import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
lowercase_  : List[Any]								=					{'LayoutLMv2Config', 'LayoutLMv3Config'}






@is_pipeline_test
class 		_lowerCamelCase		( unittest.TestCase     ):
    __a						=   MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
    __a						=   TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

    if model_mapping is not None:
        __a						=   {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
    if tf_model_mapping is not None:
        __a						=   {
            config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
        }
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase			,			lowerCAmelCase )							->       Optional[int]:
     SCREAMING_SNAKE_CASE__: str=   ZeroShotClassificationPipeline(
         model=lowerCAmelCase			,			tokenizer=lowerCAmelCase			,			candidate_labels=['''polics''', '''health'''] )
     return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
    def     UpperCamelCase_					(	self			,			lowerCAmelCase			,			lowerCAmelCase )							->       int:
     SCREAMING_SNAKE_CASE__: List[Any]=   classifier('''Who are you voting for in 2020?'''			,			candidate_labels='''politics''' )
     self.assertEqual(lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase )]} )

     # No kwarg
     SCREAMING_SNAKE_CASE__: int=   classifier('''Who are you voting for in 2020?'''			,			['''politics'''] )
     self.assertEqual(lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase )]} )

     SCREAMING_SNAKE_CASE__: List[Any]=   classifier('''Who are you voting for in 2020?'''			,			candidate_labels=['''politics'''] )
     self.assertEqual(lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase )]} )

     SCREAMING_SNAKE_CASE__: Dict=   classifier('''Who are you voting for in 2020?'''			,			candidate_labels='''politics, public health''' )
     self.assertEqual(
         lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )]} )
     self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) )			,			1.0 )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   classifier('''Who are you voting for in 2020?'''			,			candidate_labels=['''politics''', '''public health'''] )
     self.assertEqual(
         lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )]} )
     self.assertAlmostEqual(sum(nested_simplify(outputs['''scores'''] ) )			,			1.0 )

     SCREAMING_SNAKE_CASE__: Optional[Any]=   classifier(
         '''Who are you voting for in 2020?'''			,			candidate_labels='''politics'''			,			hypothesis_template='''This text is about {}''' )
     self.assertEqual(lowerCAmelCase			,			{'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase )]} )

     # https://github.com/huggingface/transformers/issues/13846
     SCREAMING_SNAKE_CASE__: str=   classifier(['''I am happy''']			,			['''positive''', '''negative'''] )
     self.assertEqual(
         lowerCAmelCase			,			[
             {'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )]}
             for i in range(1 )
         ]			,			)
     SCREAMING_SNAKE_CASE__: Tuple=   classifier(['''I am happy''', '''I am sad''']			,			['''positive''', '''negative'''] )
     self.assertEqual(
         lowerCAmelCase			,			[
             {'''sequence''': ANY(lowerCAmelCase ), '''labels''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )], '''scores''': [ANY(lowerCAmelCase ), ANY(lowerCAmelCase )]}
             for i in range(2 )
         ]			,			)

     with self.assertRaises(lowerCAmelCase ):
      classifier(''''''			,			candidate_labels='''politics''' )

     with self.assertRaises(lowerCAmelCase ):
      classifier(lowerCAmelCase			,			candidate_labels='''politics''' )

     with self.assertRaises(lowerCAmelCase ):
      classifier('''Who are you voting for in 2020?'''			,			candidate_labels='''''' )

     with self.assertRaises(lowerCAmelCase ):
      classifier('''Who are you voting for in 2020?'''			,			candidate_labels=lowerCAmelCase )

     with self.assertRaises(lowerCAmelCase ):
      classifier(
          '''Who are you voting for in 2020?'''			,			candidate_labels='''politics'''			,			hypothesis_template='''Not formatting template'''			,			)

     with self.assertRaises(lowerCAmelCase ):
      classifier(
          '''Who are you voting for in 2020?'''			,			candidate_labels='''politics'''			,			hypothesis_template=lowerCAmelCase			,			)

     self.run_entailment_id(lowerCAmelCase )
    def     UpperCamelCase_					(	self			,			lowerCAmelCase )							->       Union[str, Any]:
     SCREAMING_SNAKE_CASE__: Optional[Any]=   zero_shot_classifier.model.config
     SCREAMING_SNAKE_CASE__: str=   config.labelaid
     SCREAMING_SNAKE_CASE__: int=   zero_shot_classifier.entailment_id

