = None
lowercase__ = 1
else:
lowercase__ = input_ids.shape[0]
lowercase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowercase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
lowercase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
lowercase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowercase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowercase__ = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
lowercase__ = generated_sequence.shape[0]
if self.framework == "pt":
lowercase__ = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowercase__ = tf.reshape(_lowercase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def UpperCAmelCase ( self :Any , _lowercase :Tuple , _lowercase :str=ReturnType.FULL_TEXT , _lowercase :Dict=True ):
'''simple docstring'''
lowercase__ = model_outputs["generated_sequence"][0]
lowercase__ = model_outputs["input_ids"]
lowercase__ = model_outputs["prompt_text"]
lowercase__ = generated_sequence.numpy().tolist()
lowercase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowercase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowercase__ = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowercase__ = 0
else:
lowercase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
lowercase__ = prompt_text + text[prompt_length:]
else:
lowercase__ = text[prompt_length:]
lowercase__ = {"generated_text": all_text}
records.append(_lowercase )
return records
| 655 | 1 |
def _A ( __magic_name__ , __magic_name__ ):
if not (isinstance(__magic_name__ , __magic_name__ ) and isinstance(__magic_name__ , __magic_name__ )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
lowercase__ = len(__magic_name__ )
lowercase__ = len(__magic_name__ )
lowercase__ = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
lowercase__ = 0
lowercase__ = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
lowercase__ = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
lowercase__ = i
lowercase__ = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_snake_case = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _A ( __magic_name__ ):
lowercase__ = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__magic_name__ )[0]
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(rows * cols * num_images )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
lowercase__ = data.reshape(__magic_name__ , __magic_name__ , __magic_name__ , 1 )
return data
@deprecated(__magic_name__ , "Please use tf.one_hot on tensors." )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = labels_dense.shape[0]
lowercase__ = numpy.arange(__magic_name__ ) * num_classes
lowercase__ = numpy.zeros((num_labels, num_classes) )
lowercase__ = 1
return labels_one_hot
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(__magic_name__ )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__magic_name__ , __magic_name__ )
return labels
class lowerCAmelCase :
@deprecated(
_lowercase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Tuple=False , _lowercase :str=False , _lowercase :Dict=dtypes.floataa , _lowercase :Optional[Any]=True , _lowercase :Any=None , ):
'''simple docstring'''
lowercase__ , lowercase__ = random_seed.get_seed(_lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ = dtypes.as_dtype(_lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
lowercase__ = 1_00_00
lowercase__ = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
lowercase__ = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ = images.astype(numpy.floataa )
lowercase__ = numpy.multiply(_lowercase , 1.0 / 255.0 )
lowercase__ = images
lowercase__ = labels
lowercase__ = 0
lowercase__ = 0
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._images
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self._labels
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return self._num_examples
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._epochs_completed
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] , _lowercase :Any=False , _lowercase :Union[str, Any]=True ):
'''simple docstring'''
if fake_data:
lowercase__ = [1] * 7_84
lowercase__ = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_lowercase )],
[fake_label for _ in range(_lowercase )],
)
lowercase__ = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perma]
lowercase__ = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ = self._num_examples - start
lowercase__ = self._images[start : self._num_examples]
lowercase__ = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perm]
lowercase__ = self.labels[perm]
# Start next epoch
lowercase__ = 0
lowercase__ = batch_size - rest_num_examples
lowercase__ = self._index_in_epoch
lowercase__ = self._images[start:end]
lowercase__ = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__magic_name__ , "Please write your own downloading logic." )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if not gfile.Exists(__magic_name__ ):
gfile.MakeDirs(__magic_name__ )
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
if not gfile.Exists(__magic_name__ ):
urllib.request.urlretrieve(__magic_name__ , __magic_name__ ) # noqa: S310
with gfile.GFile(__magic_name__ ) as f:
lowercase__ = f.size()
print("Successfully downloaded" , __magic_name__ , __magic_name__ , "bytes." )
return filepath
@deprecated(
__magic_name__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=5000 , __magic_name__=None , __magic_name__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__magic_name__ , one_hot=__magic_name__ , dtype=__magic_name__ , seed=__magic_name__ )
lowercase__ = fake()
lowercase__ = fake()
lowercase__ = fake()
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
if not source_url: # empty string check
lowercase__ = DEFAULT_SOURCE_URL
lowercase__ = "train-images-idx3-ubyte.gz"
lowercase__ = "train-labels-idx1-ubyte.gz"
lowercase__ = "t10k-images-idx3-ubyte.gz"
lowercase__ = "t10k-labels-idx1-ubyte.gz"
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
if not 0 <= validation_size <= len(__magic_name__ ):
lowercase__ = (
"Validation size should be between 0 and "
f'''{len(__magic_name__ )}. Received: {validation_size}.'''
)
raise ValueError(__magic_name__ )
lowercase__ = train_images[:validation_size]
lowercase__ = train_labels[:validation_size]
lowercase__ = train_images[validation_size:]
lowercase__ = train_labels[validation_size:]
lowercase__ = {"dtype": dtype, "reshape": reshape, "seed": seed}
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
| 655 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCAmelCase ( unittest.TestCase ):
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = self.dummy_uncond_unet
lowercase__ = ScoreSdeVeScheduler()
lowercase__ = ScoreSdeVePipeline(unet=_lowercase , scheduler=_lowercase )
sde_ve.to(_lowercase )
sde_ve.set_progress_bar_config(disable=_lowercase )
lowercase__ = torch.manual_seed(0 )
lowercase__ = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=_lowercase ).images
lowercase__ = torch.manual_seed(0 )
lowercase__ = sde_ve(num_inference_steps=2 , output_type="numpy" , generator=_lowercase , return_dict=_lowercase )[
0
]
lowercase__ = image[0, -3:, -3:, -1]
lowercase__ = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
lowercase__ = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = "google/ncsnpp-church-256"
lowercase__ = UNetaDModel.from_pretrained(_lowercase )
lowercase__ = ScoreSdeVeScheduler.from_pretrained(_lowercase )
lowercase__ = ScoreSdeVePipeline(unet=_lowercase , scheduler=_lowercase )
sde_ve.to(_lowercase )
sde_ve.set_progress_bar_config(disable=_lowercase )
lowercase__ = torch.manual_seed(0 )
lowercase__ = sde_ve(num_inference_steps=10 , output_type="numpy" , generator=_lowercase ).images
lowercase__ = image[0, -3:, -3:, -1]
assert image.shape == (1, 2_56, 2_56, 3)
lowercase__ = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ):
'''simple docstring'''
lowercase__ = []
lowercase__ = self
while temp:
string_rep.append(f'''{temp.data}''' )
lowercase__ = temp.next
return "->".join(_lowercase )
def _A ( __magic_name__ ):
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase__ = lowercase__ = Node(elements_list[0] )
for i in range(1 , len(__magic_name__ ) ):
lowercase__ = Node(elements_list[i] )
lowercase__ = current.next
return head
def _A ( __magic_name__ ):
if head_node is not None and isinstance(__magic_name__ , __magic_name__ ):
print_reverse(head_node.next )
print(head_node.data )
def _A ( ):
from doctest import testmod
testmod()
lowercase__ = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(__magic_name__ )
print("Elements in Reverse:" )
print_reverse(__magic_name__ )
if __name__ == "__main__":
main()
| 655 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs
_snake_case = imread(R"""digital_image_processing/image_data/lena_small.jpg""")
_snake_case = cvtColor(img, COLOR_BGR2GRAY)
def _A ( ):
lowercase__ = cn.convert_to_negative(__magic_name__ )
# assert negative_img array for at least one True
assert negative_img.any()
def _A ( ):
with Image.open("digital_image_processing/image_data/lena_small.jpg" ) as img:
# Work around assertion for response
assert str(cc.change_contrast(__magic_name__ , 110 ) ).startswith(
"<PIL.Image.Image image mode=RGB size=100x100 at" )
def _A ( ):
lowercase__ = canny.gen_gaussian_kernel(9 , sigma=1.4 )
# Assert ambiguous array
assert resp.all()
def _A ( ):
lowercase__ = imread("digital_image_processing/image_data/lena_small.jpg" , 0 )
# assert ambiguous array for all == True
assert canny_img.all()
lowercase__ = canny.canny(__magic_name__ )
# assert canny array for at least one True
assert canny_array.any()
def _A ( ):
assert gg.gaussian_filter(__magic_name__ , 5 , sigma=0.9 ).all()
def _A ( ):
# laplace diagonals
lowercase__ = array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]] )
lowercase__ = conv.img_convolve(__magic_name__ , __magic_name__ ).astype(__magic_name__ )
assert res.any()
def _A ( ):
assert med.median_filter(__magic_name__ , 3 ).any()
def _A ( ):
lowercase__ , lowercase__ = sob.sobel_filter(__magic_name__ )
assert grad.any() and theta.any()
def _A ( ):
lowercase__ = sp.make_sepia(__magic_name__ , 20 )
assert sepia.all()
def _A ( __magic_name__ = "digital_image_processing/image_data/lena_small.jpg" ):
lowercase__ = bs.Burkes(imread(__magic_name__ , 1 ) , 120 )
burkes.process()
assert burkes.output_img.any()
def _A ( __magic_name__ = "digital_image_processing/image_data/lena_small.jpg" , ):
lowercase__ = rs.NearestNeighbour(imread(__magic_name__ , 1 ) , 400 , 200 )
nn.process()
assert nn.output.any()
def _A ( ):
lowercase__ = "digital_image_processing/image_data/lena.jpg"
# Reading the image and converting it to grayscale.
lowercase__ = imread(__magic_name__ , 0 )
# Test for get_neighbors_pixel function() return not None
lowercase__ = 0
lowercase__ = 0
lowercase__ = image[x_coordinate][y_coordinate]
lowercase__ = lbp.get_neighbors_pixel(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
assert neighbors_pixels is not None
# Test for local_binary_pattern function()
# Create a numpy array as the same height and width of read image
lowercase__ = np.zeros((image.shape[0], image.shape[1]) )
# Iterating through the image and calculating the local binary pattern value
# for each pixel.
for i in range(0 , image.shape[0] ):
for j in range(0 , image.shape[1] ):
lowercase__ = lbp.local_binary_value(__magic_name__ , __magic_name__ , __magic_name__ )
assert lbp_image.any()
| 655 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowercase__ = 0
while count < prec:
lowercase__ = random.randint(2 , n - 1 )
lowercase__ = bin_exp_mod(__magic_name__ , __magic_name__ , __magic_name__ )
if b != 1:
lowercase__ = True
for _ in range(__magic_name__ ):
if b == n - 1:
lowercase__ = False
break
lowercase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
_snake_case = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"""configuration_instructblip""": [
"""INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""InstructBlipConfig""",
"""InstructBlipQFormerConfig""",
"""InstructBlipVisionConfig""",
],
"""processing_instructblip""": ["""InstructBlipProcessor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""InstructBlipQFormerModel""",
"""InstructBlipPreTrainedModel""",
"""InstructBlipForConditionalGeneration""",
"""InstructBlipVisionModel""",
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCAmelCase :
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["prompt"]
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
if "image" in inputs:
lowercase__ = inputs["image"]
else:
lowercase__ = None
if "mask_image" in inputs:
lowercase__ = inputs["mask_image"]
else:
lowercase__ = None
if "original_image" in inputs:
lowercase__ = inputs["original_image"]
else:
lowercase__ = None
lowercase__ , lowercase__ = pipe.encode_prompt(_lowercase )
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowercase , _lowercase , _lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowercase , _lowercase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
| 655 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from transformers.utils import is_sentencepiece_available
if is_sentencepiece_available():
from transformers.models.mam_aaa.tokenization_mam_aaa import VOCAB_FILES_NAMES, save_json
from ...test_tokenization_common import TokenizerTesterMixin
if is_sentencepiece_available():
_snake_case = get_tests_dir("""fixtures/test_sentencepiece.model""")
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
_snake_case = 12_8022
_snake_case = 12_8028
@require_sentencepiece
class lowerCAmelCase ( lowercase_ , unittest.TestCase ):
__lowerCamelCase = MaMaaaTokenizer
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = True
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
super().setUp()
lowercase__ = ["</s>", "<unk>", "▁This", "▁is", "▁a", "▁t", "est", "\u0120", "<pad>"]
lowercase__ = dict(zip(_lowercase , range(len(_lowercase ) ) ) )
lowercase__ = Path(self.tmpdirname )
save_json(_lowercase , save_dir / VOCAB_FILES_NAMES["vocab_file"] )
if not (save_dir / VOCAB_FILES_NAMES["spm_file"]).exists():
copyfile(_lowercase , save_dir / VOCAB_FILES_NAMES["spm_file"] )
lowercase__ = MaMaaaTokenizer.from_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname )
def UpperCAmelCase ( self :Union[str, Any] , **_lowercase :Optional[int] ):
'''simple docstring'''
return MaMaaaTokenizer.from_pretrained(self.tmpdirname , **_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Optional[int] ):
'''simple docstring'''
return (
"This is a test",
"This is a test",
)
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = "</s>"
lowercase__ = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_lowercase ) , _lowercase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_lowercase ) , _lowercase )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = list(tokenizer.get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "</s>" )
self.assertEqual(vocab_keys[1] , "<unk>" )
self.assertEqual(vocab_keys[-1] , "<s>" )
self.assertEqual(len(_lowercase ) , tokenizer.vocab_size + len(tokenizer.get_added_vocab() ) )
@unittest.skip("Skip this test while all models are still to be uploaded." )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = tokenizer.tokenize("This is a test" )
self.assertListEqual(_lowercase , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_lowercase ) , [2, 3, 4, 5, 6] , )
lowercase__ = tokenizer.convert_ids_to_tokens([2, 3, 4, 5, 6] )
self.assertListEqual(_lowercase , ["▁This", "▁is", "▁a", "▁t", "est"] )
lowercase__ = tokenizer.convert_tokens_to_string(_lowercase )
self.assertEqual(_lowercase , "This is a test" )
@slow
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = {"input_ids": [[12_80_22, 11_01_08, 3_97, 11, 3_82_72, 22_47, 12_48_11, 2_85, 1_81_05, 15_86, 2_07, 7, 3_95_34, 44_28, 3_97, 10_19, 1_81_05, 15_86, 2_07, 7, 4_13_37, 1_67_86, 2_41, 7, 2_02_14, 17, 12_56_90, 1_03_98, 7, 4_43_78, 5_80_69, 6_83_42, 77_98, 73_43, 11, 2_99, 3_33_10, 4, 1_58, 3_73_50, 9_40_77, 45_69, 2_99, 3_33_10, 90, 4, 5_28_40, 2_90, 4, 3_12_70, 1_12, 2_99, 6_82, 4, 5_28_40, 3_99_53, 1_40_79, 1_93, 5_25_19, 9_08_94, 1_78_94, 12_06_97, 11, 4_04_45, 5_51, 17, 10_19, 5_25_19, 9_08_94, 1_77_56, 9_63, 11, 4_04_45, 4_80, 17, 97_92, 11_20, 51_73, 13_93, 62_40, 1_67_86, 2_41, 12_09_96, 28, 12_45, 13_93, 11_82_40, 1_11_23, 10_19, 9_36_12, 26_91, 1_06_18, 9_80_58, 12_04_09, 19_28, 2_79, 4, 4_06_83, 3_67, 1_78, 2_07, 10_19, 1_03, 10_31_21, 5_06, 6_52_96, 5, 2], [12_80_22, 2_12_17, 3_67, 1_17, 12_54_50, 1_28, 7_19, 7, 73_08, 40, 9_36_12, 1_26_69, 11_16, 1_67_04, 71, 1_77_85, 36_99, 1_55_92, 35, 1_44, 95_84, 2_41, 1_19_43, 7_13, 9_50, 7_99, 22_47, 8_84_27, 1_50, 1_49, 11_88_13, 12_07_06, 10_19, 10_69_06, 8_15_18, 28, 12_24, 2_27_99, 3_97, 5, 2, 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, 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], [12_80_22, 16_58, 12_33_11, 51_55, 55_78, 47_22, 2_79, 1_49_47, 23_66, 11_20, 11_97, 14, 13_48, 92_32, 5, 2, 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, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "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, 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, 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, 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, 1, 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], [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, 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]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=_lowercase , model_name="facebook/m2m100_418M" , revision="c168bae485c864188cf9aa0e4108b0b6934dc91e" , )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
__lowerCamelCase = 'facebook/m2m100_418M'
__lowerCamelCase = [
'In my opinion, there are two levels of response from the French government.',
'NSA Affair Emphasizes Complete Lack of Debate on Intelligence',
]
__lowerCamelCase = [
'Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.',
'L\'affaire NSA souligne l\'absence totale de débat sur le renseignement',
]
# fmt: off
__lowerCamelCase = [EN_CODE, 593, 1_949, 115_781, 4, 71_586, 4_234, 60_633, 126_233, 432, 123_808, 15_592, 1_197, 117_132, 120_618, 5, 2]
@classmethod
def UpperCAmelCase ( cls :Optional[Any] ):
'''simple docstring'''
lowercase__ = MaMaaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en" , tgt_lang="fr" )
lowercase__ = 1
return cls
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
self.assertEqual(self.tokenizer.get_lang_id("ar" ) , 12_80_06 )
self.assertEqual(self.tokenizer.get_lang_id("en" ) , 12_80_22 )
self.assertEqual(self.tokenizer.get_lang_id("ro" ) , 12_80_76 )
self.assertEqual(self.tokenizer.get_lang_id("mr" ) , 12_80_63 )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.tokenizer.get_vocab()
self.assertEqual(len(_lowercase ) , self.tokenizer.vocab_size )
self.assertEqual(vocab["<unk>"] , 3 )
self.assertIn(self.tokenizer.get_lang_token("en" ) , _lowercase )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = "en"
lowercase__ = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , _lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
self.assertIn(_lowercase , self.tokenizer.all_special_ids )
# fmt: off
lowercase__ = [FR_CODE, 53_64, 82, 86_42, 4, 2_94, 47, 8, 1_40_28, 1_36, 32_86, 97_06, 6, 9_07_97, 6, 14_40_12, 1_62, 8_81_28, 3_00_61, 5, 2]
# fmt: on
lowercase__ = self.tokenizer.decode(_lowercase , skip_special_tokens=_lowercase )
lowercase__ = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=_lowercase )
self.assertEqual(_lowercase , _lowercase )
self.assertNotIn(self.tokenizer.eos_token , _lowercase )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = tempfile.mkdtemp()
lowercase__ = self.tokenizer.lang_token_to_id
self.tokenizer.save_pretrained(_lowercase )
lowercase__ = MaMaaaTokenizer.from_pretrained(_lowercase )
self.assertDictEqual(new_tok.lang_token_to_id , _lowercase )
@require_torch
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = "en"
lowercase__ = "fr"
lowercase__ = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=_lowercase , return_tensors="pt" )
lowercase__ = shift_tokens_right(
batch["labels"] , self.tokenizer.pad_token_id , self.tokenizer.eos_token_id )
for k in batch:
lowercase__ = batch[k].tolist()
# batch = {k: v.tolist() for k,v in batch.items()}
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
# batch.decoder_inputs_ids[0][0] ==
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == FR_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2] == [2, FR_CODE]
@require_torch
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = "mr"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
lowercase__ = "zh"
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
@require_torch
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = "mr"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("mr" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
lowercase__ = "zh"
self.tokenizer._switch_to_target_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id("zh" )] )
self.assertListEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
self.tokenizer._switch_to_input_mode()
self.assertListEqual(self.tokenizer.prefix_tokens , [self.tokenizer.get_lang_id(self.tokenizer.src_lang )] )
@require_torch
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.tokenizer._build_translation_inputs("A test" , return_tensors="pt" , src_lang="en" , tgt_lang="ar" )
self.assertEqual(
nested_simplify(_lowercase ) , {
# en_XX, A, test, EOS
"input_ids": [[12_80_22, 58, 41_83, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 12_80_06,
} , )
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
lowercase__ = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ = model(_lowercase )["last_hidden_state"]
lowercase__ = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
lowercase__ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 655 | 1 |
from PIL import Image
def _A ( __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = 0
lowercase__ = image.load()
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
lowercase__ = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(__magic_name__ ):
for i in range(__magic_name__ ):
lowercase__ = 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""")
| 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(__magic_name__ )
lowercase__ = "".join(bin(__magic_name__ )[2:].zfill(8 ) for byte in data )
lowercase__ = len(__magic_name__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase__ = B"=" * ((6 - len(__magic_name__ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(__magic_name__ ) % 6)
else:
lowercase__ = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(__magic_name__ ) , 6 ) ).encode()
+ padding
)
def _A ( __magic_name__ ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ) and not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = (
"argument should be a bytes-like object or ASCII string, "
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(__magic_name__ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(__magic_name__ , __magic_name__ ):
try:
lowercase__ = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
lowercase__ = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(__magic_name__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase__ = encoded_data[:-padding]
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase__ = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(__magic_name__ ) , 8 )
]
return bytes(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
_snake_case = TypeVar("""T""")
_snake_case = TypeVar("""U""")
class lowerCAmelCase ( Generic[T, U] ):
def __init__( self :int , _lowercase :T | None , _lowercase :U | None ):
'''simple docstring'''
lowercase__ = key
lowercase__ = val
lowercase__ = None
lowercase__ = None
def __repr__( self :Union[str, Any] ):
'''simple docstring'''
return (
f'''Node: key: {self.key}, val: {self.val}, '''
f'''has next: {bool(self.next )}, has prev: {bool(self.prev )}'''
)
class lowerCAmelCase ( Generic[T, U] ):
def __init__( self :str ):
'''simple docstring'''
lowercase__ = DoubleLinkedListNode(_lowercase , _lowercase )
lowercase__ = DoubleLinkedListNode(_lowercase , _lowercase )
lowercase__ , lowercase__ = self.rear, self.head
def __repr__( self :str ):
'''simple docstring'''
lowercase__ = ["DoubleLinkedList"]
lowercase__ = self.head
while node.next is not None:
rep.append(str(_lowercase ) )
lowercase__ = node.next
rep.append(str(self.rear ) )
return ",\n ".join(_lowercase )
def UpperCAmelCase ( self :List[Any] , _lowercase :DoubleLinkedListNode[T, U] ):
'''simple docstring'''
lowercase__ = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
lowercase__ = node
lowercase__ = previous
lowercase__ = node
lowercase__ = self.rear
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :DoubleLinkedListNode[T, U] ):
'''simple docstring'''
if node.prev is None or node.next is None:
return None
lowercase__ = node.next
lowercase__ = node.prev
lowercase__ = None
lowercase__ = None
return node
class lowerCAmelCase ( Generic[T, U] ):
__lowerCamelCase = {}
def __init__( self :Dict , _lowercase :int ):
'''simple docstring'''
lowercase__ = DoubleLinkedList()
lowercase__ = capacity
lowercase__ = 0
lowercase__ = 0
lowercase__ = 0
lowercase__ = {}
def __repr__( self :Tuple ):
'''simple docstring'''
return (
f'''CacheInfo(hits={self.hits}, misses={self.miss}, '''
f'''capacity={self.capacity}, current size={self.num_keys})'''
)
def __contains__( self :Tuple , _lowercase :T ):
'''simple docstring'''
return key in self.cache
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :T ):
'''simple docstring'''
if key in self.cache:
self.hits += 1
lowercase__ = self.cache[key]
lowercase__ = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(_lowercase )
return node.val
self.miss += 1
return None
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :T , _lowercase :U ):
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
lowercase__ = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(_lowercase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
lowercase__ = DoubleLinkedListNode(_lowercase , _lowercase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
lowercase__ = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
lowercase__ = value
self.list.add(_lowercase )
@classmethod
def UpperCAmelCase ( cls :List[Any] , _lowercase :int = 1_28 ):
'''simple docstring'''
def cache_decorator_inner(_lowercase :Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*_lowercase :T ) -> U:
if func not in cls.decorator_function_to_instance_map:
lowercase__ = LRUCache(_lowercase )
lowercase__ = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
lowercase__ = func(*_lowercase )
cls.decorator_function_to_instance_map[func].put(args[0] , _lowercase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(_lowercase , "cache_info" , _lowercase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Dict , _lowercase :TransformeraDModel , _lowercase :AutoencoderKL , _lowercase :KarrasDiffusionSchedulers , _lowercase :Optional[Dict[int, str]] = None , ):
'''simple docstring'''
super().__init__()
self.register_modules(transformer=_lowercase , vae=_lowercase , scheduler=_lowercase )
# create a imagenet -> id dictionary for easier use
lowercase__ = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
lowercase__ = int(_lowercase )
lowercase__ = dict(sorted(self.labels.items() ) )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Union[str, List[str]] ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
lowercase__ = list(_lowercase )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self :Optional[Any] , _lowercase :List[int] , _lowercase :float = 4.0 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :int = 50 , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = len(_lowercase )
lowercase__ = self.transformer.config.sample_size
lowercase__ = self.transformer.config.in_channels
lowercase__ = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=_lowercase , device=self.device , dtype=self.transformer.dtype , )
lowercase__ = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowercase__ = torch.tensor(_lowercase , device=self.device ).reshape(-1 )
lowercase__ = torch.tensor([10_00] * batch_size , device=self.device )
lowercase__ = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(_lowercase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowercase__ = latent_model_input[: len(_lowercase ) // 2]
lowercase__ = torch.cat([half, half] , dim=0 )
lowercase__ = self.scheduler.scale_model_input(_lowercase , _lowercase )
lowercase__ = t
if not torch.is_tensor(_lowercase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
lowercase__ = latent_model_input.device.type == "mps"
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.floataa if is_mps else torch.floataa
else:
lowercase__ = torch.intaa if is_mps else torch.intaa
lowercase__ = torch.tensor([timesteps] , dtype=_lowercase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowercase__ = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase__ = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowercase__ = self.transformer(
_lowercase , timestep=_lowercase , class_labels=_lowercase ).sample
# perform guidance
if guidance_scale > 1:
lowercase__ , lowercase__ = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowercase__ , lowercase__ = torch.split(_lowercase , len(_lowercase ) // 2 , dim=0 )
lowercase__ = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowercase__ = torch.cat([half_eps, half_eps] , dim=0 )
lowercase__ = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowercase__ , lowercase__ = torch.split(_lowercase , _lowercase , dim=1 )
else:
lowercase__ = noise_pred
# compute previous image: x_t -> x_t-1
lowercase__ = self.scheduler.step(_lowercase , _lowercase , _lowercase ).prev_sample
if guidance_scale > 1:
lowercase__ , lowercase__ = latent_model_input.chunk(2 , dim=0 )
else:
lowercase__ = latent_model_input
lowercase__ = 1 / self.vae.config.scaling_factor * latents
lowercase__ = self.vae.decode(_lowercase ).sample
lowercase__ = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase__ = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 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 = {
"""hustvl/yolos-small""": """https://huggingface.co/hustvl/yolos-small/resolve/main/config.json""",
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'yolos'
def __init__( self :Dict , _lowercase :Dict=7_68 , _lowercase :List[str]=12 , _lowercase :Tuple=12 , _lowercase :Any=30_72 , _lowercase :List[str]="gelu" , _lowercase :List[Any]=0.0 , _lowercase :Optional[Any]=0.0 , _lowercase :int=0.02 , _lowercase :int=1e-12 , _lowercase :Tuple=[5_12, 8_64] , _lowercase :Dict=16 , _lowercase :List[Any]=3 , _lowercase :Union[str, Any]=True , _lowercase :Optional[int]=1_00 , _lowercase :Optional[int]=True , _lowercase :Any=False , _lowercase :Union[str, Any]=1 , _lowercase :Union[str, Any]=5 , _lowercase :List[Any]=2 , _lowercase :Tuple=5 , _lowercase :List[str]=2 , _lowercase :Tuple=0.1 , **_lowercase :Optional[Any] , ):
'''simple docstring'''
super().__init__(**_lowercase )
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__ = num_detection_tokens
lowercase__ = use_mid_position_embeddings
lowercase__ = auxiliary_loss
# Hungarian matcher
lowercase__ = class_cost
lowercase__ = bbox_cost
lowercase__ = giou_cost
# Loss coefficients
lowercase__ = bbox_loss_coefficient
lowercase__ = giou_loss_coefficient
lowercase__ = eos_coefficient
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = version.parse('1.11' )
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
return 1e-4
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return 12
| 655 |
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 ( lowercase_ ):
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = SMALL_MODEL_IDENTIFIER
lowercase__ = "pt"
lowercase__ = "tf"
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowercase )
def UpperCAmelCase ( self :Tuple , _lowercase :int ):
'''simple docstring'''
lowercase__ = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowercase )
model_tf.save_pretrained(_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = "mock_framework"
# Framework provided - return whatever the user provides
lowercase__ = FeaturesManager.determine_framework(self.test_model , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_tf )
# Both in environment -> use PyTorch
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# Both not in environment -> raise error
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
| 655 | 1 |
import unittest
from transformers import PegasusConfig, PegasusTokenizer, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
_snake_case = """platform"""
import jax
import jax.numpy as jnp
import numpy as np
from transformers import FlaxPegasusForConditionalGeneration, FlaxPegasusModel
@require_flax
class lowerCAmelCase :
__lowerCamelCase = PegasusConfig
__lowerCamelCase = {}
__lowerCamelCase = 'gelu'
def __init__( self :Any , _lowercase :List[Any] , _lowercase :int=13 , _lowercase :str=7 , _lowercase :Optional[Any]=True , _lowercase :Tuple=False , _lowercase :int=99 , _lowercase :Optional[Any]=32 , _lowercase :Any=5 , _lowercase :Any=4 , _lowercase :List[Any]=37 , _lowercase :Union[str, Any]=0.1 , _lowercase :Optional[Any]=0.1 , _lowercase :str=20 , _lowercase :List[str]=2 , _lowercase :str=1 , _lowercase :List[str]=0 , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_labels
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = eos_token_id
lowercase__ = pad_token_id
lowercase__ = bos_token_id
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ).clip(3 , self.vocab_size )
lowercase__ = np.expand_dims(np.array([self.eos_token_id] * self.batch_size ) , 1 )
lowercase__ = np.concatenate([input_ids, eos_tensor] , axis=1 )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase__ = prepare_pegasus_inputs_dict(_lowercase , _lowercase , _lowercase )
return config, inputs_dict
def UpperCAmelCase ( self :Tuple , _lowercase :List[Any] , _lowercase :Any , _lowercase :List[str] ):
'''simple docstring'''
lowercase__ = 20
lowercase__ = model_class_name(_lowercase )
lowercase__ = model.encode(inputs_dict["input_ids"] )
lowercase__ , lowercase__ = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowercase__ = model.init_cache(decoder_input_ids.shape[0] , _lowercase , _lowercase )
lowercase__ = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
lowercase__ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase__ = model.decode(
decoder_input_ids[:, :-1] , _lowercase , decoder_attention_mask=_lowercase , past_key_values=_lowercase , decoder_position_ids=_lowercase , )
lowercase__ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
lowercase__ = model.decode(
decoder_input_ids[:, -1:] , _lowercase , decoder_attention_mask=_lowercase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=_lowercase , )
lowercase__ = model.decode(_lowercase , _lowercase )
lowercase__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[Any] , _lowercase :List[Any] , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = 20
lowercase__ = model_class_name(_lowercase )
lowercase__ = model.encode(inputs_dict["input_ids"] )
lowercase__ , lowercase__ = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
lowercase__ = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
lowercase__ = model.init_cache(decoder_input_ids.shape[0] , _lowercase , _lowercase )
lowercase__ = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
lowercase__ = model.decode(
decoder_input_ids[:, :-1] , _lowercase , decoder_attention_mask=_lowercase , past_key_values=_lowercase , decoder_position_ids=_lowercase , )
lowercase__ = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
lowercase__ = model.decode(
decoder_input_ids[:, -1:] , _lowercase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=_lowercase , decoder_position_ids=_lowercase , )
lowercase__ = model.decode(_lowercase , _lowercase , decoder_attention_mask=_lowercase )
lowercase__ = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f'''Max diff is {diff}''' )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=None , __magic_name__=None , ):
if attention_mask is None:
lowercase__ = np.not_equal(__magic_name__ , config.pad_token_id ).astype(np.inta )
if decoder_attention_mask is None:
lowercase__ = np.concatenate(
[
np.ones(decoder_input_ids[:, :1].shape , dtype=np.inta ),
np.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ).astype(np.inta ),
] , axis=-1 , )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
}
@require_flax
class lowerCAmelCase ( lowercase_ , unittest.TestCase ):
__lowerCamelCase = (
(
FlaxPegasusForConditionalGeneration,
FlaxPegasusModel,
)
if is_flax_available()
else ()
)
__lowerCamelCase = (FlaxPegasusForConditionalGeneration,) if is_flax_available() else ()
__lowerCamelCase = True
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = FlaxPegasusModelTester(self )
lowercase__ = ConfigTester(self , config_class=_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(_lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(_lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase__ = self._prepare_for_class(_lowercase , _lowercase )
lowercase__ = model_class(_lowercase )
@jax.jit
def encode_jitted(_lowercase :Union[str, Any] , _lowercase :int=None , **_lowercase :str ):
return model.encode(input_ids=_lowercase , attention_mask=_lowercase )
with self.subTest("JIT Enabled" ):
lowercase__ = encode_jitted(**_lowercase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowercase__ = encode_jitted(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for jitted_output, output in zip(_lowercase , _lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
lowercase__ = model_class(_lowercase )
lowercase__ = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
lowercase__ = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(_lowercase :List[str] , _lowercase :List[str] , _lowercase :Any ):
return model.decode(
decoder_input_ids=_lowercase , decoder_attention_mask=_lowercase , encoder_outputs=_lowercase , )
with self.subTest("JIT Enabled" ):
lowercase__ = decode_jitted(**_lowercase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
lowercase__ = decode_jitted(**_lowercase ).to_tuple()
self.assertEqual(len(_lowercase ) , len(_lowercase ) )
for jitted_output, output in zip(_lowercase , _lowercase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
lowercase__ = model_class_name.from_pretrained("google/pegasus-large" , from_pt=_lowercase )
lowercase__ = np.ones((1, 1) )
lowercase__ = model(_lowercase )
self.assertIsNotNone(_lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = FlaxPegasusForConditionalGeneration.from_pretrained("google/pegasus-xsum" )
lowercase__ = PegasusTokenizer.from_pretrained("google/pegasus-xsum" )
lowercase__ = [
" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
]
lowercase__ = [
"California's largest electricity provider has turned off power to hundreds of thousands of customers.",
"Pop group N-Dubz have revealed they were surprised to get four nominations for this year's Mobo Awards.",
]
lowercase__ = tokenizer(_lowercase , return_tensors="np" , truncation=_lowercase , max_length=5_12 , padding=_lowercase )
lowercase__ = model.generate(**_lowercase , num_beams=2 ).sequences
lowercase__ = tokenizer.batch_decode(_lowercase , skip_special_tokens=_lowercase )
assert tgt_text == decoded
| 655 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git_vision_model'
def __init__( self :Dict , _lowercase :Dict=7_68 , _lowercase :Dict=30_72 , _lowercase :Tuple=12 , _lowercase :List[str]=12 , _lowercase :Tuple=3 , _lowercase :Dict=2_24 , _lowercase :Tuple=16 , _lowercase :Optional[int]="quick_gelu" , _lowercase :Union[str, Any]=1e-5 , _lowercase :Tuple=0.0 , _lowercase :Tuple=0.02 , **_lowercase :Optional[Any] , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_channels
lowercase__ = patch_size
lowercase__ = image_size
lowercase__ = initializer_range
lowercase__ = attention_dropout
lowercase__ = layer_norm_eps
lowercase__ = hidden_act
@classmethod
def UpperCAmelCase ( cls :List[str] , _lowercase :Union[str, os.PathLike] , **_lowercase :Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
lowercase__ , lowercase__ = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
lowercase__ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowercase , **_lowercase )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git'
def __init__( self :Union[str, Any] , _lowercase :Dict=None , _lowercase :List[str]=3_05_22 , _lowercase :Tuple=7_68 , _lowercase :Any=6 , _lowercase :Dict=12 , _lowercase :Any=30_72 , _lowercase :List[Any]="gelu" , _lowercase :Tuple=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :Optional[Any]=10_24 , _lowercase :Any=0.02 , _lowercase :int=1e-12 , _lowercase :List[Any]=0 , _lowercase :int="absolute" , _lowercase :List[str]=True , _lowercase :Any=False , _lowercase :int=1_01 , _lowercase :str=1_02 , _lowercase :Dict=None , **_lowercase :List[str] , ):
'''simple docstring'''
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , pad_token_id=_lowercase , **_lowercase )
if vision_config is None:
lowercase__ = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
lowercase__ = GitVisionConfig(**_lowercase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
lowercase__ = tie_word_embeddings
lowercase__ = num_image_with_embedding
lowercase__ = bos_token_id
lowercase__ = eos_token_id
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.vision_config.to_dict()
lowercase__ = self.__class__.model_type
return output
| 655 | 1 |
import unittest
from transformers import BertGenerationConfig, 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, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class lowerCAmelCase :
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Optional[Any]=13 , _lowercase :Tuple=7 , _lowercase :Optional[int]=True , _lowercase :Any=True , _lowercase :Any=99 , _lowercase :Any=32 , _lowercase :Union[str, Any]=5 , _lowercase :Dict=4 , _lowercase :int=37 , _lowercase :Dict="gelu" , _lowercase :Dict=0.1 , _lowercase :int=0.1 , _lowercase :Optional[int]=50 , _lowercase :List[str]=0.02 , _lowercase :Tuple=True , _lowercase :Union[str, Any]=None , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = seq_length
lowercase__ = is_training
lowercase__ = use_input_mask
lowercase__ = vocab_size
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__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = use_labels
lowercase__ = scope
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_input_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = self.get_config()
return config, input_ids, input_mask, token_labels
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return BertGenerationConfig(
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 , is_decoder=_lowercase , initializer_range=self.initializer_range , )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.prepare_config_and_inputs()
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase ( self :Any , _lowercase :List[str] , _lowercase :Dict , _lowercase :List[str] , _lowercase :Optional[int] , **_lowercase :Union[str, Any] , ):
'''simple docstring'''
lowercase__ = BertGenerationEncoder(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase , attention_mask=_lowercase )
lowercase__ = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self :int , _lowercase :Optional[Any] , _lowercase :Dict , _lowercase :Dict , _lowercase :Dict , _lowercase :int , _lowercase :Optional[int] , **_lowercase :str , ):
'''simple docstring'''
lowercase__ = True
lowercase__ = BertGenerationEncoder(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(
_lowercase , attention_mask=_lowercase , encoder_hidden_states=_lowercase , encoder_attention_mask=_lowercase , )
lowercase__ = model(
_lowercase , attention_mask=_lowercase , encoder_hidden_states=_lowercase , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self :Tuple , _lowercase :int , _lowercase :Any , _lowercase :Optional[int] , _lowercase :str , _lowercase :Tuple , _lowercase :List[Any] , **_lowercase :Any , ):
'''simple docstring'''
lowercase__ = True
lowercase__ = True
lowercase__ = BertGenerationDecoder(config=_lowercase ).to(_lowercase ).eval()
# first forward pass
lowercase__ = model(
_lowercase , attention_mask=_lowercase , encoder_hidden_states=_lowercase , encoder_attention_mask=_lowercase , use_cache=_lowercase , )
lowercase__ = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
lowercase__ = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase__ = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
lowercase__ = torch.cat([input_ids, next_tokens] , dim=-1 )
lowercase__ = torch.cat([input_mask, next_mask] , dim=-1 )
lowercase__ = model(
_lowercase , attention_mask=_lowercase , encoder_hidden_states=_lowercase , encoder_attention_mask=_lowercase , output_hidden_states=_lowercase , )["hidden_states"][0]
lowercase__ = model(
_lowercase , attention_mask=_lowercase , encoder_hidden_states=_lowercase , encoder_attention_mask=_lowercase , past_key_values=_lowercase , output_hidden_states=_lowercase , )["hidden_states"][0]
# select random slice
lowercase__ = ids_tensor((1,) , output_from_past.shape[-1] ).item()
lowercase__ = output_from_no_past[:, -3:, random_slice_idx].detach()
lowercase__ = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(_lowercase , _lowercase , atol=1e-3 ) )
def UpperCAmelCase ( self :List[Any] , _lowercase :Any , _lowercase :int , _lowercase :Tuple , _lowercase :Dict , *_lowercase :Dict , ):
'''simple docstring'''
lowercase__ = BertGenerationDecoder(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase , attention_mask=_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ , lowercase__ , lowercase__ , lowercase__ = self.prepare_config_and_inputs()
lowercase__ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class lowerCAmelCase ( lowercase_ , lowercase_ , lowercase_ , unittest.TestCase ):
__lowerCamelCase = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
__lowerCamelCase = (BertGenerationDecoder,) if is_torch_available() else ()
__lowerCamelCase = (
{'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
if is_torch_available()
else {}
)
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = BertGenerationEncoderTester(self )
lowercase__ = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ , lowercase__ , lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs()
lowercase__ = "bert"
self.model_tester.create_and_check_model(_lowercase , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.model_tester.prepare_config_and_inputs_for_decoder()
lowercase__ = None
self.model_tester.create_and_check_model_as_decoder(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase , )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*_lowercase )
@slow
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(_lowercase )
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
lowercase__ = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
lowercase__ = model(_lowercase )[0]
lowercase__ = torch.Size([1, 8, 10_24] )
self.assertEqual(output.shape , _lowercase )
lowercase__ = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowercase , atol=1e-4 ) )
@require_torch
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
lowercase__ = torch.tensor([[1_01, 75_92, 10_10, 20_26, 38_99, 20_03, 1_01_40, 1_02]] )
with torch.no_grad():
lowercase__ = model(_lowercase )[0]
lowercase__ = torch.Size([1, 8, 5_03_58] )
self.assertEqual(output.shape , _lowercase )
lowercase__ = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , _lowercase , atol=1e-4 ) )
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModel.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModel.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForPreTraining.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForCausalLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForMaskedLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
| 655 | 1 |
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if days_between_payments <= 0:
raise ValueError("days_between_payments must be > 0" )
if daily_interest_rate < 0:
raise ValueError("daily_interest_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * daily_interest_rate * days_between_payments
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , ):
if number_of_compounding_periods <= 0:
raise ValueError("number_of_compounding_periods must be > 0" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("nominal_annual_interest_rate_percentage must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , ):
if number_of_years <= 0:
raise ValueError("number_of_years must be > 0" )
if nominal_annual_percentage_rate < 0:
raise ValueError("nominal_annual_percentage_rate must be >= 0" )
if principal <= 0:
raise ValueError("principal must be > 0" )
return compound_interest(
__magic_name__ , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
def _A ( __magic_name__ ):
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
lowercase__ = value
else:
lowercase__ = value
return new_state_dict
def _A ( __magic_name__ ):
lowercase__ = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ = in_proj_weight_cross_attn[:256, :]
lowercase__ = in_proj_bias_cross_attn[:256]
lowercase__ = in_proj_weight_cross_attn[256:512, :]
lowercase__ = in_proj_bias_cross_attn[256:512]
lowercase__ = in_proj_weight_cross_attn[-256:, :]
lowercase__ = in_proj_bias_cross_attn[-256:]
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = max(__magic_name__ , __magic_name__ )
lowercase__ = 800 if "detection" in checkpoint_url else 1000
lowercase__ = target_max_size / current_max_size
lowercase__ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def _A ( __magic_name__ ):
lowercase__ = F.to_tensor(__magic_name__ )
lowercase__ = F.normalize(__magic_name__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
logger.info("Converting model..." )
# load original state dict
lowercase__ = torch.hub.load_state_dict_from_url(__magic_name__ , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = rename_backbone_keys(__magic_name__ )
# query, key and value matrices need special treatment
read_in_q_k_v(__magic_name__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
# create HuggingFace model and load state dict
lowercase__ = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase__ = 15
lowercase__ = 2
lowercase__ = {0: "table", 1: "table rotated"}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
else:
lowercase__ = 125
lowercase__ = 6
lowercase__ = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1000 )
lowercase__ = TableTransformerForObjectDetection(__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
# verify our conversion
lowercase__ = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
lowercase__ = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=__magic_name__ )
lowercase__ = Image.open(__magic_name__ ).convert("RGB" )
lowercase__ = normalize(resize(__magic_name__ , __magic_name__ ) ).unsqueeze(0 )
lowercase__ = model(__magic_name__ )
if "detection" in checkpoint_url:
lowercase__ = (1, 15, 3)
lowercase__ = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
lowercase__ = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
lowercase__ = (1, 125, 7)
lowercase__ = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
lowercase__ = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __magic_name__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __magic_name__ , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
lowercase__ = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(__magic_name__ )
image_processor.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 | 1 |
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("""At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training""")
# TF training parameters
_snake_case = False
_snake_case = False
def _A ( __magic_name__ ):
return TrainCommand(__magic_name__ )
class lowerCAmelCase ( lowercase_ ):
@staticmethod
def UpperCAmelCase ( _lowercase :ArgumentParser ):
'''simple docstring'''
lowercase__ = parser.add_parser("train" , help="CLI tool to train a model on a task." )
train_parser.add_argument(
"--train_data" , type=_lowercase , required=_lowercase , help="path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences." , )
train_parser.add_argument(
"--column_label" , type=_lowercase , default=0 , help="Column of the dataset csv file with example labels." )
train_parser.add_argument(
"--column_text" , type=_lowercase , default=1 , help="Column of the dataset csv file with example texts." )
train_parser.add_argument(
"--column_id" , type=_lowercase , default=2 , help="Column of the dataset csv file with example ids." )
train_parser.add_argument(
"--skip_first_row" , action="store_true" , help="Skip the first row of the csv file (headers)." )
train_parser.add_argument("--validation_data" , type=_lowercase , default="" , help="path to validation dataset." )
train_parser.add_argument(
"--validation_split" , type=_lowercase , default=0.1 , help="if validation dataset is not provided, fraction of train dataset to use as validation dataset." , )
train_parser.add_argument("--output" , type=_lowercase , default="./" , help="path to saved the trained model." )
train_parser.add_argument(
"--task" , type=_lowercase , default="text_classification" , help="Task to train the model on." )
train_parser.add_argument(
"--model" , type=_lowercase , default="bert-base-uncased" , help="Model's name or path to stored model." )
train_parser.add_argument("--train_batch_size" , type=_lowercase , default=32 , help="Batch size for training." )
train_parser.add_argument("--valid_batch_size" , type=_lowercase , default=64 , help="Batch size for validation." )
train_parser.add_argument("--learning_rate" , type=_lowercase , default=3e-5 , help="Learning rate." )
train_parser.add_argument("--adam_epsilon" , type=_lowercase , default=1e-08 , help="Epsilon for Adam optimizer." )
train_parser.set_defaults(func=_lowercase )
def __init__( self :List[Any] , _lowercase :Namespace ):
'''simple docstring'''
lowercase__ = logging.get_logger("transformers-cli/training" )
lowercase__ = "tf" if is_tf_available() else "torch"
os.makedirs(args.output , exist_ok=_lowercase )
lowercase__ = args.output
lowercase__ = args.column_label
lowercase__ = args.column_text
lowercase__ = args.column_id
self.logger.info(f'''Loading {args.task} pipeline for {args.model}''' )
if args.task == "text_classification":
lowercase__ = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(f'''Loading dataset from {args.train_data}''' )
lowercase__ = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
lowercase__ = None
if args.validation_data:
self.logger.info(f'''Loading validation dataset from {args.validation_data}''' )
lowercase__ = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
lowercase__ = args.validation_split
lowercase__ = args.train_batch_size
lowercase__ = args.valid_batch_size
lowercase__ = args.learning_rate
lowercase__ = args.adam_epsilon
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def UpperCAmelCase ( self :str ):
'''simple docstring'''
raise NotImplementedError
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 655 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
import os
from collections import deque
import torch
from torch.utils.data import Dataset
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Any , _lowercase :Tuple="" , _lowercase :List[str]="train" ):
'''simple docstring'''
assert os.path.isdir(_lowercase )
lowercase__ = []
lowercase__ = os.listdir(_lowercase )
for story_filename in story_filenames_list:
if "summary" in story_filename:
continue
lowercase__ = os.path.join(_lowercase , _lowercase )
if not os.path.isfile(_lowercase ):
continue
self.documents.append(_lowercase )
def __len__( self :Tuple ):
'''simple docstring'''
return len(self.documents )
def __getitem__( self :Union[str, Any] , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = self.documents[idx]
lowercase__ = document_path.split("/" )[-1]
with open(_lowercase , encoding="utf-8" ) as source:
lowercase__ = source.read()
lowercase__ , lowercase__ = process_story(_lowercase )
return document_name, story_lines, summary_lines
def _A ( __magic_name__ ):
lowercase__ = list(filter(lambda __magic_name__ : len(__magic_name__ ) != 0 , [line.strip() for line in raw_story.split("\n" )] ) )
# for some unknown reason some lines miss a period, add it
lowercase__ = [_add_missing_period(__magic_name__ ) for line in nonempty_lines]
# gather article lines
lowercase__ = []
lowercase__ = deque(__magic_name__ )
while True:
try:
lowercase__ = lines.popleft()
if element.startswith("@highlight" ):
break
story_lines.append(__magic_name__ )
except IndexError:
