





from collections import OrderedDict

from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES


_lowerCamelCase 					=      logging.get_logger(__name__)


_lowerCamelCase 					=      OrderedDict(
    [
        # Base model mapping
        ('albert', 'FlaxAlbertModel'),
        ('bart', 'FlaxBartModel'),
        ('beit', 'FlaxBeitModel'),
        ('bert', 'FlaxBertModel'),
        ('big_bird', 'FlaxBigBirdModel'),
        ('blenderbot', 'FlaxBlenderbotModel'),
        ('blenderbot-small', 'FlaxBlenderbotSmallModel'),
        ('clip', 'FlaxCLIPModel'),
        ('distilbert', 'FlaxDistilBertModel'),
        ('electra', 'FlaxElectraModel'),
        ('gpt-sw3', 'FlaxGPT2Model'),
        ('gpt2', 'FlaxGPT2Model'),
        ('gpt_neo', 'FlaxGPTNeoModel'),
        ('gptj', 'FlaxGPTJModel'),
        ('longt5', 'FlaxLongT5Model'),
        ('marian', 'FlaxMarianModel'),
        ('mbart', 'FlaxMBartModel'),
        ('mt5', 'FlaxMT5Model'),
        ('opt', 'FlaxOPTModel'),
        ('pegasus', 'FlaxPegasusModel'),
        ('regnet', 'FlaxRegNetModel'),
        ('resnet', 'FlaxResNetModel'),
        ('roberta', 'FlaxRobertaModel'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormModel'),
        ('roformer', 'FlaxRoFormerModel'),
        ('t5', 'FlaxT5Model'),
        ('vision-text-dual-encoder', 'FlaxVisionTextDualEncoderModel'),
        ('vit', 'FlaxViTModel'),
        ('wav2vec2', 'FlaxWav2Vec2Model'),
        ('whisper', 'FlaxWhisperModel'),
        ('xglm', 'FlaxXGLMModel'),
        ('xlm-roberta', 'FlaxXLMRobertaModel'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for pre-training mapping
        ('albert', 'FlaxAlbertForPreTraining'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForPreTraining'),
        ('big_bird', 'FlaxBigBirdForPreTraining'),
        ('electra', 'FlaxElectraForPreTraining'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
        ('wav2vec2', 'FlaxWav2Vec2ForPreTraining'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Masked LM mapping
        ('albert', 'FlaxAlbertForMaskedLM'),
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('bert', 'FlaxBertForMaskedLM'),
        ('big_bird', 'FlaxBigBirdForMaskedLM'),
        ('distilbert', 'FlaxDistilBertForMaskedLM'),
        ('electra', 'FlaxElectraForMaskedLM'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('roberta', 'FlaxRobertaForMaskedLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMaskedLM'),
        ('roformer', 'FlaxRoFormerForMaskedLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForMaskedLM'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Seq2Seq Causal LM mapping
        ('bart', 'FlaxBartForConditionalGeneration'),
        ('blenderbot', 'FlaxBlenderbotForConditionalGeneration'),
        ('blenderbot-small', 'FlaxBlenderbotSmallForConditionalGeneration'),
        ('encoder-decoder', 'FlaxEncoderDecoderModel'),
        ('longt5', 'FlaxLongT5ForConditionalGeneration'),
        ('marian', 'FlaxMarianMTModel'),
        ('mbart', 'FlaxMBartForConditionalGeneration'),
        ('mt5', 'FlaxMT5ForConditionalGeneration'),
        ('pegasus', 'FlaxPegasusForConditionalGeneration'),
        ('t5', 'FlaxT5ForConditionalGeneration'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Image-classsification
        ('beit', 'FlaxBeitForImageClassification'),
        ('regnet', 'FlaxRegNetForImageClassification'),
        ('resnet', 'FlaxResNetForImageClassification'),
        ('vit', 'FlaxViTForImageClassification'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        ('vision-encoder-decoder', 'FlaxVisionEncoderDecoderModel'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Causal LM mapping
