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

from ..utils import logging


__lowerCAmelCase	:       str						       =	logging.get_logger(__name__)





def       lowerCAmelCase					(		UpperCamelCase__				:   str=None    ,					UpperCamelCase__				:   str=None       ):


	"""simple docstring"""
	return field(default_factory=lambda: default    ,					metadata=a__       )






@dataclass
class 						A   :
							a_        =      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'''
							        )
							    }       ,	)

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

							a_        =      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'''}       ,	)

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





							def       snake_case__						(      self				:							List[Any]	)							->	List[str]:
								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.'''     ,		lowercase_     ,		)





							def       snake_case__						(      self				:							Tuple	)							->	Tuple:
								return json.dumps(dataclasses.asdict(self	)     ,		indent=2	)





							@property
							def       snake_case__						(      self				:							Any	)							->	List[str]:







								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       snake_case__						(      self				:							Any	)							->	Tuple:







								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'''


from __future__ import annotations

import unittest

from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
					import tensorflow as tf

					from transformers.models.distilbert.modeling_tf_distilbert import (
					    TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFDistilBertForMaskedLM,
					    TFDistilBertForMultipleChoice,
					    TFDistilBertForQuestionAnswering,
					    TFDistilBertForSequenceClassification,
					    TFDistilBertForTokenClassification,
					    TFDistilBertModel,
					)






class 						A   :





							def __init__(      self				:							List[Any]     ,		__a				:							Any     ,		)							->	Dict:
								__UpperCAmelCase							=			parent
								__UpperCAmelCase							=			1_3
								__UpperCAmelCase							=			7
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			False
								__UpperCAmelCase							=			True
								__UpperCAmelCase							=			9_9
								__UpperCAmelCase							=			3_2
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			3_7
								__UpperCAmelCase							=			'''gelu'''
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			0.1
								__UpperCAmelCase							=			5_1_2
								__UpperCAmelCase							=			1_6
								__UpperCAmelCase							=			2
								__UpperCAmelCase							=			0.0_2
								__UpperCAmelCase							=			3
								__UpperCAmelCase							=			4
								__UpperCAmelCase							=			None





							def       snake_case__						(      self				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size	)

								__UpperCAmelCase							=			None
								if self.use_input_mask:
									__UpperCAmelCase							=			random_attention_mask([self.batch_size, self.seq_length]	)

								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								__UpperCAmelCase							=			None
								if self.use_labels:
									__UpperCAmelCase							=			ids_tensor([self.batch_size]     ,		self.type_sequence_label_size	)
									__UpperCAmelCase							=			ids_tensor([self.batch_size, self.seq_length]     ,		self.num_labels	)
									__UpperCAmelCase							=			ids_tensor([self.batch_size]     ,		self.num_choices	)

								__UpperCAmelCase							=			DistilBertConfig(
								    vocab_size=self.vocab_size     ,		dim=self.hidden_size     ,		n_layers=self.num_hidden_layers     ,		n_heads=self.num_attention_heads     ,		hidden_dim=self.intermediate_size     ,		hidden_act=self.hidden_act     ,		dropout=self.hidden_dropout_prob     ,		attention_dropout=self.attention_probs_dropout_prob     ,		max_position_embeddings=self.max_position_embeddings     ,		initializer_range=self.initializer_range     ,		)

								return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							List[str]     ,		__a				:							int     ,		__a				:							Union[str, Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]     ,		__a				:							int	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModel(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}

								__UpperCAmelCase							=			model(__a	)

								__UpperCAmelCase							=			[input_ids, input_mask]

								__UpperCAmelCase							=			model(__a	)

								self.parent.assertEqual(result.last_hidden_state.shape     ,		(self.batch_size, self.seq_length, self.hidden_size)	)





							def       snake_case__						(      self				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							List[Any]     ,		__a				:							Union[str, Any]     ,		__a				:							List[Any]	)							->	int:
								__UpperCAmelCase							=			TFDistilBertForMaskedLM(config=__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.vocab_size)	)





							def       snake_case__						(      self				:							Optional[int]     ,		__a				:							Any     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							int     ,		__a				:							Optional[Any]     ,		__a				:							Optional[int]	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertForQuestionAnswering(config=__a	)
								__UpperCAmelCase							=			{
								    '''input_ids''': input_ids,
								    '''attention_mask''': input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.start_logits.shape     ,		(self.batch_size, self.seq_length)	)
								self.parent.assertEqual(result.end_logits.shape     ,		(self.batch_size, self.seq_length)	)





							def       snake_case__						(      self				:							Any     ,		__a				:							Optional[Any]     ,		__a				:							List[str]     ,		__a				:							Dict     ,		__a				:							Dict     ,		__a				:							int     ,		__a				:							List[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForSequenceClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_labels)	)