# if "@highlight" is absent from the file we pop
# all elements until there is None, raising an exception.
return story_lines, []
# gather summary lines
lowercase__ = list(filter(lambda __magic_name__ : not t.startswith("@highlight" ) , __magic_name__ ) )
return story_lines, summary_lines
def _A ( __magic_name__ ):
lowercase__ = [".", "!", "?", "...", "'", "`", "\"", "\u2019", "\u2019", ")"]
if line.startswith("@highlight" ):
return line
if line[-1] in END_TOKENS:
return line
return line + "."
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if len(__magic_name__ ) > block_size:
return sequence[:block_size]
else:
sequence.extend([pad_token_id] * (block_size - len(__magic_name__ )) )
return sequence
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = torch.ones_like(__magic_name__ )
lowercase__ = sequence == pad_token_id
lowercase__ = 0
return mask
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = [tokenizer.encode(__magic_name__ ) for line in story_lines]
lowercase__ = [token for sentence in story_lines_token_ids for token in sentence]
lowercase__ = [tokenizer.encode(__magic_name__ ) for line in summary_lines]
lowercase__ = [token for sentence in summary_lines_token_ids for token in sentence]
return story_token_ids, summary_token_ids
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
for sequence in batch:
lowercase__ = -1
lowercase__ = []
for s in sequence:
if s == separator_token_id:
sentence_num += 1
embeddings.append(sentence_num % 2 )
batch_embeddings.append(__magic_name__ )
return torch.tensor(__magic_name__ )
| 655 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case = """
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def _A ( __magic_name__ , __magic_name__ , __magic_name__=8 ):
lowercase__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase ( lowercase_ ):
def __init__( self :List[str] , _lowercase :UNetaDConditionModel , _lowercase :DDPMScheduler , _lowercase :VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_lowercase , scheduler=_lowercase , movq=_lowercase , )
lowercase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Tuple , _lowercase :List[str] , _lowercase :Tuple , _lowercase :Optional[Any] , _lowercase :int , _lowercase :str ):
'''simple docstring'''
if latents is None:
lowercase__ = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase__ = latents.to(_lowercase )
lowercase__ = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase ( self :int , _lowercase :int=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
lowercase__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=_lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase__ , lowercase__ = cpu_offload_with_hook(_lowercase , _lowercase , prev_module_hook=_lowercase )
# We'll offload the last model manually.
lowercase__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowercase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowercase )
def __call__( self :int , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :int = 5_12 , _lowercase :int = 5_12 , _lowercase :int = 1_00 , _lowercase :float = 4.0 , _lowercase :int = 1 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :Optional[torch.FloatTensor] = None , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = self._execution_device
lowercase__ = guidance_scale > 1.0
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
lowercase__ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
if do_classifier_free_guidance:
lowercase__ = image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = negative_image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowercase )
self.scheduler.set_timesteps(_lowercase , device=_lowercase )
lowercase__ = self.scheduler.timesteps
lowercase__ = self.unet.config.in_channels
lowercase__ , lowercase__ = downscale_height_and_width(_lowercase , _lowercase , self.movq_scale_factor )
# create initial latent
lowercase__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowercase , _lowercase , _lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowercase ) ):
# expand the latents if we are doing classifier free guidance
lowercase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase__ = {"image_embeds": image_embeds}
lowercase__ = self.unet(
sample=_lowercase , timestep=_lowercase , encoder_hidden_states=_lowercase , added_cond_kwargs=_lowercase , return_dict=_lowercase , )[0]
if do_classifier_free_guidance:
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
lowercase__ , lowercase__ = noise_pred.chunk(2 )
lowercase__ , lowercase__ = variance_pred.chunk(2 )
lowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase__ = self.scheduler.step(
_lowercase , _lowercase , _lowercase , generator=_lowercase , )[0]
# post-processing
lowercase__ = self.movq.decode(_lowercase , force_not_quantize=_lowercase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase__ = image * 0.5 + 0.5
lowercase__ = image.clamp(0 , 1 )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
import argparse
from collections import defaultdict
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = f'''{file}_{class_name}_{test_name}'''
done_test[_id] += 1
with open(__magic_name__ , "r" ) as f:
lowercase__ = f.readlines()
lowercase__ = f'''class {class_name}('''
lowercase__ = f'''{4 * ' '}def {test_name}('''
lowercase__ = f'''{8 * ' '}{correct_line.split()[0]}'''
lowercase__ = f'''{16 * ' '}{correct_line.split()[0]}'''
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = False
lowercase__ = 0
lowercase__ = 0
lowercase__ = []
for line in lines:
if line.startswith(__magic_name__ ):
lowercase__ = True
elif in_class and line.startswith(__magic_name__ ):
lowercase__ = True
elif in_class and in_func and (line.startswith(__magic_name__ ) or line.startswith(__magic_name__ )):
lowercase__ = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
lowercase__ = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
lowercase__ = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f'''{spaces * ' '}{correct_line}''' )
lowercase__ = lowercase__ = lowercase__ = lowercase__ = False
else:
new_lines.append(__magic_name__ )
with open(__magic_name__ , "w" ) as f:
for line in new_lines:
f.write(__magic_name__ )
def _A ( __magic_name__ , __magic_name__=None ):
if fail is not None:
with open(__magic_name__ , "r" ) as f:
lowercase__ = {l.strip() for l in f.readlines()}
else:
lowercase__ = None
with open(__magic_name__ , "r" ) as f:
lowercase__ = f.readlines()
lowercase__ = defaultdict(__magic_name__ )
for line in correct_lines:
lowercase__ , lowercase__ , lowercase__ , lowercase__ = line.split(";" )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument("""--correct_filename""", help="""filename of tests with expected result""")
parser.add_argument("""--fail_filename""", help="""filename of test failures""", type=str, default=None)
_snake_case = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 655 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
lowercase__ = inspect.getmembers(_lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowercase__ = "k-diffusion"
elif backend == "invisible_watermark":
lowercase__ = "invisible-watermark"
assert backend in deps, f'''{backend} is not in the deps table!'''
| 655 | 1 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class lowerCAmelCase :
def __init__( self :Dict , _lowercase :Optional[Any] , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = 13
lowercase__ = 7
lowercase__ = True
lowercase__ = True
lowercase__ = True
lowercase__ = 99
lowercase__ = 32
lowercase__ = 2
lowercase__ = 4
lowercase__ = 37
lowercase__ = "gelu"
lowercase__ = 0.1
lowercase__ = 0.1
lowercase__ = 5_12
lowercase__ = 16
lowercase__ = 2
lowercase__ = 0.02
lowercase__ = 3
lowercase__ = 4
lowercase__ = None
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase__ = None
if self.use_input_mask:
lowercase__ = random_attention_mask([self.batch_size, self.seq_length] )
lowercase__ = None
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
lowercase__ = ids_tensor([self.batch_size] , self.num_choices )
lowercase__ = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , 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 , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = self.prepare_config_and_inputs()
lowercase__ = True
lowercase__ = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
lowercase__ = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase ( self :List[str] , _lowercase :str , _lowercase :str , _lowercase :Optional[Any] , _lowercase :int , _lowercase :Optional[int] , _lowercase :List[str] ):
'''simple docstring'''
lowercase__ = TFEsmModel(config=_lowercase )
lowercase__ = {"input_ids": input_ids, "attention_mask": input_mask}
lowercase__ = model(_lowercase )
lowercase__ = [input_ids, input_mask]
lowercase__ = model(_lowercase )
lowercase__ = model(_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self :List[Any] , _lowercase :List[Any] , _lowercase :Optional[int] , _lowercase :Any , _lowercase :Dict , _lowercase :Union[str, Any] , _lowercase :Tuple , _lowercase :Tuple , _lowercase :str , ):
'''simple docstring'''
lowercase__ = True
lowercase__ = TFEsmModel(config=_lowercase )
lowercase__ = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
lowercase__ = model(_lowercase )
lowercase__ = [input_ids, input_mask]
lowercase__ = model(_lowercase , encoder_hidden_states=_lowercase )
# Also check the case where encoder outputs are not passed
lowercase__ = model(_lowercase , attention_mask=_lowercase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Tuple , _lowercase :str , _lowercase :List[Any] , _lowercase :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] ):
'''simple docstring'''
lowercase__ = TFEsmForMaskedLM(config=_lowercase )
lowercase__ = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def UpperCAmelCase ( self :List[Any] , _lowercase :str , _lowercase :Dict , _lowercase :Dict , _lowercase :Optional[Any] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.num_labels
lowercase__ = TFEsmForTokenClassification(config=_lowercase )
lowercase__ = {"input_ids": input_ids, "attention_mask": input_mask}
lowercase__ = model(_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) = config_and_inputs
lowercase__ = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
__lowerCamelCase = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
__lowerCamelCase = (
{
'feature-extraction': TFEsmModel,
'fill-mask': TFEsmForMaskedLM,
'text-classification': TFEsmForSequenceClassification,
'token-classification': TFEsmForTokenClassification,
'zero-shot': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = TFEsmModelTester(self )
lowercase__ = ConfigTester(self , config_class=_lowercase , hidden_size=37 )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
self.config_tester.run_common_tests()
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_lowercase )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = TFEsmModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
@unittest.skip("Protein models do not support embedding resizing." )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
pass
@unittest.skip("Protein models do not support embedding resizing." )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_lowercase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
lowercase__ = model.get_bias()
assert isinstance(_lowercase , _lowercase )
for k, v in name.items():
assert isinstance(_lowercase , tf.Variable )
else:
lowercase__ = model.get_output_embeddings()
assert x is None
lowercase__ = model.get_bias()
assert name is None
@require_tf
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D" )
lowercase__ = tf.constant([[0, 1, 2, 3, 4, 5]] )
lowercase__ = model(_lowercase )[0]
lowercase__ = [1, 6, 33]
self.assertEqual(list(output.numpy().shape ) , _lowercase )
# compare the actual values for a slice.
lowercase__ = tf.constant(
[
[
[8.921518, -10.589814, -6.4671307],
[-6.3967156, -13.911377, -1.1211915],
[-7.781247, -13.951557, -3.740592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2 ) )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D" )
lowercase__ = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]] )
lowercase__ = model(_lowercase )[0]
# compare the actual values for a slice.
lowercase__ = tf.constant(
[
[
[0.14443092, 0.54125327, 0.3247739],
[0.30340484, 0.00526676, 0.31077722],
[0.32278043, -0.24987096, 0.3414628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class lowerCAmelCase :
__lowerCamelCase = 42
# setable values
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = None
@classmethod
def UpperCAmelCase ( cls :Union[str, Any] , _lowercase :CommonSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray ):
'''simple docstring'''
return cls(common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase )
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
__lowerCamelCase = 42
@property
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return True
@register_to_config
def __init__( self :str , _lowercase :int = 10_00 , _lowercase :float = 0.0001 , _lowercase :float = 0.02 , _lowercase :str = "linear" , _lowercase :Optional[jnp.ndarray] = None , _lowercase :str = "fixed_small" , _lowercase :bool = True , _lowercase :str = "epsilon" , _lowercase :jnp.dtype = jnp.floataa , ):
'''simple docstring'''
lowercase__ = dtype
def UpperCAmelCase ( self :str , _lowercase :Optional[CommonSchedulerState] = None ):
'''simple docstring'''
if common is None:
lowercase__ = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
lowercase__ = jnp.array(1.0 , dtype=self.dtype )
lowercase__ = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCAmelCase ( self :List[str] , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :Tuple = () ):
'''simple docstring'''
lowercase__ = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
lowercase__ = (jnp.arange(0 , _lowercase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Tuple , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :List[str]=None , _lowercase :Tuple=None ):
'''simple docstring'''
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowercase__ = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
lowercase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
lowercase__ = jnp.clip(_lowercase , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
lowercase__ = jnp.log(jnp.clip(_lowercase , a_min=1e-20 ) )
elif variance_type == "fixed_large":
lowercase__ = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
lowercase__ = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
lowercase__ = variance
lowercase__ = state.common.betas[t]
lowercase__ = (predicted_variance + 1) / 2
lowercase__ = frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase ( self :Optional[int] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :int , _lowercase :jnp.ndarray , _lowercase :Optional[jax.random.KeyArray] = None , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = timestep
if key is None:
lowercase__ = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
lowercase__ , lowercase__ = jnp.split(_lowercase , sample.shape[1] , axis=1 )
else:
lowercase__ = None
# 1. compute alphas, betas
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
lowercase__ = 1 - alpha_prod_t
lowercase__ = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowercase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowercase__ = model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowercase__ = jnp.clip(_lowercase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
lowercase__ = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
lowercase__ = jax.random.split(_lowercase , num=1 )
lowercase__ = jax.random.normal(_lowercase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(_lowercase , _lowercase , predicted_variance=_lowercase ) ** 0.5) * noise
lowercase__ = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
lowercase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=_lowercase , state=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return add_noise_common(state.common , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Dict , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return get_velocity_common(state.common , _lowercase , _lowercase , _lowercase )
def __len__( self :List[str] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_snake_case = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
_snake_case = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
_snake_case = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
_snake_case = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_snake_case = logging.get_logger(__name__)
_snake_case = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Model type selected in the list: ' + ', '.join(lowercase_ )} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
__lowerCamelCase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
__lowerCamelCase = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
__lowerCamelCase = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
__lowerCamelCase = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
__lowerCamelCase = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'train'
__lowerCamelCase = 'dev'
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
def __init__( self :Optional[Any] , _lowercase :SquadDataTrainingArguments , _lowercase :PreTrainedTokenizer , _lowercase :Optional[int] = None , _lowercase :Union[str, Split] = Split.train , _lowercase :Optional[bool] = False , _lowercase :Optional[str] = None , _lowercase :Optional[str] = "pt" , ):
'''simple docstring'''
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = "v2" if args.version_2_with_negative else "v1"
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + ".lock"
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features["features"]
lowercase__ = self.old_features.get("dataset" , _lowercase )
lowercase__ = self.old_features.get("examples" , _lowercase )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
" future run" )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
lowercase__ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self :Dict ):
'''simple docstring'''
return len(self.features )
def __getitem__( self :Any , _lowercase :Any ):
'''simple docstring'''
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 655 | 1 |
def _A ( __magic_name__ , __magic_name__ ):
print("\nThe shortest path matrix using Floyd Warshall algorithm\n" )
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
if dist[i][j] != float("inf" ):
print(int(dist[i][j] ) , end="\t" )
else:
print("INF" , end="\t" )
print()
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = [[float("inf" ) for _ in range(__magic_name__ )] for _ in range(__magic_name__ )]
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
lowercase__ = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(__magic_name__ ):
# looping through rows of graph array
for i in range(__magic_name__ ):
# looping through columns of graph array
for j in range(__magic_name__ ):
if (
dist[i][k] != float("inf" )
and dist[k][j] != float("inf" )
and dist[i][k] + dist[k][j] < dist[i][j]
):
lowercase__ = dist[i][k] + dist[k][j]
_print_dist(__magic_name__ , __magic_name__ )
return dist, v
if __name__ == "__main__":
_snake_case = int(input("""Enter number of vertices: """))
_snake_case = int(input("""Enter number of edges: """))
_snake_case = [[float("""inf""") for i in range(v)] for j in range(v)]
for i in range(v):
_snake_case = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("""\nEdge """, i + 1)
_snake_case = int(input("""Enter source:"""))
_snake_case = int(input("""Enter destination:"""))
_snake_case = float(input("""Enter weight:"""))
_snake_case = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
_snake_case = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
_snake_case = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
_snake_case = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
_snake_case = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ["input_ids"]
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = RESOURCE_FILES_NAMES
def __init__( self :Union[str, Any] , _lowercase :Union[str, Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=False , _lowercase :Dict="utf8" , _lowercase :Optional[Any]="[UNK]" , _lowercase :Optional[int]="[SEP]" , _lowercase :List[str]="[PAD]" , _lowercase :Dict="[CLS]" , _lowercase :Optional[Any]="[MASK]" , _lowercase :Optional[Dict[str, Any]] = None , **_lowercase :Tuple , ):
'''simple docstring'''
lowercase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , vocab_file=_lowercase , encoding=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
lowercase__ = do_lower_case
lowercase__ = sentencepiece_model_ckpt
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase__ = self.load_vocab(filepath=_lowercase )
else:
lowercase__ = {self.sp_model.id_to_piece(_lowercase ): id for id in range(self.sp_model.get_piece_size() )}
lowercase__ = {v: k for k, v in self.vocab.items()}
def UpperCAmelCase ( self :Any , _lowercase :Dict ):
'''simple docstring'''
if text is None:
return None
lowercase__ = self.tokenize(_lowercase )
lowercase__ , lowercase__ = "", []
for i, ch in enumerate(_lowercase ):
if ch in self.SP_CHAR_MAPPING:
lowercase__ = self.SP_CHAR_MAPPING.get(_lowercase )
else:
lowercase__ = unicodedata.normalize("NFKC" , _lowercase )
if self.is_whitespace(_lowercase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(_lowercase ) )
lowercase__ , lowercase__ , lowercase__ = normalized_text, [], 0
if self.do_lower_case:
lowercase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase__ = token[1:]
lowercase__ = text[offset:].index(_lowercase ) + offset
lowercase__ = start + len(_lowercase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase__ = end
return token_mapping
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self :Any ):
'''simple docstring'''
lowercase__ = self.__dict__.copy()
lowercase__ = None
return state
def __setstate__( self :Optional[Any] , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ = {}
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def UpperCAmelCase ( self :List[str] , _lowercase :Optional[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(_lowercase , _lowercase ) for c in text) )
def UpperCAmelCase ( self :str , _lowercase :int , _lowercase :Union[str, Any]=False , _lowercase :Optional[int]=64 , _lowercase :Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowercase__ = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowercase__ = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowercase__ = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowercase__ = self.sp_model.EncodeAsPieces(_lowercase )
else:
lowercase__ = self.sp_model.SampleEncodeAsPieces(_lowercase , _lowercase , _lowercase )
lowercase__ = []
for pi, piece in enumerate(_lowercase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(_lowercase ) and pi != 0:
new_pieces.append(_lowercase )
continue
else:
continue
lowercase__ = 0
for i, chunk in enumerate(_lowercase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(_lowercase ) or self.is_punct(_lowercase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(_lowercase )
lowercase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
if len(_lowercase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def UpperCAmelCase ( self :Tuple , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ = self.convert_ids_to_tokens(_lowercase )
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Optional[int] ):
'''simple docstring'''
return self.vocab.get(_lowercase , self.vocab.get(self.unk_token ) )
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(_lowercase , self.unk_token )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :Tuple=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__ = [self.cls_token_id]
lowercase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def UpperCAmelCase ( self :Dict , _lowercase :int , _lowercase :Union[str, Any]=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Dict=None , _lowercase :Optional[Any]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase ( self :int , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(_lowercase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(_lowercase ) + 1) + [1] * (len(_lowercase ) + 3)
def UpperCAmelCase ( self :str , _lowercase :Optional[int] ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Dict ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def UpperCAmelCase ( self :List[str] , _lowercase :List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(_lowercase ) == 1:
lowercase__ = unicodedata.category(_lowercase )
if cat == "Zs":
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = {}
with io.open(_lowercase , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(_lowercase ):
lowercase__ = line.rstrip("\n" )
lowercase__ = int(_lowercase )
return token_to_idx
def UpperCAmelCase ( self :List[str] , _lowercase :str , _lowercase :Optional[str] = None ):
'''simple docstring'''
lowercase__ = 0
if os.path.isdir(_lowercase ):
lowercase__ = os.path.join(
_lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowercase__ = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(_lowercase , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowercase__ = token_index
writer.write(token + "\n" )
index += 1
lowercase__ = os.path.join(_lowercase , "sentencepiece.bpe.model" )
with open(_lowercase , "wb" ) as fi:
lowercase__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (vocab_file,)
| 655 | 1 |
def _A ( __magic_name__ = 100_0000 ):
lowercase__ = set(range(3 , __magic_name__ , 2 ) )
primes.add(2 )
for p in range(3 , __magic_name__ , 2 ):
if p not in primes:
continue
primes.difference_update(set(range(p * p , __magic_name__ , __magic_name__ ) ) )
lowercase__ = [float(__magic_name__ ) for n in range(limit + 1 )]
for p in primes:
for n in range(__magic_name__ , limit + 1 , __magic_name__ ):
phi[n] *= 1 - 1 / p
return int(sum(phi[2:] ) )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ = pre_numerator
lowercase__ = 2 * i // 3 if i % 3 == 0 else 1
lowercase__ = cur_numerator
lowercase__ = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 | 1 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""huggingface/autoformer-tourism-monthly""": """https://huggingface.co/huggingface/autoformer-tourism-monthly/resolve/main/config.json""",
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'autoformer'
__lowerCamelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self :str , _lowercase :Optional[int] = None , _lowercase :Optional[int] = None , _lowercase :str = "student_t" , _lowercase :str = "nll" , _lowercase :int = 1 , _lowercase :List[int] = [1, 2, 3, 4, 5, 6, 7] , _lowercase :bool = True , _lowercase :int = 0 , _lowercase :int = 0 , _lowercase :int = 0 , _lowercase :int = 0 , _lowercase :Optional[List[int]] = None , _lowercase :Optional[List[int]] = None , _lowercase :int = 64 , _lowercase :int = 2 , _lowercase :int = 2 , _lowercase :int = 2 , _lowercase :int = 2 , _lowercase :int = 32 , _lowercase :int = 32 , _lowercase :str = "gelu" , _lowercase :float = 0.1 , _lowercase :float = 0.1 , _lowercase :float = 0.1 , _lowercase :float = 0.1 , _lowercase :float = 0.1 , _lowercase :int = 1_00 , _lowercase :float = 0.02 , _lowercase :bool = True , _lowercase :Optional[Any]=True , _lowercase :int = 10 , _lowercase :int = 25 , _lowercase :int = 3 , **_lowercase :int , ):
'''simple docstring'''
lowercase__ = prediction_length
lowercase__ = context_length if context_length is not None else prediction_length
lowercase__ = distribution_output
lowercase__ = loss
lowercase__ = input_size
lowercase__ = num_time_features
lowercase__ = lags_sequence
lowercase__ = scaling
lowercase__ = num_dynamic_real_features
lowercase__ = num_static_real_features
lowercase__ = num_static_categorical_features
if cardinality is not None and num_static_categorical_features > 0:
if len(_lowercase ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
lowercase__ = cardinality
else:
lowercase__ = [0]
if embedding_dimension is not None and num_static_categorical_features > 0:
if len(_lowercase ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
lowercase__ = embedding_dimension
else:
lowercase__ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowercase__ = num_parallel_samples
# Transformer architecture configuration
lowercase__ = input_size * len(self.lags_sequence ) + self._number_of_features
lowercase__ = d_model
lowercase__ = encoder_attention_heads
lowercase__ = decoder_attention_heads
lowercase__ = encoder_ffn_dim
lowercase__ = decoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = decoder_layers
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = use_cache
# Autoformer
lowercase__ = label_length
lowercase__ = moving_average
lowercase__ = autocorrelation_factor
super().__init__(is_encoder_decoder=_lowercase , **_lowercase )
@property
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 655 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'AutoTokenizer'
__lowerCamelCase = ['tokenizer']
__lowerCamelCase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self :Dict , _lowercase :List[str] , _lowercase :List[Any]=None ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = speaker_embeddings
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :int , _lowercase :str="speaker_embeddings_path.json" , **_lowercase :List[str] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
lowercase__ = get_file_from_repo(
_lowercase , _lowercase , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_lowercase , _lowercase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase__ = None
else:
with open(_lowercase ) as speaker_embeddings_json:
lowercase__ = json.load(_lowercase )
else:
lowercase__ = None
lowercase__ = AutoTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(tokenizer=_lowercase , speaker_embeddings=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :List[str]="speaker_embeddings_path.json" , _lowercase :Any="speaker_embeddings" , _lowercase :bool = False , **_lowercase :Any , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_lowercase , _lowercase , "v2" ) , exist_ok=_lowercase )
lowercase__ = {}
lowercase__ = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase__ = self._load_voice_preset(_lowercase )
lowercase__ = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , _lowercase , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_lowercase , )
lowercase__ = os.path.join(_lowercase , f'''{prompt_key}_{key}.npy''' )
lowercase__ = tmp_dict
with open(os.path.join(_lowercase , _lowercase ) , "w" ) as fp:
json.dump(_lowercase , _lowercase )
super().save_pretrained(_lowercase , _lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :str = None , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.speaker_embeddings[voice_preset]
lowercase__ = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase__ = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase__ = np.load(_lowercase )
return voice_preset_dict
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=None , _lowercase :List[str]="pt" , _lowercase :List[Any]=2_56 , _lowercase :List[str]=False , _lowercase :Union[str, Any]=True , _lowercase :Dict=False , **_lowercase :Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(_lowercase , _lowercase ):
if (
isinstance(_lowercase , _lowercase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase__ = self._load_voice_preset(_lowercase )
else:
if isinstance(_lowercase , _lowercase ) and not voice_preset.endswith(".npz" ):
lowercase__ = voice_preset + ".npz"
lowercase__ = np.load(_lowercase )
if voice_preset is not None:
self._validate_voice_preset_dict(_lowercase , **_lowercase )
lowercase__ = BatchFeature(data=_lowercase , tensor_type=_lowercase )
lowercase__ = self.tokenizer(
_lowercase , return_tensors=_lowercase , padding="max_length" , max_length=_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
if voice_preset is not None:
lowercase__ = voice_preset
return encoded_text
| 655 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_snake_case = logging.getLogger(__name__)
class lowerCAmelCase :
def __init__( self :str ):
'''simple docstring'''
lowercase__ = False
def UpperCAmelCase ( self :List[str] , _lowercase :Dict , _lowercase :Optional[Any] , _lowercase :Tuple , _lowercase :Dict ):
'''simple docstring'''
if not self.initialized:
lowercase__ = RagRetriever(
_lowercase , question_encoder_tokenizer=_lowercase , generator_tokenizer=_lowercase , index=_lowercase , init_retrieval=_lowercase , )
lowercase__ = True
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
self.retriever.index.init_index()
def UpperCAmelCase ( self :Optional[int] , _lowercase :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ , lowercase__ = self.retriever._main_retrieve(_lowercase , _lowercase )
return doc_ids, retrieved_doc_embeds
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Dict , _lowercase :int , _lowercase :List[str] , _lowercase :Dict , _lowercase :int , _lowercase :Tuple=None ):
'''simple docstring'''
if index is not None and index.is_initialized() and len(_lowercase ) > 0:
raise ValueError(
"When using Ray for distributed fine-tuning, "
"you'll need to provide the paths instead, "
"as the dataset and the index are loaded "
"separately. More info in examples/rag/use_own_knowledge_dataset.py " )
super().__init__(
_lowercase , question_encoder_tokenizer=_lowercase , generator_tokenizer=_lowercase , index=_lowercase , init_retrieval=_lowercase , )
lowercase__ = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(_lowercase , _lowercase , _lowercase , _lowercase )
for worker in self.retrieval_workers
] )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
logger.info("initializing retrieval" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def UpperCAmelCase ( self :Dict , _lowercase :Any , _lowercase :Union[str, Any] ):
'''simple docstring'''
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
lowercase__ = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
lowercase__ , lowercase__ = ray.get(random_worker.retrieve.remote(_lowercase , _lowercase ) )
else:
lowercase__ , lowercase__ = self._main_retrieve(_lowercase , _lowercase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowercase )
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :Union[str, Any] , _lowercase :int=None , **_lowercase :str ):
'''simple docstring'''
return super(_lowercase , cls ).get_tokenizers(_lowercase , _lowercase , **_lowercase )
@classmethod
def UpperCAmelCase ( cls :int , _lowercase :str , _lowercase :str , _lowercase :Optional[Any]=None , **_lowercase :Optional[Any] ):
'''simple docstring'''
lowercase__ = kwargs.pop("config" , _lowercase ) or RagConfig.from_pretrained(_lowercase , **_lowercase )
lowercase__ = RagTokenizer.from_pretrained(_lowercase , config=_lowercase )
lowercase__ = rag_tokenizer.question_encoder
lowercase__ = rag_tokenizer.generator
if indexed_dataset is not None:
lowercase__ = "custom"
lowercase__ = CustomHFIndex(config.retrieval_vector_size , _lowercase )
else:
lowercase__ = cls._build_index(_lowercase )
return cls(
_lowercase , question_encoder_tokenizer=_lowercase , generator_tokenizer=_lowercase , retrieval_workers=_lowercase , index=_lowercase , )
| 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 | 1 |
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
_snake_case = Mapping[str, np.ndarray]
_snake_case = Mapping[str, Any] # Is a nested dict.
