

'''simple docstring'''





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


A_	= logging.get_logger(__name__)

A_	= {
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					'deit'
		def __init__(					self   ,			SCREAMING_SNAKE_CASE_=768   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=12   ,			SCREAMING_SNAKE_CASE_=3072   ,			SCREAMING_SNAKE_CASE_="gelu"   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.0   ,			SCREAMING_SNAKE_CASE_=0.02   ,			SCREAMING_SNAKE_CASE_=1E-12   ,			SCREAMING_SNAKE_CASE_=224   ,			SCREAMING_SNAKE_CASE_=16   ,			SCREAMING_SNAKE_CASE_=3   ,			SCREAMING_SNAKE_CASE_=True   ,			SCREAMING_SNAKE_CASE_=16   ,			**SCREAMING_SNAKE_CASE_   ,			)  ->     int:






							'''simple docstring'''



							super().__init__(**SCREAMING_SNAKE_CASE_		)

							lowerCamelCase_  				=      hidden_size
							lowerCamelCase_  				=      num_hidden_layers
							lowerCamelCase_  				=      num_attention_heads
							lowerCamelCase_  				=      intermediate_size
							lowerCamelCase_  				=      hidden_act
							lowerCamelCase_  				=      hidden_dropout_prob
							lowerCamelCase_  				=      attention_probs_dropout_prob
							lowerCamelCase_  				=      initializer_range
							lowerCamelCase_  				=      layer_norm_eps
							lowerCamelCase_  				=      image_size
							lowerCamelCase_  				=      patch_size
							lowerCamelCase_  				=      num_channels
							lowerCamelCase_  				=      qkv_bias
							lowerCamelCase_  				=      encoder_stride


class  UpperCAmelCase (							UpperCAmelCase__    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					version.parse('1.11'    )
		@property
		def 			UpperCamelCase(					self		)  ->     Mapping[str, Mapping[int, str]]:






							'''simple docstring'''



							return OrderedDict(
							    [
							        ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
							    ]		)






		@property
		def 			UpperCamelCase(					self		)  ->     float:






							'''simple docstring'''



							return 1E-4









'''simple docstring'''

from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, 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 numpy as np
							import tensorflow as tf

							from transformers import (
							    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
							    FlaubertConfig,
							    TFFlaubertForMultipleChoice,
							    TFFlaubertForQuestionAnsweringSimple,
							    TFFlaubertForSequenceClassification,
							    TFFlaubertForTokenClassification,
							    TFFlaubertModel,
							    TFFlaubertWithLMHeadModel,
							)






class 		a							:


				def __init__(						self       :   Tuple			,							lowercase_       :   Any			,							):
									snake_case_		     =							parent
									snake_case_		     =							13
									snake_case_		     =							7
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							True
									snake_case_		     =							False
									snake_case_		     =							False
									snake_case_		     =							False
									snake_case_		     =							2
									snake_case_		     =							99
									snake_case_		     =							0
									snake_case_		     =							32
									snake_case_		     =							2
									snake_case_		     =							4
									snake_case_		     =							0.1
									snake_case_		     =							0.1
									snake_case_		     =							512
									snake_case_		     =							16
									snake_case_		     =							2
									snake_case_		     =							0.02
									snake_case_		     =							3
									snake_case_		     =							4
									snake_case_		     =							'''last'''
									snake_case_		     =							True
									snake_case_		     =							None
									snake_case_		     =							0