							def       snake_case__						(      self				:							Union[str, Any]     ,		__a				:							int     ,		__a				:							str     ,		__a				:							Union[str, Any]     ,		__a				:							Optional[int]     ,		__a				:							List[str]     ,		__a				:							Dict	)							->	str:
								__UpperCAmelCase							=			self.num_choices
								__UpperCAmelCase							=			TFDistilBertForMultipleChoice(__a	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			tf.tile(tf.expand_dims(__a     ,		1	)     ,		(1, self.num_choices, 1)	)
								__UpperCAmelCase							=			{
								    '''input_ids''': multiple_choice_inputs_ids,
								    '''attention_mask''': multiple_choice_input_mask,
								}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.num_choices)	)





							def       snake_case__						(      self				:							int     ,		__a				:							Optional[Any]     ,		__a				:							int     ,		__a				:							Tuple     ,		__a				:							int     ,		__a				:							Optional[int]     ,		__a				:							Optional[int]	)							->	int:
								__UpperCAmelCase							=			self.num_labels
								__UpperCAmelCase							=			TFDistilBertForTokenClassification(__a	)
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								__UpperCAmelCase							=			model(__a	)
								self.parent.assertEqual(result.logits.shape     ,		(self.batch_size, self.seq_length, self.num_labels)	)






							def       snake_case__						(      self				:							str	)							->	Any:
								__UpperCAmelCase							=			self.prepare_config_and_inputs()
								((__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase)				,			(__UpperCAmelCase))							=			config_and_inputs
								__UpperCAmelCase							=			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
								return config, inputs_dict






@require_tf
class 						A   (   UpperCAmelCase       ,	UpperCAmelCase       ,	unittest.TestCase   ):
							a_        =      (
							    (
							        TFDistilBertModel,
							        TFDistilBertForMaskedLM,
							        TFDistilBertForQuestionAnswering,
							        TFDistilBertForSequenceClassification,
							        TFDistilBertForTokenClassification,
							        TFDistilBertForMultipleChoice,
							    )
							    if is_tf_available()
							    else None
							)
							a_        =      (
							    {
							        '''feature-extraction''': TFDistilBertModel,
							        '''fill-mask''': TFDistilBertForMaskedLM,
							        '''question-answering''': TFDistilBertForQuestionAnswering,
							        '''text-classification''': TFDistilBertForSequenceClassification,
							        '''token-classification''': TFDistilBertForTokenClassification,
							        '''zero-shot''': TFDistilBertForSequenceClassification,
							    }
							    if is_tf_available()
							    else {}
							)
							a_        =      False
							a_        =      False





							def       snake_case__						(      self				:							Any	)							->	Any:
								__UpperCAmelCase							=			TFDistilBertModelTester(self	)
								__UpperCAmelCase							=			ConfigTester(self     ,		config_class=__a     ,		dim=3_7	)





							def       snake_case__						(      self				:							List[Any]	)							->	Optional[int]:
								self.config_tester.run_common_tests()





							def       snake_case__						(      self				:							Any	)							->	str:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_model(*__a	)





							def       snake_case__						(      self				:							Tuple	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_masked_lm(*__a	)





							def       snake_case__						(      self				:							Union[str, Any]	)							->	Any:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_question_answering(*__a	)





							def       snake_case__						(      self				:							Optional[Any]	)							->	Dict:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_sequence_classification(*__a	)





							def       snake_case__						(      self				:							Any	)							->	int:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_multiple_choice(*__a	)





							def       snake_case__						(      self				:							List[str]	)							->	List[Any]:
								__UpperCAmelCase							=			self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_distilbert_for_token_classification(*__a	)






							@slow
							def       snake_case__						(      self				:							Dict	)							->	Tuple:
								for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]	):
									__UpperCAmelCase							=			TFDistilBertModel.from_pretrained(__a	)
									self.assertIsNotNone(__a	)






@require_tf
class 						A   (   unittest.TestCase   ):





							@slow
							def       snake_case__						(      self				:							int	)							->	Dict:
								__UpperCAmelCase							=			TFDistilBertModel.from_pretrained('''distilbert-base-uncased'''	)
								__UpperCAmelCase							=			tf.constant([[0, 1, 2, 3, 4, 5]]	)
								__UpperCAmelCase							=			model(__a	)[0]

								__UpperCAmelCase							=			[1, 6, 7_6_8]
								self.assertEqual(output.shape     ,		__a	)

								__UpperCAmelCase							=			tf.constant(
								    [
								        [
								            [0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
								            [0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
								            [0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
								        ]
								    ]	)
								tf.debugging.assert_near(output[:, :3, :3]     ,		__a     ,		atol=1e-4	)