_snake_case = 0.01
@dataclasses.dataclass(frozen=lowercase_ )
class lowerCAmelCase :
__lowerCamelCase = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
__lowerCamelCase = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
__lowerCamelCase = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
__lowerCamelCase = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
__lowerCamelCase = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
__lowerCamelCase = None
# Optional remark about the protein. Included as a comment in output PDB
# files
__lowerCamelCase = None
# Templates used to generate this protein (prediction-only)
__lowerCamelCase = None
# Chain corresponding to each parent
__lowerCamelCase = None
def _A ( __magic_name__ ):
lowercase__ = R"(\[[A-Z]+\]\n)"
lowercase__ = [tag.strip() for tag in re.split(__magic_name__ , __magic_name__ ) if len(__magic_name__ ) > 0]
lowercase__ = zip(tags[0::2] , [l.split("\n" ) for l in tags[1::2]] )
lowercase__ = ["N", "CA", "C"]
lowercase__ = None
lowercase__ = None
lowercase__ = None
for g in groups:
if "[PRIMARY]" == g[0]:
lowercase__ = g[1][0].strip()
for i in range(len(__magic_name__ ) ):
if seq[i] not in residue_constants.restypes:
lowercase__ = "X" # FIXME: strings are immutable
lowercase__ = np.array(
[residue_constants.restype_order.get(__magic_name__ , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
lowercase__ = []
for axis in range(3 ):
tertiary.append(list(map(__magic_name__ , g[1][axis].split() ) ) )
lowercase__ = np.array(__magic_name__ )
lowercase__ = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(__magic_name__ ):
lowercase__ = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
lowercase__ = np.array(list(map({"-": 0, "+": 1}.get , g[1][0].strip() ) ) )
lowercase__ = np.zeros(
(
len(__magic_name__ ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(__magic_name__ ):
lowercase__ = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=__magic_name__ , atom_mask=__magic_name__ , aatype=__magic_name__ , residue_index=np.arange(len(__magic_name__ ) ) , b_factors=__magic_name__ , )
def _A ( __magic_name__ , __magic_name__ = 0 ):
lowercase__ = []
lowercase__ = prot.remark
if remark is not None:
pdb_headers.append(f'''REMARK {remark}''' )
lowercase__ = prot.parents
lowercase__ = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
lowercase__ = [p for i, p in zip(__magic_name__ , __magic_name__ ) if i == chain_id]
if parents is None or len(__magic_name__ ) == 0:
lowercase__ = ["N/A"]
pdb_headers.append(f'''PARENT {' '.join(__magic_name__ )}''' )
return pdb_headers
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
lowercase__ = pdb_str.split("\n" )
lowercase__ = prot.remark
if remark is not None:
out_pdb_lines.append(f'''REMARK {remark}''' )
lowercase__ = 42
if prot.parents is not None and len(prot.parents ) > 0:
lowercase__ = []
if prot.parents_chain_index is not None:
lowercase__ = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(__magic_name__ ) , [] )
parent_dict[str(__magic_name__ )].append(__magic_name__ )
lowercase__ = max([int(__magic_name__ ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
lowercase__ = parent_dict.get(str(__magic_name__ ) , ["N/A"] )
parents_per_chain.append(__magic_name__ )
else:
parents_per_chain.append(list(prot.parents ) )
else:
lowercase__ = [["N/A"]]
def make_parent_line(__magic_name__ ) -> str:
return f'''PARENT {' '.join(__magic_name__ )}'''
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
lowercase__ = 0
for i, l in enumerate(__magic_name__ ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(__magic_name__ )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(__magic_name__ ):
lowercase__ = parents_per_chain[chain_counter]
else:
lowercase__ = ["N/A"]
out_pdb_lines.append(make_parent_line(__magic_name__ ) )
return "\n".join(__magic_name__ )
def _A ( __magic_name__ ):
lowercase__ = residue_constants.restypes + ["X"]
def res_atoa(__magic_name__ ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , "UNK" )
lowercase__ = residue_constants.atom_types
lowercase__ = []
lowercase__ = prot.atom_mask
lowercase__ = prot.aatype
lowercase__ = prot.atom_positions
lowercase__ = prot.residue_index.astype(np.intaa )
lowercase__ = prot.b_factors
lowercase__ = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("Invalid aatypes." )
lowercase__ = get_pdb_headers(__magic_name__ )
if len(__magic_name__ ) > 0:
pdb_lines.extend(__magic_name__ )
lowercase__ = aatype.shape[0]
lowercase__ = 1
lowercase__ = 0
lowercase__ = string.ascii_uppercase
lowercase__ = None
# Add all atom sites.
for i in range(__magic_name__ ):
lowercase__ = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(__magic_name__ , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
lowercase__ = "ATOM"
lowercase__ = atom_name if len(__magic_name__ ) == 4 else f''' {atom_name}'''
lowercase__ = ""
lowercase__ = ""
lowercase__ = 1.00
lowercase__ = atom_name[0] # Protein supports only C, N, O, S, this works.
lowercase__ = ""
lowercase__ = "A"
if chain_index is not None:
lowercase__ = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
lowercase__ = (
f'''{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'''
f'''{res_name_a:>3} {chain_tag:>1}'''
f'''{residue_index[i]:>4}{insertion_code:>1} '''
f'''{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'''
f'''{occupancy:>6.2f}{b_factor:>6.2f} '''
f'''{element:>2}{charge:>2}'''
)
pdb_lines.append(__magic_name__ )
atom_index += 1
lowercase__ = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
lowercase__ = True
lowercase__ = chain_index[i + 1]
if should_terminate:
# Close the chain.
lowercase__ = "TER"
lowercase__ = (
f'''{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'''
)
pdb_lines.append(__magic_name__ )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(__magic_name__ , __magic_name__ ) )
pdb_lines.append("END" )
pdb_lines.append("" )
return "\n".join(__magic_name__ )
def _A ( __magic_name__ ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def _A ( __magic_name__ , __magic_name__ , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , __magic_name__ = None , ):
return Protein(
aatype=features["aatype"] , atom_positions=result["final_atom_positions"] , atom_mask=result["final_atom_mask"] , residue_index=features["residue_index"] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] ) , chain_index=__magic_name__ , remark=__magic_name__ , parents=__magic_name__ , parents_chain_index=__magic_name__ , )
| 655 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'van'
def __init__( self :Optional[Any] , _lowercase :Dict=2_24 , _lowercase :Union[str, Any]=3 , _lowercase :List[Any]=[7, 3, 3, 3] , _lowercase :Any=[4, 2, 2, 2] , _lowercase :Union[str, Any]=[64, 1_28, 3_20, 5_12] , _lowercase :List[Any]=[3, 3, 12, 3] , _lowercase :Dict=[8, 8, 4, 4] , _lowercase :int="gelu" , _lowercase :List[Any]=0.02 , _lowercase :List[Any]=1e-6 , _lowercase :Any=1e-2 , _lowercase :int=0.0 , _lowercase :int=0.0 , **_lowercase :Dict , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = patch_sizes
lowercase__ = strides
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = mlp_ratios
lowercase__ = hidden_act
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = layer_scale_init_value
lowercase__ = drop_path_rate
lowercase__ = dropout_rate
| 655 | 1 |
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :int , _lowercase :int , _lowercase :int , _lowercase :float , **_lowercase :Union[str, Any] ):
'''simple docstring'''
lowercase__ = feature_size
lowercase__ = sampling_rate
lowercase__ = padding_value
lowercase__ = kwargs.pop("padding_side" , "right" )
lowercase__ = kwargs.pop("return_attention_mask" , _lowercase )
super().__init__(**_lowercase )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[
BatchFeature,
List[BatchFeature],
Dict[str, BatchFeature],
Dict[str, List[BatchFeature]],
List[Dict[str, BatchFeature]],
] , _lowercase :Union[bool, str, PaddingStrategy] = True , _lowercase :Optional[int] = None , _lowercase :bool = False , _lowercase :Optional[int] = None , _lowercase :Optional[bool] = None , _lowercase :Optional[Union[str, TensorType]] = None , ):
'''simple docstring'''
if isinstance(_lowercase , (list, tuple) ) and isinstance(processed_features[0] , (dict, BatchFeature) ):
lowercase__ = {
key: [example[key] for example in processed_features] for key in processed_features[0].keys()
}
# The model's main input name, usually `input_values`, has be passed for padding
if self.model_input_names[0] not in processed_features:
raise ValueError(
"You should supply an instance of `transformers.BatchFeature` or list of `transformers.BatchFeature`"
f''' to this method that includes {self.model_input_names[0]}, but you provided'''
f''' {list(processed_features.keys() )}''' )
lowercase__ = processed_features[self.model_input_names[0]]
lowercase__ = (
return_attention_mask if return_attention_mask is not None else self.return_attention_mask
)
if len(_lowercase ) == 0:
if return_attention_mask:
lowercase__ = []
return processed_features
# If we have PyTorch/TF tensors or lists as inputs, we cast them as Numpy arrays
# and rebuild them afterwards if no return_tensors is specified
# Note that we lose the specific device the tensor may be on for PyTorch
lowercase__ = required_input[0]
if isinstance(_lowercase , (list, tuple) ):
# first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
lowercase__ = 0
while len(required_input[index] ) == 0:
index += 1
if index < len(_lowercase ):
lowercase__ = required_input[index][0]
if return_tensors is None:
if is_tf_tensor(_lowercase ):
lowercase__ = "tf"
elif is_torch_tensor(_lowercase ):
lowercase__ = "pt"
elif isinstance(_lowercase , (int, float, list, tuple, np.ndarray) ):
lowercase__ = "np"
else:
raise ValueError(
f'''type of {first_element} unknown: {type(_lowercase )}. '''
"Should be one of a python, numpy, pytorch or tensorflow object." )
for key, value in processed_features.items():
if isinstance(value[0] , (int, float) ):
lowercase__ = to_numpy(_lowercase )
else:
lowercase__ = [to_numpy(_lowercase ) for v in value]
# Convert padding_strategy in PaddingStrategy
lowercase__ = self._get_padding_strategies(padding=_lowercase , max_length=_lowercase )
lowercase__ = processed_features[self.model_input_names[0]]
lowercase__ = len(_lowercase )
if not all(len(_lowercase ) == batch_size for v in processed_features.values() ):
raise ValueError("Some items in the output dictionary have a different batch size than others." )
lowercase__ = []
for i in range(_lowercase ):
lowercase__ = {k: v[i] for k, v in processed_features.items()}
# truncation
lowercase__ = self._truncate(
_lowercase , max_length=_lowercase , pad_to_multiple_of=_lowercase , truncation=_lowercase , )
truncated_inputs.append(_lowercase )
if padding_strategy == PaddingStrategy.LONGEST:
# make sure that `max_length` cannot be longer than the longest truncated length
lowercase__ = max(len(input_slice[self.model_input_names[0]] ) for input_slice in truncated_inputs )
lowercase__ = PaddingStrategy.MAX_LENGTH
lowercase__ = {}
for i in range(_lowercase ):
# padding
lowercase__ = self._pad(
truncated_inputs[i] , max_length=_lowercase , padding_strategy=_lowercase , pad_to_multiple_of=_lowercase , return_attention_mask=_lowercase , )
for key, value in outputs.items():
if key not in batch_outputs:
lowercase__ = []
if value.dtype is np.dtype(np.floataa ):
lowercase__ = value.astype(np.floataa )
batch_outputs[key].append(_lowercase )
return BatchFeature(_lowercase , tensor_type=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :Union[Dict[str, np.ndarray], BatchFeature] , _lowercase :Optional[int] = None , _lowercase :PaddingStrategy = PaddingStrategy.DO_NOT_PAD , _lowercase :Optional[int] = None , _lowercase :Optional[bool] = None , ):
'''simple docstring'''
lowercase__ = processed_features[self.model_input_names[0]]
if padding_strategy == PaddingStrategy.LONGEST:
lowercase__ = len(_lowercase )
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowercase__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowercase__ = padding_strategy != PaddingStrategy.DO_NOT_PAD and len(_lowercase ) < max_length
if return_attention_mask and "attention_mask" not in processed_features:
lowercase__ = np.ones(len(_lowercase ) , dtype=np.intaa )
if needs_to_be_padded:
lowercase__ = max_length - len(_lowercase )
if self.padding_side == "right":
if return_attention_mask:
lowercase__ = np.pad(
processed_features["attention_mask"] , (0, difference) )
lowercase__ = ((0, difference), (0, 0)) if self.feature_size > 1 else (0, difference)
lowercase__ = np.pad(
_lowercase , _lowercase , "constant" , constant_values=self.padding_value )
elif self.padding_side == "left":
if return_attention_mask:
lowercase__ = np.pad(
processed_features["attention_mask"] , (difference, 0) )
lowercase__ = ((difference, 0), (0, 0)) if self.feature_size > 1 else (difference, 0)
lowercase__ = np.pad(
_lowercase , _lowercase , "constant" , constant_values=self.padding_value )
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return processed_features
def UpperCAmelCase ( self :Any , _lowercase :Union[Dict[str, np.ndarray], BatchFeature] , _lowercase :Optional[int] = None , _lowercase :Optional[int] = None , _lowercase :Optional[bool] = None , ):
'''simple docstring'''
if not truncation:
return processed_features
elif truncation and max_length is None:
raise ValueError("When setting ``truncation=True``, make sure that ``max_length`` is defined." )
lowercase__ = processed_features[self.model_input_names[0]]
# find `max_length` that fits `pad_to_multiple_of`
if max_length is not None and pad_to_multiple_of is not None and (max_length % pad_to_multiple_of != 0):
lowercase__ = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of
lowercase__ = len(_lowercase ) > max_length
if needs_to_be_truncated:
lowercase__ = processed_features[self.model_input_names[0]][:max_length]
if "attention_mask" in processed_features:
lowercase__ = processed_features["attention_mask"][:max_length]
return processed_features
def UpperCAmelCase ( self :int , _lowercase :str=False , _lowercase :int=None ):
'''simple docstring'''
if padding is not False:
if padding is True:
lowercase__ = PaddingStrategy.LONGEST # Default to pad to the longest sequence in the batch
elif not isinstance(_lowercase , _lowercase ):
lowercase__ = PaddingStrategy(_lowercase )
elif isinstance(_lowercase , _lowercase ):
lowercase__ = padding
else:
lowercase__ = PaddingStrategy.DO_NOT_PAD
# Set max length if needed
if max_length is None:
if padding_strategy == PaddingStrategy.MAX_LENGTH:
raise ValueError(
f'''When setting ``padding={PaddingStrategy.MAX_LENGTH}``, make sure that max_length is defined''' )
# Test if we have a padding value
if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.padding_value is None):
raise ValueError(
"Asking to pad but the feature_extractor does not have a padding value. Please select a value to use"
" as `padding_value`. For example: `feature_extractor.padding_value = 0.0`." )
return padding_strategy
| 655 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase ( enum.Enum ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 2
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self :Any , *_lowercase :Optional[Any] , **_lowercase :Union[str, Any] ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowercase__ = None
if self.model.config.prefix is not None:
lowercase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowercase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowercase__ , lowercase__ , lowercase__ = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
lowercase__ = {**self._preprocess_params, **preprocess_params}
lowercase__ = {**self._forward_params, **forward_params}
def UpperCAmelCase ( self :Tuple , _lowercase :Optional[Any]=None , _lowercase :List[Any]=None , _lowercase :List[str]=None , _lowercase :Optional[Any]=None , _lowercase :Optional[int]=None , _lowercase :Any=None , _lowercase :Any=None , _lowercase :Dict=None , **_lowercase :Union[str, Any] , ):
'''simple docstring'''
lowercase__ = {}
if prefix is not None:
lowercase__ = prefix
if prefix:
lowercase__ = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
lowercase__ = handle_long_generation
preprocess_params.update(_lowercase )
lowercase__ = generate_kwargs
lowercase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.TENSORS
if return_type is not None:
lowercase__ = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
lowercase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self :int , *_lowercase :Optional[int] , **_lowercase :List[str] ):
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self :Union[str, Any] , _lowercase :Dict , **_lowercase :Tuple ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple , _lowercase :Optional[int]="" , _lowercase :Tuple=None , **_lowercase :List[str] ):
'''simple docstring'''
lowercase__ = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prompt_text
if handle_long_generation == "hole":
lowercase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowercase__ = generate_kwargs["max_new_tokens"]
else:
lowercase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowercase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
lowercase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
lowercase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def UpperCAmelCase ( self :str , _lowercase :int , **_lowercase :str ):
'''simple docstring'''
lowercase__ = model_inputs["input_ids"]
lowercase__ = model_inputs.get("attention_mask" , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowercase__ = None
lowercase__ = None
lowercase__ = 1
else:
lowercase__ = input_ids.shape[0]
lowercase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowercase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
lowercase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
lowercase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowercase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowercase__ = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
lowercase__ = generated_sequence.shape[0]
if self.framework == "pt":
lowercase__ = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowercase__ = tf.reshape(_lowercase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def UpperCAmelCase ( self :Any , _lowercase :Tuple , _lowercase :str=ReturnType.FULL_TEXT , _lowercase :Dict=True ):
'''simple docstring'''
lowercase__ = model_outputs["generated_sequence"][0]
lowercase__ = model_outputs["input_ids"]
lowercase__ = model_outputs["prompt_text"]
lowercase__ = generated_sequence.numpy().tolist()
lowercase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowercase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowercase__ = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowercase__ = 0
else:
lowercase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
lowercase__ = prompt_text + text[prompt_length:]
else:
lowercase__ = text[prompt_length:]
lowercase__ = {"generated_text": all_text}
records.append(_lowercase )
return records
| 655 | 1 |
import copy
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 ..auto import CONFIG_MAPPING
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/conditional-detr-resnet-50""": (
"""https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'conditional_detr'
__lowerCamelCase = ['past_key_values']
__lowerCamelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self :Union[str, Any] , _lowercase :Union[str, Any]=True , _lowercase :str=None , _lowercase :Dict=3 , _lowercase :int=3_00 , _lowercase :Tuple=6 , _lowercase :List[str]=20_48 , _lowercase :Union[str, Any]=8 , _lowercase :Any=6 , _lowercase :Tuple=20_48 , _lowercase :str=8 , _lowercase :Any=0.0 , _lowercase :int=0.0 , _lowercase :Any=True , _lowercase :Tuple="relu" , _lowercase :Optional[Any]=2_56 , _lowercase :int=0.1 , _lowercase :Dict=0.0 , _lowercase :List[Any]=0.0 , _lowercase :Union[str, Any]=0.02 , _lowercase :List[str]=1.0 , _lowercase :Optional[int]=False , _lowercase :List[Any]="sine" , _lowercase :str="resnet50" , _lowercase :int=True , _lowercase :str=False , _lowercase :List[str]=2 , _lowercase :int=5 , _lowercase :Union[str, Any]=2 , _lowercase :Any=1 , _lowercase :int=1 , _lowercase :Union[str, Any]=2 , _lowercase :Optional[Any]=5 , _lowercase :List[Any]=2 , _lowercase :Any=0.25 , **_lowercase :str , ):
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
lowercase__ = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(_lowercase , _lowercase ):
lowercase__ = backbone_config.get("model_type" )
lowercase__ = CONFIG_MAPPING[backbone_model_type]
lowercase__ = config_class.from_dict(_lowercase )
lowercase__ = use_timm_backbone
lowercase__ = backbone_config
lowercase__ = num_channels
lowercase__ = num_queries
lowercase__ = d_model
lowercase__ = encoder_ffn_dim
lowercase__ = encoder_layers
lowercase__ = encoder_attention_heads
lowercase__ = decoder_ffn_dim
lowercase__ = decoder_layers
lowercase__ = decoder_attention_heads
lowercase__ = dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = activation_function
lowercase__ = init_std
lowercase__ = init_xavier_std
lowercase__ = encoder_layerdrop
lowercase__ = decoder_layerdrop
lowercase__ = encoder_layers
lowercase__ = auxiliary_loss
lowercase__ = position_embedding_type
lowercase__ = backbone
lowercase__ = use_pretrained_backbone
lowercase__ = dilation
# Hungarian matcher
lowercase__ = class_cost
lowercase__ = bbox_cost
lowercase__ = giou_cost
# Loss coefficients
lowercase__ = mask_loss_coefficient
lowercase__ = dice_loss_coefficient
lowercase__ = cls_loss_coefficient
lowercase__ = bbox_loss_coefficient
lowercase__ = giou_loss_coefficient
lowercase__ = focal_alpha
super().__init__(is_encoder_decoder=_lowercase , **_lowercase )
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self.encoder_attention_heads
@property
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
return self.d_model
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
lowercase__ = self.backbone_config.to_dict()
lowercase__ = self.__class__.model_type
return output
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = version.parse('1.11' )
@property
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return 1e-5
@property
def UpperCAmelCase ( self :int ):
'''simple docstring'''
return 12
| 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_snake_case = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _A ( __magic_name__ ):
lowercase__ = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__magic_name__ )[0]
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(rows * cols * num_images )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
lowercase__ = data.reshape(__magic_name__ , __magic_name__ , __magic_name__ , 1 )
return data
@deprecated(__magic_name__ , "Please use tf.one_hot on tensors." )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = labels_dense.shape[0]
lowercase__ = numpy.arange(__magic_name__ ) * num_classes
lowercase__ = numpy.zeros((num_labels, num_classes) )
lowercase__ = 1
return labels_one_hot
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(__magic_name__ )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__magic_name__ , __magic_name__ )
return labels
class lowerCAmelCase :
@deprecated(
_lowercase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Tuple=False , _lowercase :str=False , _lowercase :Dict=dtypes.floataa , _lowercase :Optional[Any]=True , _lowercase :Any=None , ):
'''simple docstring'''
lowercase__ , lowercase__ = random_seed.get_seed(_lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ = dtypes.as_dtype(_lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
lowercase__ = 1_00_00
lowercase__ = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
lowercase__ = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ = images.astype(numpy.floataa )
lowercase__ = numpy.multiply(_lowercase , 1.0 / 255.0 )
lowercase__ = images
lowercase__ = labels
lowercase__ = 0
lowercase__ = 0
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._images
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self._labels
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return self._num_examples
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._epochs_completed
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] , _lowercase :Any=False , _lowercase :Union[str, Any]=True ):
'''simple docstring'''
if fake_data:
lowercase__ = [1] * 7_84
lowercase__ = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_lowercase )],
[fake_label for _ in range(_lowercase )],
)
lowercase__ = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perma]
lowercase__ = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ = self._num_examples - start
lowercase__ = self._images[start : self._num_examples]
lowercase__ = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perm]
lowercase__ = self.labels[perm]
# Start next epoch
lowercase__ = 0
lowercase__ = batch_size - rest_num_examples
lowercase__ = self._index_in_epoch
lowercase__ = self._images[start:end]
lowercase__ = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__magic_name__ , "Please write your own downloading logic." )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if not gfile.Exists(__magic_name__ ):
gfile.MakeDirs(__magic_name__ )
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
if not gfile.Exists(__magic_name__ ):
urllib.request.urlretrieve(__magic_name__ , __magic_name__ ) # noqa: S310
with gfile.GFile(__magic_name__ ) as f:
lowercase__ = f.size()
print("Successfully downloaded" , __magic_name__ , __magic_name__ , "bytes." )
return filepath
@deprecated(
__magic_name__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=5000 , __magic_name__=None , __magic_name__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__magic_name__ , one_hot=__magic_name__ , dtype=__magic_name__ , seed=__magic_name__ )
lowercase__ = fake()
lowercase__ = fake()
lowercase__ = fake()
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
if not source_url: # empty string check
lowercase__ = DEFAULT_SOURCE_URL
lowercase__ = "train-images-idx3-ubyte.gz"
lowercase__ = "train-labels-idx1-ubyte.gz"
lowercase__ = "t10k-images-idx3-ubyte.gz"
lowercase__ = "t10k-labels-idx1-ubyte.gz"
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
if not 0 <= validation_size <= len(__magic_name__ ):
lowercase__ = (
"Validation size should be between 0 and "
f'''{len(__magic_name__ )}. Received: {validation_size}.'''
)
raise ValueError(__magic_name__ )
lowercase__ = train_images[:validation_size]
lowercase__ = train_labels[:validation_size]
lowercase__ = train_images[validation_size:]
lowercase__ = train_labels[validation_size:]
lowercase__ = {"dtype": dtype, "reshape": reshape, "seed": seed}
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
| 655 | 1 |
import numpy as np
import skfuzzy as fuzz
if __name__ == "__main__":
# Create universe of discourse in Python using linspace ()
_snake_case = np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)
# Create two fuzzy sets by defining any membership function
# (trapmf(), gbellmf(), gaussmf(), etc).
_snake_case = [0, 25, 50]
_snake_case = [25, 50, 75]
_snake_case = fuzz.membership.trimf(X, abca)
_snake_case = fuzz.membership.trimf(X, abca)
# Compute the different operations using inbuilt functions.
_snake_case = np.ones(75)
_snake_case = np.zeros((75,))
# 1. Union = max(µA(x), µB(x))
_snake_case = fuzz.fuzzy_or(X, young, X, middle_aged)[1]
# 2. Intersection = min(µA(x), µB(x))
_snake_case = fuzz.fuzzy_and(X, young, X, middle_aged)[1]
# 3. Complement (A) = (1- min(µA(x))
_snake_case = fuzz.fuzzy_not(young)
# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
_snake_case = fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
_snake_case = young + middle_aged - (young * middle_aged)
# 6. Algebraic Product = (µA(x) * µB(x))
_snake_case = young * middle_aged
# 7. Bounded Sum = min[1,(µA(x), µB(x))]
_snake_case = fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
# 8. Bounded difference = min[0,(µA(x), µB(x))]
_snake_case = fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]
# max-min composition
# max-product composition
# Plot each set A, set B and each operation result using plot() and subplot().
from matplotlib import pyplot as plt
plt.figure()
plt.subplot(4, 3, 1)
plt.plot(X, young)
plt.title("""Young""")
plt.grid(True)
plt.subplot(4, 3, 2)
plt.plot(X, middle_aged)
plt.title("""Middle aged""")
plt.grid(True)
plt.subplot(4, 3, 3)
plt.plot(X, union)
plt.title("""union""")
plt.grid(True)
plt.subplot(4, 3, 4)
plt.plot(X, intersection)
plt.title("""intersection""")
plt.grid(True)
plt.subplot(4, 3, 5)
plt.plot(X, complement_a)
plt.title("""complement_a""")
plt.grid(True)
plt.subplot(4, 3, 6)
plt.plot(X, difference)
plt.title("""difference a/b""")
plt.grid(True)
plt.subplot(4, 3, 7)
plt.plot(X, alg_sum)
plt.title("""alg_sum""")
plt.grid(True)
plt.subplot(4, 3, 8)
plt.plot(X, alg_product)
plt.title("""alg_product""")
plt.grid(True)
plt.subplot(4, 3, 9)
plt.plot(X, bdd_sum)
plt.title("""bdd_sum""")
plt.grid(True)
plt.subplot(4, 3, 10)
plt.plot(X, bdd_difference)
plt.title("""bdd_difference""")
plt.grid(True)
plt.subplots_adjust(hspace=0.5)
plt.show()
| 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ):
'''simple docstring'''
lowercase__ = []
lowercase__ = self
while temp:
string_rep.append(f'''{temp.data}''' )
lowercase__ = temp.next
return "->".join(_lowercase )
def _A ( __magic_name__ ):
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase__ = lowercase__ = Node(elements_list[0] )
for i in range(1 , len(__magic_name__ ) ):
lowercase__ = Node(elements_list[i] )
lowercase__ = current.next
return head
def _A ( __magic_name__ ):
if head_node is not None and isinstance(__magic_name__ , __magic_name__ ):
print_reverse(head_node.next )
print(head_node.data )
def _A ( ):
from doctest import testmod
testmod()
lowercase__ = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(__magic_name__ )
print("Elements in Reverse:" )
print_reverse(__magic_name__ )
if __name__ == "__main__":
main()
| 655 | 1 |
from __future__ import annotations
def _A ( __magic_name__ , __magic_name__ ):
if nth_term == "":
return [""]
lowercase__ = int(__magic_name__ )
lowercase__ = int(__magic_name__ )
lowercase__ = []
for temp in range(int(__magic_name__ ) ):
series.append(f'''1 / {pow(temp + 1 , int(__magic_name__ ) )}''' if series else "1" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Enter the last number (nth term) of the P-Series"""))
_snake_case = int(input("""Enter the power for P-Series"""))
print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
print(p_series(nth_term, power))
| 655 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowercase__ = 0
while count < prec:
lowercase__ = random.randint(2 , n - 1 )
lowercase__ = bin_exp_mod(__magic_name__ , __magic_name__ , __magic_name__ )
if b != 1:
lowercase__ = True
for _ in range(__magic_name__ ):
if b == n - 1:
lowercase__ = False
break
lowercase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
_snake_case = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 655 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
_snake_case = """0.12""" # assumed parallelism: 8
@require_flax
@is_staging_test
class lowerCAmelCase ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls :Union[str, Any] ):
'''simple docstring'''
lowercase__ = TOKEN
HfFolder.save_token(_lowercase )
@classmethod
def UpperCAmelCase ( cls :Optional[Any] ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
lowercase__ = FlaxBertModel(_lowercase )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
lowercase__ = FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax''' )
lowercase__ = flatten_dict(unfreeze(model.params ) )
lowercase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowercase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1e-3 , msg=f'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(_lowercase , repo_id="test-model-flax" , push_to_hub=_lowercase , use_auth_token=self._token )
lowercase__ = FlaxBertModel.from_pretrained(f'''{USER}/test-model-flax''' )
lowercase__ = flatten_dict(unfreeze(model.params ) )
lowercase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowercase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1e-3 , msg=f'''{key} not identical''' )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
lowercase__ = FlaxBertModel(_lowercase )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
lowercase__ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
lowercase__ = flatten_dict(unfreeze(model.params ) )
lowercase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowercase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1e-3 , msg=f'''{key} not identical''' )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
_lowercase , repo_id="valid_org/test-model-flax-org" , push_to_hub=_lowercase , use_auth_token=self._token )
lowercase__ = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
lowercase__ = flatten_dict(unfreeze(model.params ) )
lowercase__ = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
lowercase__ = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(_lowercase , 1e-3 , msg=f'''{key} not identical''' )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = True
lowercase__ = flatten_dict(modela.params )
lowercase__ = flatten_dict(modela.params )
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key] ) ) > 1e-4:
lowercase__ = False
return models_are_equal
@require_flax
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
lowercase__ = FlaxBertModel(_lowercase )
lowercase__ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_lowercase , _lowercase ) )
with self.assertRaises(_lowercase ):
lowercase__ = FlaxBertModel.from_pretrained(_lowercase )
lowercase__ = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertTrue(check_models_equal(_lowercase , _lowercase ) )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
lowercase__ = FlaxBertModel(_lowercase )
lowercase__ = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(_lowercase , _lowercase ) , max_shard_size="10KB" )
with self.assertRaises(_lowercase ):
lowercase__ = FlaxBertModel.from_pretrained(_lowercase )
lowercase__ = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertTrue(check_models_equal(_lowercase , _lowercase ) )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = "bert"
lowercase__ = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(_lowercase ):
lowercase__ = FlaxBertModel.from_pretrained(_lowercase )
lowercase__ = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertIsNotNone(_lowercase )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = "bert"
lowercase__ = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(_lowercase ):
lowercase__ = FlaxBertModel.from_pretrained(_lowercase )
lowercase__ = FlaxBertModel.from_pretrained(_lowercase , subfolder=_lowercase )
self.assertIsNotNone(_lowercase )
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCAmelCase :
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["prompt"]
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
if "image" in inputs:
lowercase__ = inputs["image"]
else:
lowercase__ = None
if "mask_image" in inputs:
lowercase__ = inputs["mask_image"]
else:
lowercase__ = None
if "original_image" in inputs:
lowercase__ = inputs["original_image"]
else:
lowercase__ = None
lowercase__ , lowercase__ = pipe.encode_prompt(_lowercase )
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowercase , _lowercase , _lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowercase , _lowercase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
| 655 | 1 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
_snake_case = logging.get_logger(__name__)
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[Any] , *_lowercase :List[Any] , **_lowercase :List[Any] ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
requires_backends(self , "vision" )
self.check_model_type(_lowercase )
def __call__( self :Union[str, Any] , _lowercase :Union[str, List[str], "Image.Image", List["Image.Image"]] , **_lowercase :Optional[Any] ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :List[str] , **_lowercase :Union[str, Any] ):
'''simple docstring'''
return {}, {}, {}
def UpperCAmelCase ( self :Optional[int] , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = load_image(_lowercase )
lowercase__ = image.size
lowercase__ = self.image_processor(images=_lowercase , return_tensors=self.framework )
return model_inputs
def UpperCAmelCase ( self :Dict , _lowercase :Optional[Any] ):
'''simple docstring'''
lowercase__ = self.model(**_lowercase )
return model_outputs
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :List[str] ):
'''simple docstring'''
lowercase__ = model_outputs.predicted_depth
lowercase__ = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode="bicubic" , align_corners=_lowercase )
lowercase__ = prediction.squeeze().cpu().numpy()
lowercase__ = (output * 2_55 / np.max(_lowercase )).astype("uint8" )
lowercase__ = Image.fromarray(_lowercase )
lowercase__ = {}
lowercase__ = predicted_depth
lowercase__ = depth
return output_dict
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
lowercase__ = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ = model(_lowercase )["last_hidden_state"]
lowercase__ = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
lowercase__ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 655 | 1 |
def _A ( __magic_name__ , __magic_name__ ):
if mass < 0:
raise ValueError("The mass of a body cannot be negative" )
return 0.5 * mass * abs(__magic_name__ ) * abs(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(__magic_name__ )
lowercase__ = "".join(bin(__magic_name__ )[2:].zfill(8 ) for byte in data )
lowercase__ = len(__magic_name__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase__ = B"=" * ((6 - len(__magic_name__ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(__magic_name__ ) % 6)
else:
lowercase__ = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(__magic_name__ ) , 6 ) ).encode()
+ padding
)
def _A ( __magic_name__ ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ) and not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = (
"argument should be a bytes-like object or ASCII string, "
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(__magic_name__ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(__magic_name__ , __magic_name__ ):
try:
lowercase__ = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
lowercase__ = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(__magic_name__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase__ = encoded_data[:-padding]
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase__ = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(__magic_name__ ) , 8 )
]
return bytes(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from __future__ import annotations
_snake_case = """#"""
class lowerCAmelCase :
def __init__( self :List[Any] ):
'''simple docstring'''
lowercase__ = {}
def UpperCAmelCase ( self :Dict , _lowercase :str ):
'''simple docstring'''
lowercase__ = self._trie
for char in text:
if char not in trie:
lowercase__ = {}
lowercase__ = trie[char]
lowercase__ = True
def UpperCAmelCase ( self :Dict , _lowercase :str ):
'''simple docstring'''
lowercase__ = self._trie
for char in prefix:
if char in trie:
lowercase__ = trie[char]
else:
return []
return self._elements(_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :dict ):
'''simple docstring'''
lowercase__ = []
for c, v in d.items():
lowercase__ = [" "] if c == END else [(c + s) for s in self._elements(_lowercase )]
result.extend(_lowercase )
return tuple(_lowercase )
_snake_case = Trie()
_snake_case = ("""depart""", """detergent""", """daring""", """dog""", """deer""", """deal""")
for word in words:
trie.insert_word(word)
def _A ( __magic_name__ ):
lowercase__ = trie.find_word(__magic_name__ )
return tuple(string + word for word in suffixes )
def _A ( ):
print(autocomplete_using_trie("de" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 655 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Dict , _lowercase :TransformeraDModel , _lowercase :AutoencoderKL , _lowercase :KarrasDiffusionSchedulers , _lowercase :Optional[Dict[int, str]] = None , ):
'''simple docstring'''
super().__init__()
self.register_modules(transformer=_lowercase , vae=_lowercase , scheduler=_lowercase )
# create a imagenet -> id dictionary for easier use
lowercase__ = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
lowercase__ = int(_lowercase )
lowercase__ = dict(sorted(self.labels.items() ) )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Union[str, List[str]] ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
lowercase__ = list(_lowercase )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self :Optional[Any] , _lowercase :List[int] , _lowercase :float = 4.0 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :int = 50 , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = len(_lowercase )
lowercase__ = self.transformer.config.sample_size
lowercase__ = self.transformer.config.in_channels
lowercase__ = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=_lowercase , device=self.device , dtype=self.transformer.dtype , )
lowercase__ = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowercase__ = torch.tensor(_lowercase , device=self.device ).reshape(-1 )
lowercase__ = torch.tensor([10_00] * batch_size , device=self.device )
lowercase__ = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(_lowercase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowercase__ = latent_model_input[: len(_lowercase ) // 2]
lowercase__ = torch.cat([half, half] , dim=0 )
lowercase__ = self.scheduler.scale_model_input(_lowercase , _lowercase )
lowercase__ = t
if not torch.is_tensor(_lowercase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
lowercase__ = latent_model_input.device.type == "mps"
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.floataa if is_mps else torch.floataa
else:
lowercase__ = torch.intaa if is_mps else torch.intaa
lowercase__ = torch.tensor([timesteps] , dtype=_lowercase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowercase__ = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase__ = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowercase__ = self.transformer(
_lowercase , timestep=_lowercase , class_labels=_lowercase ).sample
# perform guidance
if guidance_scale > 1:
lowercase__ , lowercase__ = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowercase__ , lowercase__ = torch.split(_lowercase , len(_lowercase ) // 2 , dim=0 )
lowercase__ = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowercase__ = torch.cat([half_eps, half_eps] , dim=0 )
lowercase__ = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowercase__ , lowercase__ = torch.split(_lowercase , _lowercase , dim=1 )
else:
lowercase__ = noise_pred
# compute previous image: x_t -> x_t-1
lowercase__ = self.scheduler.step(_lowercase , _lowercase , _lowercase ).prev_sample
if guidance_scale > 1:
lowercase__ , lowercase__ = latent_model_input.chunk(2 , dim=0 )
else:
lowercase__ = latent_model_input
lowercase__ = 1 / self.vae.config.scaling_factor * latents
lowercase__ = self.vae.decode(_lowercase ).sample
lowercase__ = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase__ = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class lowerCAmelCase ( lowercase_ , unittest.TestCase ):
__lowerCamelCase = MobileBertTokenizer
__lowerCamelCase = MobileBertTokenizerFast
__lowerCamelCase = True
__lowerCamelCase = True
__lowerCamelCase = filter_non_english
__lowerCamelCase = 'google/mobilebert-uncased'
def UpperCAmelCase ( self :str ):
'''simple docstring'''
super().setUp()
lowercase__ = [
"[UNK]",
"[CLS]",
"[SEP]",
"[PAD]",
"[MASK]",
"want",
"##want",
"##ed",
"wa",
"un",
"runn",
"##ing",
",",
"low",
"lowest",
]
lowercase__ = 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] ) )
lowercase__ = [
(tokenizer_def[0], self.pre_trained_model_path, tokenizer_def[2]) # else the 'google/' prefix is stripped
for tokenizer_def in self.tokenizers_list
]
def UpperCAmelCase ( self :Tuple , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = "UNwant\u00E9d,running"
lowercase__ = "unwanted, running"
return input_text, output_text
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = self.tokenizer_class(self.vocab_file )
lowercase__ = tokenizer.tokenize("UNwant\u00E9d,running" )
self.assertListEqual(_lowercase , ["un", "##want", "##ed", ",", "runn", "##ing"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase ) , [9, 6, 7, 12, 10, 11] )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = "UNwant\u00E9d,running"
lowercase__ = tokenizer.tokenize(_lowercase )
lowercase__ = rust_tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
lowercase__ = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
lowercase__ = rust_tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
lowercase__ = self.get_rust_tokenizer()
lowercase__ = tokenizer.encode(_lowercase )
lowercase__ = rust_tokenizer.encode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
# With lower casing
lowercase__ = self.get_tokenizer(do_lower_case=_lowercase )
lowercase__ = self.get_rust_tokenizer(do_lower_case=_lowercase )
lowercase__ = "UNwant\u00E9d,running"
lowercase__ = tokenizer.tokenize(_lowercase )
lowercase__ = rust_tokenizer.tokenize(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
lowercase__ = tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
lowercase__ = rust_tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
self.assertListEqual(_lowercase , _lowercase )
lowercase__ = self.get_rust_tokenizer()
lowercase__ = tokenizer.encode(_lowercase )
lowercase__ = rust_tokenizer.encode(_lowercase )
self.assertListEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize("ah\u535A\u63A8zz" ) , ["ah", "\u535A", "\u63A8", "zz"] )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["hello", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase , strip_accents=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hällo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["h\u00E9llo"] )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase , strip_accents=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["hallo", "!", "how", "are", "you", "?"] )
self.assertListEqual(tokenizer.tokenize("H\u00E9llo" ) , ["hello"] )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase , strip_accents=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HäLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase , strip_accents=_lowercase )
self.assertListEqual(
tokenizer.tokenize(" \tHäLLo!how \n Are yoU? " ) , ["HaLLo", "!", "how", "Are", "yoU", "?"] )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = BasicTokenizer(do_lower_case=_lowercase , never_split=["[UNK]"] )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo!how \n Are yoU? [UNK]" ) , ["HeLLo", "!", "how", "Are", "yoU", "?", "[UNK]"] )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = ["[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn", "##ing"]
lowercase__ = {}
for i, token in enumerate(_lowercase ):
lowercase__ = i
lowercase__ = WordpieceTokenizer(vocab=_lowercase , unk_token="[UNK]" )
self.assertListEqual(tokenizer.tokenize("" ) , [] )
self.assertListEqual(tokenizer.tokenize("unwanted running" ) , ["un", "##want", "##ed", "runn", "##ing"] )
self.assertListEqual(tokenizer.tokenize("unwantedX running" ) , ["[UNK]", "runn", "##ing"] )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
self.assertTrue(_is_whitespace(" " ) )
self.assertTrue(_is_whitespace("\t" ) )
self.assertTrue(_is_whitespace("\r" ) )
self.assertTrue(_is_whitespace("\n" ) )
self.assertTrue(_is_whitespace("\u00A0" ) )
self.assertFalse(_is_whitespace("A" ) )
self.assertFalse(_is_whitespace("-" ) )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
self.assertTrue(_is_control("\u0005" ) )
self.assertFalse(_is_control("A" ) )
self.assertFalse(_is_control(" " ) )
self.assertFalse(_is_control("\t" ) )
self.assertFalse(_is_control("\r" ) )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
self.assertTrue(_is_punctuation("-" ) )
self.assertTrue(_is_punctuation("$" ) )
self.assertTrue(_is_punctuation("`" ) )
self.assertTrue(_is_punctuation("." ) )
self.assertFalse(_is_punctuation("A" ) )
self.assertFalse(_is_punctuation(" " ) )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(_lowercase ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
self.assertListEqual(
[rust_tokenizer.tokenize(_lowercase ) for t in ["Test", "\xad", "test"]] , [["[UNK]"], [], ["[UNK]"]] )
@slow
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.tokenizer_class.from_pretrained("google/mobilebert-uncased" )
lowercase__ = tokenizer.encode("sequence builders" , add_special_tokens=_lowercase )
lowercase__ = tokenizer.encode("multi-sequence build" , add_special_tokens=_lowercase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(_lowercase )
lowercase__ = tokenizer.build_inputs_with_special_tokens(_lowercase , _lowercase )
assert encoded_sentence == [1_01] + text + [1_02]
assert encoded_pair == [1_01] + text + [1_02] + text_a + [1_02]
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
lowercase__ = f'''A, naïve {tokenizer_r.mask_token} AllenNLP sentence.'''
