

'''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


lowerCAmelCase__						:       Tuple										=						logging.get_logger(__name__)




def 	_a       (				__lowerCAmelCase		:      List[Any]=None   ,							__lowerCAmelCase		:      Tuple=None     ):

		"""simple docstring"""

		return field(default_factory=lambda: default   ,							metadata=__lowerCAmelCase     )





@dataclass
class     a			:






						"""simple docstring"""




						__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, 32, 128, 512]			,			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       __magic_name__							(							self					:      Optional[int]  ):



								'''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       __magic_name__							(							self					:      List[str]  ):



								'''simple docstring'''



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







						@property
						def       __magic_name__							(							self					:      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					:      List[str]  ):



								'''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 copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs




class     a			(				SCREAMING_SNAKE_CASE       ):






						"""simple docstring"""




						def __init__(							self					:      Dict			,			snake_case_					:      str			,			snake_case_					:      Optional[int]=None			,			snake_case_					:      List[Any]=True			,			snake_case_					:      Any=None			,			**snake_case_					:      List[str]  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  parent
								snake_case__						:       List[Any]       						=  config_class
								snake_case__						:       Optional[int]       						=  has_text_modality
								snake_case__						:       str       						=  kwargs
								snake_case__						:       Dict       						=  common_properties







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       List[Any]       						=  (
								    ['''hidden_size''', '''num_attention_heads''', '''num_hidden_layers''']
								    if self.common_properties is None
								    else self.common_properties
								)

								# Add common fields for text models
								if self.has_text_modality:
										common_properties.extend(['''vocab_size''']  )

								# Test that config has the common properties as getters
								for prop in common_properties:
										self.parent.assertTrue(hasattr(snake_case_			,			snake_case_  )			,			msg=F"""`{prop}` does not exist"""  )

								# Test that config has the common properties as setter
								for idx, name in enumerate(snake_case_  ):
										try:
												setattr(snake_case_			,			snake_case_			,			snake_case_  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass

        # Test if config class can be called with Config(prop_name=..)
								for idx, name in enumerate(snake_case_  ):
										try:
												snake_case__						:       Dict       						=  self.config_class(**{name: idx}  )
												self.parent.assertEqual(
												    getattr(snake_case_			,			snake_case_  )			,			snake_case_			,			msg=F"""`{name} value {idx} expected, but was {getattr(snake_case_			,			snake_case_  )}"""  )
										except NotImplementedError:
												# Some models might not be able to implement setters for common_properties
												# In that case, a NotImplementedError is raised
												pass







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Tuple       						=  self.config_class(**self.inputs_dict  )
								snake_case__						:       str       						=  json.loads(config.to_json_string()  )
								for key, value in self.inputs_dict.items():
										self.parent.assertEqual(obj[key]			,			snake_case_  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								snake_case__						:       Optional[int]       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[Any]       						=  os.path.join(snake_case_			,			'''config.json'''  )
										config_first.to_json_file(snake_case_  )
										snake_case__						:       Dict       						=  self.config_class.from_json_file(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								with tempfile.TemporaryDirectory() as tmpdirname:
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       Optional[Any]       						=  self.config_class.from_pretrained(snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      str  ):



								'''simple docstring'''



								snake_case__						:       int       						=  self.config_class(**self.inputs_dict  )

								snake_case__						:       str       						=  '''test'''
								with tempfile.TemporaryDirectory() as tmpdirname:
										snake_case__						:       List[str]       						=  os.path.join(snake_case_			,			snake_case_  )
										config_first.save_pretrained(snake_case_  )
										snake_case__						:       List[str]       						=  self.config_class.from_pretrained(snake_case_			,			subfolder=snake_case_  )

								self.parent.assertEqual(config_second.to_dict()			,			config_first.to_dict()  )







						def       __magic_name__							(							self					:      Dict  ):



								'''simple docstring'''



								snake_case__						:       Any       						=  self.config_class(**self.inputs_dict			,			num_labels=5  )
								self.parent.assertEqual(len(config.idalabel  )			,			5  )
								self.parent.assertEqual(len(config.labelaid  )			,			5  )

								snake_case__						:       List[Any]       						=  3
								self.parent.assertEqual(len(config.idalabel  )			,			3  )
								self.parent.assertEqual(len(config.labelaid  )			,			3  )







						def       __magic_name__							(							self					:      Optional[int]  ):



								'''simple docstring'''



								if self.config_class.is_composition:
										return
								snake_case__						:       Any       						=  self.config_class()
								self.parent.assertIsNotNone(snake_case_  )







						def       __magic_name__							(							self					:      int  ):



								'''simple docstring'''



								snake_case__						:       Optional[Any]       						=  copy.deepcopy(snake_case_  )
								snake_case__						:       Union[str, Any]       						=  self.config_class(**snake_case_  )
								snake_case__						:       List[str]       						=  []
								for key, value in config_common_kwargs.items():
										if key == "torch_dtype":
												if not is_torch_available():
														continue
												else:
														import torch

														if config.torch_dtype != torch.floataa:
																wrong_values.append(('''torch_dtype''', config.torch_dtype, torch.floataa)  )
										elif getattr(snake_case_			,			snake_case_  ) != value:
												wrong_values.append((key, getattr(snake_case_			,			snake_case_  ), value)  )

								if len(snake_case_  ) > 0:
										snake_case__						:       Dict       						=  '''\n'''.join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values]  )
										raise ValueError(F"""The following keys were not properly set in the config:\n{errors}"""  )







						def       __magic_name__							(							self					:      Union[str, Any]  ):



								'''simple docstring'''



								self.create_and_test_config_common_properties()
								self.create_and_test_config_to_json_string()
								self.create_and_test_config_to_json_file()
								self.create_and_test_config_from_and_save_pretrained()
								self.create_and_test_config_from_and_save_pretrained_subfolder()
								self.create_and_test_config_with_num_labels()
								self.check_config_can_be_init_without_params()
								self.check_config_arguments_init()