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.vocab_size   )
									snake_case_		     =							random_attention_mask([self.batch_size, self.seq_length]			,							dtype=tf.floataa   )

									snake_case_		     =							None
									if self.use_input_lengths:
														snake_case_		     =							(
														    ids_tensor([self.batch_size]			,							vocab_size=2   ) + self.seq_length - 2
														)  # small variation of seq_length

									snake_case_		     =							None
									if self.use_token_type_ids:
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.n_langs   )

									snake_case_		     =							None
									snake_case_		     =							None
									snake_case_		     =							None
									if self.use_labels:
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.type_sequence_label_size   )
														snake_case_		     =							ids_tensor([self.batch_size, self.seq_length]			,							self.num_labels   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							2			,							dtype=tf.floataa   )
														snake_case_		     =							ids_tensor([self.batch_size]			,							self.num_choices   )

									snake_case_		     =							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			,							bos_token_id=self.bos_token_id			,							)

									return (
									    config,
									    input_ids,
									    token_type_ids,
									    input_lengths,
									    sequence_labels,
									    token_labels,
									    is_impossible_labels,
									    choice_labels,
									    input_mask,
									)


				def     A_					(						self       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   Optional[int]			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   Optional[int]			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   str			,							lowercase_       :   int			,							):
									snake_case_		     =							TFFlaubertModel(config=lowercase_   )
									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
									snake_case_		     =							model(lowercase_   )

									snake_case_		     =							[input_ids, input_mask]
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.last_hidden_state.shape			,							(self.batch_size, self.seq_length, self.hidden_size)   )


				def     A_					(						self       :   List[str]			,							lowercase_       :   int			,							lowercase_       :   List[str]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   str			,							lowercase_       :   Tuple			,							lowercase_       :   Tuple			,							lowercase_       :   Any			,							lowercase_       :   Dict			,							lowercase_       :   List[Any]			,							):
									snake_case_		     =							TFFlaubertWithLMHeadModel(lowercase_   )

									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
									snake_case_		     =							model(lowercase_   )

									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.vocab_size)   )


				def     A_					(						self       :   str			,							lowercase_       :   Dict			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Dict			,							lowercase_       :   Any			,							lowercase_       :   str			,							lowercase_       :   List[str]			,							lowercase_       :   List[Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   str			,							):
									snake_case_		     =							TFFlaubertForQuestionAnsweringSimple(lowercase_   )

									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths}

									snake_case_		     =							model(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     A_					(						self       :   Optional[Any]			,							lowercase_       :   Any			,							lowercase_       :   int			,							lowercase_       :   List[Any]			,							lowercase_       :   Any			,							lowercase_       :   Optional[Any]			,							lowercase_       :   str			,							lowercase_       :   int			,							lowercase_       :   int			,							lowercase_       :   Dict			,							):
									snake_case_		     =							TFFlaubertForSequenceClassification(lowercase_   )

									snake_case_		     =							{'''input_ids''': input_ids, '''lengths''': input_lengths}

									snake_case_		     =							model(lowercase_   )

									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.type_sequence_label_size)   )


				def     A_					(						self       :   List[Any]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Any			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   List[Any]			,							lowercase_       :   Dict			,							lowercase_       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   Union[str, Any]			,							):
									snake_case_		     =							self.num_labels
									snake_case_		     =							TFFlaubertForTokenClassification(config=lowercase_   )
									snake_case_		     =							{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.seq_length, self.num_labels)   )


				def     A_					(						self       :   Dict			,							lowercase_       :   Tuple			,							lowercase_       :   Optional[int]			,							lowercase_       :   Any			,							lowercase_       :   List[Any]			,							lowercase_       :   int			,							lowercase_       :   Tuple			,							lowercase_       :   List[str]			,							lowercase_       :   List[Any]			,							lowercase_       :   Any			,							):
									snake_case_		     =							self.num_choices
									snake_case_		     =							TFFlaubertForMultipleChoice(config=lowercase_   )
									snake_case_		     =							tf.tile(tf.expand_dims(lowercase_			,							1   )			,							(1, self.num_choices, 1)   )
									snake_case_		     =							tf.tile(tf.expand_dims(lowercase_			,							1   )			,							(1, self.num_choices, 1)   )
									snake_case_		     =							tf.tile(tf.expand_dims(lowercase_			,							1   )			,							(1, self.num_choices, 1)   )
									snake_case_		     =							{
									    '''input_ids''': multiple_choice_inputs_ids,
									    '''attention_mask''': multiple_choice_input_mask,
									    '''token_type_ids''': multiple_choice_token_type_ids,
									}
									snake_case_		     =							model(lowercase_   )
									self.parent.assertEqual(result.logits.shape			,							(self.batch_size, self.num_choices)   )