lowercase__ = tokenizer_r.encode_plus(
_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , return_offsets_mapping=_lowercase , add_special_tokens=_lowercase , )
lowercase__ = tokenizer_r.do_lower_case if hasattr(_lowercase , "do_lower_case" ) else False
lowercase__ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), "A"),
((1, 2), ","),
((3, 5), "na"),
((5, 6), "##ï"),
((6, 8), "##ve"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "Allen"),
((21, 23), "##NL"),
((23, 24), "##P"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), "a"),
((1, 2), ","),
((3, 8), "naive"),
((9, 15), tokenizer_r.mask_token),
((16, 21), "allen"),
((21, 23), "##nl"),
((23, 24), "##p"),
((25, 33), "sentence"),
((33, 34), "."),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens["input_ids"] ) )
self.assertEqual([e[0] for e in expected_results] , tokens["offset_mapping"] )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = ["的", "人", "有"]
lowercase__ = "".join(_lowercase )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowercase__ = True
lowercase__ = self.tokenizer_class.from_pretrained(_lowercase , **_lowercase )
lowercase__ = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
lowercase__ = tokenizer_p.encode(_lowercase , add_special_tokens=_lowercase )
lowercase__ = tokenizer_r.encode(_lowercase , add_special_tokens=_lowercase )
lowercase__ = tokenizer_r.convert_ids_to_tokens(_lowercase )
lowercase__ = tokenizer_p.convert_ids_to_tokens(_lowercase )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(_lowercase , _lowercase )
self.assertListEqual(_lowercase , _lowercase )
lowercase__ = False
lowercase__ = self.rust_tokenizer_class.from_pretrained(_lowercase , **_lowercase )
lowercase__ = self.tokenizer_class.from_pretrained(_lowercase , **_lowercase )
lowercase__ = tokenizer_r.encode(_lowercase , add_special_tokens=_lowercase )
lowercase__ = tokenizer_p.encode(_lowercase , add_special_tokens=_lowercase )
lowercase__ = tokenizer_r.convert_ids_to_tokens(_lowercase )
lowercase__ = tokenizer_p.convert_ids_to_tokens(_lowercase )
# it is expected that only the first Chinese character is not preceded by "##".
lowercase__ = [
f'''##{token}''' if idx != 0 else token for idx, token in enumerate(_lowercase )
]
self.assertListEqual(_lowercase , _lowercase )
self.assertListEqual(_lowercase , _lowercase )
| 655 |
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 ( lowercase_ ):
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = SMALL_MODEL_IDENTIFIER
lowercase__ = "pt"
lowercase__ = "tf"
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowercase )
def UpperCAmelCase ( self :Tuple , _lowercase :int ):
'''simple docstring'''
lowercase__ = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowercase )
model_tf.save_pretrained(_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = "mock_framework"
# Framework provided - return whatever the user provides
lowercase__ = FeaturesManager.determine_framework(self.test_model , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_tf )
# Both in environment -> use PyTorch
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# Both not in environment -> raise error
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
| 655 | 1 |
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
_snake_case = """
Hugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company that developed a chatbot app targeted at teenagers.[2] After open-sourcing the model behind the chatbot, the company pivoted to focus on being a platform for machine learning.
In March 2021, Hugging Face raised $40 million in a Series B funding round.[3]
On April 28, 2021, the company launched the BigScience Research Workshop in collaboration with several other research groups to release an open large language model.[4] In 2022, the workshop concluded with the announcement of BLOOM, a multilingual large language model with 176 billion parameters.[5]
"""
class lowerCAmelCase ( unittest.TestCase , lowercase_ ):
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = load_tool("text-question-answering" )
self.tool.setup()
lowercase__ = load_tool("text-question-answering" , remote=_lowercase )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = self.tool(_lowercase , "What did Hugging Face do in April 2021?" )
self.assertEqual(_lowercase , "launched the BigScience Research Workshop" )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = self.remote_tool(_lowercase , "What did Hugging Face do in April 2021?" )
self.assertEqual(_lowercase , "launched the BigScience Research Workshop" )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.tool(text=_lowercase , question="What did Hugging Face do in April 2021?" )
self.assertEqual(_lowercase , "launched the BigScience Research Workshop" )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.remote_tool(text=_lowercase , question="What did Hugging Face do in April 2021?" )
self.assertEqual(_lowercase , "launched the BigScience Research Workshop" )
| 655 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git_vision_model'
def __init__( self :Dict , _lowercase :Dict=7_68 , _lowercase :Dict=30_72 , _lowercase :Tuple=12 , _lowercase :List[str]=12 , _lowercase :Tuple=3 , _lowercase :Dict=2_24 , _lowercase :Tuple=16 , _lowercase :Optional[int]="quick_gelu" , _lowercase :Union[str, Any]=1e-5 , _lowercase :Tuple=0.0 , _lowercase :Tuple=0.02 , **_lowercase :Optional[Any] , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_channels
lowercase__ = patch_size
lowercase__ = image_size
lowercase__ = initializer_range
lowercase__ = attention_dropout
lowercase__ = layer_norm_eps
lowercase__ = hidden_act
@classmethod
def UpperCAmelCase ( cls :List[str] , _lowercase :Union[str, os.PathLike] , **_lowercase :Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
lowercase__ , lowercase__ = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
lowercase__ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowercase , **_lowercase )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git'
def __init__( self :Union[str, Any] , _lowercase :Dict=None , _lowercase :List[str]=3_05_22 , _lowercase :Tuple=7_68 , _lowercase :Any=6 , _lowercase :Dict=12 , _lowercase :Any=30_72 , _lowercase :List[Any]="gelu" , _lowercase :Tuple=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :Optional[Any]=10_24 , _lowercase :Any=0.02 , _lowercase :int=1e-12 , _lowercase :List[Any]=0 , _lowercase :int="absolute" , _lowercase :List[str]=True , _lowercase :Any=False , _lowercase :int=1_01 , _lowercase :str=1_02 , _lowercase :Dict=None , **_lowercase :List[str] , ):
'''simple docstring'''
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , pad_token_id=_lowercase , **_lowercase )
if vision_config is None:
lowercase__ = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
lowercase__ = GitVisionConfig(**_lowercase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
lowercase__ = tie_word_embeddings
lowercase__ = num_image_with_embedding
lowercase__ = bos_token_id
lowercase__ = eos_token_id
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.vision_config.to_dict()
lowercase__ = self.__class__.model_type
return output
| 655 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
_snake_case = _symbol_database.Default()
_snake_case = _descriptor_pool.Default().AddSerializedFile(
b"""\n\x19sentencepiece_model.proto\x12\rsentencepiece\"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12\"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12\"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18\" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse\"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32\".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL\"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03"""
)
_snake_case = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, """sentencepiece_model_pb2""", _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
_snake_case = None
_snake_case = b"""H\003"""
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
_snake_case = 45
_snake_case = 1581
_snake_case = 1517
_snake_case = 1570
_snake_case = 1584
_snake_case = 1793
_snake_case = 1795
_snake_case = 1916
_snake_case = 1864
_snake_case = 1905
_snake_case = 1919
_snake_case = 2429
_snake_case = 2208
_snake_case = 2418
_snake_case = 2323
_snake_case = 2407
# @@protoc_insertion_point(module_scope)
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModel.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModel.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForPreTraining.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForCausalLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForMaskedLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
| 655 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git_vision_model'
def __init__( self :Dict , _lowercase :Dict=7_68 , _lowercase :Dict=30_72 , _lowercase :Tuple=12 , _lowercase :List[str]=12 , _lowercase :Tuple=3 , _lowercase :Dict=2_24 , _lowercase :Tuple=16 , _lowercase :Optional[int]="quick_gelu" , _lowercase :Union[str, Any]=1e-5 , _lowercase :Tuple=0.0 , _lowercase :Tuple=0.02 , **_lowercase :Optional[Any] , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_channels
lowercase__ = patch_size
lowercase__ = image_size
lowercase__ = initializer_range
lowercase__ = attention_dropout
lowercase__ = layer_norm_eps
lowercase__ = hidden_act
@classmethod
def UpperCAmelCase ( cls :List[str] , _lowercase :Union[str, os.PathLike] , **_lowercase :Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
lowercase__ , lowercase__ = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
lowercase__ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowercase , **_lowercase )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git'
def __init__( self :Union[str, Any] , _lowercase :Dict=None , _lowercase :List[str]=3_05_22 , _lowercase :Tuple=7_68 , _lowercase :Any=6 , _lowercase :Dict=12 , _lowercase :Any=30_72 , _lowercase :List[Any]="gelu" , _lowercase :Tuple=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :Optional[Any]=10_24 , _lowercase :Any=0.02 , _lowercase :int=1e-12 , _lowercase :List[Any]=0 , _lowercase :int="absolute" , _lowercase :List[str]=True , _lowercase :Any=False , _lowercase :int=1_01 , _lowercase :str=1_02 , _lowercase :Dict=None , **_lowercase :List[str] , ):
'''simple docstring'''
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , pad_token_id=_lowercase , **_lowercase )
if vision_config is None:
lowercase__ = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
lowercase__ = GitVisionConfig(**_lowercase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
lowercase__ = tie_word_embeddings
lowercase__ = num_image_with_embedding
lowercase__ = bos_token_id
lowercase__ = eos_token_id
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.vision_config.to_dict()
lowercase__ = self.__class__.model_type
return output
| 655 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
def _A ( __magic_name__ ):
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
lowercase__ = value
else:
lowercase__ = value
return new_state_dict
def _A ( __magic_name__ ):
lowercase__ = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ = in_proj_weight_cross_attn[:256, :]
lowercase__ = in_proj_bias_cross_attn[:256]
lowercase__ = in_proj_weight_cross_attn[256:512, :]
lowercase__ = in_proj_bias_cross_attn[256:512]
lowercase__ = in_proj_weight_cross_attn[-256:, :]
lowercase__ = in_proj_bias_cross_attn[-256:]
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = max(__magic_name__ , __magic_name__ )
lowercase__ = 800 if "detection" in checkpoint_url else 1000
lowercase__ = target_max_size / current_max_size
lowercase__ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def _A ( __magic_name__ ):
lowercase__ = F.to_tensor(__magic_name__ )
lowercase__ = F.normalize(__magic_name__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
logger.info("Converting model..." )
# load original state dict
lowercase__ = torch.hub.load_state_dict_from_url(__magic_name__ , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = rename_backbone_keys(__magic_name__ )
# query, key and value matrices need special treatment
read_in_q_k_v(__magic_name__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
# create HuggingFace model and load state dict
lowercase__ = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase__ = 15
lowercase__ = 2
lowercase__ = {0: "table", 1: "table rotated"}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
else:
lowercase__ = 125
lowercase__ = 6
lowercase__ = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1000 )
lowercase__ = TableTransformerForObjectDetection(__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
# verify our conversion
lowercase__ = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
lowercase__ = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=__magic_name__ )
lowercase__ = Image.open(__magic_name__ ).convert("RGB" )
lowercase__ = normalize(resize(__magic_name__ , __magic_name__ ) ).unsqueeze(0 )
lowercase__ = model(__magic_name__ )
if "detection" in checkpoint_url:
lowercase__ = (1, 15, 3)
lowercase__ = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
lowercase__ = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
lowercase__ = (1, 125, 7)
lowercase__ = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
lowercase__ = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __magic_name__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __magic_name__ , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
lowercase__ = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(__magic_name__ )
image_processor.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 | 1 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase ( metaclass=lowercase_ ):
__lowerCamelCase = ['onnx']
def __init__( self :Optional[Any] , *_lowercase :Any , **_lowercase :List[str] ):
'''simple docstring'''
requires_backends(self , ["onnx"] )
@classmethod
def UpperCAmelCase ( cls :int , *_lowercase :Union[str, Any] , **_lowercase :Tuple ):
'''simple docstring'''
requires_backends(cls , ["onnx"] )
@classmethod
def UpperCAmelCase ( cls :Optional[Any] , *_lowercase :Dict , **_lowercase :Union[str, Any] ):
'''simple docstring'''
requires_backends(cls , ["onnx"] )
| 655 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
lowercase__ = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ = model(_lowercase )["last_hidden_state"]
lowercase__ = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
lowercase__ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 655 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case = """
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def _A ( __magic_name__ , __magic_name__ , __magic_name__=8 ):
lowercase__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase ( lowercase_ ):
def __init__( self :List[str] , _lowercase :UNetaDConditionModel , _lowercase :DDPMScheduler , _lowercase :VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_lowercase , scheduler=_lowercase , movq=_lowercase , )
lowercase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Tuple , _lowercase :List[str] , _lowercase :Tuple , _lowercase :Optional[Any] , _lowercase :int , _lowercase :str ):
'''simple docstring'''
if latents is None:
lowercase__ = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase__ = latents.to(_lowercase )
lowercase__ = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase ( self :int , _lowercase :int=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
lowercase__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=_lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase__ , lowercase__ = cpu_offload_with_hook(_lowercase , _lowercase , prev_module_hook=_lowercase )
# We'll offload the last model manually.
lowercase__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowercase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowercase )
def __call__( self :int , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :int = 5_12 , _lowercase :int = 5_12 , _lowercase :int = 1_00 , _lowercase :float = 4.0 , _lowercase :int = 1 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :Optional[torch.FloatTensor] = None , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = self._execution_device
lowercase__ = guidance_scale > 1.0
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
lowercase__ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
if do_classifier_free_guidance:
lowercase__ = image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = negative_image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowercase )
self.scheduler.set_timesteps(_lowercase , device=_lowercase )
lowercase__ = self.scheduler.timesteps
lowercase__ = self.unet.config.in_channels
lowercase__ , lowercase__ = downscale_height_and_width(_lowercase , _lowercase , self.movq_scale_factor )
# create initial latent
lowercase__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowercase , _lowercase , _lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowercase ) ):
# expand the latents if we are doing classifier free guidance
lowercase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase__ = {"image_embeds": image_embeds}
lowercase__ = self.unet(
sample=_lowercase , timestep=_lowercase , encoder_hidden_states=_lowercase , added_cond_kwargs=_lowercase , return_dict=_lowercase , )[0]
if do_classifier_free_guidance:
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
lowercase__ , lowercase__ = noise_pred.chunk(2 )
lowercase__ , lowercase__ = variance_pred.chunk(2 )
lowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase__ = self.scheduler.step(
_lowercase , _lowercase , _lowercase , generator=_lowercase , )[0]
# post-processing
lowercase__ = self.movq.decode(_lowercase , force_not_quantize=_lowercase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase__ = image * 0.5 + 0.5
lowercase__ = image.clamp(0 , 1 )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
_snake_case = logging.getLogger(__name__)
_snake_case = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={
'help': (
'The model checkpoint for weights initialization. Leave None if you want to train a model from'
' scratch.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(lowercase_ )} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The input training data file (a text file).'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={
'help': (
'The input training data files (multiple files in glob format). '
'Very often splitting large files to smaller files can prevent tokenizer going out of memory'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'An optional input train ref data file for whole word mask in Chinese.'} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'An optional input eval ref data file for whole word mask in Chinese.'} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Whether distinct lines of text in the dataset are to be handled as distinct sequences.'} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Train with masked-language modeling loss instead of language modeling.'} )
__lowerCamelCase = field(default=lowercase_ , metadata={'help': 'Whether ot not to use whole word mask.'} )
__lowerCamelCase = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
__lowerCamelCase = field(
default=1 / 6 , metadata={
'help': (
'Ratio of length of a span of masked tokens to surrounding context length for permutation language'
' modeling.'
)
} , )
__lowerCamelCase = field(
default=5 , metadata={'help': 'Maximum length of a span of masked tokens for permutation language modeling.'} )
__lowerCamelCase = field(
default=-1 , metadata={
'help': (
'Optional input sequence length after tokenization.'
'The training dataset will be truncated in block of this size for training.'
'Default to the model max input length for single sentence inputs (take into account special tokens).'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ = False , __magic_name__ = None , ):
def _dataset(__magic_name__ , __magic_name__=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError("You need to set world whole masking and mlm to True for Chinese Whole Word Mask" )
return LineByLineWithRefDataset(
tokenizer=__magic_name__ , file_path=__magic_name__ , block_size=args.block_size , ref_path=__magic_name__ , )
return LineByLineTextDataset(tokenizer=__magic_name__ , file_path=__magic_name__ , block_size=args.block_size )
else:
return TextDataset(
tokenizer=__magic_name__ , file_path=__magic_name__ , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=__magic_name__ , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(__magic_name__ ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def _A ( ):
# 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.
lowercase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase__ , lowercase__ , lowercase__ = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
"or remove the --do_eval argument." )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , 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" , __magic_name__ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
lowercase__ = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
lowercase__ = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.tokenizer_name:
lowercase__ = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another"
" script, save it,and load it from here, using --tokenizer_name" )
if model_args.model_name_or_path:
lowercase__ = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool(".ckpt" in model_args.model_name_or_path ) , config=__magic_name__ , cache_dir=model_args.cache_dir , )
else:
logger.info("Training new model from scratch" )
lowercase__ = AutoModelWithLMHead.from_config(__magic_name__ )
model.resize_token_embeddings(len(__magic_name__ ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
"BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the"
"--mlm flag (masked language modeling)." )
if data_args.block_size <= 0:
lowercase__ = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
lowercase__ = min(data_args.block_size , tokenizer.max_len )
# Get datasets
lowercase__ = (
get_dataset(__magic_name__ , tokenizer=__magic_name__ , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
lowercase__ = (
get_dataset(__magic_name__ , tokenizer=__magic_name__ , evaluate=__magic_name__ , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
lowercase__ = DataCollatorForPermutationLanguageModeling(
tokenizer=__magic_name__ , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
lowercase__ = DataCollatorForWholeWordMask(
tokenizer=__magic_name__ , mlm_probability=data_args.mlm_probability )
else:
lowercase__ = DataCollatorForLanguageModeling(
tokenizer=__magic_name__ , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
lowercase__ = Trainer(
model=__magic_name__ , args=__magic_name__ , data_collator=__magic_name__ , train_dataset=__magic_name__ , eval_dataset=__magic_name__ , prediction_loss_only=__magic_name__ , )
# Training
if training_args.do_train:
lowercase__ = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=__magic_name__ )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
lowercase__ = trainer.evaluate()
lowercase__ = math.exp(eval_output["eval_loss"] )
lowercase__ = {"perplexity": perplexity}
lowercase__ = os.path.join(training_args.output_dir , "eval_results_lm.txt" )
if trainer.is_world_master():
with open(__magic_name__ , "w" ) as writer:
logger.info("***** Eval results *****" )
for key in sorted(result.keys() ):
logger.info(" %s = %s" , __magic_name__ , str(result[key] ) )
writer.write("%s = %s\n" % (key, str(result[key] )) )
results.update(__magic_name__ )
return results
def _A ( __magic_name__ ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 655 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
lowercase__ = inspect.getmembers(_lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowercase__ = "k-diffusion"
elif backend == "invisible_watermark":
lowercase__ = "invisible-watermark"
assert backend in deps, f'''{backend} is not in the deps table!'''
| 655 | 1 |
from __future__ import annotations
from collections import namedtuple
from dataclasses import dataclass
@dataclass
class lowerCAmelCase :
__lowerCamelCase = 42
__lowerCamelCase = None
__lowerCamelCase = None
_snake_case = namedtuple("""CoinsDistribResult""", """moves excess""")
def _A ( __magic_name__ ):
if root is None:
return 0
# Validation
def count_nodes(__magic_name__ ) -> int:
if node is None:
return 0
return count_nodes(node.left ) + count_nodes(node.right ) + 1
def count_coins(__magic_name__ ) -> int:
if node is None:
return 0
return count_coins(node.left ) + count_coins(node.right ) + node.data
if count_nodes(__magic_name__ ) != count_coins(__magic_name__ ):
raise ValueError("The nodes number should be same as the number of coins" )
# Main calculation
def get_distrib(__magic_name__ ) -> CoinsDistribResult:
if node is None:
return CoinsDistribResult(0 , 1 )
lowercase__ , lowercase__ = get_distrib(node.left )
lowercase__ , lowercase__ = get_distrib(node.right )
lowercase__ = 1 - left_distrib_excess
lowercase__ = 1 - right_distrib_excess
lowercase__ = (
left_distrib_moves
+ right_distrib_moves
+ abs(__magic_name__ )
+ abs(__magic_name__ )
)
lowercase__ = node.data - coins_to_left - coins_to_right
return CoinsDistribResult(__magic_name__ , __magic_name__ )
return get_distrib(__magic_name__ )[0]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class lowerCAmelCase :
__lowerCamelCase = 42
# setable values
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = None
@classmethod
def UpperCAmelCase ( cls :Union[str, Any] , _lowercase :CommonSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray ):
'''simple docstring'''
return cls(common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase )
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
__lowerCamelCase = 42
@property
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return True
@register_to_config
def __init__( self :str , _lowercase :int = 10_00 , _lowercase :float = 0.0001 , _lowercase :float = 0.02 , _lowercase :str = "linear" , _lowercase :Optional[jnp.ndarray] = None , _lowercase :str = "fixed_small" , _lowercase :bool = True , _lowercase :str = "epsilon" , _lowercase :jnp.dtype = jnp.floataa , ):
'''simple docstring'''
lowercase__ = dtype
def UpperCAmelCase ( self :str , _lowercase :Optional[CommonSchedulerState] = None ):
'''simple docstring'''
if common is None:
lowercase__ = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
lowercase__ = jnp.array(1.0 , dtype=self.dtype )
lowercase__ = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCAmelCase ( self :List[str] , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :Tuple = () ):
'''simple docstring'''
lowercase__ = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
lowercase__ = (jnp.arange(0 , _lowercase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Tuple , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :List[str]=None , _lowercase :Tuple=None ):
'''simple docstring'''
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowercase__ = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
lowercase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
lowercase__ = jnp.clip(_lowercase , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
lowercase__ = jnp.log(jnp.clip(_lowercase , a_min=1e-20 ) )
elif variance_type == "fixed_large":
lowercase__ = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
lowercase__ = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
lowercase__ = variance
lowercase__ = state.common.betas[t]
lowercase__ = (predicted_variance + 1) / 2
lowercase__ = frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase ( self :Optional[int] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :int , _lowercase :jnp.ndarray , _lowercase :Optional[jax.random.KeyArray] = None , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = timestep
if key is None:
lowercase__ = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
lowercase__ , lowercase__ = jnp.split(_lowercase , sample.shape[1] , axis=1 )
else:
lowercase__ = None
# 1. compute alphas, betas
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
lowercase__ = 1 - alpha_prod_t
lowercase__ = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowercase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowercase__ = model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowercase__ = jnp.clip(_lowercase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
lowercase__ = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
lowercase__ = jax.random.split(_lowercase , num=1 )
lowercase__ = jax.random.normal(_lowercase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(_lowercase , _lowercase , predicted_variance=_lowercase ) ** 0.5) * noise
lowercase__ = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
lowercase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=_lowercase , state=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return add_noise_common(state.common , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Dict , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return get_velocity_common(state.common , _lowercase , _lowercase , _lowercase )
def __len__( self :List[str] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_snake_case = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
"""PoolFormerOnnxConfig""",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""PoolFormerFeatureExtractor"""]
_snake_case = ["""PoolFormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PoolFormerForImageClassification""",
"""PoolFormerModel""",
"""PoolFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_poolformer import (
POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
PoolFormerConfig,
PoolFormerOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_poolformer import PoolFormerFeatureExtractor
from .image_processing_poolformer import PoolFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_poolformer import (
POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
PoolFormerForImageClassification,
PoolFormerModel,
PoolFormerPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 655 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_snake_case = logging.get_logger(__name__)
_snake_case = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Model type selected in the list: ' + ', '.join(lowercase_ )} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
__lowerCamelCase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
__lowerCamelCase = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
__lowerCamelCase = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
__lowerCamelCase = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
__lowerCamelCase = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'train'
__lowerCamelCase = 'dev'
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
def __init__( self :Optional[Any] , _lowercase :SquadDataTrainingArguments , _lowercase :PreTrainedTokenizer , _lowercase :Optional[int] = None , _lowercase :Union[str, Split] = Split.train , _lowercase :Optional[bool] = False , _lowercase :Optional[str] = None , _lowercase :Optional[str] = "pt" , ):
'''simple docstring'''
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = "v2" if args.version_2_with_negative else "v1"
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + ".lock"
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features["features"]
lowercase__ = self.old_features.get("dataset" , _lowercase )
lowercase__ = self.old_features.get("examples" , _lowercase )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
" future run" )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
lowercase__ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self :Dict ):
'''simple docstring'''
return len(self.features )
def __getitem__( self :Any , _lowercase :Any ):
'''simple docstring'''
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 655 | 1 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case = """
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def _A ( __magic_name__ , __magic_name__ , __magic_name__=8 ):
lowercase__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase ( lowercase_ ):
def __init__( self :List[str] , _lowercase :UNetaDConditionModel , _lowercase :DDPMScheduler , _lowercase :VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_lowercase , scheduler=_lowercase , movq=_lowercase , )
lowercase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Tuple , _lowercase :List[str] , _lowercase :Tuple , _lowercase :Optional[Any] , _lowercase :int , _lowercase :str ):
'''simple docstring'''
if latents is None:
lowercase__ = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase__ = latents.to(_lowercase )
lowercase__ = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase ( self :int , _lowercase :int=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
lowercase__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=_lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase__ , lowercase__ = cpu_offload_with_hook(_lowercase , _lowercase , prev_module_hook=_lowercase )
# We'll offload the last model manually.
lowercase__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowercase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowercase )
def __call__( self :int , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :int = 5_12 , _lowercase :int = 5_12 , _lowercase :int = 1_00 , _lowercase :float = 4.0 , _lowercase :int = 1 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :Optional[torch.FloatTensor] = None , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = self._execution_device
lowercase__ = guidance_scale > 1.0
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
lowercase__ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
if do_classifier_free_guidance:
lowercase__ = image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = negative_image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowercase )
self.scheduler.set_timesteps(_lowercase , device=_lowercase )
lowercase__ = self.scheduler.timesteps
lowercase__ = self.unet.config.in_channels
lowercase__ , lowercase__ = downscale_height_and_width(_lowercase , _lowercase , self.movq_scale_factor )
# create initial latent
lowercase__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowercase , _lowercase , _lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowercase ) ):
# expand the latents if we are doing classifier free guidance
lowercase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase__ = {"image_embeds": image_embeds}
lowercase__ = self.unet(
sample=_lowercase , timestep=_lowercase , encoder_hidden_states=_lowercase , added_cond_kwargs=_lowercase , return_dict=_lowercase , )[0]
if do_classifier_free_guidance:
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
lowercase__ , lowercase__ = noise_pred.chunk(2 )
lowercase__ , lowercase__ = variance_pred.chunk(2 )
lowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase__ = self.scheduler.step(
_lowercase , _lowercase , _lowercase , generator=_lowercase , )[0]
# post-processing
lowercase__ = self.movq.decode(_lowercase , force_not_quantize=_lowercase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase__ = image * 0.5 + 0.5
lowercase__ = image.clamp(0 , 1 )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
_snake_case = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
_snake_case = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
_snake_case = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
_snake_case = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ["input_ids"]
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = RESOURCE_FILES_NAMES
def __init__( self :Union[str, Any] , _lowercase :Union[str, Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=False , _lowercase :Dict="utf8" , _lowercase :Optional[Any]="[UNK]" , _lowercase :Optional[int]="[SEP]" , _lowercase :List[str]="[PAD]" , _lowercase :Dict="[CLS]" , _lowercase :Optional[Any]="[MASK]" , _lowercase :Optional[Dict[str, Any]] = None , **_lowercase :Tuple , ):
'''simple docstring'''
lowercase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , vocab_file=_lowercase , encoding=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
lowercase__ = do_lower_case
lowercase__ = sentencepiece_model_ckpt
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase__ = self.load_vocab(filepath=_lowercase )
else:
lowercase__ = {self.sp_model.id_to_piece(_lowercase ): id for id in range(self.sp_model.get_piece_size() )}
lowercase__ = {v: k for k, v in self.vocab.items()}
def UpperCAmelCase ( self :Any , _lowercase :Dict ):
'''simple docstring'''
if text is None:
return None
lowercase__ = self.tokenize(_lowercase )
lowercase__ , lowercase__ = "", []
for i, ch in enumerate(_lowercase ):
if ch in self.SP_CHAR_MAPPING:
lowercase__ = self.SP_CHAR_MAPPING.get(_lowercase )
else:
lowercase__ = unicodedata.normalize("NFKC" , _lowercase )
if self.is_whitespace(_lowercase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(_lowercase ) )
lowercase__ , lowercase__ , lowercase__ = normalized_text, [], 0
if self.do_lower_case:
lowercase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase__ = token[1:]
lowercase__ = text[offset:].index(_lowercase ) + offset
lowercase__ = start + len(_lowercase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase__ = end
return token_mapping
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self :Any ):
'''simple docstring'''
lowercase__ = self.__dict__.copy()
lowercase__ = None
return state
def __setstate__( self :Optional[Any] , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ = {}
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def UpperCAmelCase ( self :List[str] , _lowercase :Optional[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(_lowercase , _lowercase ) for c in text) )
def UpperCAmelCase ( self :str , _lowercase :int , _lowercase :Union[str, Any]=False , _lowercase :Optional[int]=64 , _lowercase :Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowercase__ = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowercase__ = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowercase__ = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowercase__ = self.sp_model.EncodeAsPieces(_lowercase )
else:
lowercase__ = self.sp_model.SampleEncodeAsPieces(_lowercase , _lowercase , _lowercase )
lowercase__ = []
for pi, piece in enumerate(_lowercase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(_lowercase ) and pi != 0:
new_pieces.append(_lowercase )
continue
else:
continue
lowercase__ = 0
for i, chunk in enumerate(_lowercase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(_lowercase ) or self.is_punct(_lowercase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(_lowercase )
lowercase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
if len(_lowercase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def UpperCAmelCase ( self :Tuple , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ = self.convert_ids_to_tokens(_lowercase )
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Optional[int] ):
'''simple docstring'''
return self.vocab.get(_lowercase , self.vocab.get(self.unk_token ) )
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(_lowercase , self.unk_token )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :Tuple=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__ = [self.cls_token_id]
lowercase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def UpperCAmelCase ( self :Dict , _lowercase :int , _lowercase :Union[str, Any]=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Dict=None , _lowercase :Optional[Any]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase ( self :int , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(_lowercase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(_lowercase ) + 1) + [1] * (len(_lowercase ) + 3)
def UpperCAmelCase ( self :str , _lowercase :Optional[int] ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Dict ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def UpperCAmelCase ( self :List[str] , _lowercase :List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(_lowercase ) == 1:
lowercase__ = unicodedata.category(_lowercase )
if cat == "Zs":
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = {}
with io.open(_lowercase , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(_lowercase ):
lowercase__ = line.rstrip("\n" )
lowercase__ = int(_lowercase )
return token_to_idx
def UpperCAmelCase ( self :List[str] , _lowercase :str , _lowercase :Optional[str] = None ):
'''simple docstring'''
lowercase__ = 0
if os.path.isdir(_lowercase ):
lowercase__ = os.path.join(
_lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowercase__ = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(_lowercase , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowercase__ = token_index
writer.write(token + "\n" )
index += 1
lowercase__ = os.path.join(_lowercase , "sentencepiece.bpe.model" )
with open(_lowercase , "wb" ) as fi:
lowercase__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (vocab_file,)
| 655 | 1 |
# 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 ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'naver-clova-ix/donut-base-finetuned-docvqa'
__lowerCamelCase = (
'This is a tool that answers a question about an document (pdf). It takes an input named `document` which '
'should be the document containing the information, as well as a `question` that is the question about the '
'document. It returns a text that contains the answer to the question.'
