



import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


UpperCAmelCase__       =	logging.get_logger(__name__)





def 							A     (					_UpperCAmelCase    :       List[Any]=None							,       _UpperCAmelCase    :       str=None   )    ->			Optional[Any]:







			'''simple docstring'''
			return field(default_factory=lambda: default							,       metadata=_UpperCAmelCase   )







@dataclass
class       __lowerCAmelCase   :
			UpperCamelCase        =					list_field(
			    default=[]    ,						metadata={
			        '''help''': (
			            '''Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version'''
			            ''' of all available models'''
			        )
			    }    ,						)

			UpperCamelCase        =					list_field(
			    default=[8]    ,						metadata={'''help''': '''List of batch sizes for which memory and time performance will be evaluated'''}						)

			UpperCamelCase        =					list_field(
			    default=[8, 3_2, 1_2_8, 5_1_2]    ,						metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}    ,						)

			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Use FP16 to accelerate inference.'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Benchmark training of model'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Verbose memory tracing'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}    ,						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
			    }    ,						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Trace memory line by line'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Save result to a CSV file'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Save all print statements in a log file'''}						)
			UpperCamelCase        =					field(default=A    ,						metadata={'''help''': '''Whether to print environment information'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use'''
			            ''' multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled'''
			            ''' for debugging / testing and on TPU.'''
			        )
			    }    ,						)
			UpperCamelCase        =					field(
			    default=f'''inference_time_{round(time()						)}.csv'''    ,						metadata={'''help''': '''CSV filename used if saving time results to csv.'''}    ,						)
			UpperCamelCase        =					field(
			    default=f'''inference_memory_{round(time()						)}.csv'''    ,						metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}    ,						)
			UpperCamelCase        =					field(
			    default=f'''train_time_{round(time()						)}.csv'''    ,						metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}    ,						)
			UpperCamelCase        =					field(
			    default=f'''train_memory_{round(time()						)}.csv'''    ,						metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}    ,						)
			UpperCamelCase        =					field(
			    default=f'''env_info_{round(time()						)}.csv'''    ,						metadata={'''help''': '''CSV filename used if saving environment information.'''}    ,						)
			UpperCamelCase        =					field(
			    default=f'''log_{round(time()						)}.csv'''    ,						metadata={'''help''': '''Log filename used if print statements are saved in log.'''}    ,						)
			UpperCamelCase        =					field(default=3    ,						metadata={'''help''': '''Times an experiment will be run.'''}						)
			UpperCamelCase        =					field(
			    default=A    ,						metadata={
			        '''help''': (
			            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
			            ''' model weights.'''
			        )
			    }    ,						)



			def    _lowerCamelCase	(						self       :  int)       ->   Dict:



						"""simple docstring"""






						warnings.warn(
						    F"The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"
						    ' are deprecated in general and it is advised to use external Benchmarking libraries '
						    ' to benchmark Transformer models.'			,		A			,		)



			def    _lowerCamelCase	(						self       :  List[Any])       ->   int:



						"""simple docstring"""






						return json.dumps(dataclasses.asdict(self)			,		indent=2)



			@property
			def    _lowerCamelCase	(						self       :  Optional[int])       ->   List[str]:



						"""simple docstring"""






						if len(self.models) <= 0:
									raise ValueError(
									    'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
									    ' bert-base-cased` or `args.models = [\'bert-base-cased\'].')
						return self.models



			@property
			def    _lowerCamelCase	(						self       :  List[Any])       ->   Tuple:



						"""simple docstring"""






						if not self.multi_process:
									return False
						elif self.is_tpu:
									logger.info('Multiprocessing is currently not possible on TPU.')
									return False
						else:
									return True





import argparse
import os

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
UpperCAmelCase__       =	"src/transformers"
UpperCAmelCase__       =	"docs/source/en/tasks"





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Any   )    ->			int:







			'''simple docstring'''
			with open(_UpperCAmelCase							,       'r'							,       encoding='utf-8'							,       newline='\n'   ) as f:
						_UpperCAmelCase			=       f.readlines()
			# Find the start prompt.
			_UpperCAmelCase			=       0
			while not lines[start_index].startswith(_UpperCAmelCase   ):
						start_index += 1
			start_index += 1

			_UpperCAmelCase			=       start_index
			while not lines[end_index].startswith(_UpperCAmelCase   ):
						end_index += 1
			end_index -= 1

			while len(lines[start_index]   ) <= 1:
						start_index += 1
			while len(lines[end_index]   ) <= 1:
						end_index -= 1
			end_index += 1
			return "".join(lines[start_index:end_index]   ), start_index, end_index, lines


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase__       =	direct_transformers_import(TRANSFORMERS_PATH)

UpperCAmelCase__       =	{
    "asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
    "audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
    "language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    "image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
    "masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
    "multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
    "object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
    "question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
    "semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
    "sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
    "summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
    "translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
    "video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
    "document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
    "monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}

# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
UpperCAmelCase__       =	{
    "summarization.md": ("nllb",),
    "translation.md": ("nllb",),
}





def 							A     (					_UpperCAmelCase    :       List[Any]   )    ->			int:







			'''simple docstring'''
			_UpperCAmelCase			=       TASK_GUIDE_TO_MODELS[task_guide]
			_UpperCAmelCase			=       SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(_UpperCAmelCase							,       set()   )
			_UpperCAmelCase			=       {
			    code: name
			    for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
			    if (code in model_maping_names or code in special_model_types)
			}
			return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()]   ) + "\n"





def 							A     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       Dict=False   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase			=       _find_text_in_file(
			    filename=os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       start_prompt='<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->'							,       end_prompt='<!--End of the generated tip-->'							,       )

			_UpperCAmelCase			=       get_model_list_for_task(_UpperCAmelCase   )

			if current_list != new_list:
						if overwrite:
									with open(os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   )							,       'w'							,       encoding='utf-8'							,       newline='\n'   ) as f:
												f.writelines(lines[:start_index] + [new_list] + lines[end_index:]   )
						else:
									raise ValueError(
									    F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
									    ' to fix this.'   )


if __name__ == "__main__":
							UpperCAmelCase__       =	argparse.ArgumentParser()
							parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
							UpperCAmelCase__       =	parser.parse_args()

							for task_guide in TASK_GUIDE_TO_MODELS.keys():
														check_model_list_for_task(task_guide, args.fix_and_overwrite)