				def     A_					(						self       :   Dict   ):
									snake_case_		     =							self.prepare_config_and_inputs()
									(
									    (
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,(
									    snake_case_
									)     ,
									)		     =							config_and_inputs
									snake_case_		     =							{
									    '''input_ids''': input_ids,
									    '''token_type_ids''': token_type_ids,
									    '''langs''': token_type_ids,
									    '''lengths''': input_lengths,
									}
									return config, inputs_dict







@require_tf
class 		a							(  _lowerCamelCase  ,	_lowerCamelCase  ,	unittest.TestCase	):
				snake_case_           =						(
				    (
				        TFFlaubertModel,
				        TFFlaubertWithLMHeadModel,
				        TFFlaubertForSequenceClassification,
				        TFFlaubertForQuestionAnsweringSimple,
				        TFFlaubertForTokenClassification,
				        TFFlaubertForMultipleChoice,
				    )
				    if is_tf_available()
				    else ()
				)
				snake_case_           =						(
				    (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
				)  # TODO (PVP): Check other models whether language generation is also applicable
				snake_case_           =						(
				    {
				        "feature-extraction": TFFlaubertModel,
				        "fill-mask": TFFlaubertWithLMHeadModel,
				        "question-answering": TFFlaubertForQuestionAnsweringSimple,
				        "text-classification": TFFlaubertForSequenceClassification,
				        "token-classification": TFFlaubertForTokenClassification,
				        "zero-shot": TFFlaubertForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)
				snake_case_           =						False
				snake_case_           =						False


				def     A_					(						self       :   str			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Optional[Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Union[str, Any]			,							lowercase_       :   Optional[Any]   ):
									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     A_					(						self       :   int   ):
									snake_case_		     =							TFFlaubertModelTester(self   )
									snake_case_		     =							ConfigTester(self			,							config_class=lowercase_			,							emb_dim=37   )


				def     A_					(						self       :   List[str]   ):
									self.config_tester.run_common_tests()


				def     A_					(						self       :   Optional[Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_model(*lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_lm_head(*lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_qa(*lowercase_   )


				def     A_					(						self       :   Tuple   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_sequence_classif(*lowercase_   )


				def     A_					(						self       :   List[str]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_token_classification(*lowercase_   )


				def     A_					(						self       :   Union[str, Any]   ):
									snake_case_		     =							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_flaubert_for_multiple_choice(*lowercase_   )


				@slow
				def     A_					(						self       :   Optional[int]   ):
									for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														snake_case_		     =							TFFlaubertModel.from_pretrained(lowercase_   )
														self.assertIsNotNone(lowercase_   )







@require_tf
@require_sentencepiece
@require_tokenizers
class 		a							(  unittest.TestCase	):


				@slow
				def     A_					(						self       :   int   ):
									snake_case_		     =							TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''   )

									snake_case_		     =							tf.convert_to_tensor(
									    [[0, 158, 735, 2592, 1424, 6727, 82, 1]]			,							dtype=tf.intaa			,							)  # "J'aime flaubert !"

									snake_case_		     =							model(lowercase_   )[0]
									snake_case_		     =							tf.TensorShape((1, 8, 512)   )
									self.assertEqual(output.shape			,							lowercase_   )
									# compare the actual values for a slice.
									snake_case_		     =							tf.convert_to_tensor(
									    [
									        [
									            [-1.876_8773, -1.56_6555, 0.2707_2418],
									            [-1.692_0038, -0.587_3505, 1.932_9599],
									            [-2.956_3985, -1.699_3835, 1.797_2052],
									        ]
									    ]			,							dtype=tf.floataa			,							)

									self.assertTrue(np.allclose(output[:, :3, :3].numpy()			,							expected_slice.numpy()			,							atol=1e-4   )   )