)
__lowerCamelCase = 'document_qa'
__lowerCamelCase = AutoProcessor
__lowerCamelCase = VisionEncoderDecoderModel
__lowerCamelCase = ['image', 'text']
__lowerCamelCase = ['text']
def __init__( self :Dict , *_lowercase :List[str] , **_lowercase :List[str] ):
'''simple docstring'''
if not is_vision_available():
raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool." )
super().__init__(*_lowercase , **_lowercase )
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :"Image" , _lowercase :str ):
'''simple docstring'''
lowercase__ = "<s_docvqa><s_question>{user_input}</s_question><s_answer>"
lowercase__ = task_prompt.replace("{user_input}" , _lowercase )
lowercase__ = self.pre_processor.tokenizer(
_lowercase , add_special_tokens=_lowercase , return_tensors="pt" ).input_ids
lowercase__ = self.pre_processor(_lowercase , return_tensors="pt" ).pixel_values
return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] ):
'''simple docstring'''
return self.model.generate(
inputs["pixel_values"].to(self.device ) , decoder_input_ids=inputs["decoder_input_ids"].to(self.device ) , max_length=self.model.decoder.config.max_position_embeddings , early_stopping=_lowercase , pad_token_id=self.pre_processor.tokenizer.pad_token_id , eos_token_id=self.pre_processor.tokenizer.eos_token_id , use_cache=_lowercase , num_beams=1 , bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]] , return_dict_in_generate=_lowercase , ).sequences
def UpperCAmelCase ( self :str , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = self.pre_processor.batch_decode(_lowercase )[0]
lowercase__ = sequence.replace(self.pre_processor.tokenizer.eos_token , "" )
lowercase__ = sequence.replace(self.pre_processor.tokenizer.pad_token , "" )
lowercase__ = re.sub(r"<.*?>" , "" , _lowercase , count=1 ).strip() # remove first task start token
lowercase__ = self.pre_processor.tokenajson(_lowercase )
return sequence["answer"]
| 655 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ = pre_numerator
lowercase__ = 2 * i // 3 if i % 3 == 0 else 1
lowercase__ = cur_numerator
lowercase__ = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 | 1 |
def _A ( __magic_name__ , __magic_name__ , __magic_name__=False ):
if isinstance(__magic_name__ , __magic_name__ ) and isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = len(set_a.intersection(__magic_name__ ) )
if alternative_union:
lowercase__ = len(__magic_name__ ) + len(__magic_name__ )
else:
lowercase__ = len(set_a.union(__magic_name__ ) )
return intersection / union
if isinstance(__magic_name__ , (list, tuple) ) and isinstance(__magic_name__ , (list, tuple) ):
lowercase__ = [element for element in set_a if element in set_b]
if alternative_union:
lowercase__ = len(__magic_name__ ) + len(__magic_name__ )
return len(__magic_name__ ) / union
else:
lowercase__ = set_a + [element for element in set_b if element not in set_a]
return len(__magic_name__ ) / len(__magic_name__ )
return len(__magic_name__ ) / len(__magic_name__ )
return None
if __name__ == "__main__":
_snake_case = {"""a""", """b""", """c""", """d""", """e"""}
_snake_case = {"""c""", """d""", """e""", """f""", """h""", """i"""}
print(jaccard_similarity(set_a, set_b))
| 655 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'AutoTokenizer'
__lowerCamelCase = ['tokenizer']
__lowerCamelCase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self :Dict , _lowercase :List[str] , _lowercase :List[Any]=None ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = speaker_embeddings
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :int , _lowercase :str="speaker_embeddings_path.json" , **_lowercase :List[str] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
lowercase__ = get_file_from_repo(
_lowercase , _lowercase , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_lowercase , _lowercase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase__ = None
else:
with open(_lowercase ) as speaker_embeddings_json:
lowercase__ = json.load(_lowercase )
else:
lowercase__ = None
lowercase__ = AutoTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(tokenizer=_lowercase , speaker_embeddings=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :List[str]="speaker_embeddings_path.json" , _lowercase :Any="speaker_embeddings" , _lowercase :bool = False , **_lowercase :Any , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_lowercase , _lowercase , "v2" ) , exist_ok=_lowercase )
lowercase__ = {}
lowercase__ = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase__ = self._load_voice_preset(_lowercase )
lowercase__ = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , _lowercase , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_lowercase , )
lowercase__ = os.path.join(_lowercase , f'''{prompt_key}_{key}.npy''' )
lowercase__ = tmp_dict
with open(os.path.join(_lowercase , _lowercase ) , "w" ) as fp:
json.dump(_lowercase , _lowercase )
super().save_pretrained(_lowercase , _lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :str = None , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.speaker_embeddings[voice_preset]
lowercase__ = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase__ = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase__ = np.load(_lowercase )
return voice_preset_dict
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=None , _lowercase :List[str]="pt" , _lowercase :List[Any]=2_56 , _lowercase :List[str]=False , _lowercase :Union[str, Any]=True , _lowercase :Dict=False , **_lowercase :Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(_lowercase , _lowercase ):
if (
isinstance(_lowercase , _lowercase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase__ = self._load_voice_preset(_lowercase )
else:
if isinstance(_lowercase , _lowercase ) and not voice_preset.endswith(".npz" ):
lowercase__ = voice_preset + ".npz"
lowercase__ = np.load(_lowercase )
if voice_preset is not None:
self._validate_voice_preset_dict(_lowercase , **_lowercase )
lowercase__ = BatchFeature(data=_lowercase , tensor_type=_lowercase )
lowercase__ = self.tokenizer(
_lowercase , return_tensors=_lowercase , padding="max_length" , max_length=_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
if voice_preset is not None:
lowercase__ = voice_preset
return encoded_text
| 655 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""tanreinama/GPTSAN-2.8B-spout_is_uniform""": (
"""https://huggingface.co/tanreinama/GPTSAN-2.8B-spout_is_uniform/resolve/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'gptsan-japanese'
__lowerCamelCase = [
'past_key_values',
]
__lowerCamelCase = {
'hidden_size': 'd_model',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self :Any , _lowercase :List[Any]=3_60_00 , _lowercase :Dict=12_80 , _lowercase :Tuple=10_24 , _lowercase :Optional[Any]=81_92 , _lowercase :Any=40_96 , _lowercase :List[str]=1_28 , _lowercase :Optional[int]=10 , _lowercase :Optional[int]=0 , _lowercase :int=16 , _lowercase :Optional[int]=16 , _lowercase :List[str]=1_28 , _lowercase :Any=0.0 , _lowercase :List[Any]=1e-5 , _lowercase :List[str]=False , _lowercase :int=0.0 , _lowercase :str="float32" , _lowercase :str=False , _lowercase :str=False , _lowercase :Union[str, Any]=False , _lowercase :str=0.002 , _lowercase :Optional[Any]=False , _lowercase :Optional[Any]=True , _lowercase :Dict=3_59_98 , _lowercase :Dict=3_59_95 , _lowercase :int=3_59_99 , **_lowercase :Dict , ):
'''simple docstring'''
lowercase__ = vocab_size
lowercase__ = max_position_embeddings
lowercase__ = d_model
lowercase__ = d_ff
lowercase__ = d_ext
lowercase__ = d_spout
lowercase__ = num_switch_layers
lowercase__ = num_ext_layers
lowercase__ = num_switch_layers + num_ext_layers
lowercase__ = num_heads
lowercase__ = num_experts
lowercase__ = expert_capacity
lowercase__ = dropout_rate
lowercase__ = layer_norm_epsilon
lowercase__ = router_bias
lowercase__ = router_jitter_noise
lowercase__ = router_dtype
lowercase__ = router_ignore_padding_tokens
lowercase__ = output_hidden_states
lowercase__ = output_attentions
lowercase__ = initializer_factor
lowercase__ = output_router_logits
lowercase__ = use_cache
super().__init__(
separator_token_id=_lowercase , pad_token_id=_lowercase , eos_token_id=_lowercase , **_lowercase , )
| 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class lowerCAmelCase ( lowercase_ , lowercase_ ):
@register_to_config
def __init__( self :Union[str, Any] , _lowercase :int = 1_28 , _lowercase :int = 2_56 , _lowercase :float = 2000.0 , _lowercase :int = 7_68 , _lowercase :int = 12 , _lowercase :int = 12 , _lowercase :int = 64 , _lowercase :int = 20_48 , _lowercase :float = 0.1 , ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Sequential(
nn.Linear(_lowercase , d_model * 4 , bias=_lowercase ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=_lowercase ) , nn.SiLU() , )
lowercase__ = nn.Embedding(_lowercase , _lowercase )
lowercase__ = False
lowercase__ = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
lowercase__ = nn.Dropout(p=_lowercase )
lowercase__ = nn.ModuleList()
for lyr_num in range(_lowercase ):
# FiLM conditional T5 decoder
lowercase__ = DecoderLayer(d_model=_lowercase , d_kv=_lowercase , num_heads=_lowercase , d_ff=_lowercase , dropout_rate=_lowercase )
self.decoders.append(_lowercase )
lowercase__ = TaLayerNorm(_lowercase )
lowercase__ = nn.Dropout(p=_lowercase )
lowercase__ = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
def UpperCAmelCase ( self :List[Any] , _lowercase :Any , _lowercase :Optional[Any] ):
'''simple docstring'''
lowercase__ = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def UpperCAmelCase ( self :List[Any] , _lowercase :Dict , _lowercase :int , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ , lowercase__ , lowercase__ = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
lowercase__ = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
lowercase__ = self.conditioning_emb(_lowercase ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
lowercase__ = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
lowercase__ = torch.broadcast_to(
torch.arange(_lowercase , device=decoder_input_tokens.device ) , (batch, seq_length) , )
lowercase__ = self.position_encoding(_lowercase )
lowercase__ = self.continuous_inputs_projection(_lowercase )
inputs += position_encodings
lowercase__ = self.dropout(_lowercase )
# decoder: No padding present.
lowercase__ = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
lowercase__ = [(x, self.encoder_decoder_mask(_lowercase , _lowercase )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
lowercase__ = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
lowercase__ = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
lowercase__ = lyr(
_lowercase , conditioning_emb=_lowercase , encoder_hidden_states=_lowercase , encoder_attention_mask=_lowercase , )[0]
lowercase__ = self.decoder_norm(_lowercase )
lowercase__ = self.post_dropout(_lowercase )
lowercase__ = self.spec_out(_lowercase )
return spec_out
class lowerCAmelCase ( nn.Module ):
def __init__( self :Dict , _lowercase :List[str] , _lowercase :Optional[int] , _lowercase :Union[str, Any] , _lowercase :List[str] , _lowercase :Union[str, Any] , _lowercase :Tuple=1e-6 ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=_lowercase , d_kv=_lowercase , num_heads=_lowercase , dropout_rate=_lowercase ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=_lowercase , d_kv=_lowercase , num_heads=_lowercase , dropout_rate=_lowercase , layer_norm_epsilon=_lowercase , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=_lowercase , d_ff=_lowercase , dropout_rate=_lowercase , layer_norm_epsilon=_lowercase ) )
def UpperCAmelCase ( self :Dict , _lowercase :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :Any=None , _lowercase :str=None , _lowercase :Optional[Any]=None , _lowercase :Dict=None , ):
'''simple docstring'''
lowercase__ = self.layer[0](
_lowercase , conditioning_emb=_lowercase , attention_mask=_lowercase , )
if encoder_hidden_states is not None:
lowercase__ = torch.where(encoder_attention_mask > 0 , 0 , -1e10 ).to(
encoder_hidden_states.dtype )
lowercase__ = self.layer[1](
_lowercase , key_value_states=_lowercase , attention_mask=_lowercase , )
# Apply Film Conditional Feed Forward layer
lowercase__ = self.layer[-1](_lowercase , _lowercase )
return (hidden_states,)
class lowerCAmelCase ( nn.Module ):
def __init__( self :Optional[int] , _lowercase :List[str] , _lowercase :Union[str, Any] , _lowercase :Optional[Any] , _lowercase :List[str] ):
'''simple docstring'''
super().__init__()
lowercase__ = TaLayerNorm(_lowercase )
lowercase__ = TaFiLMLayer(in_features=d_model * 4 , out_features=_lowercase )
lowercase__ = Attention(query_dim=_lowercase , heads=_lowercase , dim_head=_lowercase , out_bias=_lowercase , scale_qk=_lowercase )
lowercase__ = nn.Dropout(_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Optional[int] , _lowercase :Any=None , _lowercase :Tuple=None , ):
'''simple docstring'''
lowercase__ = self.layer_norm(_lowercase )
if conditioning_emb is not None:
lowercase__ = self.FiLMLayer(_lowercase , _lowercase )
# Self-attention block
lowercase__ = self.attention(_lowercase )
lowercase__ = hidden_states + self.dropout(_lowercase )
return hidden_states
class lowerCAmelCase ( nn.Module ):
def __init__( self :Any , _lowercase :List[Any] , _lowercase :str , _lowercase :List[str] , _lowercase :Any , _lowercase :List[str] ):
'''simple docstring'''
super().__init__()
lowercase__ = Attention(query_dim=_lowercase , heads=_lowercase , dim_head=_lowercase , out_bias=_lowercase , scale_qk=_lowercase )
lowercase__ = TaLayerNorm(_lowercase , eps=_lowercase )
lowercase__ = nn.Dropout(_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[int] , _lowercase :Tuple=None , _lowercase :Tuple=None , ):
'''simple docstring'''
lowercase__ = self.layer_norm(_lowercase )
lowercase__ = self.attention(
_lowercase , encoder_hidden_states=_lowercase , attention_mask=attention_mask.squeeze(1 ) , )
lowercase__ = hidden_states + self.dropout(_lowercase )
return layer_output
class lowerCAmelCase ( nn.Module ):
def __init__( self :int , _lowercase :int , _lowercase :Optional[int] , _lowercase :Union[str, Any] , _lowercase :int ):
'''simple docstring'''
super().__init__()
lowercase__ = TaDenseGatedActDense(d_model=_lowercase , d_ff=_lowercase , dropout_rate=_lowercase )
lowercase__ = TaFiLMLayer(in_features=d_model * 4 , out_features=_lowercase )
lowercase__ = TaLayerNorm(_lowercase , eps=_lowercase )
lowercase__ = nn.Dropout(_lowercase )
def UpperCAmelCase ( self :int , _lowercase :Union[str, Any] , _lowercase :Any=None ):
'''simple docstring'''
lowercase__ = self.layer_norm(_lowercase )
if conditioning_emb is not None:
lowercase__ = self.film(_lowercase , _lowercase )
lowercase__ = self.DenseReluDense(_lowercase )
lowercase__ = hidden_states + self.dropout(_lowercase )
return hidden_states
class lowerCAmelCase ( nn.Module ):
def __init__( self :List[Any] , _lowercase :Any , _lowercase :Optional[int] , _lowercase :Optional[int] ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
lowercase__ = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
lowercase__ = nn.Linear(_lowercase , _lowercase , bias=_lowercase )
lowercase__ = nn.Dropout(_lowercase )
lowercase__ = NewGELUActivation()
def UpperCAmelCase ( self :Optional[int] , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.act(self.wi_a(_lowercase ) )
lowercase__ = self.wi_a(_lowercase )
lowercase__ = hidden_gelu * hidden_linear
lowercase__ = self.dropout(_lowercase )
lowercase__ = self.wo(_lowercase )
return hidden_states
class lowerCAmelCase ( nn.Module ):
def __init__( self :Dict , _lowercase :str , _lowercase :Optional[Any]=1e-6 ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Parameter(torch.ones(_lowercase ) )
lowercase__ = eps
def UpperCAmelCase ( self :Dict , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=_lowercase )
lowercase__ = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
lowercase__ = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class lowerCAmelCase ( nn.Module ):
def UpperCAmelCase ( self :Tuple , _lowercase :torch.Tensor ):
'''simple docstring'''
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044715 * torch.pow(_lowercase , 3.0 )) ))
class lowerCAmelCase ( nn.Module ):
def __init__( self :Optional[Any] , _lowercase :Optional[int] , _lowercase :int ):
'''simple docstring'''
super().__init__()
lowercase__ = nn.Linear(_lowercase , out_features * 2 , bias=_lowercase )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :List[str] , _lowercase :int ):
'''simple docstring'''
lowercase__ = self.scale_bias(_lowercase )
lowercase__ , lowercase__ = torch.chunk(_lowercase , 2 , -1 )
lowercase__ = x * (1 + scale) + shift
return x
| 655 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'van'
def __init__( self :Optional[Any] , _lowercase :Dict=2_24 , _lowercase :Union[str, Any]=3 , _lowercase :List[Any]=[7, 3, 3, 3] , _lowercase :Any=[4, 2, 2, 2] , _lowercase :Union[str, Any]=[64, 1_28, 3_20, 5_12] , _lowercase :List[Any]=[3, 3, 12, 3] , _lowercase :Dict=[8, 8, 4, 4] , _lowercase :int="gelu" , _lowercase :List[Any]=0.02 , _lowercase :List[Any]=1e-6 , _lowercase :Any=1e-2 , _lowercase :int=0.0 , _lowercase :int=0.0 , **_lowercase :Dict , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = patch_sizes
lowercase__ = strides
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = mlp_ratios
lowercase__ = hidden_act
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = layer_scale_init_value
lowercase__ = drop_path_rate
lowercase__ = dropout_rate
| 655 | 1 |
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 ( lowercase_ ):
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = SMALL_MODEL_IDENTIFIER
lowercase__ = "pt"
lowercase__ = "tf"
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowercase )
def UpperCAmelCase ( self :Tuple , _lowercase :int ):
'''simple docstring'''
lowercase__ = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowercase )
model_tf.save_pretrained(_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = "mock_framework"
# Framework provided - return whatever the user provides
lowercase__ = FeaturesManager.determine_framework(self.test_model , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_tf )
# Both in environment -> use PyTorch
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# Both not in environment -> raise error
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
| 655 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase ( enum.Enum ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 2
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self :Any , *_lowercase :Optional[Any] , **_lowercase :Union[str, Any] ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowercase__ = None
if self.model.config.prefix is not None:
lowercase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowercase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowercase__ , lowercase__ , lowercase__ = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
lowercase__ = {**self._preprocess_params, **preprocess_params}
lowercase__ = {**self._forward_params, **forward_params}
def UpperCAmelCase ( self :Tuple , _lowercase :Optional[Any]=None , _lowercase :List[Any]=None , _lowercase :List[str]=None , _lowercase :Optional[Any]=None , _lowercase :Optional[int]=None , _lowercase :Any=None , _lowercase :Any=None , _lowercase :Dict=None , **_lowercase :Union[str, Any] , ):
'''simple docstring'''
lowercase__ = {}
if prefix is not None:
lowercase__ = prefix
if prefix:
lowercase__ = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
lowercase__ = handle_long_generation
preprocess_params.update(_lowercase )
lowercase__ = generate_kwargs
lowercase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.TENSORS
if return_type is not None:
lowercase__ = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
lowercase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self :int , *_lowercase :Optional[int] , **_lowercase :List[str] ):
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self :Union[str, Any] , _lowercase :Dict , **_lowercase :Tuple ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple , _lowercase :Optional[int]="" , _lowercase :Tuple=None , **_lowercase :List[str] ):
'''simple docstring'''
lowercase__ = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prompt_text
if handle_long_generation == "hole":
lowercase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowercase__ = generate_kwargs["max_new_tokens"]
else:
lowercase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowercase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
lowercase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
lowercase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def UpperCAmelCase ( self :str , _lowercase :int , **_lowercase :str ):
'''simple docstring'''
lowercase__ = model_inputs["input_ids"]
lowercase__ = model_inputs.get("attention_mask" , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowercase__ = None
lowercase__ = None
lowercase__ = 1
else:
lowercase__ = input_ids.shape[0]
lowercase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowercase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
lowercase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
lowercase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowercase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowercase__ = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
lowercase__ = generated_sequence.shape[0]
if self.framework == "pt":
lowercase__ = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowercase__ = tf.reshape(_lowercase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def UpperCAmelCase ( self :Any , _lowercase :Tuple , _lowercase :str=ReturnType.FULL_TEXT , _lowercase :Dict=True ):
'''simple docstring'''
lowercase__ = model_outputs["generated_sequence"][0]
lowercase__ = model_outputs["input_ids"]
lowercase__ = model_outputs["prompt_text"]
lowercase__ = generated_sequence.numpy().tolist()
lowercase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowercase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowercase__ = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowercase__ = 0
else:
lowercase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
lowercase__ = prompt_text + text[prompt_length:]
else:
lowercase__ = text[prompt_length:]
lowercase__ = {"generated_text": all_text}
records.append(_lowercase )
return records
| 655 | 1 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
_snake_case = datasets.logging.get_logger(__name__)
_snake_case = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
"""
_snake_case = """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""
_snake_case = """
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
'scores': List of scores.
Examples:
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> bleurt = datasets.load_metric(\"bleurt\")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results[\"scores\"]])
[1.03, 1.04]
"""
_snake_case = {
"""bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
"""bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
"""bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
"""bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
"""bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
"""bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
"""BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
"""BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
"""BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
"""BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase ( datasets.Metric ):
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://github.com/google-research/bleurt" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/google-research/bleurt"] , reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"] , )
def UpperCAmelCase ( self :int , _lowercase :Tuple ):
'''simple docstring'''
if self.config_name == "default":
logger.warning(
"Using default BLEURT-Base checkpoint for sequence maximum length 128. "
"You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')." )
lowercase__ = "bleurt-base-128"
if self.config_name.lower() in CHECKPOINT_URLS:
lowercase__ = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
lowercase__ = self.config_name.upper()
else:
raise KeyError(
f'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}''' )
# download the model checkpoint specified by self.config_name and set up the scorer
lowercase__ = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
lowercase__ = score.BleurtScorer(os.path.join(_lowercase , _lowercase ) )
def UpperCAmelCase ( self :int , _lowercase :Dict , _lowercase :int ):
'''simple docstring'''
lowercase__ = self.scorer.score(references=_lowercase , candidates=_lowercase )
return {"scores": scores}
| 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_snake_case = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _A ( __magic_name__ ):
lowercase__ = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__magic_name__ )[0]
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(rows * cols * num_images )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
lowercase__ = data.reshape(__magic_name__ , __magic_name__ , __magic_name__ , 1 )
return data
@deprecated(__magic_name__ , "Please use tf.one_hot on tensors." )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = labels_dense.shape[0]
lowercase__ = numpy.arange(__magic_name__ ) * num_classes
lowercase__ = numpy.zeros((num_labels, num_classes) )
lowercase__ = 1
return labels_one_hot
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(__magic_name__ )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__magic_name__ , __magic_name__ )
return labels
class lowerCAmelCase :
@deprecated(
_lowercase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Tuple=False , _lowercase :str=False , _lowercase :Dict=dtypes.floataa , _lowercase :Optional[Any]=True , _lowercase :Any=None , ):
'''simple docstring'''
lowercase__ , lowercase__ = random_seed.get_seed(_lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ = dtypes.as_dtype(_lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
lowercase__ = 1_00_00
lowercase__ = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
lowercase__ = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ = images.astype(numpy.floataa )
lowercase__ = numpy.multiply(_lowercase , 1.0 / 255.0 )
lowercase__ = images
lowercase__ = labels
lowercase__ = 0
lowercase__ = 0
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._images
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self._labels
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return self._num_examples
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._epochs_completed
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] , _lowercase :Any=False , _lowercase :Union[str, Any]=True ):
'''simple docstring'''
if fake_data:
lowercase__ = [1] * 7_84
lowercase__ = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_lowercase )],
[fake_label for _ in range(_lowercase )],
)
lowercase__ = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perma]
lowercase__ = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ = self._num_examples - start
lowercase__ = self._images[start : self._num_examples]
lowercase__ = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perm]
lowercase__ = self.labels[perm]
# Start next epoch
lowercase__ = 0
lowercase__ = batch_size - rest_num_examples
lowercase__ = self._index_in_epoch
lowercase__ = self._images[start:end]
lowercase__ = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__magic_name__ , "Please write your own downloading logic." )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if not gfile.Exists(__magic_name__ ):
gfile.MakeDirs(__magic_name__ )
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
if not gfile.Exists(__magic_name__ ):
urllib.request.urlretrieve(__magic_name__ , __magic_name__ ) # noqa: S310
with gfile.GFile(__magic_name__ ) as f:
lowercase__ = f.size()
print("Successfully downloaded" , __magic_name__ , __magic_name__ , "bytes." )
return filepath
@deprecated(
__magic_name__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=5000 , __magic_name__=None , __magic_name__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__magic_name__ , one_hot=__magic_name__ , dtype=__magic_name__ , seed=__magic_name__ )
lowercase__ = fake()
lowercase__ = fake()
lowercase__ = fake()
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
if not source_url: # empty string check
lowercase__ = DEFAULT_SOURCE_URL
lowercase__ = "train-images-idx3-ubyte.gz"
lowercase__ = "train-labels-idx1-ubyte.gz"
lowercase__ = "t10k-images-idx3-ubyte.gz"
lowercase__ = "t10k-labels-idx1-ubyte.gz"
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
if not 0 <= validation_size <= len(__magic_name__ ):
lowercase__ = (
"Validation size should be between 0 and "
f'''{len(__magic_name__ )}. Received: {validation_size}.'''
)
raise ValueError(__magic_name__ )
lowercase__ = train_images[:validation_size]
lowercase__ = train_labels[:validation_size]
lowercase__ = train_images[validation_size:]
lowercase__ = train_labels[validation_size:]
lowercase__ = {"dtype": dtype, "reshape": reshape, "seed": seed}
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
| 655 | 1 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Any , *_lowercase :Dict , **_lowercase :List[str] ):
'''simple docstring'''
warnings.warn(
"The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use CLIPImageProcessor instead." , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ):
'''simple docstring'''
lowercase__ = []
lowercase__ = self
while temp:
string_rep.append(f'''{temp.data}''' )
lowercase__ = temp.next
return "->".join(_lowercase )
def _A ( __magic_name__ ):
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase__ = lowercase__ = Node(elements_list[0] )
for i in range(1 , len(__magic_name__ ) ):
lowercase__ = Node(elements_list[i] )
lowercase__ = current.next
return head
def _A ( __magic_name__ ):
if head_node is not None and isinstance(__magic_name__ , __magic_name__ ):
print_reverse(head_node.next )
print(head_node.data )
def _A ( ):
from doctest import testmod
testmod()
lowercase__ = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(__magic_name__ )
print("Elements in Reverse:" )
print_reverse(__magic_name__ )
if __name__ == "__main__":
main()
| 655 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = list(__magic_name__ )
lowercase__ = list(__magic_name__ )
lowercase__ = 0
for i in range(len(__magic_name__ ) ):
if lista[i] != lista[i]:
count += 1
lowercase__ = "_"
if count > 1:
return False
else:
return "".join(__magic_name__ )
def _A ( __magic_name__ ):
lowercase__ = []
while True:
lowercase__ = ["$"] * len(__magic_name__ )
lowercase__ = []
for i in range(len(__magic_name__ ) ):
for j in range(i + 1 , len(__magic_name__ ) ):
lowercase__ = compare_string(binary[i] , binary[j] )
if k is False:
lowercase__ = "*"
lowercase__ = "*"
temp.append("X" )
for i in range(len(__magic_name__ ) ):
if checka[i] == "$":
pi.append(binary[i] )
if len(__magic_name__ ) == 0:
return pi
lowercase__ = list(set(__magic_name__ ) )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
for minterm in minterms:
lowercase__ = ""
for _ in range(__magic_name__ ):
lowercase__ = str(minterm % 2 ) + string
minterm //= 2
temp.append(__magic_name__ )
return temp
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = list(__magic_name__ )
lowercase__ = list(__magic_name__ )
lowercase__ = 0
for i in range(len(__magic_name__ ) ):
if lista[i] != lista[i]:
count_n += 1
return count_n == count
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = []
lowercase__ = [0] * len(__magic_name__ )
for i in range(len(chart[0] ) ):
lowercase__ = 0
lowercase__ = -1
for j in range(len(__magic_name__ ) ):
if chart[j][i] == 1:
count += 1
lowercase__ = j
if count == 1:
lowercase__ = 1
for i in range(len(__magic_name__ ) ):
if select[i] == 1:
for j in range(len(chart[0] ) ):
if chart[i][j] == 1:
for k in range(len(__magic_name__ ) ):
lowercase__ = 0
temp.append(prime_implicants[i] )
while True:
lowercase__ = 0
lowercase__ = -1
lowercase__ = 0
for i in range(len(__magic_name__ ) ):
lowercase__ = chart[i].count(1 )
if count_n > max_n:
lowercase__ = count_n
lowercase__ = i
if max_n == 0:
return temp
temp.append(prime_implicants[rem] )
for i in range(len(chart[0] ) ):
if chart[rem][i] == 1:
for j in range(len(__magic_name__ ) ):
lowercase__ = 0
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = [[0 for x in range(len(__magic_name__ ) )] for x in range(len(__magic_name__ ) )]
for i in range(len(__magic_name__ ) ):
lowercase__ = prime_implicants[i].count("_" )
for j in range(len(__magic_name__ ) ):
if is_for_table(prime_implicants[i] , binary[j] , __magic_name__ ):
lowercase__ = 1
return chart
def _A ( ):
lowercase__ = int(input("Enter the no. of variables\n" ) )
lowercase__ = [
float(__magic_name__ )
for x in input(
"Enter the decimal representation of Minterms 'Spaces Separated'\n" ).split()
]
lowercase__ = decimal_to_binary(__magic_name__ , __magic_name__ )
lowercase__ = check(__magic_name__ )
print("Prime Implicants are:" )
print(__magic_name__ )
lowercase__ = prime_implicant_chart(__magic_name__ , __magic_name__ )
lowercase__ = selection(__magic_name__ , __magic_name__ )
print("Essential Prime Implicants are:" )
print(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 655 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowercase__ = 0
while count < prec:
lowercase__ = random.randint(2 , n - 1 )
lowercase__ = bin_exp_mod(__magic_name__ , __magic_name__ , __magic_name__ )
if b != 1:
lowercase__ = True
for _ in range(__magic_name__ ):
if b == n - 1:
lowercase__ = False
break
lowercase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
_snake_case = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 655 | 1 |
import numpy as np
import datasets
_snake_case = """
Compute the Mahalanobis Distance
Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""
_snake_case = """\
@article{de2000mahalanobis,
title={The mahalanobis distance},
author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
journal={Chemometrics and intelligent laboratory systems},
volume={50},
number={1},
pages={1--18},
year={2000},
publisher={Elsevier}
}
"""
_snake_case = """
Args:
X: List of datapoints to be compared with the `reference_distribution`.
reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:
>>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
>>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
>>> print(results)
{'mahalanobis': array([0.5])}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase ( datasets.Metric ):
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"X": datasets.Sequence(datasets.Value("float" , id="sequence" ) , id="X" ),
} ) , )
def UpperCAmelCase ( self :List[str] , _lowercase :List[Any] , _lowercase :List[str] ):
'''simple docstring'''
lowercase__ = np.array(_lowercase )
lowercase__ = np.array(_lowercase )
# Assert that arrays are 2D
if len(X.shape ) != 2:
raise ValueError("Expected `X` to be a 2D vector" )
if len(reference_distribution.shape ) != 2:
raise ValueError("Expected `reference_distribution` to be a 2D vector" )
if reference_distribution.shape[0] < 2:
raise ValueError(
"Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension" )
# Get mahalanobis distance for each prediction
lowercase__ = X - np.mean(_lowercase )
lowercase__ = np.cov(reference_distribution.T )
try:
lowercase__ = np.linalg.inv(_lowercase )
except np.linalg.LinAlgError:
lowercase__ = np.linalg.pinv(_lowercase )
lowercase__ = np.dot(_lowercase , _lowercase )
lowercase__ = np.dot(_lowercase , X_minus_mu.T ).diagonal()
return {"mahalanobis": mahal_dist}
| 655 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowerCAmelCase :
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowercase__ = TaEncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5" )
torch.manual_seed(0 )
lowercase__ = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"ResnetDownsampleBlock2D",
"SimpleCrossAttnDownBlock2D",
] , mid_block_type="UNetMidBlock2DSimpleCrossAttn" , up_block_types=["SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="text" , addition_embed_type_num_heads=2 , cross_attention_norm="group_norm" , resnet_time_scale_shift="scale_shift" , act_fn="gelu" , class_embed_type="timestep" , mid_block_scale_factor=1.414 , time_embedding_act_fn="gelu" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , thresholding=_lowercase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="epsilon" , variance_type="learned_range" , )
torch.manual_seed(0 )
lowercase__ = DDPMScheduler(
num_train_timesteps=10_00 , beta_schedule="squaredcos_cap_v2" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
lowercase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["prompt"]
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
if "image" in inputs:
lowercase__ = inputs["image"]
else:
lowercase__ = None
if "mask_image" in inputs:
lowercase__ = inputs["mask_image"]
else:
lowercase__ = None
if "original_image" in inputs:
lowercase__ = inputs["original_image"]
else:
lowercase__ = None
lowercase__ , lowercase__ = pipe.encode_prompt(_lowercase )
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(_lowercase , _lowercase , _lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(_lowercase , _lowercase ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = inputs["generator"]
lowercase__ = inputs["num_inference_steps"]
lowercase__ = inputs["output_type"]
# inputs with prompt converted to embeddings
lowercase__ = {
"prompt_embeds": prompt_embeds,
"negative_prompt_embeds": negative_prompt_embeds,
"generator": generator,
"num_inference_steps": num_inference_steps,
"output_type": output_type,
}
if image is not None:
lowercase__ = image
if mask_image is not None:
lowercase__ = mask_image
if original_image is not None:
lowercase__ = original_image
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.get_dummy_components()
lowercase__ = self.pipeline_class(**_lowercase )
pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe(**_lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(_lowercase )
lowercase__ = self.pipeline_class.from_pretrained(_lowercase )
pipe_loaded.to(_lowercase )
pipe_loaded.set_progress_bar_config(disable=_lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowercase__ = self.get_dummy_inputs(_lowercase )
lowercase__ = pipe_loaded(**_lowercase )[0]
lowercase__ = np.abs(to_np(_lowercase ) - to_np(_lowercase ) ).max()
self.assertLess(_lowercase , 1e-4 )
| 655 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/unispeech-large-1500h-cv""": (
"""https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json"""
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'unispeech'
def __init__( self :str , _lowercase :List[Any]=32 , _lowercase :Any=7_68 , _lowercase :Optional[int]=12 , _lowercase :Union[str, Any]=12 , _lowercase :Dict=30_72 , _lowercase :Optional[int]="gelu" , _lowercase :int=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :Dict=0.1 , _lowercase :int=0.0 , _lowercase :str=0.0 , _lowercase :Optional[int]=0.1 , _lowercase :Union[str, Any]=0.1 , _lowercase :str=0.02 , _lowercase :Dict=1e-5 , _lowercase :Tuple="group" , _lowercase :List[str]="gelu" , _lowercase :Optional[int]=(5_12, 5_12, 5_12, 5_12, 5_12, 5_12, 5_12) , _lowercase :Tuple=(5, 2, 2, 2, 2, 2, 2) , _lowercase :Tuple=(10, 3, 3, 3, 3, 2, 2) , _lowercase :List[Any]=False , _lowercase :Optional[Any]=1_28 , _lowercase :List[Any]=16 , _lowercase :str=False , _lowercase :str=True , _lowercase :Optional[int]=0.05 , _lowercase :Dict=10 , _lowercase :Dict=2 , _lowercase :int=0.0 , _lowercase :Dict=10 , _lowercase :int=0 , _lowercase :Optional[int]=3_20 , _lowercase :Any=2 , _lowercase :Tuple=0.1 , _lowercase :Tuple=1_00 , _lowercase :List[str]=2_56 , _lowercase :List[str]=2_56 , _lowercase :int=0.1 , _lowercase :int="mean" , _lowercase :Union[str, Any]=False , _lowercase :Optional[Any]=False , _lowercase :str=2_56 , _lowercase :List[str]=80 , _lowercase :Union[str, Any]=0 , _lowercase :Optional[Any]=1 , _lowercase :Any=2 , _lowercase :Optional[int]=0.5 , **_lowercase :Optional[int] , ):
'''simple docstring'''
super().__init__(**_lowercase , pad_token_id=_lowercase , bos_token_id=_lowercase , eos_token_id=_lowercase )
lowercase__ = hidden_size
lowercase__ = feat_extract_norm
lowercase__ = feat_extract_activation
lowercase__ = list(_lowercase )
lowercase__ = list(_lowercase )
lowercase__ = list(_lowercase )
lowercase__ = conv_bias
lowercase__ = num_conv_pos_embeddings
lowercase__ = num_conv_pos_embedding_groups
lowercase__ = len(self.conv_dim )
lowercase__ = num_hidden_layers
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_attention_heads
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = feat_proj_dropout
lowercase__ = final_dropout
lowercase__ = layerdrop
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
lowercase__ = num_ctc_classes
lowercase__ = vocab_size
lowercase__ = do_stable_layer_norm
lowercase__ = use_weighted_layer_sum
lowercase__ = classifier_proj_size
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
lowercase__ = apply_spec_augment
lowercase__ = mask_time_prob
lowercase__ = mask_time_length
lowercase__ = mask_time_min_masks
lowercase__ = mask_feature_prob
lowercase__ = mask_feature_length
lowercase__ = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowercase__ = num_codevectors_per_group
lowercase__ = num_codevector_groups
lowercase__ = contrastive_logits_temperature
lowercase__ = feat_quantizer_dropout
lowercase__ = num_negatives
lowercase__ = codevector_dim
lowercase__ = proj_codevector_dim
lowercase__ = diversity_loss_weight
# ctc loss
lowercase__ = ctc_loss_reduction
lowercase__ = ctc_zero_infinity
# pretraining loss
lowercase__ = replace_prob
@property
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
@require_tf
@require_sentencepiece
@require_tokenizers
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = TFCamembertModel.from_pretrained("jplu/tf-camembert-base" )
lowercase__ = tf.convert_to_tensor(
[[5, 1_21, 11, 6_60, 16, 7_30, 2_55_43, 1_10, 83, 6]] , dtype=tf.intaa , ) # J'aime le camembert !"
lowercase__ = model(_lowercase )["last_hidden_state"]
lowercase__ = tf.TensorShape((1, 10, 7_68) )
self.assertEqual(output.shape , _lowercase )
# compare the actual values for a slice.
lowercase__ = tf.convert_to_tensor(
[[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]] , dtype=tf.floataa , )
# camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
# camembert.eval()
# expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()
self.assertTrue(np.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 655 | 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 lowerCAmelCase ( lowercase_ ):
__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 :str , _lowercase :Any=None , _lowercase :Any=None , _lowercase :List[Any]=False , _lowercase :Union[str, Any]=True , _lowercase :Optional[Any]=True , _lowercase :int="<unk>" , _lowercase :Optional[int]="[SEP]" , _lowercase :int="<pad>" , _lowercase :Union[str, Any]="[CLS]" , _lowercase :Dict="[MASK]" , **_lowercase :Optional[Any] , ):
'''simple docstring'''
lowercase__ = (
AddedToken(_lowercase , lstrip=_lowercase , rstrip=_lowercase , normalized=_lowercase )
if isinstance(_lowercase , _lowercase )
else mask_token
)
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , remove_space=_lowercase , keep_accents=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , **_lowercase , )
lowercase__ = do_lower_case
lowercase__ = remove_space
lowercase__ = keep_accents
lowercase__ = vocab_file
lowercase__ = False if not self.vocab_file else True
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [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 UpperCAmelCase ( self :Optional[Any] , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [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 UpperCAmelCase ( self :Optional[int] , _lowercase :str , _lowercase :Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_lowercase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase__ = 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 ):
copyfile(self.vocab_file , _lowercase )
return (out_vocab_file,)
| 655 |
_snake_case = """ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/"""
def _A ( __magic_name__ ):
# Make sure the supplied data is a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = f'''a bytes-like object is required, not \'{data.__class__.__name__}\''''
raise TypeError(__magic_name__ )
lowercase__ = "".join(bin(__magic_name__ )[2:].zfill(8 ) for byte in data )
lowercase__ = len(__magic_name__ ) % 6 != 0
if padding_needed:
# The padding that will be added later
lowercase__ = B"=" * ((6 - len(__magic_name__ ) % 6) // 2)
# Append binary_stream with arbitrary binary digits (0's by default) to make its
# length a multiple of 6.
binary_stream += "0" * (6 - len(__magic_name__ ) % 6)
else:
lowercase__ = B""
# Encode every 6 binary digits to their corresponding Base64 character
return (
"".join(
B64_CHARSET[int(binary_stream[index : index + 6] , 2 )]
for index in range(0 , len(__magic_name__ ) , 6 ) ).encode()
+ padding
)
def _A ( __magic_name__ ):
# Make sure encoded_data is either a string or a bytes-like object
if not isinstance(__magic_name__ , __magic_name__ ) and not isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = (
"argument should be a bytes-like object or ASCII string, "
f'''not \'{encoded_data.__class__.__name__}\''''
)
raise TypeError(__magic_name__ )
# In case encoded_data is a bytes-like object, make sure it contains only
# ASCII characters so we convert it to a string object
if isinstance(__magic_name__ , __magic_name__ ):
try:
lowercase__ = encoded_data.decode("utf-8" )
except UnicodeDecodeError:
raise ValueError("base64 encoded data should only contain ASCII characters" )
lowercase__ = encoded_data.count("=" )
# Check if the encoded string contains non base64 characters
if padding:
assert all(
char in B64_CHARSET for char in encoded_data[:-padding] ), "Invalid base64 character(s) found."
else:
assert all(
char in B64_CHARSET for char in encoded_data ), "Invalid base64 character(s) found."