     SCREAMING_SNAKE_CASE__: str=   {'''LABEL_0''': 0, '''LABEL_1''': 1, '''LABEL_2''': 2}
     self.assertEqual(zero_shot_classifier.entailment_id			,			-1 )

     SCREAMING_SNAKE_CASE__: Any=   {'''entailment''': 0, '''neutral''': 1, '''contradiction''': 2}
     self.assertEqual(zero_shot_classifier.entailment_id			,			0 )

     SCREAMING_SNAKE_CASE__: List[Any]=   {'''ENTAIL''': 0, '''NON-ENTAIL''': 1}
     self.assertEqual(zero_shot_classifier.entailment_id			,			0 )

     SCREAMING_SNAKE_CASE__: Tuple=   {'''ENTAIL''': 2, '''NEUTRAL''': 1, '''CONTR''': 0}
     self.assertEqual(zero_shot_classifier.entailment_id			,			2 )

     SCREAMING_SNAKE_CASE__: Dict=   original_labelaid
     self.assertEqual(lowerCAmelCase			,			zero_shot_classifier.entailment_id )
    @require_torch
    def     UpperCamelCase_					(	self )							->       int:
     SCREAMING_SNAKE_CASE__: Union[str, Any]=   pipeline(
         '''zero-shot-classification'''			,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''			,			framework='''pt'''			,			)
     # There was a regression in 4.10 for this
     # Adding a test so we don't make the mistake again.
     # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
     zero_shot_classifier(
         '''Who are you voting for in 2020?''' * 100			,			candidate_labels=['''politics''', '''public health''', '''science'''] )
    @require_torch
    def     UpperCamelCase_					(	self )							->       Dict:
     SCREAMING_SNAKE_CASE__: Optional[int]=   pipeline(
         '''zero-shot-classification'''			,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''			,			framework='''pt'''			,			)
     SCREAMING_SNAKE_CASE__: Any=   zero_shot_classifier(
         '''Who are you voting for in 2020?'''			,			candidate_labels=['''politics''', '''public health''', '''science'''] )

     self.assertEqual(
         nested_simplify(lowerCAmelCase )			,			{
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''science''', '''public health''', '''politics'''],
             '''scores''': [0.333, 0.333, 0.333],
         }			,			)
    @require_tf
    def     UpperCamelCase_					(	self )							->       str:
     SCREAMING_SNAKE_CASE__: Tuple=   pipeline(
         '''zero-shot-classification'''			,			model='''sshleifer/tiny-distilbert-base-cased-distilled-squad'''			,			framework='''tf'''			,			)
     SCREAMING_SNAKE_CASE__: List[str]=   zero_shot_classifier(
         '''Who are you voting for in 2020?'''			,			candidate_labels=['''politics''', '''public health''', '''science'''] )

     self.assertEqual(
         nested_simplify(lowerCAmelCase )			,			{
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''science''', '''public health''', '''politics'''],
             '''scores''': [0.333, 0.333, 0.333],
         }			,			)
    @slow
    @require_torch
    def     UpperCamelCase_					(	self )							->       List[Any]:
     SCREAMING_SNAKE_CASE__: int=   pipeline('''zero-shot-classification'''			,			model='''roberta-large-mnli'''			,			framework='''pt''' )
     SCREAMING_SNAKE_CASE__: List[Any]=   zero_shot_classifier(
         '''Who are you voting for in 2020?'''			,			candidate_labels=['''politics''', '''public health''', '''science'''] )

     self.assertEqual(
         nested_simplify(lowerCAmelCase )			,			{
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''politics''', '''public health''', '''science'''],
             '''scores''': [0.976, 0.015, 0.009],
         }			,			)
     SCREAMING_SNAKE_CASE__: Dict=   zero_shot_classifier(
         '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
         ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
         ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
         ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
         ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
         ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
         ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
         ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
         ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
         ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
         ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
         ''' large and limited training data.'''			,			candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']			,			multi_label=lowerCAmelCase			,			)
     self.assertEqual(
         nested_simplify(lowerCAmelCase )			,			{
             '''sequence''': (
                 '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                 ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                 ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                 ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                 ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                 ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                 ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                 ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                 ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                 ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                 ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                 ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                 ''' limited training data.'''
             ),
             '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
             '''scores''': [0.817, 0.713, 0.018, 0.018],
         }			,			)



