
"""simple docstring"""




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

from ..utils import logging


UpperCAmelCase__   :					Optional[int]												=				logging.get_logger(__name__)

def lowercase_						(     _snake_case=None     ,_snake_case=None		):
							return field(default_factory=lambda: default     ,metadata=_snake_case		)



@dataclass
class 			lowerCAmelCase_		:


				"""simple docstring"""



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

				__UpperCamelCase   :		List[int]       =     list_field(
				    default=[8, 32, 128, 512]				,      metadata={'''help''': '''List of sequence lengths for which memory and time performance will be evaluated'''}				,      )

				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Whether to benchmark inference of model. Inference can be disabled via --no-inference.'''}				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Whether to run on available cuda devices. Cuda can be disabled via --no-cuda.'''}				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Whether to run on available tpu devices. TPU can be disabled via --no-tpu.'''}						)
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Use FP16 to accelerate inference.'''}						)
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Benchmark training of model'''}						)
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Verbose memory tracing'''}						)
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={'''help''': '''Whether to perform speed measurements. Speed measurements can be disabled via --no-speed.'''}				,      )
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={
				        '''help''': '''Whether to perform memory measurements. Memory measurements can be disabled via --no-memory'''
				    }				,      )
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Trace memory line by line'''}						)
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Save result to a CSV file'''}						)
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Save all print statements in a log file'''}						)
				__UpperCamelCase   :		bool       =     field(default=a__				,      metadata={'''help''': '''Whether to print environment information'''}						)
				__UpperCamelCase   :		bool       =     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   :		str       =     field(
				    default=F"inference_time_{round(time()						)}.csv"				,      metadata={'''help''': '''CSV filename used if saving time results to csv.'''}				,      )
				__UpperCamelCase   :		str       =     field(
				    default=F"inference_memory_{round(time()						)}.csv"				,      metadata={'''help''': '''CSV filename used if saving memory results to csv.'''}				,      )
				__UpperCamelCase   :		str       =     field(
				    default=F"train_time_{round(time()						)}.csv"				,      metadata={'''help''': '''CSV filename used if saving time results to csv for training.'''}				,      )
				__UpperCamelCase   :		str       =     field(
				    default=F"train_memory_{round(time()						)}.csv"				,      metadata={'''help''': '''CSV filename used if saving memory results to csv for training.'''}				,      )
				__UpperCamelCase   :		str       =     field(
				    default=F"env_info_{round(time()						)}.csv"				,      metadata={'''help''': '''CSV filename used if saving environment information.'''}				,      )
				__UpperCamelCase   :		str       =     field(
				    default=F"log_{round(time()						)}.csv"				,      metadata={'''help''': '''Log filename used if print statements are saved in log.'''}				,      )
				__UpperCamelCase   :		int       =     field(default=3				,      metadata={'''help''': '''Times an experiment will be run.'''}						)
				__UpperCamelCase   :		bool       =     field(
				    default=a__				,      metadata={
				        '''help''': (
				            '''Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain'''
				            ''' model weights.'''
				        )
				    }				,      )



				def 			__magic_name__					(self					)      ->	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."""     , SCREAMING_SNAKE_CASE__     , )



				def 			__magic_name__					(self					)      ->	Dict:



											"""simple docstring"""




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



				@property
				def 			__magic_name__					(self					)      ->	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 			__magic_name__					(self					)      ->	Dict:



											"""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






"""simple docstring"""




import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union

import transformers
from transformers.testing_utils import require_tf, require_torch, slow


UpperCAmelCase__   :					Any												=				logging.getLogger()



@unittest.skip('''Temporarily disable the doc tests.'''						)
@require_torch
@require_tf
@slow
class 			lowerCAmelCase_		(unittest.TestCase						):


				"""simple docstring"""



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = None     , SCREAMING_SNAKE_CASE__ = True     , )      ->	int:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Tuple     =     [file for file in os.listdir(SCREAMING_SNAKE_CASE__					) if os.path.isfile(os.path.join(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)]

											if identifier is not None:
																		SCREAMING_SNAKE_CASE__		: int     =     [file for file in files if identifier in file]

											if n_identifier is not None:
																		if isinstance(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					):
																									for n_ in n_identifier:
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     [file for file in files if n_ not in file]
																		else:
																									SCREAMING_SNAKE_CASE__		: List[Any]     =     [file for file in files if n_identifier not in file]

											SCREAMING_SNAKE_CASE__		: int     =     ignore_files or []
											ignore_files.append("""__init__.py"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     [file for file in files if file not in ignore_files]

											for file in files:
																		# Open all files
																		print("""Testing"""     , SCREAMING_SNAKE_CASE__					)

																		if only_modules:
																									SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     file.split("""."""					)[0]
																									try:
																																SCREAMING_SNAKE_CASE__		: Optional[Any]     =     getattr(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																																SCREAMING_SNAKE_CASE__		: Optional[int]     =     doctest.DocTestSuite(SCREAMING_SNAKE_CASE__					)
																																SCREAMING_SNAKE_CASE__		: int     =     unittest.TextTestRunner().run(SCREAMING_SNAKE_CASE__					)
																																self.assertIs(len(result.failures					)     , 0					)
																									except AttributeError:
																																logger.info(F'''{module_identifier} is not a module.'''					)
																		else:
																									SCREAMING_SNAKE_CASE__		: Optional[int]     =     doctest.testfile(str("""..""" / directory / file					)     , optionflags=doctest.ELLIPSIS					)
																									self.assertIs(result.failed     , 0					)



				def 			__magic_name__					(self					)      ->	Any:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     """modeling"""
											SCREAMING_SNAKE_CASE__		: int     =     [
											    """modeling_ctrl.py""",
											    """modeling_tf_ctrl.py""",
											]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__     , ignore_files=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """tokenization"""
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	List[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: List[str]     =     """configuration"""
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Path("""src/transformers"""					)
											SCREAMING_SNAKE_CASE__		: Dict     =     ["""configuration""", """modeling""", """tokenization"""]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , n_identifier=SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Optional[int]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     Path("""docs/source"""					)
											SCREAMING_SNAKE_CASE__		: Any     =     ["""favicon.ico"""]
											self.analyze_directory(SCREAMING_SNAKE_CASE__     , ignore_files=SCREAMING_SNAKE_CASE__     , only_modules=SCREAMING_SNAKE_CASE__					)