# Check the padding
assert len(__magic_name__ ) % 4 == 0 and padding < 3, "Incorrect padding"
if padding:
# Remove padding if there is one
lowercase__ = encoded_data[:-padding]
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )[: -padding * 2]
else:
lowercase__ = "".join(
bin(B64_CHARSET.index(__magic_name__ ) )[2:].zfill(6 ) for char in encoded_data )
lowercase__ = [
int(binary_stream[index : index + 8] , 2 )
for index in range(0 , len(__magic_name__ ) , 8 )
]
return bytes(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 | 1 |
from dataclasses import dataclass
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .attention_processor import AttentionProcessor, AttnProcessor
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, DiagonalGaussianDistribution, Encoder
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = True
@register_to_config
def __init__( self :int , _lowercase :int = 3 , _lowercase :int = 3 , _lowercase :Tuple[str] = ("DownEncoderBlock2D",) , _lowercase :Tuple[str] = ("UpDecoderBlock2D",) , _lowercase :Tuple[int] = (64,) , _lowercase :int = 1 , _lowercase :str = "silu" , _lowercase :int = 4 , _lowercase :int = 32 , _lowercase :int = 32 , _lowercase :float = 0.18215 , ):
'''simple docstring'''
super().__init__()
# pass init params to Encoder
lowercase__ = Encoder(
in_channels=_lowercase , out_channels=_lowercase , down_block_types=_lowercase , block_out_channels=_lowercase , layers_per_block=_lowercase , act_fn=_lowercase , norm_num_groups=_lowercase , double_z=_lowercase , )
# pass init params to Decoder
lowercase__ = Decoder(
in_channels=_lowercase , out_channels=_lowercase , up_block_types=_lowercase , block_out_channels=_lowercase , layers_per_block=_lowercase , norm_num_groups=_lowercase , act_fn=_lowercase , )
lowercase__ = nn.Convad(2 * latent_channels , 2 * latent_channels , 1 )
lowercase__ = nn.Convad(_lowercase , _lowercase , 1 )
lowercase__ = False
lowercase__ = False
# only relevant if vae tiling is enabled
lowercase__ = self.config.sample_size
lowercase__ = (
self.config.sample_size[0]
if isinstance(self.config.sample_size , (list, tuple) )
else self.config.sample_size
)
lowercase__ = int(sample_size / (2 ** (len(self.config.block_out_channels ) - 1)) )
lowercase__ = 0.25
def UpperCAmelCase ( self :str , _lowercase :int , _lowercase :int=False ):
'''simple docstring'''
if isinstance(_lowercase , (Encoder, Decoder) ):
lowercase__ = value
def UpperCAmelCase ( self :Optional[Any] , _lowercase :bool = True ):
'''simple docstring'''
lowercase__ = use_tiling
def UpperCAmelCase ( self :int ):
'''simple docstring'''
self.enable_tiling(_lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = True
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = False
@property
# Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = {}
def fn_recursive_add_processors(_lowercase :str , _lowercase :torch.nn.Module , _lowercase :Dict[str, AttentionProcessor] ):
if hasattr(_lowercase , "set_processor" ):
lowercase__ = module.processor
for sub_name, child in module.named_children():
fn_recursive_add_processors(f'''{name}.{sub_name}''' , _lowercase , _lowercase )
return processors
for name, module in self.named_children():
fn_recursive_add_processors(_lowercase , _lowercase , _lowercase )
return processors
def UpperCAmelCase ( self :int , _lowercase :Union[AttentionProcessor, Dict[str, AttentionProcessor]] ):
'''simple docstring'''
lowercase__ = len(self.attn_processors.keys() )
if isinstance(_lowercase , _lowercase ) and len(_lowercase ) != count:
raise ValueError(
f'''A dict of processors was passed, but the number of processors {len(_lowercase )} does not match the'''
f''' number of attention layers: {count}. Please make sure to pass {count} processor classes.''' )
def fn_recursive_attn_processor(_lowercase :str , _lowercase :torch.nn.Module , _lowercase :Any ):
if hasattr(_lowercase , "set_processor" ):
if not isinstance(_lowercase , _lowercase ):
module.set_processor(_lowercase )
else:
module.set_processor(processor.pop(f'''{name}.processor''' ) )
for sub_name, child in module.named_children():
fn_recursive_attn_processor(f'''{name}.{sub_name}''' , _lowercase , _lowercase )
for name, module in self.named_children():
fn_recursive_attn_processor(_lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
self.set_attn_processor(AttnProcessor() )
@apply_forward_hook
def UpperCAmelCase ( self :Any , _lowercase :torch.FloatTensor , _lowercase :bool = True ):
'''simple docstring'''
if self.use_tiling and (x.shape[-1] > self.tile_sample_min_size or x.shape[-2] > self.tile_sample_min_size):
return self.tiled_encode(_lowercase , return_dict=_lowercase )
if self.use_slicing and x.shape[0] > 1:
lowercase__ = [self.encoder(_lowercase ) for x_slice in x.split(1 )]
lowercase__ = torch.cat(_lowercase )
else:
lowercase__ = self.encoder(_lowercase )
lowercase__ = self.quant_conv(_lowercase )
lowercase__ = DiagonalGaussianDistribution(_lowercase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :torch.FloatTensor , _lowercase :bool = True ):
'''simple docstring'''
if self.use_tiling and (z.shape[-1] > self.tile_latent_min_size or z.shape[-2] > self.tile_latent_min_size):
return self.tiled_decode(_lowercase , return_dict=_lowercase )
lowercase__ = self.post_quant_conv(_lowercase )
lowercase__ = self.decoder(_lowercase )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowercase )
@apply_forward_hook
def UpperCAmelCase ( self :int , _lowercase :torch.FloatTensor , _lowercase :bool = True ):
'''simple docstring'''
if self.use_slicing and z.shape[0] > 1:
lowercase__ = [self._decode(_lowercase ).sample for z_slice in z.split(1 )]
lowercase__ = torch.cat(_lowercase )
else:
lowercase__ = self._decode(_lowercase ).sample
if not return_dict:
return (decoded,)
return DecoderOutput(sample=_lowercase )
def UpperCAmelCase ( self :Dict , _lowercase :str , _lowercase :Any , _lowercase :Optional[Any] ):
'''simple docstring'''
lowercase__ = min(a.shape[2] , b.shape[2] , _lowercase )
for y in range(_lowercase ):
lowercase__ = a[:, :, -blend_extent + y, :] * (1 - y / blend_extent) + b[:, :, y, :] * (y / blend_extent)
return b
def UpperCAmelCase ( self :Optional[int] , _lowercase :Dict , _lowercase :List[str] , _lowercase :List[str] ):
'''simple docstring'''
lowercase__ = min(a.shape[3] , b.shape[3] , _lowercase )
for x in range(_lowercase ):
lowercase__ = a[:, :, :, -blend_extent + x] * (1 - x / blend_extent) + b[:, :, :, x] * (x / blend_extent)
return b
def UpperCAmelCase ( self :List[Any] , _lowercase :torch.FloatTensor , _lowercase :bool = True ):
'''simple docstring'''
lowercase__ = int(self.tile_sample_min_size * (1 - self.tile_overlap_factor) )
lowercase__ = int(self.tile_latent_min_size * self.tile_overlap_factor )
lowercase__ = self.tile_latent_min_size - blend_extent
# Split the image into 512x512 tiles and encode them separately.
lowercase__ = []
for i in range(0 , x.shape[2] , _lowercase ):
lowercase__ = []
for j in range(0 , x.shape[3] , _lowercase ):
lowercase__ = x[:, :, i : i + self.tile_sample_min_size, j : j + self.tile_sample_min_size]
lowercase__ = self.encoder(_lowercase )
lowercase__ = self.quant_conv(_lowercase )
row.append(_lowercase )
rows.append(_lowercase )
lowercase__ = []
for i, row in enumerate(_lowercase ):
lowercase__ = []
for j, tile in enumerate(_lowercase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
lowercase__ = self.blend_v(rows[i - 1][j] , _lowercase , _lowercase )
if j > 0:
lowercase__ = self.blend_h(row[j - 1] , _lowercase , _lowercase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(_lowercase , dim=3 ) )
lowercase__ = torch.cat(_lowercase , dim=2 )
lowercase__ = DiagonalGaussianDistribution(_lowercase )
if not return_dict:
return (posterior,)
return AutoencoderKLOutput(latent_dist=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :torch.FloatTensor , _lowercase :bool = True ):
'''simple docstring'''
lowercase__ = int(self.tile_latent_min_size * (1 - self.tile_overlap_factor) )
lowercase__ = int(self.tile_sample_min_size * self.tile_overlap_factor )
lowercase__ = self.tile_sample_min_size - blend_extent
# Split z into overlapping 64x64 tiles and decode them separately.
# The tiles have an overlap to avoid seams between tiles.
lowercase__ = []
for i in range(0 , z.shape[2] , _lowercase ):
lowercase__ = []
for j in range(0 , z.shape[3] , _lowercase ):
lowercase__ = z[:, :, i : i + self.tile_latent_min_size, j : j + self.tile_latent_min_size]
lowercase__ = self.post_quant_conv(_lowercase )
lowercase__ = self.decoder(_lowercase )
row.append(_lowercase )
rows.append(_lowercase )
lowercase__ = []
for i, row in enumerate(_lowercase ):
lowercase__ = []
for j, tile in enumerate(_lowercase ):
# blend the above tile and the left tile
# to the current tile and add the current tile to the result row
if i > 0:
lowercase__ = self.blend_v(rows[i - 1][j] , _lowercase , _lowercase )
if j > 0:
lowercase__ = self.blend_h(row[j - 1] , _lowercase , _lowercase )
result_row.append(tile[:, :, :row_limit, :row_limit] )
result_rows.append(torch.cat(_lowercase , dim=3 ) )
lowercase__ = torch.cat(_lowercase , dim=2 )
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :torch.FloatTensor , _lowercase :bool = False , _lowercase :bool = True , _lowercase :Optional[torch.Generator] = None , ):
'''simple docstring'''
lowercase__ = sample
lowercase__ = self.encode(_lowercase ).latent_dist
if sample_posterior:
lowercase__ = posterior.sample(generator=_lowercase )
else:
lowercase__ = posterior.mode()
lowercase__ = self.decode(_lowercase ).sample
if not return_dict:
return (dec,)
return DecoderOutput(sample=_lowercase )
| 655 |
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Dict , _lowercase :TransformeraDModel , _lowercase :AutoencoderKL , _lowercase :KarrasDiffusionSchedulers , _lowercase :Optional[Dict[int, str]] = None , ):
'''simple docstring'''
super().__init__()
self.register_modules(transformer=_lowercase , vae=_lowercase , scheduler=_lowercase )
# create a imagenet -> id dictionary for easier use
lowercase__ = {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
lowercase__ = int(_lowercase )
lowercase__ = dict(sorted(self.labels.items() ) )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Union[str, List[str]] ):
'''simple docstring'''
if not isinstance(_lowercase , _lowercase ):
lowercase__ = list(_lowercase )
for l in label:
if l not in self.labels:
raise ValueError(
f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self :Optional[Any] , _lowercase :List[int] , _lowercase :float = 4.0 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :int = 50 , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = len(_lowercase )
lowercase__ = self.transformer.config.sample_size
lowercase__ = self.transformer.config.in_channels
lowercase__ = randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=_lowercase , device=self.device , dtype=self.transformer.dtype , )
lowercase__ = torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowercase__ = torch.tensor(_lowercase , device=self.device ).reshape(-1 )
lowercase__ = torch.tensor([10_00] * batch_size , device=self.device )
lowercase__ = torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(_lowercase )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowercase__ = latent_model_input[: len(_lowercase ) // 2]
lowercase__ = torch.cat([half, half] , dim=0 )
lowercase__ = self.scheduler.scale_model_input(_lowercase , _lowercase )
lowercase__ = t
if not torch.is_tensor(_lowercase ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
lowercase__ = latent_model_input.device.type == "mps"
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.floataa if is_mps else torch.floataa
else:
lowercase__ = torch.intaa if is_mps else torch.intaa
lowercase__ = torch.tensor([timesteps] , dtype=_lowercase , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowercase__ = timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase__ = timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowercase__ = self.transformer(
_lowercase , timestep=_lowercase , class_labels=_lowercase ).sample
# perform guidance
if guidance_scale > 1:
lowercase__ , lowercase__ = noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowercase__ , lowercase__ = torch.split(_lowercase , len(_lowercase ) // 2 , dim=0 )
lowercase__ = uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowercase__ = torch.cat([half_eps, half_eps] , dim=0 )
lowercase__ = torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowercase__ , lowercase__ = torch.split(_lowercase , _lowercase , dim=1 )
else:
lowercase__ = noise_pred
# compute previous image: x_t -> x_t-1
lowercase__ = self.scheduler.step(_lowercase , _lowercase , _lowercase ).prev_sample
if guidance_scale > 1:
lowercase__ , lowercase__ = latent_model_input.chunk(2 , dim=0 )
else:
lowercase__ = latent_model_input
lowercase__ = 1 / self.vae.config.scaling_factor * latents
lowercase__ = self.vae.decode(_lowercase ).sample
lowercase__ = (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase__ = samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
def __init__( self :Optional[Any] , *_lowercase :int , **_lowercase :Optional[int] ):
'''simple docstring'''
warnings.warn(
"The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ChineseCLIPImageProcessor instead." , _lowercase , )
super().__init__(*_lowercase , **_lowercase )
| 655 |
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 ( lowercase_ ):
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = SMALL_MODEL_IDENTIFIER
lowercase__ = "pt"
lowercase__ = "tf"
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = AutoModel.from_pretrained(self.test_model )
model_pt.save_pretrained(_lowercase )
def UpperCAmelCase ( self :Tuple , _lowercase :int ):
'''simple docstring'''
lowercase__ = TFAutoModel.from_pretrained(self.test_model , from_pt=_lowercase )
model_tf.save_pretrained(_lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = "mock_framework"
# Framework provided - return whatever the user provides
lowercase__ = FeaturesManager.determine_framework(self.test_model , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# Local checkpoint and framework provided - return provided framework
# PyTorch checkpoint
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase , _lowercase )
self.assertEqual(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
with TemporaryDirectory() as local_pt_ckpt:
self._setup_pt_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_pt )
# TensorFlow checkpoint
with TemporaryDirectory() as local_tf_ckpt:
self._setup_tf_ckpt(_lowercase )
lowercase__ = FeaturesManager.determine_framework(_lowercase )
self.assertEqual(_lowercase , self.framework_tf )
# Invalid local checkpoint
with TemporaryDirectory() as local_invalid_ckpt:
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(_lowercase )
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# PyTorch not in environment -> use TensorFlow
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_tf )
# Both in environment -> use PyTorch
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
self.assertEqual(_lowercase , self.framework_pt )
# Both not in environment -> raise error
lowercase__ = MagicMock(return_value=_lowercase )
lowercase__ = MagicMock(return_value=_lowercase )
with patch("transformers.onnx.features.is_tf_available" , _lowercase ), patch(
"transformers.onnx.features.is_torch_available" , _lowercase ):
with self.assertRaises(_lowercase ):
lowercase__ = FeaturesManager.determine_framework(self.test_model )
| 655 | 1 |
import argparse
import gc
import json
import os
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
_snake_case = 16
_snake_case = 32
def _A ( __magic_name__ ):
return int(x / 2**20 )
class lowerCAmelCase :
def __enter__( self :List[str] ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated() # reset the peak gauge to zero
lowercase__ = torch.cuda.memory_allocated()
return self
def __exit__( self :Optional[int] , *_lowercase :Optional[int] ):
'''simple docstring'''
gc.collect()
torch.cuda.empty_cache()
lowercase__ = torch.cuda.memory_allocated()
lowercase__ = torch.cuda.max_memory_allocated()
lowercase__ = bamb(self.end - self.begin )
lowercase__ = bamb(self.peak - self.begin )
# print(f"delta used/peak {self.used:4d}/{self.peaked:4d}")
def _A ( __magic_name__ , __magic_name__ = 16 , __magic_name__ = "bert-base-cased" , __magic_name__ = 320 , __magic_name__ = 160 , ):
lowercase__ = AutoTokenizer.from_pretrained(__magic_name__ )
lowercase__ = load_dataset(
"glue" , "mrpc" , split={"train": f'''train[:{n_train}]''', "validation": f'''validation[:{n_val}]'''} )
def tokenize_function(__magic_name__ ):
# max_length=None => use the model max length (it's actually the default)
lowercase__ = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=__magic_name__ , max_length=__magic_name__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
lowercase__ = datasets.map(
__magic_name__ , batched=__magic_name__ , remove_columns=["idx", "sentence1", "sentence2"] , load_from_cache_file=__magic_name__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase__ = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__magic_name__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__magic_name__ , padding="max_length" , max_length=128 , return_tensors="pt" )
return tokenizer.pad(__magic_name__ , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
lowercase__ = DataLoader(
tokenized_datasets["train"] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=__magic_name__ )
lowercase__ = DataLoader(
tokenized_datasets["validation"] , shuffle=__magic_name__ , collate_fn=__magic_name__ , batch_size=__magic_name__ )
return train_dataloader, eval_dataloader
def _A ( __magic_name__ , __magic_name__ ):
# Initialize accelerator
lowercase__ = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ = config["lr"]
lowercase__ = int(config["num_epochs"] )
lowercase__ = int(config["seed"] )
lowercase__ = int(config["batch_size"] )
lowercase__ = args.model_name_or_path
set_seed(__magic_name__ )
lowercase__ , lowercase__ = get_dataloaders(__magic_name__ , __magic_name__ , __magic_name__ , args.n_train , args.n_val )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ = AutoModelForSequenceClassification.from_pretrained(__magic_name__ , return_dict=__magic_name__ )
# Instantiate optimizer
lowercase__ = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
lowercase__ = optimizer_cls(params=model.parameters() , lr=__magic_name__ )
if accelerator.state.deepspeed_plugin is not None:
lowercase__ = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
lowercase__ = 1
lowercase__ = (len(__magic_name__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
lowercase__ = get_linear_schedule_with_warmup(
optimizer=__magic_name__ , num_warmup_steps=0 , num_training_steps=__magic_name__ , )
else:
lowercase__ = DummyScheduler(__magic_name__ , total_num_steps=__magic_name__ , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ = accelerator.prepare(
__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
# We need to keep track of how many total steps we have iterated over
lowercase__ = 0
# We also need to keep track of the stating epoch so files are named properly
lowercase__ = 0
# Now we train the model
lowercase__ = {}
for epoch in range(__magic_name__ , __magic_name__ ):
with TorchTracemalloc() as tracemalloc:
model.train()
for step, batch in enumerate(__magic_name__ ):
lowercase__ = model(**__magic_name__ )
lowercase__ = outputs.loss
lowercase__ = loss / gradient_accumulation_steps
accelerator.backward(__magic_name__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
# Printing the GPU memory usage details such as allocated memory, peak memory, and total memory usage
accelerator.print("Memory before entering the train : {}".format(bamb(tracemalloc.begin ) ) )
accelerator.print("Memory consumed at the end of the train (end-begin): {}".format(tracemalloc.used ) )
accelerator.print("Peak Memory consumed during the train (max-begin): {}".format(tracemalloc.peaked ) )
accelerator.print(
"Total Peak Memory consumed during the train (max): {}".format(
tracemalloc.peaked + bamb(tracemalloc.begin ) ) )
lowercase__ = tracemalloc.peaked + bamb(tracemalloc.begin )
if args.peak_memory_upper_bound is not None:
assert (
train_total_peak_memory[f'''epoch-{epoch}'''] <= args.peak_memory_upper_bound
), "Peak memory usage exceeded the upper bound"
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , "peak_memory_utilization.json" ) , "w" ) as f:
json.dump(__magic_name__ , __magic_name__ )
def _A ( ):
lowercase__ = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path" , type=__magic_name__ , default="bert-base-cased" , help="Path to pretrained model or model identifier from huggingface.co/models." , required=__magic_name__ , )
parser.add_argument(
"--output_dir" , type=__magic_name__ , default="." , help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory." , )
parser.add_argument(
"--peak_memory_upper_bound" , type=__magic_name__ , default=__magic_name__ , help="The upper bound of peak memory usage in MB. If set, the training will throw an error if the peak memory usage exceeds this value." , )
parser.add_argument(
"--n_train" , type=__magic_name__ , default=320 , help="Number of training examples to use." , )
parser.add_argument(
"--n_val" , type=__magic_name__ , default=160 , help="Number of validation examples to use." , )
parser.add_argument(
"--num_epochs" , type=__magic_name__ , default=1 , help="Number of train epochs." , )
lowercase__ = parser.parse_args()
lowercase__ = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__magic_name__ , __magic_name__ )
if __name__ == "__main__":
main()
| 655 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git_vision_model'
def __init__( self :Dict , _lowercase :Dict=7_68 , _lowercase :Dict=30_72 , _lowercase :Tuple=12 , _lowercase :List[str]=12 , _lowercase :Tuple=3 , _lowercase :Dict=2_24 , _lowercase :Tuple=16 , _lowercase :Optional[int]="quick_gelu" , _lowercase :Union[str, Any]=1e-5 , _lowercase :Tuple=0.0 , _lowercase :Tuple=0.02 , **_lowercase :Optional[Any] , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = hidden_size
lowercase__ = intermediate_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = num_channels
lowercase__ = patch_size
lowercase__ = image_size
lowercase__ = initializer_range
lowercase__ = attention_dropout
lowercase__ = layer_norm_eps
lowercase__ = hidden_act
@classmethod
def UpperCAmelCase ( cls :List[str] , _lowercase :Union[str, os.PathLike] , **_lowercase :Optional[int] ):
'''simple docstring'''
cls._set_token_in_kwargs(_lowercase )
lowercase__ , lowercase__ = cls.get_config_dict(_lowercase , **_lowercase )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("model_type" ) == "git":
lowercase__ = config_dict["vision_config"]
if "model_type" in config_dict and hasattr(cls , "model_type" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(_lowercase , **_lowercase )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'git'
def __init__( self :Union[str, Any] , _lowercase :Dict=None , _lowercase :List[str]=3_05_22 , _lowercase :Tuple=7_68 , _lowercase :Any=6 , _lowercase :Dict=12 , _lowercase :Any=30_72 , _lowercase :List[Any]="gelu" , _lowercase :Tuple=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :Optional[Any]=10_24 , _lowercase :Any=0.02 , _lowercase :int=1e-12 , _lowercase :List[Any]=0 , _lowercase :int="absolute" , _lowercase :List[str]=True , _lowercase :Any=False , _lowercase :int=1_01 , _lowercase :str=1_02 , _lowercase :Dict=None , **_lowercase :List[str] , ):
'''simple docstring'''
super().__init__(bos_token_id=_lowercase , eos_token_id=_lowercase , pad_token_id=_lowercase , **_lowercase )
if vision_config is None:
lowercase__ = {}
logger.info("vision_config is None. initializing the GitVisionConfig with default values." )
lowercase__ = GitVisionConfig(**_lowercase )
lowercase__ = vocab_size
lowercase__ = hidden_size
lowercase__ = num_hidden_layers
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = intermediate_size
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = max_position_embeddings
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = position_embedding_type
lowercase__ = use_cache
lowercase__ = tie_word_embeddings
lowercase__ = num_image_with_embedding
lowercase__ = bos_token_id
lowercase__ = eos_token_id
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = copy.deepcopy(self.__dict__ )
lowercase__ = self.vision_config.to_dict()
lowercase__ = self.__class__.model_type
return output
| 655 | 1 |
from dataclasses import dataclass, field
from typing import Optional
from transformers import AutoConfig, AutoImageProcessor, AutoTokenizer, FlaxVisionEncoderDecoderModel, HfArgumentParser
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
metadata={'help': 'The output directory where the model will be written.'} , )
__lowerCamelCase = field(
metadata={
'help': (
'The encoder model checkpoint for weights initialization.'
'Don\'t set if you want to train an encoder model from scratch.'
)
} , )
__lowerCamelCase = field(
metadata={
'help': (
'The decoder model checkpoint for weights initialization.'
'Don\'t set if you want to train a decoder model from scratch.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Pretrained encoder config name or path if not the same as encoder_model_name'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Pretrained decoder config name or path if not the same as decoder_model_name'} )
def _A ( ):
lowercase__ = HfArgumentParser((ModelArguments,) )
((lowercase__) , ) = parser.parse_args_into_dataclasses()
# Load pretrained model and tokenizer
# Use explicit specified encoder config
if model_args.encoder_config_name:
lowercase__ = AutoConfig.from_pretrained(model_args.encoder_config_name )
# Use pretrained encoder model's config
else:
lowercase__ = AutoConfig.from_pretrained(model_args.encoder_model_name_or_path )
# Use explicit specified decoder config
if model_args.decoder_config_name:
lowercase__ = AutoConfig.from_pretrained(model_args.decoder_config_name )
# Use pretrained decoder model's config
else:
lowercase__ = AutoConfig.from_pretrained(model_args.decoder_model_name_or_path )
# necessary for `from_encoder_decoder_pretrained` when `decoder_config` is passed
lowercase__ = True
lowercase__ = True
lowercase__ = FlaxVisionEncoderDecoderModel.from_encoder_decoder_pretrained(
encoder_pretrained_model_name_or_path=model_args.encoder_model_name_or_path , decoder_pretrained_model_name_or_path=model_args.decoder_model_name_or_path , encoder_config=__magic_name__ , decoder_config=__magic_name__ , )
# GPT2 only has bos/eos tokens but not decoder_start/pad tokens
lowercase__ = decoder_config.decoder_start_token_id
lowercase__ = decoder_config.pad_token_id
if decoder_start_token_id is None:
lowercase__ = decoder_config.bos_token_id
if pad_token_id is None:
lowercase__ = decoder_config.eos_token_id
# This is necessary to make Flax's generate() work
lowercase__ = decoder_config.eos_token_id
lowercase__ = decoder_start_token_id
lowercase__ = pad_token_id
lowercase__ = AutoImageProcessor.from_pretrained(model_args.encoder_model_name_or_path )
lowercase__ = AutoTokenizer.from_pretrained(model_args.decoder_model_name_or_path )
lowercase__ = tokenizer.convert_ids_to_tokens(model.config.pad_token_id )
model.save_pretrained(model_args.output_dir )
image_processor.save_pretrained(model_args.output_dir )
tokenizer.save_pretrained(model_args.output_dir )
if __name__ == "__main__":
main()
| 655 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class lowerCAmelCase ( unittest.TestCase ):
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModel.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModel.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForPreTraining.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForPreTraining.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForCausalLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForCausalLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForCausalLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForMaskedLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForMaskedLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForMaskedLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_pt=_lowercase )
lowercase__ , lowercase__ = TFAutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(_lowercase , from_tf=_lowercase )
lowercase__ , lowercase__ = AutoModelForSeqaSeqLM.from_pretrained(
_lowercase , output_loading_info=_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForSequenceClassification.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForSequenceClassification.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
lowercase__ = AutoConfig.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = TFAutoModelForQuestionAnswering.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
lowercase__ = AutoModelForQuestionAnswering.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsNotNone(_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = TFAutoModelWithLMHead.from_pretrained(_lowercase , from_pt=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
lowercase__ = AutoModelWithLMHead.from_pretrained(_lowercase , from_tf=_lowercase )
self.assertIsInstance(_lowercase , _lowercase )
self.assertEqual(model.num_parameters() , 1_44_10 )
self.assertEqual(model.num_parameters(only_trainable=_lowercase ) , 1_44_10 )
| 655 | 1 |
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
_snake_case = logging.getLogger(__name__)
def _A ( __magic_name__ , __magic_name__ ):
# save results
if os.path.exists(__magic_name__ ):
if os.path.exists(os.path.join(__magic_name__ , "config.json" ) ) and os.path.isfile(
os.path.join(__magic_name__ , "config.json" ) ):
os.remove(os.path.join(__magic_name__ , "config.json" ) )
if os.path.exists(os.path.join(__magic_name__ , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(__magic_name__ , "pytorch_model.bin" ) ):
os.remove(os.path.join(__magic_name__ , "pytorch_model.bin" ) )
else:
os.makedirs(__magic_name__ )
model.save_pretrained(__magic_name__ )
def _A ( __magic_name__ , __magic_name__=False ):
lowercase__ = 2
if unlogit:
lowercase__ = torch.pow(__magic_name__ , __magic_name__ )
lowercase__ = p * torch.log(__magic_name__ )
lowercase__ = 0
return -plogp.sum(dim=-1 )
def _A ( __magic_name__ ):
logger.info("lv, h >\t" + "\t".join(f'''{x + 1}''' for x in range(len(__magic_name__ ) ) ) )
for row in range(len(__magic_name__ ) ):
if tensor.dtype != torch.long:
logger.info(f'''layer {row + 1}:\t''' + "\t".join(f'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(f'''layer {row + 1}:\t''' + "\t".join(f'''{x:d}''' for x in tensor[row].cpu().data ) )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=True , __magic_name__=True , __magic_name__=None , __magic_name__=False ):
lowercase__ , lowercase__ = model.config.num_hidden_layers, model.config.num_attention_heads
lowercase__ = torch.zeros(__magic_name__ , __magic_name__ ).to(args.device )
lowercase__ = torch.zeros(__magic_name__ , __magic_name__ ).to(args.device )
if head_mask is None:
lowercase__ = torch.ones(__magic_name__ , __magic_name__ ).to(args.device )
head_mask.requires_grad_(requires_grad=__magic_name__ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
lowercase__ = None
lowercase__ = 0.0
lowercase__ = 0.0
for step, inputs in enumerate(tqdm(__magic_name__ , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
lowercase__ = tuple(t.to(args.device ) for t in inputs )
((lowercase__) , ) = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
lowercase__ = model(__magic_name__ , labels=__magic_name__ , head_mask=__magic_name__ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
lowercase__ , lowercase__ , lowercase__ = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(__magic_name__ ):
lowercase__ = entropy(attn.detach() , __magic_name__ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(__magic_name__ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
lowercase__ = 2
lowercase__ = torch.pow(torch.pow(__magic_name__ , __magic_name__ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
lowercase__ = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(__magic_name__ )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(__magic_name__ )
logger.info("Head ranked by importance scores" )
lowercase__ = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
lowercase__ = torch.arange(
head_importance.numel() , device=args.device )
lowercase__ = head_ranks.view_as(__magic_name__ )
print_ad_tensor(__magic_name__ )
return attn_entropy, head_importance, total_loss
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ , lowercase__ = compute_heads_importance(__magic_name__ , __magic_name__ , __magic_name__ , compute_entropy=__magic_name__ )
lowercase__ = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , __magic_name__ , original_score * args.masking_threshold )
lowercase__ = torch.ones_like(__magic_name__ )
lowercase__ = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
lowercase__ = original_score
while current_score >= original_score * args.masking_threshold:
lowercase__ = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
lowercase__ = float("Inf" )
lowercase__ = head_importance.view(-1 ).sort()[1]
if len(__magic_name__ ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
lowercase__ = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
lowercase__ = new_head_mask.view(-1 )
lowercase__ = 0.0
lowercase__ = new_head_mask.view_as(__magic_name__ )
lowercase__ = new_head_mask.clone().detach()
print_ad_tensor(__magic_name__ )
# Compute metric and head importance again
lowercase__ , lowercase__ , lowercase__ = compute_heads_importance(
__magic_name__ , __magic_name__ , __magic_name__ , compute_entropy=__magic_name__ , head_mask=__magic_name__ )
lowercase__ = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , __magic_name__ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(__magic_name__ )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = datetime.now()
lowercase__ , lowercase__ , lowercase__ = compute_heads_importance(
__magic_name__ , __magic_name__ , __magic_name__ , compute_entropy=__magic_name__ , compute_importance=__magic_name__ , head_mask=__magic_name__ )
lowercase__ = 1 / loss
lowercase__ = datetime.now() - before_time
lowercase__ = sum(p.numel() for p in model.parameters() )
lowercase__ = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(__magic_name__ ) )
}
for k, v in heads_to_prune.items():
if isinstance(__magic_name__ , __magic_name__ ):
lowercase__ = [
v,
]
assert sum(len(__magic_name__ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(__magic_name__ )
lowercase__ = sum(p.numel() for p in model.parameters() )
lowercase__ = datetime.now()
lowercase__ , lowercase__ , lowercase__ = compute_heads_importance(
__magic_name__ , __magic_name__ , __magic_name__ , compute_entropy=__magic_name__ , compute_importance=__magic_name__ , head_mask=__magic_name__ , actually_pruned=__magic_name__ , )
lowercase__ = 1 / loss
lowercase__ = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , __magic_name__ , __magic_name__ , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , __magic_name__ , __magic_name__ )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(__magic_name__ , args.output_dir )
def _A ( ):
lowercase__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="The input data dir. Should contain the .tsv files (or other data files) for the task." , )
parser.add_argument(
"--model_name_or_path" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=__magic_name__ , type=__magic_name__ , required=__magic_name__ , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=__magic_name__ , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=__magic_name__ , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=__magic_name__ , type=__magic_name__ , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=__magic_name__ , default=-1 , help="If > 0: limit the data to a subset of data_subset instances." )
parser.add_argument(
"--overwrite_output_dir" , action="store_true" , help="Whether to overwrite data in output directory" )
parser.add_argument(
"--overwrite_cache" , action="store_true" , help="Overwrite the cached training and evaluation sets" )
parser.add_argument(
"--dont_normalize_importance_by_layer" , action="store_true" , help="Don't normalize importance score by layers" )
parser.add_argument(
"--dont_normalize_global_importance" , action="store_true" , help="Don't normalize all importance scores between 0 and 1" , )
parser.add_argument(
"--try_masking" , action="store_true" , help="Whether to try to mask head until a threshold of accuracy." )
parser.add_argument(
"--masking_threshold" , default=0.9 , type=__magic_name__ , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=__magic_name__ , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=__magic_name__ , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=__magic_name__ , help=(
"The maximum total input sequence length after WordPiece tokenization. \n"
"Sequences longer than this will be truncated, sequences shorter padded."
) , )
parser.add_argument("--batch_size" , default=1 , type=__magic_name__ , help="Batch size." )
parser.add_argument("--seed" , type=__magic_name__ , default=42 )
parser.add_argument("--local_rank" , type=__magic_name__ , default=-1 , help="local_rank for distributed training on gpus" )
parser.add_argument("--no_cuda" , action="store_true" , help="Whether not to use CUDA when available" )
parser.add_argument("--server_ip" , type=__magic_name__ , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=__magic_name__ , default="" , help="Can be used for distant debugging." )
lowercase__ = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach" )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=__magic_name__ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
lowercase__ = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
lowercase__ = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
lowercase__ = torch.device("cuda" , args.local_rank )
lowercase__ = 1
torch.distributed.init_process_group(backend="nccl" ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info("device: {} n_gpu: {}, distributed: {}".format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
lowercase__ = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
lowercase__ = nn.parallel.DistributedDataParallel(
__magic_name__ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=__magic_name__ )
elif args.n_gpu > 1:
lowercase__ = nn.DataParallel(__magic_name__ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=__magic_name__ )
torch.save(__magic_name__ , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , __magic_name__ )
# Prepare dataset
lowercase__ = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
lowercase__ = (torch.from_numpy(__magic_name__ ),)
lowercase__ = TensorDataset(*__magic_name__ )
lowercase__ = RandomSampler(__magic_name__ )
lowercase__ = DataLoader(__magic_name__ , sampler=__magic_name__ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(__magic_name__ , __magic_name__ , __magic_name__ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
lowercase__ = mask_heads(__magic_name__ , __magic_name__ , __magic_name__ )
prune_heads(__magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ )
if __name__ == "__main__":
main()
| 655 |
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F
from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_snake_case = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""", F"""encoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias"""))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""", F"""decoder.layers.{i}.self_attn.out_proj.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""")
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
)
)
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
)
)
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias"""))
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""")
)
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""")
)
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight"""))
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias"""))
# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("""input_proj.weight""", """input_projection.weight"""),
("""input_proj.bias""", """input_projection.bias"""),
("""query_embed.weight""", """query_position_embeddings.weight"""),
("""transformer.encoder.norm.weight""", """encoder.layernorm.weight"""),
("""transformer.encoder.norm.bias""", """encoder.layernorm.bias"""),
("""transformer.decoder.norm.weight""", """decoder.layernorm.weight"""),
("""transformer.decoder.norm.bias""", """decoder.layernorm.bias"""),
("""class_embed.weight""", """class_labels_classifier.weight"""),
("""class_embed.bias""", """class_labels_classifier.bias"""),
("""bbox_embed.layers.0.weight""", """bbox_predictor.layers.0.weight"""),
("""bbox_embed.layers.0.bias""", """bbox_predictor.layers.0.bias"""),
("""bbox_embed.layers.1.weight""", """bbox_predictor.layers.1.weight"""),
("""bbox_embed.layers.1.bias""", """bbox_predictor.layers.1.bias"""),
("""bbox_embed.layers.2.weight""", """bbox_predictor.layers.2.weight"""),
("""bbox_embed.layers.2.bias""", """bbox_predictor.layers.2.bias"""),
]
)
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
def _A ( __magic_name__ ):
lowercase__ = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
lowercase__ = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
lowercase__ = value
else:
lowercase__ = value
return new_state_dict
def _A ( __magic_name__ ):
lowercase__ = ""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase__ = in_proj_weight[:256, :]
lowercase__ = in_proj_bias[:256]
lowercase__ = in_proj_weight[256:512, :]
lowercase__ = in_proj_bias[256:512]
lowercase__ = in_proj_weight[-256:, :]
lowercase__ = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
lowercase__ = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
lowercase__ = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
lowercase__ = in_proj_weight_cross_attn[:256, :]
lowercase__ = in_proj_bias_cross_attn[:256]
lowercase__ = in_proj_weight_cross_attn[256:512, :]
lowercase__ = in_proj_bias_cross_attn[256:512]
lowercase__ = in_proj_weight_cross_attn[-256:, :]
lowercase__ = in_proj_bias_cross_attn[-256:]
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ , lowercase__ = image.size
lowercase__ = max(__magic_name__ , __magic_name__ )
lowercase__ = 800 if "detection" in checkpoint_url else 1000
lowercase__ = target_max_size / current_max_size
lowercase__ = image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )
return resized_image
def _A ( __magic_name__ ):
lowercase__ = F.to_tensor(__magic_name__ )
lowercase__ = F.normalize(__magic_name__ , mean=[0.485, 0.456, 0.406] , std=[0.229, 0.224, 0.225] )
return image
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
logger.info("Converting model..." )
# load original state dict
lowercase__ = torch.hub.load_state_dict_from_url(__magic_name__ , map_location="cpu" )
# rename keys
for src, dest in rename_keys:
rename_key(__magic_name__ , __magic_name__ , __magic_name__ )
lowercase__ = rename_backbone_keys(__magic_name__ )
# query, key and value matrices need special treatment
read_in_q_k_v(__magic_name__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
lowercase__ = "model."