        ('bart', 'FlaxBartForCausalLM'),
        ('bert', 'FlaxBertForCausalLM'),
        ('big_bird', 'FlaxBigBirdForCausalLM'),
        ('electra', 'FlaxElectraForCausalLM'),
        ('gpt-sw3', 'FlaxGPT2LMHeadModel'),
        ('gpt2', 'FlaxGPT2LMHeadModel'),
        ('gpt_neo', 'FlaxGPTNeoForCausalLM'),
        ('gptj', 'FlaxGPTJForCausalLM'),
        ('opt', 'FlaxOPTForCausalLM'),
        ('roberta', 'FlaxRobertaForCausalLM'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForCausalLM'),
        ('xglm', 'FlaxXGLMForCausalLM'),
        ('xlm-roberta', 'FlaxXLMRobertaForCausalLM'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Sequence Classification mapping
        ('albert', 'FlaxAlbertForSequenceClassification'),
        ('bart', 'FlaxBartForSequenceClassification'),
        ('bert', 'FlaxBertForSequenceClassification'),
        ('big_bird', 'FlaxBigBirdForSequenceClassification'),
        ('distilbert', 'FlaxDistilBertForSequenceClassification'),
        ('electra', 'FlaxElectraForSequenceClassification'),
        ('mbart', 'FlaxMBartForSequenceClassification'),
        ('roberta', 'FlaxRobertaForSequenceClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForSequenceClassification'),
        ('roformer', 'FlaxRoFormerForSequenceClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForSequenceClassification'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Question Answering mapping
        ('albert', 'FlaxAlbertForQuestionAnswering'),
        ('bart', 'FlaxBartForQuestionAnswering'),
        ('bert', 'FlaxBertForQuestionAnswering'),
        ('big_bird', 'FlaxBigBirdForQuestionAnswering'),
        ('distilbert', 'FlaxDistilBertForQuestionAnswering'),
        ('electra', 'FlaxElectraForQuestionAnswering'),
        ('mbart', 'FlaxMBartForQuestionAnswering'),
        ('roberta', 'FlaxRobertaForQuestionAnswering'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForQuestionAnswering'),
        ('roformer', 'FlaxRoFormerForQuestionAnswering'),
        ('xlm-roberta', 'FlaxXLMRobertaForQuestionAnswering'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Token Classification mapping
        ('albert', 'FlaxAlbertForTokenClassification'),
        ('bert', 'FlaxBertForTokenClassification'),
        ('big_bird', 'FlaxBigBirdForTokenClassification'),
        ('distilbert', 'FlaxDistilBertForTokenClassification'),
        ('electra', 'FlaxElectraForTokenClassification'),
        ('roberta', 'FlaxRobertaForTokenClassification'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForTokenClassification'),
        ('roformer', 'FlaxRoFormerForTokenClassification'),
        ('xlm-roberta', 'FlaxXLMRobertaForTokenClassification'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        # Model for Multiple Choice mapping
        ('albert', 'FlaxAlbertForMultipleChoice'),
        ('bert', 'FlaxBertForMultipleChoice'),
        ('big_bird', 'FlaxBigBirdForMultipleChoice'),
        ('distilbert', 'FlaxDistilBertForMultipleChoice'),
        ('electra', 'FlaxElectraForMultipleChoice'),
        ('roberta', 'FlaxRobertaForMultipleChoice'),
        ('roberta-prelayernorm', 'FlaxRobertaPreLayerNormForMultipleChoice'),
        ('roformer', 'FlaxRoFormerForMultipleChoice'),
        ('xlm-roberta', 'FlaxXLMRobertaForMultipleChoice'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        ('bert', 'FlaxBertForNextSentencePrediction'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        ('speech-encoder-decoder', 'FlaxSpeechEncoderDecoderModel'),
        ('whisper', 'FlaxWhisperForConditionalGeneration'),
    ]
)