for key in state_dict.copy().keys():
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
lowercase__ = state_dict.pop(__magic_name__ )
lowercase__ = val
# create HuggingFace model and load state dict
lowercase__ = TableTransformerConfig(
backbone="resnet18" , mask_loss_coefficient=1 , dice_loss_coefficient=1 , ce_loss_coefficient=1 , bbox_loss_coefficient=5 , giou_loss_coefficient=2 , eos_coefficient=0.4 , class_cost=1 , bbox_cost=5 , giou_cost=2 , )
if "detection" in checkpoint_url:
lowercase__ = 15
lowercase__ = 2
lowercase__ = {0: "table", 1: "table rotated"}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
else:
lowercase__ = 125
lowercase__ = 6
lowercase__ = {
0: "table",
1: "table column",
2: "table row",
3: "table column header",
4: "table projected row header",
5: "table spanning cell",
}
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = DetrImageProcessor(
format="coco_detection" , max_size=800 if "detection" in checkpoint_url else 1000 )
lowercase__ = TableTransformerForObjectDetection(__magic_name__ )
model.load_state_dict(__magic_name__ )
model.eval()
# verify our conversion
lowercase__ = "example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
lowercase__ = hf_hub_download(repo_id="nielsr/example-pdf" , repo_type="dataset" , filename=__magic_name__ )
lowercase__ = Image.open(__magic_name__ ).convert("RGB" )
lowercase__ = normalize(resize(__magic_name__ , __magic_name__ ) ).unsqueeze(0 )
lowercase__ = model(__magic_name__ )
if "detection" in checkpoint_url:
lowercase__ = (1, 15, 3)
lowercase__ = torch.tensor(
[[-6.7_897, -16.9_985, 6.7_937], [-8.0_186, -22.2_192, 6.9_677], [-7.3_117, -21.0_708, 7.4_055]] )
lowercase__ = torch.tensor([[0.4_867, 0.1_767, 0.6_732], [0.6_718, 0.4_479, 0.3_830], [0.4_716, 0.1_760, 0.6_364]] )
else:
lowercase__ = (1, 125, 7)
lowercase__ = torch.tensor(
[[-18.1_430, -8.3_214, 4.8_274], [-18.4_685, -7.1_361, -4.2_667], [-26.3_693, -9.3_429, -4.9_962]] )
lowercase__ = torch.tensor([[0.4_983, 0.5_595, 0.9_440], [0.4_916, 0.6_315, 0.5_954], [0.6_108, 0.8_637, 0.1_135]] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, :3, :3] , __magic_name__ , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes[0, :3, :3] , __magic_name__ , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
model.save_pretrained(__magic_name__ )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
# Push model to HF hub
logger.info("Pushing model to the hub..." )
lowercase__ = (
"microsoft/table-transformer-detection"
if "detection" in checkpoint_url
else "microsoft/table-transformer-structure-recognition"
)
model.push_to_hub(__magic_name__ )
image_processor.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_url""",
default="""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
type=str,
choices=[
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth""",
"""https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth""",
],
help="""URL of the Table Transformer checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 655 | 1 |
import argparse
import json
import re
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileNetVaConfig,
MobileNetVaForImageClassification,
MobileNetVaImageProcessor,
load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def _A ( __magic_name__ ):
lowercase__ = MobileNetVaConfig(layer_norm_eps=0.001 )
if "_quant" in model_name:
raise ValueError("Quantized models are not supported." )
lowercase__ = re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$" , __magic_name__ )
if matches:
lowercase__ = float(matches[1] )
lowercase__ = int(matches[2] )
# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
# the usual 1000. The first class (index 0) is "background".
lowercase__ = 1001
lowercase__ = "imagenet-1k-id2label.json"
lowercase__ = "huggingface/label-files"
lowercase__ = json.load(open(hf_hub_download(__magic_name__ , __magic_name__ , repo_type="dataset" ) , "r" ) )
lowercase__ = {int(__magic_name__ ) + 1: v for k, v in idalabel.items()}
lowercase__ = "background"
lowercase__ = idalabel
lowercase__ = {v: k for k, v in idalabel.items()}
return config
def _A ( ):
lowercase__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowercase__ = Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw )
return im
@torch.no_grad()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__=False ):
lowercase__ = get_mobilenet_va_config(__magic_name__ )
# Load 🤗 model
lowercase__ = MobileNetVaForImageClassification(__magic_name__ ).eval()
# Load weights from TensorFlow checkpoint
load_tf_weights_in_mobilenet_va(__magic_name__ , __magic_name__ , __magic_name__ )
# Check outputs on an image, prepared by MobileNetV1ImageProcessor
lowercase__ = MobileNetVaImageProcessor(
crop_size={"width": config.image_size, "height": config.image_size} , size={"shortest_edge": config.image_size + 32} , )
lowercase__ = image_processor(images=prepare_img() , return_tensors="pt" )
lowercase__ = model(**__magic_name__ )
lowercase__ = outputs.logits
assert logits.shape == (1, 1001)
if model_name == "mobilenet_v1_1.0_224":
lowercase__ = torch.tensor([-4.1_739, -1.1_233, 3.1_205] )
elif model_name == "mobilenet_v1_0.75_192":
lowercase__ = torch.tensor([-3.9_440, -2.3_141, -0.3_333] )
else:
lowercase__ = None
if expected_logits is not None:
assert torch.allclose(logits[0, :3] , __magic_name__ , atol=1e-4 )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__magic_name__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
print("Pushing to the hub..." )
lowercase__ = "google/" + model_name
image_processor.push_to_hub(__magic_name__ )
model.push_to_hub(__magic_name__ )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""mobilenet_v1_1.0_224""",
type=str,
help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_snake_case = parser.parse_args()
convert_movilevit_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 655 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 | 1 |
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if len(__magic_name__ ) != len(__magic_name__ ):
raise ValueError("The length of profit and weight must be same." )
if max_weight <= 0:
raise ValueError("max_weight must greater than zero." )
if any(p < 0 for p in profit ):
raise ValueError("Profit can not be negative." )
if any(w < 0 for w in weight ):
raise ValueError("Weight can not be negative." )
# List created to store profit gained for the 1kg in case of each weight
# respectively. Calculate and append profit/weight for each element.
lowercase__ = [p / w for p, w in zip(__magic_name__ , __magic_name__ )]
# Creating a copy of the list and sorting profit/weight in ascending order
lowercase__ = sorted(__magic_name__ )
# declaring useful variables
lowercase__ = len(__magic_name__ )
lowercase__ = 0
lowercase__ = 0
lowercase__ = 0
# loop till the total weight do not reach max limit e.g. 15 kg and till i<length
while limit <= max_weight and i < length:
# flag value for encountered greatest element in sorted_profit_by_weight
lowercase__ = sorted_profit_by_weight[length - i - 1]
lowercase__ = profit_by_weight.index(__magic_name__ )
lowercase__ = -1
# check if the weight encountered is less than the total weight
# encountered before.
if max_weight - limit >= weight[index]:
limit += weight[index]
# Adding profit gained for the given weight 1 ===
# weight[index]/weight[index]
gain += 1 * profit[index]
else:
# Since the weight encountered is greater than limit, therefore take the
# required number of remaining kgs and calculate profit for it.
# weight remaining / weight[index]
gain += (max_weight - limit) / weight[index] * profit[index]
break
i += 1
return gain
if __name__ == "__main__":
print(
"""Input profits, weights, and then max_weight (all positive ints) separated by """
"""spaces."""
)
_snake_case = [int(x) for x in input("""Input profits separated by spaces: """).split()]
_snake_case = [int(x) for x in input("""Input weights separated by spaces: """).split()]
_snake_case = int(input("""Max weight allowed: """))
# Function Call
calc_profit(profit, weight, max_weight)
| 655 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case = """
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")
>>> pipe_prior.to(\"cuda\")
>>> prompt = \"red cat, 4k photo\"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")
>>> pipe.to(\"cuda\")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save(\"cat.png\")
```
"""
def _A ( __magic_name__ , __magic_name__ , __magic_name__=8 ):
lowercase__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowercase__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class lowerCAmelCase ( lowercase_ ):
def __init__( self :List[str] , _lowercase :UNetaDConditionModel , _lowercase :DDPMScheduler , _lowercase :VQModel , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=_lowercase , scheduler=_lowercase , movq=_lowercase , )
lowercase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Tuple , _lowercase :List[str] , _lowercase :Tuple , _lowercase :Optional[Any] , _lowercase :int , _lowercase :str ):
'''simple docstring'''
if latents is None:
lowercase__ = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
else:
if latents.shape != shape:
raise ValueError(f'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
lowercase__ = latents.to(_lowercase )
lowercase__ = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase ( self :int , _lowercase :int=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("Please install accelerate via `pip install accelerate`" )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
lowercase__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(">=" , "0.17.0.dev0" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )
lowercase__ = torch.device(f'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("cpu" , silence_dtype_warnings=_lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowercase__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowercase__ , lowercase__ = cpu_offload_with_hook(_lowercase , _lowercase , prev_module_hook=_lowercase )
# We'll offload the last model manually.
lowercase__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
if not hasattr(self.unet , "_hf_hook" ):
return self.device
for module in self.unet.modules():
if (
hasattr(_lowercase , "_hf_hook" )
and hasattr(module._hf_hook , "execution_device" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_lowercase )
def __call__( self :int , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :Union[torch.FloatTensor, List[torch.FloatTensor]] , _lowercase :int = 5_12 , _lowercase :int = 5_12 , _lowercase :int = 1_00 , _lowercase :float = 4.0 , _lowercase :int = 1 , _lowercase :Optional[Union[torch.Generator, List[torch.Generator]]] = None , _lowercase :Optional[torch.FloatTensor] = None , _lowercase :Optional[str] = "pil" , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = self._execution_device
lowercase__ = guidance_scale > 1.0
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
lowercase__ = image_embeds.shape[0] * num_images_per_prompt
if isinstance(_lowercase , _lowercase ):
lowercase__ = torch.cat(_lowercase , dim=0 )
if do_classifier_free_guidance:
lowercase__ = image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = negative_image_embeds.repeat_interleave(_lowercase , dim=0 )
lowercase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_lowercase )
self.scheduler.set_timesteps(_lowercase , device=_lowercase )
lowercase__ = self.scheduler.timesteps
lowercase__ = self.unet.config.in_channels
lowercase__ , lowercase__ = downscale_height_and_width(_lowercase , _lowercase , self.movq_scale_factor )
# create initial latent
lowercase__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _lowercase , _lowercase , _lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(_lowercase ) ):
# expand the latents if we are doing classifier free guidance
lowercase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowercase__ = {"image_embeds": image_embeds}
lowercase__ = self.unet(
sample=_lowercase , timestep=_lowercase , encoder_hidden_states=_lowercase , added_cond_kwargs=_lowercase , return_dict=_lowercase , )[0]
if do_classifier_free_guidance:
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
lowercase__ , lowercase__ = noise_pred.chunk(2 )
lowercase__ , lowercase__ = variance_pred.chunk(2 )
lowercase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowercase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , "variance_type" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowercase__ , lowercase__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowercase__ = self.scheduler.step(
_lowercase , _lowercase , _lowercase , generator=_lowercase , )[0]
# post-processing
lowercase__ = self.movq.decode(_lowercase , force_not_quantize=_lowercase )["sample"]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
lowercase__ = image * 0.5 + 0.5
lowercase__ = image.clamp(0 , 1 )
lowercase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ = self.numpy_to_pil(_lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_lowercase )
| 655 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _A ( __magic_name__ , __magic_name__ , __magic_name__=1e-12 ):
lowercase__ = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__magic_name__ , axis=1 ) , a_min=__magic_name__ ) ).T
lowercase__ = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__magic_name__ , axis=1 ) , a_min=__magic_name__ ) ).T
return jnp.matmul(__magic_name__ , norm_emb_a.T )
class lowerCAmelCase ( nn.Module ):
__lowerCamelCase = 42
__lowerCamelCase = jnp.floataa
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = FlaxCLIPVisionModule(self.config.vision_config )
lowercase__ = nn.Dense(self.config.projection_dim , use_bias=_lowercase , dtype=self.dtype )
lowercase__ = self.param("concept_embeds" , jax.nn.initializers.ones , (17, self.config.projection_dim) )
lowercase__ = self.param(
"special_care_embeds" , jax.nn.initializers.ones , (3, self.config.projection_dim) )
lowercase__ = self.param("concept_embeds_weights" , jax.nn.initializers.ones , (17,) )
lowercase__ = self.param("special_care_embeds_weights" , jax.nn.initializers.ones , (3,) )
def __call__( self :Any , _lowercase :int ):
'''simple docstring'''
lowercase__ = self.vision_model(_lowercase )[1]
lowercase__ = self.visual_projection(_lowercase )
lowercase__ = jax_cosine_distance(_lowercase , self.special_care_embeds )
lowercase__ = jax_cosine_distance(_lowercase , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
lowercase__ = 0.0
lowercase__ = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
lowercase__ = jnp.round(_lowercase , 3 )
lowercase__ = jnp.any(special_scores > 0 , axis=1 , keepdims=_lowercase )
# Use a lower threshold if an image has any special care concept
lowercase__ = is_special_care * 0.01
lowercase__ = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
lowercase__ = jnp.round(_lowercase , 3 )
lowercase__ = jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = CLIPConfig
__lowerCamelCase = 'clip_input'
__lowerCamelCase = FlaxStableDiffusionSafetyCheckerModule
def __init__( self :List[str] , _lowercase :CLIPConfig , _lowercase :Optional[Tuple] = None , _lowercase :int = 0 , _lowercase :jnp.dtype = jnp.floataa , _lowercase :bool = True , **_lowercase :Tuple , ):
'''simple docstring'''
if input_shape is None:
lowercase__ = (1, 2_24, 2_24, 3)
lowercase__ = self.module_class(config=_lowercase , dtype=_lowercase , **_lowercase )
super().__init__(_lowercase , _lowercase , input_shape=_lowercase , seed=_lowercase , dtype=_lowercase , _do_init=_do_init )
def UpperCAmelCase ( self :List[str] , _lowercase :jax.random.KeyArray , _lowercase :Tuple , _lowercase :FrozenDict = None ):
'''simple docstring'''
lowercase__ = jax.random.normal(_lowercase , _lowercase )
lowercase__ , lowercase__ = jax.random.split(_lowercase )
lowercase__ = {"params": params_rng, "dropout": dropout_rng}
lowercase__ = self.module.init(_lowercase , _lowercase )["params"]
return random_params
def __call__( self :Dict , _lowercase :Optional[int] , _lowercase :dict = None , ):
'''simple docstring'''
lowercase__ = jnp.transpose(_lowercase , (0, 2, 3, 1) )
return self.module.apply(
{"params": params or self.params} , jnp.array(_lowercase , dtype=jnp.floataa ) , rngs={} , )
| 655 |
import inspect
import unittest
class lowerCAmelCase ( unittest.TestCase ):
def UpperCAmelCase ( self :int ):
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
assert False
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
import diffusers
from diffusers.dependency_versions_table import deps
lowercase__ = inspect.getmembers(_lowercase , inspect.isclass )
for cls_name, cls_module in all_classes:
if "dummy_" in cls_module.__module__:
for backend in cls_module._backends:
if backend == "k_diffusion":
lowercase__ = "k-diffusion"
elif backend == "invisible_watermark":
lowercase__ = "invisible-watermark"
assert backend in deps, f'''{backend} is not in the deps table!'''
| 655 | 1 |
import numpy as np
def _A ( __magic_name__ ):
return 1 / (1 + np.exp(-vector ))
def _A ( __magic_name__ ):
return vector * sigmoid(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 655 |
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax
import jax.numpy as jnp
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
CommonSchedulerState,
FlaxKarrasDiffusionSchedulers,
FlaxSchedulerMixin,
FlaxSchedulerOutput,
add_noise_common,
get_velocity_common,
)
@flax.struct.dataclass
class lowerCAmelCase :
__lowerCamelCase = 42
# setable values
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = None
@classmethod
def UpperCAmelCase ( cls :Union[str, Any] , _lowercase :CommonSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray ):
'''simple docstring'''
return cls(common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase )
@dataclass
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = [e.name for e in FlaxKarrasDiffusionSchedulers]
__lowerCamelCase = 42
@property
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
return True
@register_to_config
def __init__( self :str , _lowercase :int = 10_00 , _lowercase :float = 0.0001 , _lowercase :float = 0.02 , _lowercase :str = "linear" , _lowercase :Optional[jnp.ndarray] = None , _lowercase :str = "fixed_small" , _lowercase :bool = True , _lowercase :str = "epsilon" , _lowercase :jnp.dtype = jnp.floataa , ):
'''simple docstring'''
lowercase__ = dtype
def UpperCAmelCase ( self :str , _lowercase :Optional[CommonSchedulerState] = None ):
'''simple docstring'''
if common is None:
lowercase__ = CommonSchedulerState.create(self )
# standard deviation of the initial noise distribution
lowercase__ = jnp.array(1.0 , dtype=self.dtype )
lowercase__ = jnp.arange(0 , self.config.num_train_timesteps ).round()[::-1]
return DDPMSchedulerState.create(
common=_lowercase , init_noise_sigma=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Optional[Any] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCAmelCase ( self :List[str] , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :Tuple = () ):
'''simple docstring'''
lowercase__ = self.config.num_train_timesteps // num_inference_steps
# creates integer timesteps by multiplying by ratio
# rounding to avoid issues when num_inference_step is power of 3
lowercase__ = (jnp.arange(0 , _lowercase ) * step_ratio).round()[::-1]
return state.replace(
num_inference_steps=_lowercase , timesteps=_lowercase , )
def UpperCAmelCase ( self :Tuple , _lowercase :DDPMSchedulerState , _lowercase :int , _lowercase :List[str]=None , _lowercase :Tuple=None ):
'''simple docstring'''
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
lowercase__ = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]
if variance_type is None:
lowercase__ = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small":
lowercase__ = jnp.clip(_lowercase , a_min=1e-20 )
# for rl-diffuser https://arxiv.org/abs/2205.09991
elif variance_type == "fixed_small_log":
lowercase__ = jnp.log(jnp.clip(_lowercase , a_min=1e-20 ) )
elif variance_type == "fixed_large":
lowercase__ = state.common.betas[t]
elif variance_type == "fixed_large_log":
# Glide max_log
lowercase__ = jnp.log(state.common.betas[t] )
elif variance_type == "learned":
return predicted_variance
elif variance_type == "learned_range":
lowercase__ = variance
lowercase__ = state.common.betas[t]
lowercase__ = (predicted_variance + 1) / 2
lowercase__ = frac * max_log + (1 - frac) * min_log
return variance
def UpperCAmelCase ( self :Optional[int] , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :int , _lowercase :jnp.ndarray , _lowercase :Optional[jax.random.KeyArray] = None , _lowercase :bool = True , ):
'''simple docstring'''
lowercase__ = timestep
if key is None:
lowercase__ = jax.random.PRNGKey(0 )
if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
lowercase__ , lowercase__ = jnp.split(_lowercase , sample.shape[1] , axis=1 )
else:
lowercase__ = None
# 1. compute alphas, betas
lowercase__ = state.common.alphas_cumprod[t]
lowercase__ = jnp.where(t > 0 , state.common.alphas_cumprod[t - 1] , jnp.array(1.0 , dtype=self.dtype ) )
lowercase__ = 1 - alpha_prod_t
lowercase__ = 1 - alpha_prod_t_prev
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
lowercase__ = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
lowercase__ = model_output
elif self.config.prediction_type == "v_prediction":
lowercase__ = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
" for the FlaxDDPMScheduler." )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
lowercase__ = jnp.clip(_lowercase , -1 , 1 )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
lowercase__ = state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
lowercase__ = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
def random_variance():
lowercase__ = jax.random.split(_lowercase , num=1 )
lowercase__ = jax.random.normal(_lowercase , shape=model_output.shape , dtype=self.dtype )
return (self._get_variance(_lowercase , _lowercase , predicted_variance=_lowercase ) ** 0.5) * noise
lowercase__ = jnp.where(t > 0 , random_variance() , jnp.zeros(model_output.shape , dtype=self.dtype ) )
lowercase__ = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample, state)
return FlaxDDPMSchedulerOutput(prev_sample=_lowercase , state=_lowercase )
def UpperCAmelCase ( self :int , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return add_noise_common(state.common , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Dict , _lowercase :DDPMSchedulerState , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , _lowercase :jnp.ndarray , ):
'''simple docstring'''
return get_velocity_common(state.common , _lowercase , _lowercase , _lowercase )
def __len__( self :List[str] ):
'''simple docstring'''
return self.config.num_train_timesteps
| 655 | 1 |
# 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.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'openai/whisper-base'
__lowerCamelCase = (
'This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the '
'transcribed text.'
)
__lowerCamelCase = 'transcriber'
__lowerCamelCase = WhisperProcessor
__lowerCamelCase = WhisperForConditionalGeneration
__lowerCamelCase = ['audio']
__lowerCamelCase = ['text']
def UpperCAmelCase ( self :List[str] , _lowercase :Any ):
'''simple docstring'''
return self.pre_processor(_lowercase , return_tensors="pt" ).input_features
def UpperCAmelCase ( self :List[Any] , _lowercase :str ):
'''simple docstring'''
return self.model.generate(inputs=_lowercase )
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] ):
'''simple docstring'''
return self.pre_processor.batch_decode(_lowercase , skip_special_tokens=_lowercase )[0]
| 655 |
import os
import time
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...models.auto.modeling_auto import MODEL_FOR_QUESTION_ANSWERING_MAPPING
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
from ..processors.squad import SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
_snake_case = logging.get_logger(__name__)
_snake_case = list(MODEL_FOR_QUESTION_ANSWERING_MAPPING.keys())
_snake_case = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class lowerCAmelCase :
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Model type selected in the list: ' + ', '.join(lowercase_ )} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'The input data dir. Should contain the .json files for the SQuAD task.'} )
__lowerCamelCase = field(
default=128 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__lowerCamelCase = field(
default=128 , metadata={'help': 'When splitting up a long document into chunks, how much stride to take between chunks.'} , )
__lowerCamelCase = field(
default=64 , metadata={
'help': (
'The maximum number of tokens for the question. Questions longer than this will '
'be truncated to this length.'
)
} , )
__lowerCamelCase = field(
default=30 , metadata={
'help': (
'The maximum length of an answer that can be generated. This is needed because the start '
'and end predictions are not conditioned on one another.'
)
} , )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
__lowerCamelCase = field(
default=lowercase_ , metadata={'help': 'If true, the SQuAD examples contain some that do not have an answer.'} )
__lowerCamelCase = field(
default=0.0 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=20 , metadata={'help': 'If null_score - best_non_null is greater than the threshold predict null.'} )
__lowerCamelCase = field(
default=0 , metadata={
'help': (
'language id of input for language-specific xlm models (see'
' tokenization_xlm.PRETRAINED_INIT_CONFIGURATION)'
)
} , )
__lowerCamelCase = field(default=1 , metadata={'help': 'multiple threads for converting example to features'} )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'train'
__lowerCamelCase = 'dev'
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
__lowerCamelCase = 42
def __init__( self :Optional[Any] , _lowercase :SquadDataTrainingArguments , _lowercase :PreTrainedTokenizer , _lowercase :Optional[int] = None , _lowercase :Union[str, Split] = Split.train , _lowercase :Optional[bool] = False , _lowercase :Optional[str] = None , _lowercase :Optional[str] = "pt" , ):
'''simple docstring'''
lowercase__ = args
lowercase__ = is_language_sensitive
lowercase__ = SquadVaProcessor() if args.version_2_with_negative else SquadVaProcessor()
if isinstance(_lowercase , _lowercase ):
try:
lowercase__ = Split[mode]
except KeyError:
raise KeyError("mode is not a valid split name" )
lowercase__ = mode
# Load data features from cache or dataset file
lowercase__ = "v2" if args.version_2_with_negative else "v1"
lowercase__ = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , f'''cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{version_tag}''' , )
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
lowercase__ = cached_features_file + ".lock"
with FileLock(_lowercase ):
if os.path.exists(_lowercase ) and not args.overwrite_cache:
lowercase__ = time.time()
lowercase__ = torch.load(_lowercase )
# Legacy cache files have only features, while new cache files
# will have dataset and examples also.
lowercase__ = self.old_features["features"]
lowercase__ = self.old_features.get("dataset" , _lowercase )
lowercase__ = self.old_features.get("examples" , _lowercase )
logger.info(
f'''Loading features from cached file {cached_features_file} [took %.3f s]''' , time.time() - start )
if self.dataset is None or self.examples is None:
logger.warning(
f'''Deleting cached file {cached_features_file} will allow dataset and examples to be cached in'''
" future run" )
else:
if mode == Split.dev:
lowercase__ = self.processor.get_dev_examples(args.data_dir )
else:
lowercase__ = self.processor.get_train_examples(args.data_dir )
lowercase__ , lowercase__ = squad_convert_examples_to_features(
examples=self.examples , tokenizer=_lowercase , max_seq_length=args.max_seq_length , doc_stride=args.doc_stride , max_query_length=args.max_query_length , is_training=mode == Split.train , threads=args.threads , return_dataset=_lowercase , )
lowercase__ = time.time()
torch.save(
{"features": self.features, "dataset": self.dataset, "examples": self.examples} , _lowercase , )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
f'''Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]''' )
def __len__( self :Dict ):
'''simple docstring'''
return len(self.features )
def __getitem__( self :Any , _lowercase :Any ):
'''simple docstring'''
lowercase__ = self.features[i]
lowercase__ = torch.tensor(feature.input_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.attention_mask , dtype=torch.long )
lowercase__ = torch.tensor(feature.token_type_ids , dtype=torch.long )
lowercase__ = torch.tensor(feature.cls_index , dtype=torch.long )
lowercase__ = torch.tensor(feature.p_mask , dtype=torch.float )
lowercase__ = torch.tensor(feature.is_impossible , dtype=torch.float )
lowercase__ = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"token_type_ids": token_type_ids,
}
if self.args.model_type in ["xlm", "roberta", "distilbert", "camembert"]:
del inputs["token_type_ids"]
if self.args.model_type in ["xlnet", "xlm"]:
inputs.update({"cls_index": cls_index, "p_mask": p_mask} )
if self.args.version_2_with_negative:
inputs.update({"is_impossible": is_impossible} )
if self.is_language_sensitive:
inputs.update({"langs": (torch.ones(input_ids.shape , dtype=torch.intaa ) * self.args.lang_id)} )
if self.mode == Split.train:
lowercase__ = torch.tensor(feature.start_position , dtype=torch.long )
lowercase__ = torch.tensor(feature.end_position , dtype=torch.long )
inputs.update({"start_positions": start_positions, "end_positions": end_positions} )
return inputs
| 655 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ , lowercase_ ):
__lowerCamelCase = 'maskformer-swin'
__lowerCamelCase = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self :Optional[Any] , _lowercase :Optional[int]=2_24 , _lowercase :Optional[Any]=4 , _lowercase :Optional[Any]=3 , _lowercase :int=96 , _lowercase :Tuple=[2, 2, 6, 2] , _lowercase :str=[3, 6, 12, 24] , _lowercase :List[Any]=7 , _lowercase :str=4.0 , _lowercase :Union[str, Any]=True , _lowercase :int=0.0 , _lowercase :Any=0.0 , _lowercase :List[str]=0.1 , _lowercase :str="gelu" , _lowercase :Union[str, Any]=False , _lowercase :List[str]=0.02 , _lowercase :Any=1e-5 , _lowercase :Optional[Any]=None , _lowercase :int=None , **_lowercase :Optional[int] , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = embed_dim
lowercase__ = depths
lowercase__ = len(_lowercase )
lowercase__ = num_heads
lowercase__ = window_size
lowercase__ = mlp_ratio
lowercase__ = qkv_bias
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = drop_path_rate
lowercase__ = hidden_act
lowercase__ = use_absolute_embeddings
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
# 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
lowercase__ = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
lowercase__ = ["stem"] + [f'''stage{idx}''' for idx in range(1 , len(_lowercase ) + 1 )]
lowercase__ , lowercase__ = get_aligned_output_features_output_indices(
out_features=_lowercase , out_indices=_lowercase , stage_names=self.stage_names )
| 655 |
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = """▁"""
_snake_case = {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}
_snake_case = {
"""sentencepiece_model_file""": """sentencepiece.bpe.model""",
"""vocab_file""": """vocab.txt""",
}
_snake_case = {
"""vocab_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt""",
},
"""sentencepiece_model_file""": {
"""ernie-m-base""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
"""ernie-m-large""": """https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model""",
},
}
_snake_case = {
"""ernie-m-base""": 514,
"""ernie-m-large""": 514,
}
_snake_case = {
"""ernie-m-base""": {"""do_lower_case""": False},
"""ernie-m-large""": {"""do_lower_case""": False},
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = ["input_ids"]
__lowerCamelCase = VOCAB_FILES_NAMES
__lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase = RESOURCE_FILES_NAMES
def __init__( self :Union[str, Any] , _lowercase :Union[str, Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=False , _lowercase :Dict="utf8" , _lowercase :Optional[Any]="[UNK]" , _lowercase :Optional[int]="[SEP]" , _lowercase :List[str]="[PAD]" , _lowercase :Dict="[CLS]" , _lowercase :Optional[Any]="[MASK]" , _lowercase :Optional[Dict[str, Any]] = None , **_lowercase :Tuple , ):
'''simple docstring'''
lowercase__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , vocab_file=_lowercase , encoding=_lowercase , sp_model_kwargs=self.sp_model_kwargs , **_lowercase , )
lowercase__ = do_lower_case
lowercase__ = sentencepiece_model_ckpt
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_lowercase )
# to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
if vocab_file is not None:
lowercase__ = self.load_vocab(filepath=_lowercase )
else:
lowercase__ = {self.sp_model.id_to_piece(_lowercase ): id for id in range(self.sp_model.get_piece_size() )}
lowercase__ = {v: k for k, v in self.vocab.items()}
def UpperCAmelCase ( self :Any , _lowercase :Dict ):
'''simple docstring'''
if text is None:
return None
lowercase__ = self.tokenize(_lowercase )
lowercase__ , lowercase__ = "", []
for i, ch in enumerate(_lowercase ):
if ch in self.SP_CHAR_MAPPING:
lowercase__ = self.SP_CHAR_MAPPING.get(_lowercase )
else:
lowercase__ = unicodedata.normalize("NFKC" , _lowercase )
if self.is_whitespace(_lowercase ):
continue
normalized_text += ch
char_mapping.extend([i] * len(_lowercase ) )
lowercase__ , lowercase__ , lowercase__ = normalized_text, [], 0
if self.do_lower_case:
lowercase__ = text.lower()
for token in split_tokens:
if token[:1] == "▁":
lowercase__ = token[1:]
lowercase__ = text[offset:].index(_lowercase ) + offset
lowercase__ = start + len(_lowercase )
token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1) )
lowercase__ = end
return token_mapping
@property
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
return len(self.vocab )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def __getstate__( self :Any ):
'''simple docstring'''
lowercase__ = self.__dict__.copy()
lowercase__ = None
return state
def __setstate__( self :Optional[Any] , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
lowercase__ = {}
lowercase__ = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.sentencepiece_model_ckpt )
def UpperCAmelCase ( self :List[str] , _lowercase :Optional[Any] ):
'''simple docstring'''
return "".join((self.SP_CHAR_MAPPING.get(_lowercase , _lowercase ) for c in text) )
def UpperCAmelCase ( self :str , _lowercase :int , _lowercase :Union[str, Any]=False , _lowercase :Optional[int]=64 , _lowercase :Any=0.1 ):
'''simple docstring'''
if self.sp_model_kwargs.get("enable_sampling" ) is True:
lowercase__ = True
if self.sp_model_kwargs.get("alpha" ) is not None:
lowercase__ = self.sp_model_kwargs.get("alpha" )
if self.sp_model_kwargs.get("nbest_size" ) is not None:
lowercase__ = self.sp_model_kwargs.get("nbest_size" )
if not enable_sampling:
lowercase__ = self.sp_model.EncodeAsPieces(_lowercase )
else:
lowercase__ = self.sp_model.SampleEncodeAsPieces(_lowercase , _lowercase , _lowercase )
lowercase__ = []
for pi, piece in enumerate(_lowercase ):
if piece == SPIECE_UNDERLINE:
if not pieces[pi + 1].startswith(_lowercase ) and pi != 0:
new_pieces.append(_lowercase )
continue
else:
continue
lowercase__ = 0
for i, chunk in enumerate(_lowercase ):
if chunk == SPIECE_UNDERLINE:
continue
if self.is_ch_char(_lowercase ) or self.is_punct(_lowercase ):
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
new_pieces.append(_lowercase )
lowercase__ = i + 1
elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
new_pieces.append(piece[lst_i:i] )
lowercase__ = i
if len(_lowercase ) > lst_i:
new_pieces.append(piece[lst_i:] )
return new_pieces
def UpperCAmelCase ( self :Tuple , _lowercase :Tuple ):
'''simple docstring'''
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Any , _lowercase :str ):
'''simple docstring'''
lowercase__ = self.convert_ids_to_tokens(_lowercase )
lowercase__ = "".join(_lowercase ).replace(_lowercase , " " ).strip()
return out_string
def UpperCAmelCase ( self :Union[str, Any] , _lowercase :Optional[int] ):
'''simple docstring'''
return self.vocab.get(_lowercase , self.vocab.get(self.unk_token ) )
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
return self.reverse_vocab.get(_lowercase , self.unk_token )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :Tuple=None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowercase__ = [self.cls_token_id]
lowercase__ = [self.sep_token_id]
return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep
def UpperCAmelCase ( self :Dict , _lowercase :int , _lowercase :Union[str, Any]=None ):
'''simple docstring'''
if offset_mapping_a is None:
return [(0, 0)] + offset_mapping_a + [(0, 0)]
return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]
def UpperCAmelCase ( self :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Dict=None , _lowercase :Optional[Any]=False ):
'''simple docstring'''
if already_has_special_tokens:
if token_ids_a is not None:
raise ValueError(
"You should not supply a second sequence if the provided sequence of "
"ids is already formatted with special tokens for the model." )
return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]
if token_ids_a is not None:
return [1] + ([0] * len(_lowercase )) + [1, 1] + ([0] * len(_lowercase )) + [1]
return [1] + ([0] * len(_lowercase )) + [1]
def UpperCAmelCase ( self :int , _lowercase :List[int] , _lowercase :Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
# [CLS] X [SEP]
return (len(_lowercase ) + 2) * [0]
# [CLS] A [SEP] [SEP] B [SEP]
return [0] * (len(_lowercase ) + 1) + [1] * (len(_lowercase ) + 3)
def UpperCAmelCase ( self :str , _lowercase :Optional[int] ):
'''simple docstring'''
if "\u4e00" <= char <= "\u9fff":
return True
return False
def UpperCAmelCase ( self :Tuple , _lowercase :List[str] ):
'''simple docstring'''
if ("a" <= char <= "z") or ("A" <= char <= "Z"):
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Dict ):
'''simple docstring'''
if char in ",;:.?!~，；：。？！《》【】":
return True
return False
def UpperCAmelCase ( self :List[str] , _lowercase :List[str] ):
'''simple docstring'''
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
if len(_lowercase ) == 1:
lowercase__ = unicodedata.category(_lowercase )
if cat == "Zs":
return True
return False
def UpperCAmelCase ( self :int , _lowercase :Optional[int] ):
'''simple docstring'''
lowercase__ = {}
with io.open(_lowercase , "r" , encoding="utf-8" ) as f:
for index, line in enumerate(_lowercase ):
lowercase__ = line.rstrip("\n" )
lowercase__ = int(_lowercase )
return token_to_idx
def UpperCAmelCase ( self :List[str] , _lowercase :str , _lowercase :Optional[str] = None ):
'''simple docstring'''
lowercase__ = 0
if os.path.isdir(_lowercase ):
lowercase__ = os.path.join(
_lowercase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
else:
lowercase__ = (filename_prefix + "-" if filename_prefix else "") + save_directory
with open(_lowercase , "w" , encoding="utf-8" ) as writer:
for token, token_index in sorted(self.vocab.items() , key=lambda _lowercase : kv[1] ):
if index != token_index:
logger.warning(
f'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
" Please check that the vocabulary is not corrupted!" )
lowercase__ = token_index
writer.write(token + "\n" )
index += 1
lowercase__ = os.path.join(_lowercase , "sentencepiece.bpe.model" )
with open(_lowercase , "wb" ) as fi:
lowercase__ = self.sp_model.serialized_model_proto()
fi.write(_lowercase )
return (vocab_file,)
| 655 | 1 |
import json
import sys
def _A ( __magic_name__ , __magic_name__ ):
with open(__magic_name__ , encoding="utf-8" ) as f:
lowercase__ = json.load(__magic_name__ )
lowercase__ = ["<details>", "<summary>Show updated benchmarks!</summary>", " "]
for benchmark_name in sorted(__magic_name__ ):
lowercase__ = results[benchmark_name]
lowercase__ = benchmark_name.split("/" )[-1]
output_md.append(f'''### Benchmark: {benchmark_file_name}''' )
lowercase__ = "| metric |"
lowercase__ = "|--------|"
lowercase__ = "| new / old (diff) |"
for metric_name in sorted(__magic_name__ ):
lowercase__ = benchmark_res[metric_name]
lowercase__ = metric_vals["new"]
lowercase__ = metric_vals.get("old" , __magic_name__ )
lowercase__ = metric_vals.get("diff" , __magic_name__ )
lowercase__ = f''' {new_val:f}''' if isinstance(__magic_name__ , (int, float) ) else "None"
if old_val is not None:
val_str += f''' / {old_val:f}''' if isinstance(__magic_name__ , (int, float) ) else "None"
if dif_val is not None:
val_str += f''' ({dif_val:f})''' if isinstance(__magic_name__ , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append("</details>" )
with open(__magic_name__ , "w" , encoding="utf-8" ) as f:
f.writelines("\n".join(__magic_name__ ) )
if __name__ == "__main__":
_snake_case = sys.argv[1]
_snake_case = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 655 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ = pre_numerator
lowercase__ = 2 * i // 3 if i % 3 == 0 else 1
lowercase__ = cur_numerator
lowercase__ = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_snake_case = {"""configuration_encoder_decoder""": ["""EncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""EncoderDecoderModel"""]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""TFEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = ["""FlaxEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_encoder_decoder import EncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encoder_decoder import EncoderDecoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_encoder_decoder import TFEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
_snake_case = logging.get_logger(__name__)
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'AutoTokenizer'
__lowerCamelCase = ['tokenizer']
__lowerCamelCase = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self :Dict , _lowercase :List[str] , _lowercase :List[Any]=None ):
'''simple docstring'''
super().__init__(_lowercase )
lowercase__ = speaker_embeddings
@classmethod
def UpperCAmelCase ( cls :Any , _lowercase :int , _lowercase :str="speaker_embeddings_path.json" , **_lowercase :List[str] ):
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
lowercase__ = get_file_from_repo(
_lowercase , _lowercase , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if speaker_embeddings_path is None:
logger.warning(
f'''`{os.path.join(_lowercase , _lowercase )}` does not exists
, no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json
dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`.''' )
lowercase__ = None
else:
with open(_lowercase ) as speaker_embeddings_json:
lowercase__ = json.load(_lowercase )
else:
lowercase__ = None
lowercase__ = AutoTokenizer.from_pretrained(_lowercase , **_lowercase )
return cls(tokenizer=_lowercase , speaker_embeddings=_lowercase )
def UpperCAmelCase ( self :Any , _lowercase :Any , _lowercase :List[str]="speaker_embeddings_path.json" , _lowercase :Any="speaker_embeddings" , _lowercase :bool = False , **_lowercase :Any , ):
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_lowercase , _lowercase , "v2" ) , exist_ok=_lowercase )
lowercase__ = {}
lowercase__ = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
lowercase__ = self._load_voice_preset(_lowercase )
lowercase__ = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["repo_or_path"] , _lowercase , f'''{prompt_key}_{key}''' ) , voice_preset[key] , allow_pickle=_lowercase , )
lowercase__ = os.path.join(_lowercase , f'''{prompt_key}_{key}.npy''' )
lowercase__ = tmp_dict
with open(os.path.join(_lowercase , _lowercase ) , "w" ) as fp:
json.dump(_lowercase , _lowercase )
super().save_pretrained(_lowercase , _lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :str = None , **_lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = self.speaker_embeddings[voice_preset]
lowercase__ = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
f'''Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}].''' )
lowercase__ = get_file_from_repo(
self.speaker_embeddings.get("repo_or_path" , "/" ) , voice_preset_paths[key] , subfolder=kwargs.pop("subfolder" , _lowercase ) , cache_dir=kwargs.pop("cache_dir" , _lowercase ) , force_download=kwargs.pop("force_download" , _lowercase ) , proxies=kwargs.pop("proxies" , _lowercase ) , resume_download=kwargs.pop("resume_download" , _lowercase ) , local_files_only=kwargs.pop("local_files_only" , _lowercase ) , use_auth_token=kwargs.pop("use_auth_token" , _lowercase ) , revision=kwargs.pop("revision" , _lowercase ) , )
if path is None:
raise ValueError(
f'''`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists
, no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}
embeddings.''' )
lowercase__ = np.load(_lowercase )
return voice_preset_dict
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[dict] = None ):
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(f'''Voice preset unrecognized, missing {key} as a key.''' )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(f'''{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray.''' )
def __call__( self :Optional[Any] , _lowercase :Optional[Any]=None , _lowercase :List[str]=None , _lowercase :List[str]="pt" , _lowercase :List[Any]=2_56 , _lowercase :List[str]=False , _lowercase :Union[str, Any]=True , _lowercase :Dict=False , **_lowercase :Tuple , ):
'''simple docstring'''
if voice_preset is not None and not isinstance(_lowercase , _lowercase ):
if (
isinstance(_lowercase , _lowercase )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
lowercase__ = self._load_voice_preset(_lowercase )
else:
if isinstance(_lowercase , _lowercase ) and not voice_preset.endswith(".npz" ):
lowercase__ = voice_preset + ".npz"
lowercase__ = np.load(_lowercase )
if voice_preset is not None:
self._validate_voice_preset_dict(_lowercase , **_lowercase )
lowercase__ = BatchFeature(data=_lowercase , tensor_type=_lowercase )
lowercase__ = self.tokenizer(
_lowercase , return_tensors=_lowercase , padding="max_length" , max_length=_lowercase , return_attention_mask=_lowercase , return_token_type_ids=_lowercase , add_special_tokens=_lowercase , **_lowercase , )
if voice_preset is not None:
lowercase__ = voice_preset
return encoded_text
| 655 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
def get_masked_lm_array(__magic_name__ ):
lowercase__ = f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
lowercase__ = tf.train.load_variable(__magic_name__ , __magic_name__ )
if "kernel" in name:
lowercase__ = array.transpose()
return torch.from_numpy(__magic_name__ )
def get_encoder_array(__magic_name__ ):
lowercase__ = f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
lowercase__ = tf.train.load_variable(__magic_name__ , __magic_name__ )
if "kernel" in name:
lowercase__ = array.transpose()
return torch.from_numpy(__magic_name__ )
def get_encoder_layer_array(__magic_name__ , __magic_name__ ):
lowercase__ = f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
lowercase__ = tf.train.load_variable(__magic_name__ , __magic_name__ )
if "kernel" in name:
lowercase__ = array.transpose()
return torch.from_numpy(__magic_name__ )
def get_encoder_attention_layer_array(__magic_name__ , __magic_name__ , __magic_name__ ):
lowercase__ = f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
lowercase__ = tf.train.load_variable(__magic_name__ , __magic_name__ )
lowercase__ = array.reshape(__magic_name__ )
if "kernel" in name:
lowercase__ = array.transpose()
return torch.from_numpy(__magic_name__ )
print(f'''Loading model based on config from {config_path}...''' )
lowercase__ = BertConfig.from_json_file(__magic_name__ )
lowercase__ = BertForMaskedLM(__magic_name__ )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
lowercase__ = model.bert.encoder.layer[layer_index]
# Self-attention
lowercase__ = layer.attention.self
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_query_dense/kernel" , self_attn.query.weight.data.shape )
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_query_dense/bias" , self_attn.query.bias.data.shape )
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_key_dense/kernel" , self_attn.key.weight.data.shape )
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_key_dense/bias" , self_attn.key.bias.data.shape )
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_value_dense/kernel" , self_attn.value.weight.data.shape )
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_value_dense/bias" , self_attn.value.bias.data.shape )
# Self-attention Output
lowercase__ = layer.attention.output
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_output_dense/kernel" , self_output.dense.weight.data.shape )
lowercase__ = get_encoder_attention_layer_array(
__magic_name__ , "_output_dense/bias" , self_output.dense.bias.data.shape )
lowercase__ = get_encoder_layer_array(__magic_name__ , "_attention_layer_norm/gamma" )
lowercase__ = get_encoder_layer_array(__magic_name__ , "_attention_layer_norm/beta" )
# Intermediate
lowercase__ = layer.intermediate
lowercase__ = get_encoder_layer_array(__magic_name__ , "_intermediate_dense/kernel" )
lowercase__ = get_encoder_layer_array(__magic_name__ , "_intermediate_dense/bias" )
# Output
lowercase__ = layer.output
lowercase__ = get_encoder_layer_array(__magic_name__ , "_output_dense/kernel" )
lowercase__ = get_encoder_layer_array(__magic_name__ , "_output_dense/bias" )
lowercase__ = get_encoder_layer_array(__magic_name__ , "_output_layer_norm/gamma" )
lowercase__ = get_encoder_layer_array(__magic_name__ , "_output_layer_norm/beta" )
# Embeddings
lowercase__ = get_encoder_array("_position_embedding_layer/embeddings" )
lowercase__ = get_encoder_array("_type_embedding_layer/embeddings" )
lowercase__ = get_encoder_array("_embedding_norm_layer/gamma" )
lowercase__ = get_encoder_array("_embedding_norm_layer/beta" )
# LM Head
lowercase__ = model.cls.predictions.transform
lowercase__ = get_masked_lm_array("dense/kernel" )
lowercase__ = get_masked_lm_array("dense/bias" )
lowercase__ = get_masked_lm_array("layer_norm/gamma" )
lowercase__ = get_masked_lm_array("layer_norm/beta" )
lowercase__ = get_masked_lm_array("embedding_table" )
# Pooling
lowercase__ = BertPooler(config=__magic_name__ )
lowercase__ = get_encoder_array("_pooler_layer/kernel" )
lowercase__ = get_encoder_array("_pooler_layer/bias" )
# Export final model
model.save_pretrained(__magic_name__ )
# Integration test - should load without any errors ;)
lowercase__ = BertForMaskedLM.from_pretrained(__magic_name__ )
print(new_model.eval() )
print("Model conversion was done sucessfully!" )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"""--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
)
parser.add_argument(
"""--bert_config_file""",
type=str,
required=True,
help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""",
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
_snake_case = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 655 |
import math
import random
def _A ( __magic_name__ , __magic_name__ = False ):
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
_snake_case = 0.02
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = float(2 * (random.randint(1 , 100 )) - 1 )
for _ in range(__magic_name__ ):
# Forward propagation
lowercase__ = sigmoid_function(INITIAL_VALUE * weight )
# How much did we miss?
lowercase__ = (expected / 100) - layer_a
# Error delta
lowercase__ = layer_1_error * sigmoid_function(__magic_name__ , __magic_name__ )
# Update weight
weight += INITIAL_VALUE * layer_1_delta
return layer_a * 100
if __name__ == "__main__":
import doctest
doctest.testmod()
_snake_case = int(input("""Expected value: """))
_snake_case = int(input("""Number of propagations: """))
print(forward_propagation(expected, number_propagations))
| 655 | 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 DetaImageProcessor
class lowerCAmelCase ( unittest.TestCase ):
def __init__( self :int , _lowercase :Union[str, Any] , _lowercase :Optional[int]=7 , _lowercase :int=3 , _lowercase :Optional[int]=30 , _lowercase :Tuple=4_00 , _lowercase :List[str]=True , _lowercase :Union[str, Any]=None , _lowercase :Dict=True , _lowercase :Union[str, Any]=[0.5, 0.5, 0.5] , _lowercase :Optional[Any]=[0.5, 0.5, 0.5] , _lowercase :int=True , _lowercase :List[Any]=1 / 2_55 , _lowercase :List[Any]=True , ):
'''simple docstring'''
lowercase__ = size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = num_channels
lowercase__ = min_resolution
lowercase__ = max_resolution
lowercase__ = do_resize
lowercase__ = size
lowercase__ = do_normalize
lowercase__ = image_mean
lowercase__ = image_std
lowercase__ = do_rescale
lowercase__ = rescale_factor
lowercase__ = do_pad
def UpperCAmelCase ( self :int ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def UpperCAmelCase ( self :Optional[int] , _lowercase :Optional[Any] , _lowercase :Any=False ):
'''simple docstring'''
if not batched:
lowercase__ = image_inputs[0]
if isinstance(_lowercase , Image.Image ):
lowercase__ , lowercase__ = image.size
else:
lowercase__ , lowercase__ = image.shape[1], image.shape[2]
if w < h:
lowercase__ = int(self.size["shortest_edge"] * h / w )
lowercase__ = self.size["shortest_edge"]
elif w > h:
lowercase__ = self.size["shortest_edge"]
lowercase__ = int(self.size["shortest_edge"] * w / h )
else:
lowercase__ = self.size["shortest_edge"]
lowercase__ = self.size["shortest_edge"]
else:
lowercase__ = []
for image in image_inputs:
lowercase__ , lowercase__ = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase__ = max(_lowercase , key=lambda _lowercase : item[0] )[0]
lowercase__ = max(_lowercase , key=lambda _lowercase : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class lowerCAmelCase ( lowercase_ , unittest.TestCase ):
__lowerCamelCase = DetaImageProcessor if is_vision_available() else None
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = DetaImageProcessingTester(self )
@property
def UpperCAmelCase ( self :str ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowercase , "image_mean" ) )
self.assertTrue(hasattr(_lowercase , "image_std" ) )
self.assertTrue(hasattr(_lowercase , "do_normalize" ) )
self.assertTrue(hasattr(_lowercase , "do_resize" ) )
self.assertTrue(hasattr(_lowercase , "do_rescale" ) )
self.assertTrue(hasattr(_lowercase , "do_pad" ) )
self.assertTrue(hasattr(_lowercase , "size" ) )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 13_33} )
self.assertEqual(image_processor.do_pad , _lowercase )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , Image.Image )
# Test not batched input
lowercase__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowercase__ , lowercase__ = self.image_processor_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ , lowercase__ = self.image_processor_tester.get_expected_values(_lowercase , batched=_lowercase )
lowercase__ = image_processing(_lowercase , 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 UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , numpify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , np.ndarray )
# Test not batched input
lowercase__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowercase__ , lowercase__ = self.image_processor_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ = image_processing(_lowercase , return_tensors="pt" ).pixel_values
lowercase__ , lowercase__ = self.image_processor_tester.get_expected_values(_lowercase , batched=_lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__ = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowercase , torchify=_lowercase )
for image in image_inputs:
self.assertIsInstance(_lowercase , torch.Tensor )
# Test not batched input
lowercase__ = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
lowercase__ , lowercase__ = self.image_processor_tester.get_expected_values(_lowercase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ = image_processing(_lowercase , return_tensors="pt" ).pixel_values
lowercase__ , lowercase__ = self.image_processor_tester.get_expected_values(_lowercase , batched=_lowercase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
lowercase__ = json.loads(f.read() )
lowercase__ = {"image_id": 3_97_69, "annotations": target}
# encode them
lowercase__ = DetaImageProcessor()
lowercase__ = image_processing(images=_lowercase , annotations=_lowercase , return_tensors="pt" )
# verify pixel values
lowercase__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , _lowercase )
lowercase__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _lowercase , atol=1e-4 ) )
# verify area
lowercase__ = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _lowercase ) )
# verify boxes
lowercase__ = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , _lowercase )
lowercase__ = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _lowercase , atol=1e-3 ) )
# verify image_id
lowercase__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _lowercase ) )
# verify is_crowd
lowercase__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _lowercase ) )
# verify class_labels
lowercase__ = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _lowercase ) )
# verify orig_size
lowercase__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _lowercase ) )
# verify size
lowercase__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _lowercase ) )
@slow
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
lowercase__ = json.loads(f.read() )
lowercase__ = {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}
lowercase__ = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
lowercase__ = DetaImageProcessor(format="coco_panoptic" )
lowercase__ = image_processing(images=_lowercase , annotations=_lowercase , masks_path=_lowercase , return_tensors="pt" )
# verify pixel values
lowercase__ = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , _lowercase )
lowercase__ = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , _lowercase , atol=1e-4 ) )
# verify area
lowercase__ = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , _lowercase ) )
# verify boxes
lowercase__ = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , _lowercase )
lowercase__ = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , _lowercase , atol=1e-3 ) )
# verify image_id
lowercase__ = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , _lowercase ) )
# verify is_crowd
lowercase__ = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , _lowercase ) )
# verify class_labels
lowercase__ = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , _lowercase ) )
# verify masks
lowercase__ = 82_28_73
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , _lowercase )
# verify orig_size
lowercase__ = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , _lowercase ) )
# verify size
lowercase__ = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , _lowercase ) )
| 655 |
from ....configuration_utils import PretrainedConfig
from ....utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
"""Visual-Attention-Network/van-base""": (
"""https://huggingface.co/Visual-Attention-Network/van-base/blob/main/config.json"""
),
}
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = 'van'
def __init__( self :Optional[Any] , _lowercase :Dict=2_24 , _lowercase :Union[str, Any]=3 , _lowercase :List[Any]=[7, 3, 3, 3] , _lowercase :Any=[4, 2, 2, 2] , _lowercase :Union[str, Any]=[64, 1_28, 3_20, 5_12] , _lowercase :List[Any]=[3, 3, 12, 3] , _lowercase :Dict=[8, 8, 4, 4] , _lowercase :int="gelu" , _lowercase :List[Any]=0.02 , _lowercase :List[Any]=1e-6 , _lowercase :Any=1e-2 , _lowercase :int=0.0 , _lowercase :int=0.0 , **_lowercase :Dict , ):
'''simple docstring'''
super().__init__(**_lowercase )
lowercase__ = image_size
lowercase__ = num_channels
lowercase__ = patch_sizes
lowercase__ = strides
lowercase__ = hidden_sizes
lowercase__ = depths
lowercase__ = mlp_ratios
lowercase__ = hidden_act
lowercase__ = initializer_range
lowercase__ = layer_norm_eps
lowercase__ = layer_scale_init_value
lowercase__ = drop_path_rate
lowercase__ = dropout_rate
| 655 | 1 |
def _A ( __magic_name__ ):
lowercase__ = 0
while num > 0:
digit_sum += num % 10
num //= 10
return digit_sum
def _A ( __magic_name__ = 100 ):
lowercase__ = 1
lowercase__ = 2
for i in range(2 , max_n + 1 ):
lowercase__ = pre_numerator
lowercase__ = 2 * i // 3 if i % 3 == 0 else 1
lowercase__ = cur_numerator
lowercase__ = e_cont * pre_numerator + temp
return sum_digits(__magic_name__ )
if __name__ == "__main__":
print(F"""{solution() = }""")
| 655 |
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
class lowerCAmelCase ( enum.Enum ):
__lowerCamelCase = 0
__lowerCamelCase = 1
__lowerCamelCase = 2
@add_end_docstrings(lowercase_ )
class lowerCAmelCase ( lowercase_ ):
__lowerCamelCase = '\n In 1991, the remains of Russian Tsar Nicholas II and his family (except for Alexei and Maria) are discovered. The\n voice of Nicholas\'s young son, Tsarevich Alexei Nikolaevich, narrates the remainder of the story. 1883 Western\n Siberia, a young Grigori Rasputin is asked by his father and a group of men to perform magic. Rasputin has a vision\n and denounces one of the men as a horse thief. Although his father initially slaps him for making such an\n accusation, Rasputin watches as the man is chased outside and beaten. Twenty years later, Rasputin sees a vision of\n the Virgin Mary, prompting him to become a priest. Rasputin quickly becomes famous, with people, even a bishop,\n begging for his blessing. <eod> </s> <eos>\n '
def __init__( self :Any , *_lowercase :Optional[Any] , **_lowercase :Union[str, Any] ):
'''simple docstring'''
super().__init__(*_lowercase , **_lowercase )
self.check_model_type(
TF_MODEL_FOR_CAUSAL_LM_MAPPING if self.framework == "tf" else MODEL_FOR_CAUSAL_LM_MAPPING )
if "prefix" not in self._preprocess_params:
# This is very specific. The logic is quite complex and needs to be done
# as a "default".
# It also defines both some preprocess_kwargs and generate_kwargs
# which is why we cannot put them in their respective methods.
lowercase__ = None
if self.model.config.prefix is not None:
lowercase__ = self.model.config.prefix
if prefix is None and self.model.__class__.__name__ in [
"XLNetLMHeadModel",
"TransfoXLLMHeadModel",
"TFXLNetLMHeadModel",
"TFTransfoXLLMHeadModel",
]:
# For XLNet and TransformerXL we add an article to the prompt to give more state to the model.
lowercase__ = self.XL_PREFIX
if prefix is not None:
# Recalculate some generate_kwargs linked to prefix.
lowercase__ , lowercase__ , lowercase__ = self._sanitize_parameters(prefix=_lowercase , **self._forward_params )
lowercase__ = {**self._preprocess_params, **preprocess_params}
lowercase__ = {**self._forward_params, **forward_params}
def UpperCAmelCase ( self :Tuple , _lowercase :Optional[Any]=None , _lowercase :List[Any]=None , _lowercase :List[str]=None , _lowercase :Optional[Any]=None , _lowercase :Optional[int]=None , _lowercase :Any=None , _lowercase :Any=None , _lowercase :Dict=None , **_lowercase :Union[str, Any] , ):
'''simple docstring'''
lowercase__ = {}
if prefix is not None:
lowercase__ = prefix
if prefix:
lowercase__ = self.tokenizer(
_lowercase , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prefix_inputs["input_ids"].shape[-1]
if handle_long_generation is not None:
if handle_long_generation not in {"hole"}:
raise ValueError(
f'''{handle_long_generation} is not a valid value for `handle_long_generation` parameter expected'''
" [None, 'hole']" )
lowercase__ = handle_long_generation
preprocess_params.update(_lowercase )
lowercase__ = generate_kwargs
lowercase__ = {}
if return_full_text is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_full_text`" )
if return_tensors is not None:
raise ValueError("`return_full_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.FULL_TEXT if return_full_text else ReturnType.NEW_TEXT
if return_tensors is not None and return_type is None:
if return_text is not None:
raise ValueError("`return_text` is mutually exclusive with `return_tensors`" )
lowercase__ = ReturnType.TENSORS
if return_type is not None:
lowercase__ = return_type
if clean_up_tokenization_spaces is not None:
lowercase__ = clean_up_tokenization_spaces
if stop_sequence is not None:
lowercase__ = self.tokenizer.encode(_lowercase , add_special_tokens=_lowercase )
if len(_lowercase ) > 1:
warnings.warn(
"Stopping on a multiple token sequence is not yet supported on transformers. The first token of"
" the stop sequence will be used as the stop sequence string in the interim." )
lowercase__ = stop_sequence_ids[0]
return preprocess_params, forward_params, postprocess_params
def UpperCAmelCase ( self :int , *_lowercase :Optional[int] , **_lowercase :List[str] ):
'''simple docstring'''
if self.model.__class__.__name__ in ["TransfoXLLMHeadModel"]:
kwargs.update({"add_space_before_punct_symbol": True} )
return super()._parse_and_tokenize(*_lowercase , **_lowercase )
def __call__( self :Union[str, Any] , _lowercase :Dict , **_lowercase :Tuple ):
'''simple docstring'''
return super().__call__(_lowercase , **_lowercase )
def UpperCAmelCase ( self :Optional[int] , _lowercase :Tuple , _lowercase :Optional[int]="" , _lowercase :Tuple=None , **_lowercase :List[str] ):
'''simple docstring'''
lowercase__ = self.tokenizer(
prefix + prompt_text , padding=_lowercase , add_special_tokens=_lowercase , return_tensors=self.framework )
lowercase__ = prompt_text
if handle_long_generation == "hole":
lowercase__ = inputs["input_ids"].shape[-1]
if "max_new_tokens" in generate_kwargs:
lowercase__ = generate_kwargs["max_new_tokens"]
else:
lowercase__ = generate_kwargs.get("max_length" , self.model.config.max_length ) - cur_len
if new_tokens < 0:
raise ValueError("We cannot infer how many new tokens are expected" )
if cur_len + new_tokens > self.tokenizer.model_max_length:
lowercase__ = self.tokenizer.model_max_length - new_tokens
if keep_length <= 0:
raise ValueError(
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
" models max length" )
lowercase__ = inputs["input_ids"][:, -keep_length:]
if "attention_mask" in inputs:
lowercase__ = inputs["attention_mask"][:, -keep_length:]
return inputs
def UpperCAmelCase ( self :str , _lowercase :int , **_lowercase :str ):
'''simple docstring'''
lowercase__ = model_inputs["input_ids"]
lowercase__ = model_inputs.get("attention_mask" , _lowercase )
# Allow empty prompts
if input_ids.shape[1] == 0:
lowercase__ = None
lowercase__ = None
lowercase__ = 1
else:
lowercase__ = input_ids.shape[0]
lowercase__ = model_inputs.pop("prompt_text" )
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
lowercase__ = generate_kwargs.pop("prefix_length" , 0 )
if prefix_length > 0:
lowercase__ = "max_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].max_new_tokens is not None
)
if not has_max_new_tokens:
lowercase__ = generate_kwargs.get("max_length" ) or self.model.config.max_length
generate_kwargs["max_length"] += prefix_length
lowercase__ = "min_new_tokens" in generate_kwargs or (
"generation_config" in generate_kwargs
and generate_kwargs["generation_config"].min_new_tokens is not None
)
if not has_min_new_tokens and "min_length" in generate_kwargs:
generate_kwargs["min_length"] += prefix_length
# BS x SL
lowercase__ = self.model.generate(input_ids=_lowercase , attention_mask=_lowercase , **_lowercase )
lowercase__ = generated_sequence.shape[0]
if self.framework == "pt":
lowercase__ = generated_sequence.reshape(_lowercase , out_b // in_b , *generated_sequence.shape[1:] )
elif self.framework == "tf":
lowercase__ = tf.reshape(_lowercase , (in_b, out_b // in_b, *generated_sequence.shape[1:]) )
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text}
def UpperCAmelCase ( self :Any , _lowercase :Tuple , _lowercase :str=ReturnType.FULL_TEXT , _lowercase :Dict=True ):
'''simple docstring'''
lowercase__ = model_outputs["generated_sequence"][0]
lowercase__ = model_outputs["input_ids"]
lowercase__ = model_outputs["prompt_text"]
lowercase__ = generated_sequence.numpy().tolist()
lowercase__ = []
for sequence in generated_sequence:
if return_type == ReturnType.TENSORS:
lowercase__ = {"generated_token_ids": sequence}
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
# Decode text
lowercase__ = self.tokenizer.decode(
_lowercase , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , )
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
if input_ids is None:
lowercase__ = 0
else:
lowercase__ = len(
self.tokenizer.decode(
input_ids[0] , skip_special_tokens=_lowercase , clean_up_tokenization_spaces=_lowercase , ) )
if return_type == ReturnType.FULL_TEXT:
lowercase__ = prompt_text + text[prompt_length:]
else:
lowercase__ = text[prompt_length:]
lowercase__ = {"generated_text": all_text}
records.append(_lowercase )
return records
| 655 | 1 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_snake_case = {"""tokenization_byt5""": ["""ByT5Tokenizer"""]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 655 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
_snake_case = collections.namedtuple("""_Datasets""", ["""train""", """validation""", """test"""])
# CVDF mirror of http://yann.lecun.com/exdb/mnist/
_snake_case = """https://storage.googleapis.com/cvdf-datasets/mnist/"""
def _A ( __magic_name__ ):
lowercase__ = numpy.dtype(numpy.uintaa ).newbyteorder(">" )
return numpy.frombuffer(bytestream.read(4 ) , dtype=__magic_name__ )[0]
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2051:
raise ValueError(
"Invalid magic number %d in MNIST image file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(rows * cols * num_images )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
lowercase__ = data.reshape(__magic_name__ , __magic_name__ , __magic_name__ , 1 )
return data
@deprecated(__magic_name__ , "Please use tf.one_hot on tensors." )
def _A ( __magic_name__ , __magic_name__ ):
lowercase__ = labels_dense.shape[0]
lowercase__ = numpy.arange(__magic_name__ ) * num_classes
lowercase__ = numpy.zeros((num_labels, num_classes) )
lowercase__ = 1
return labels_one_hot
@deprecated(__magic_name__ , "Please use tf.data to implement this functionality." )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=10 ):
print("Extracting" , f.name )
with gzip.GzipFile(fileobj=__magic_name__ ) as bytestream:
lowercase__ = _readaa(__magic_name__ )
if magic != 2049:
raise ValueError(
"Invalid magic number %d in MNIST label file: %s" % (magic, f.name) )
lowercase__ = _readaa(__magic_name__ )
lowercase__ = bytestream.read(__magic_name__ )
lowercase__ = numpy.frombuffer(__magic_name__ , dtype=numpy.uinta )
if one_hot:
return _dense_to_one_hot(__magic_name__ , __magic_name__ )
return labels
class lowerCAmelCase :
@deprecated(
_lowercase , "Please use alternatives such as official/mnist/_DataSet.py"
" from tensorflow/models." , )
def __init__( self :List[str] , _lowercase :Optional[Any] , _lowercase :Union[str, Any] , _lowercase :Tuple=False , _lowercase :str=False , _lowercase :Dict=dtypes.floataa , _lowercase :Optional[Any]=True , _lowercase :Any=None , ):
'''simple docstring'''
lowercase__ , lowercase__ = random_seed.get_seed(_lowercase )
# If op level seed is not set, use whatever graph level seed is returned
numpy.random.seed(seeda if seed is None else seeda )
lowercase__ = dtypes.as_dtype(_lowercase ).base_dtype
if dtype not in (dtypes.uinta, dtypes.floataa):
raise TypeError("Invalid image dtype %r, expected uint8 or float32" % dtype )
if fake_data:
lowercase__ = 1_00_00
lowercase__ = one_hot
else:
assert (
images.shape[0] == labels.shape[0]
), f'''images.shape: {images.shape} labels.shape: {labels.shape}'''
lowercase__ = images.shape[0]
# Convert shape from [num examples, rows, columns, depth]
# to [num examples, rows*columns] (assuming depth == 1)
if reshape:
assert images.shape[3] == 1
lowercase__ = images.reshape(
images.shape[0] , images.shape[1] * images.shape[2] )
if dtype == dtypes.floataa:
# Convert from [0, 255] -> [0.0, 1.0].
lowercase__ = images.astype(numpy.floataa )
lowercase__ = numpy.multiply(_lowercase , 1.0 / 255.0 )
lowercase__ = images
lowercase__ = labels
lowercase__ = 0
lowercase__ = 0
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._images
@property
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
return self._labels
@property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return self._num_examples
@property
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
return self._epochs_completed
def UpperCAmelCase ( self :str , _lowercase :Union[str, Any] , _lowercase :Any=False , _lowercase :Union[str, Any]=True ):
'''simple docstring'''
if fake_data:
lowercase__ = [1] * 7_84
lowercase__ = [1] + [0] * 9 if self.one_hot else 0
return (
[fake_image for _ in range(_lowercase )],
[fake_label for _ in range(_lowercase )],
)
lowercase__ = self._index_in_epoch
# Shuffle for the first epoch
if self._epochs_completed == 0 and start == 0 and shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perma]
lowercase__ = self.labels[perma]
# Go to the next epoch
if start + batch_size > self._num_examples:
# Finished epoch
self._epochs_completed += 1
# Get the rest examples in this epoch
lowercase__ = self._num_examples - start
lowercase__ = self._images[start : self._num_examples]
lowercase__ = self._labels[start : self._num_examples]
# Shuffle the data
if shuffle:
lowercase__ = numpy.arange(self._num_examples )
numpy.random.shuffle(_lowercase )
lowercase__ = self.images[perm]
lowercase__ = self.labels[perm]
# Start next epoch
lowercase__ = 0
lowercase__ = batch_size - rest_num_examples
lowercase__ = self._index_in_epoch
lowercase__ = self._images[start:end]
lowercase__ = self._labels[start:end]
return (
numpy.concatenate((images_rest_part, images_new_part) , axis=0 ),
numpy.concatenate((labels_rest_part, labels_new_part) , axis=0 ),
)
else:
self._index_in_epoch += batch_size
lowercase__ = self._index_in_epoch
return self._images[start:end], self._labels[start:end]
@deprecated(__magic_name__ , "Please write your own downloading logic." )
def _A ( __magic_name__ , __magic_name__ , __magic_name__ ):
if not gfile.Exists(__magic_name__ ):
gfile.MakeDirs(__magic_name__ )
lowercase__ = os.path.join(__magic_name__ , __magic_name__ )
if not gfile.Exists(__magic_name__ ):
urllib.request.urlretrieve(__magic_name__ , __magic_name__ ) # noqa: S310
with gfile.GFile(__magic_name__ ) as f:
lowercase__ = f.size()
print("Successfully downloaded" , __magic_name__ , __magic_name__ , "bytes." )
return filepath
@deprecated(
__magic_name__ , "Please use alternatives such as:" " tensorflow_datasets.load('mnist')" )
def _A ( __magic_name__ , __magic_name__=False , __magic_name__=False , __magic_name__=dtypes.floataa , __magic_name__=True , __magic_name__=5000 , __magic_name__=None , __magic_name__=DEFAULT_SOURCE_URL , ):
if fake_data:
def fake():
return _DataSet(
[] , [] , fake_data=__magic_name__ , one_hot=__magic_name__ , dtype=__magic_name__ , seed=__magic_name__ )
lowercase__ = fake()
lowercase__ = fake()
lowercase__ = fake()
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
if not source_url: # empty string check
lowercase__ = DEFAULT_SOURCE_URL
lowercase__ = "train-images-idx3-ubyte.gz"
lowercase__ = "train-labels-idx1-ubyte.gz"
lowercase__ = "t10k-images-idx3-ubyte.gz"
lowercase__ = "t10k-labels-idx1-ubyte.gz"
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + train_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_images_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_images(__magic_name__ )
lowercase__ = _maybe_download(
__magic_name__ , __magic_name__ , source_url + test_labels_file )
with gfile.Open(__magic_name__ , "rb" ) as f:
lowercase__ = _extract_labels(__magic_name__ , one_hot=__magic_name__ )
if not 0 <= validation_size <= len(__magic_name__ ):
lowercase__ = (
"Validation size should be between 0 and "
f'''{len(__magic_name__ )}. Received: {validation_size}.'''
)
raise ValueError(__magic_name__ )
lowercase__ = train_images[:validation_size]
lowercase__ = train_labels[:validation_size]
lowercase__ = train_images[validation_size:]
lowercase__ = train_labels[validation_size:]
lowercase__ = {"dtype": dtype, "reshape": reshape, "seed": seed}
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
lowercase__ = _DataSet(__magic_name__ , __magic_name__ , **__magic_name__ )
return _Datasets(train=__magic_name__ , validation=__magic_name__ , test=__magic_name__ )
| 655 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def _A ( ):
lowercase__ = ArgumentParser("Transformers CLI tool" , usage="transformers-cli <command> [<args>]" )
lowercase__ = parser.add_subparsers(help="transformers-cli command helpers" )
# Register commands
ConvertCommand.register_subcommand(__magic_name__ )
DownloadCommand.register_subcommand(__magic_name__ )
EnvironmentCommand.register_subcommand(__magic_name__ )
RunCommand.register_subcommand(__magic_name__ )
ServeCommand.register_subcommand(__magic_name__ )
UserCommands.register_subcommand(__magic_name__ )
AddNewModelCommand.register_subcommand(__magic_name__ )
AddNewModelLikeCommand.register_subcommand(__magic_name__ )
LfsCommands.register_subcommand(__magic_name__ )
PTtoTFCommand.register_subcommand(__magic_name__ )
# Let's go
lowercase__ = parser.parse_args()
if not hasattr(__magic_name__ , "func" ):
parser.print_help()
exit(1 )
# Run
lowercase__ = args.func(__magic_name__ )
service.run()
if __name__ == "__main__":
main()
| 655 |
from __future__ import annotations
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :List[Any]=None ):
'''simple docstring'''
lowercase__ = data
lowercase__ = None
def __repr__( self :Dict ):
'''simple docstring'''
lowercase__ = []
lowercase__ = self
while temp:
string_rep.append(f'''{temp.data}''' )
lowercase__ = temp.next
return "->".join(_lowercase )
def _A ( __magic_name__ ):
if not elements_list:
raise Exception("The Elements List is empty" )
lowercase__ = lowercase__ = Node(elements_list[0] )
for i in range(1 , len(__magic_name__ ) ):
lowercase__ = Node(elements_list[i] )
lowercase__ = current.next
return head
def _A ( __magic_name__ ):
if head_node is not None and isinstance(__magic_name__ , __magic_name__ ):
print_reverse(head_node.next )
print(head_node.data )
def _A ( ):
from doctest import testmod
testmod()
lowercase__ = make_linked_list([14, 52, 14, 12, 43] )
print("Linked List:" )
print(__magic_name__ )
print("Elements in Reverse:" )
print_reverse(__magic_name__ )
if __name__ == "__main__":
main()
| 655 | 1 |
import inspect
import unittest
from transformers import MobileViTConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileViTForImageClassification, MobileViTForSemanticSegmentation, MobileViTModel
from transformers.models.mobilevit.modeling_mobilevit import MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class lowerCAmelCase ( lowercase_ ):
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_lowercase , "hidden_sizes" ) )
self.parent.assertTrue(hasattr(_lowercase , "neck_hidden_sizes" ) )
self.parent.assertTrue(hasattr(_lowercase , "num_attention_heads" ) )
class lowerCAmelCase :
def __init__( self :Optional[Any] , _lowercase :Tuple , _lowercase :Optional[int]=13 , _lowercase :List[Any]=32 , _lowercase :Optional[int]=2 , _lowercase :int=3 , _lowercase :Optional[Any]=6_40 , _lowercase :Optional[int]=4 , _lowercase :Optional[Any]="silu" , _lowercase :Union[str, Any]=3 , _lowercase :str=32 , _lowercase :List[str]=0.1 , _lowercase :str=0.1 , _lowercase :Optional[int]=0.1 , _lowercase :int=0.02 , _lowercase :List[Any]=True , _lowercase :List[Any]=True , _lowercase :str=10 , _lowercase :str=None , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = last_hidden_size
lowercase__ = num_attention_heads
lowercase__ = hidden_act
lowercase__ = conv_kernel_size
lowercase__ = output_stride
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = classifier_dropout_prob
lowercase__ = use_labels
lowercase__ = is_training
lowercase__ = num_labels
lowercase__ = initializer_range
lowercase__ = scope
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.num_labels )
lowercase__ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase__ = self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
return MobileViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_attention_heads=self.num_attention_heads , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def UpperCAmelCase ( self :int , _lowercase :List[str] , _lowercase :List[str] , _lowercase :List[str] , _lowercase :List[Any] ):
'''simple docstring'''
lowercase__ = MobileViTModel(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCAmelCase ( self :str , _lowercase :Optional[Any] , _lowercase :str , _lowercase :str , _lowercase :Dict ):
'''simple docstring'''
lowercase__ = self.num_labels
lowercase__ = MobileViTForImageClassification(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase , labels=_lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def UpperCAmelCase ( self :Optional[int] , _lowercase :List[Any] , _lowercase :Tuple , _lowercase :int , _lowercase :int ):
'''simple docstring'''
lowercase__ = self.num_labels
lowercase__ = MobileViTForSemanticSegmentation(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowercase__ = model(_lowercase , labels=_lowercase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
__lowerCamelCase = (
(MobileViTModel, MobileViTForImageClassification, MobileViTForSemanticSegmentation)
if is_torch_available()
else ()
)
__lowerCamelCase = (
{
'feature-extraction': MobileViTModel,
'image-classification': MobileViTForImageClassification,
'image-segmentation': MobileViTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = MobileViTModelTester(self )
lowercase__ = MobileViTConfigTester(self , config_class=_lowercase , has_text_modality=_lowercase )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="MobileViT does not use inputs_embeds" )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
pass
@unittest.skip(reason="MobileViT does not support input and output embeddings" )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
pass
@unittest.skip(reason="MobileViT does not output attentions" )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_lowercase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowercase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
def check_hidden_states_output(_lowercase :int , _lowercase :Any , _lowercase :Dict ):
lowercase__ = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_lowercase , _lowercase ) )
lowercase__ = outputs.hidden_states
lowercase__ = 5
self.assertEqual(len(_lowercase ) , _lowercase )
# MobileViT's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
lowercase__ = 2
for i in range(len(_lowercase ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = True
check_hidden_states_output(_lowercase , _lowercase , _lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
check_hidden_states_output(_lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowercase )
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase )
@slow
def UpperCAmelCase ( self :str ):
'''simple docstring'''
for model_name in MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ = MobileViTModel.from_pretrained(_lowercase )
self.assertIsNotNone(_lowercase )
def _A ( ):
lowercase__ = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class lowerCAmelCase ( unittest.TestCase ):
@cached_property
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
return MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small" ) if is_vision_available() else None
@slow
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = MobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small" ).to(_lowercase )
lowercase__ = self.default_image_processor
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_lowercase , return_tensors="pt" ).to(_lowercase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_lowercase )
# verify the logits
lowercase__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , _lowercase )
lowercase__ = torch.tensor([-1.9364, -1.2327, -0.4653] ).to(_lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowercase , atol=1e-4 ) )
@slow
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
lowercase__ = model.to(_lowercase )
lowercase__ = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_lowercase , return_tensors="pt" ).to(_lowercase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_lowercase )
lowercase__ = outputs.logits
# verify the logits
lowercase__ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , _lowercase )
lowercase__ = torch.tensor(
[
[[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]],
[[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]],
[[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]],
] , device=_lowercase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , _lowercase , atol=1e-4 ) )
@slow
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
lowercase__ = MobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
lowercase__ = model.to(_lowercase )
lowercase__ = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small" )
lowercase__ = prepare_img()
lowercase__ = image_processor(images=_lowercase , return_tensors="pt" ).to(_lowercase )
# forward pass
with torch.no_grad():
lowercase__ = model(**_lowercase )
lowercase__ = outputs.logits.detach().cpu()
lowercase__ = image_processor.post_process_semantic_segmentation(outputs=_lowercase , target_sizes=[(50, 60)] )
lowercase__ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , _lowercase )
lowercase__ = image_processor.post_process_semantic_segmentation(outputs=_lowercase )
lowercase__ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , _lowercase )
| 655 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( __magic_name__ , __magic_name__=1000 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase__ = n - 1
lowercase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowercase__ = 0
while count < prec:
lowercase__ = random.randint(2 , n - 1 )
lowercase__ = bin_exp_mod(__magic_name__ , __magic_name__ , __magic_name__ )
if b != 1:
lowercase__ = True
for _ in range(__magic_name__ ):
if b == n - 1:
lowercase__ = False
break
lowercase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
_snake_case = abs(int(input("""Enter bound : """).strip()))
print("""Here's the list of primes:""")
print(""", """.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 655 | 1 |
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