_lowerCamelCase 					=      OrderedDict(
    [
        ('whisper', 'FlaxWhisperForAudioClassification'),
    ]
)

_lowerCamelCase 					=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
_lowerCamelCase 					=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
_lowerCamelCase 					=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
_lowerCamelCase 					=      _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
_lowerCamelCase 					=      _LazyAutoMapping(
    CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModel)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_PRETRAINING_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModelForPreTraining, head_doc='pretraining')




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_CAUSAL_LM_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModelForCausalLM, head_doc='causal language modeling')




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_MASKED_LM_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModelForMaskedLM, head_doc='masked language modeling')




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



_lowerCamelCase 					=      auto_class_update(
    FlaxAutoModelForSeqaSeqLM, head_doc='sequence-to-sequence language modeling', checkpoint_for_example='t5-base'
)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING



_lowerCamelCase 					=      auto_class_update(
    FlaxAutoModelForSequenceClassification, head_doc='sequence classification'
)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc='question answering')




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING



_lowerCamelCase 					=      auto_class_update(
    FlaxAutoModelForTokenClassification, head_doc='token classification'
)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModelForMultipleChoice, head_doc='multiple choice')




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING



_lowerCamelCase 					=      auto_class_update(
    FlaxAutoModelForNextSentencePrediction, head_doc='next sentence prediction'
)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING



_lowerCamelCase 					=      auto_class_update(
    FlaxAutoModelForImageClassification, head_doc='image classification'
)




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING



_lowerCamelCase 					=      auto_class_update(FlaxAutoModelForVisionaSeq, head_doc='vision-to-text modeling')




class 	UpperCamelCase_							(  _BaseAutoModelClass     ):
							lowerCamelCase_							     =       FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING



_lowerCamelCase 					=      auto_class_update(
    FlaxAutoModelForSpeechSeqaSeq, head_doc='sequence-to-sequence speech-to-text modeling'
)



import inspect
import unittest

import numpy as np

from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor


if is_flax_available():
		import jax

		from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel

class 						__a (							unittest.TestCase     ):
		def __init__(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE=13     ,       _SCREAMING_SNAKE_CASE=30     ,       _SCREAMING_SNAKE_CASE=2     ,       _SCREAMING_SNAKE_CASE=3     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=True     ,       _SCREAMING_SNAKE_CASE=32     ,       _SCREAMING_SNAKE_CASE=5     ,       _SCREAMING_SNAKE_CASE=4     ,       _SCREAMING_SNAKE_CASE=37     ,       _SCREAMING_SNAKE_CASE="gelu"     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=0.1     ,       _SCREAMING_SNAKE_CASE=10     ,       _SCREAMING_SNAKE_CASE=0.02     ,       )					->			int:




									"""simple docstring"""





									_UpperCAmelCase											=     parent
									_UpperCAmelCase											=     batch_size
									_UpperCAmelCase											=     image_size
									_UpperCAmelCase											=     patch_size
									_UpperCAmelCase											=     num_channels
									_UpperCAmelCase											=     is_training
									_UpperCAmelCase											=     use_labels
									_UpperCAmelCase											=     hidden_size
									_UpperCAmelCase											=     num_hidden_layers
									_UpperCAmelCase											=     num_attention_heads
									_UpperCAmelCase											=     intermediate_size
									_UpperCAmelCase											=     hidden_act
									_UpperCAmelCase											=     hidden_dropout_prob
									_UpperCAmelCase											=     attention_probs_dropout_prob
									_UpperCAmelCase											=     type_sequence_label_size
									_UpperCAmelCase											=     initializer_range

									# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (image_size // patch_size) ** 2
									_UpperCAmelCase											=     num_patches + 1
		def       UpperCAmelCase__							(     self		)					->			Optional[int]:




									"""simple docstring"""





									_UpperCAmelCase											=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

									_UpperCAmelCase											=     ViTConfig(
									    image_size=self.image_size     ,       patch_size=self.patch_size     ,       num_channels=self.num_channels     ,       hidden_size=self.hidden_size     ,       num_hidden_layers=self.num_hidden_layers     ,       num_attention_heads=self.num_attention_heads     ,       intermediate_size=self.intermediate_size     ,       hidden_act=self.hidden_act     ,       hidden_dropout_prob=self.hidden_dropout_prob     ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob     ,       is_decoder=_SCREAMING_SNAKE_CASE     ,       initializer_range=self.initializer_range     ,       )

									return config, pixel_values
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     FlaxViTModel(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
									_UpperCAmelCase											=     (self.image_size, self.image_size)
									_UpperCAmelCase											=     (self.patch_size, self.patch_size)
									_UpperCAmelCase											=     (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
									self.parent.assertEqual(result.last_hidden_state.shape     ,       (self.batch_size, num_patches + 1, self.hidden_size)		)
		def       UpperCAmelCase__							(     self     ,       _SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)					->			Any:




									"""simple docstring"""





									_UpperCAmelCase											=     self.type_sequence_label_size
									_UpperCAmelCase											=     FlaxViTForImageClassification(config=_SCREAMING_SNAKE_CASE		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)
									self.parent.assertEqual(result.logits.shape     ,       (self.batch_size, self.type_sequence_label_size)		)

									# test greyscale images
									_UpperCAmelCase											=     1
									_UpperCAmelCase											=     FlaxViTForImageClassification(_SCREAMING_SNAKE_CASE		)

									_UpperCAmelCase											=     floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
									_UpperCAmelCase											=     model(_SCREAMING_SNAKE_CASE		)






		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase											=     self.prepare_config_and_inputs()
									(
									    (
									    _UpperCAmelCase
									)						,			(
									    _UpperCAmelCase
									)						,			
									)											=     config_and_inputs
									_UpperCAmelCase											=     {'pixel_values': pixel_values}
									return config, inputs_dict



@require_flax
class 						__a (							UpperCAmelCase       , unittest.TestCase     ):
		_a      :    List[str]           =      (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
		def       UpperCAmelCase__							(     self		)					->			None:




									"""simple docstring"""





									_UpperCAmelCase											=     FlaxViTModelTester(self		)
									_UpperCAmelCase											=     ConfigTester(self     ,       config_class=_SCREAMING_SNAKE_CASE     ,       has_text_modality=_SCREAMING_SNAKE_CASE     ,       hidden_size=37		)
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									self.config_tester.run_common_tests()
		def       UpperCAmelCase__							(     self		)					->			Optional[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			List[Any]:




									"""simple docstring"""





									_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			str:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)
																_UpperCAmelCase											=     inspect.signature(model.__call__		)
																# signature.parameters is an OrderedDict => so arg_names order is deterministic
																_UpperCAmelCase											=     [*signature.parameters.keys()]

																_UpperCAmelCase											=     ['pixel_values']
																self.assertListEqual(arg_names[:1]     ,       _SCREAMING_SNAKE_CASE		)
		def       UpperCAmelCase__							(     self		)					->			int:




									"""simple docstring"""





									_UpperCAmelCase						,			_UpperCAmelCase											=     self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
																with self.subTest(model_class.__name__		):
																							_UpperCAmelCase											=     self._prepare_for_class(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		)
																							_UpperCAmelCase											=     model_class(_SCREAMING_SNAKE_CASE		)

																							@jax.jit
																							def model_jitted(_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		):
																														return model(pixel_values=_SCREAMING_SNAKE_CASE     ,       **_SCREAMING_SNAKE_CASE		)

																							with self.subTest('JIT Enabled'		):
																														_UpperCAmelCase											=     model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							with self.subTest('JIT Disabled'		):
																														with jax.disable_jit():
																																					_UpperCAmelCase											=     model_jitted(**_SCREAMING_SNAKE_CASE		).to_tuple()

																							self.assertEqual(len(_SCREAMING_SNAKE_CASE		)     ,       len(_SCREAMING_SNAKE_CASE		)		)
																							for jitted_output, output in zip(_SCREAMING_SNAKE_CASE     ,       _SCREAMING_SNAKE_CASE		):
																														self.assertEqual(jitted_output.shape     ,       output.shape		)






		@slow
		def       UpperCAmelCase__							(     self		)					->			Dict:




									"""simple docstring"""





									for model_class_name in self.all_model_classes:
																_UpperCAmelCase											=     model_class_name.from_pretrained('google/vit-base-patch16-224'		)
																_UpperCAmelCase											=     model(np.ones((1, 3, 224, 224)		)		)
																self.assertIsNotNone(_SCREAMING_SNAKE_CASE		)