    @slow
    @require_tf
    def     UpperCamelCase_					(	self )							->       Tuple:
     SCREAMING_SNAKE_CASE__: Any=   pipeline('''zero-shot-classification'''			,			model='''roberta-large-mnli'''			,			framework='''tf''' )
     SCREAMING_SNAKE_CASE__: Tuple=   zero_shot_classifier(
         '''Who are you voting for in 2020?'''			,			candidate_labels=['''politics''', '''public health''', '''science'''] )

     self.assertEqual(
         nested_simplify(lowerCAmelCase )			,			{
             '''sequence''': '''Who are you voting for in 2020?''',
             '''labels''': ['''politics''', '''public health''', '''science'''],
             '''scores''': [0.976, 0.015, 0.009],
         }			,			)
     SCREAMING_SNAKE_CASE__: Any=   zero_shot_classifier(
         '''The dominant sequence transduction models are based on complex recurrent or convolutional neural networks'''
         ''' in an encoder-decoder configuration. The best performing models also connect the encoder and decoder'''
         ''' through an attention mechanism. We propose a new simple network architecture, the Transformer, based'''
         ''' solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two'''
         ''' machine translation tasks show these models to be superior in quality while being more parallelizable'''
         ''' and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014'''
         ''' English-to-German translation task, improving over the existing best results, including ensembles by'''
         ''' over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new'''
         ''' single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small'''
         ''' fraction of the training costs of the best models from the literature. We show that the Transformer'''
         ''' generalizes well to other tasks by applying it successfully to English constituency parsing both with'''
         ''' large and limited training data.'''			,			candidate_labels=['''machine learning''', '''statistics''', '''translation''', '''vision''']			,			multi_label=lowerCAmelCase			,			)
     self.assertEqual(
         nested_simplify(lowerCAmelCase )			,			{
             '''sequence''': (
                 '''The dominant sequence transduction models are based on complex recurrent or convolutional neural'''
                 ''' networks in an encoder-decoder configuration. The best performing models also connect the'''
                 ''' encoder and decoder through an attention mechanism. We propose a new simple network'''
                 ''' architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence'''
                 ''' and convolutions entirely. Experiments on two machine translation tasks show these models to be'''
                 ''' superior in quality while being more parallelizable and requiring significantly less time to'''
                 ''' train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,'''
                 ''' improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014'''
                 ''' English-to-French translation task, our model establishes a new single-model state-of-the-art'''
                 ''' BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training'''
                 ''' costs of the best models from the literature. We show that the Transformer generalizes well to'''
                 ''' other tasks by applying it successfully to English constituency parsing both with large and'''
                 ''' limited training data.'''
             ),
             '''labels''': ['''translation''', '''machine learning''', '''vision''', '''statistics'''],
             '''scores''': [0.817, 0.713, 0.018, 0.018],
         }			,			)







import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers.models.esm.modeling_esmfold import EsmForProteinFolding







class        __lowerCAmelCase					:

			"""simple docstring"""







			def __init__( self     ,		lowerCamelCase__     ,		lowerCamelCase__=13     ,		lowerCamelCase__=7     ,		lowerCamelCase__=False     ,		lowerCamelCase__=True     ,		lowerCamelCase__=False     ,		lowerCamelCase__=False     ,		lowerCamelCase__=19     ,		lowerCamelCase__=32     ,		lowerCamelCase__=5     ,		lowerCamelCase__=4     ,		lowerCamelCase__=37     ,		lowerCamelCase__="gelu"     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=0.1     ,		lowerCamelCase__=512     ,		lowerCamelCase__=16     ,		lowerCamelCase__=2     ,		lowerCamelCase__=0.02     ,		lowerCamelCase__=3     ,		lowerCamelCase__=4     ,		lowerCamelCase__=None     ,		)       -> Any:



							'''simple docstring'''
							__lowerCamelCase									=       parent
							__lowerCamelCase									=       batch_size
							__lowerCamelCase									=       seq_length
							__lowerCamelCase									=       is_training
							__lowerCamelCase									=       use_input_mask
							__lowerCamelCase									=       use_token_type_ids
							__lowerCamelCase									=       use_labels
							__lowerCamelCase									=       vocab_size
							__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									=       max_position_embeddings
							__lowerCamelCase									=       type_vocab_size
							__lowerCamelCase									=       type_sequence_label_size
							__lowerCamelCase									=       initializer_range
							__lowerCamelCase									=       num_labels
							__lowerCamelCase									=       num_choices
							__lowerCamelCase									=       scope


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       ids_tensor([self.batch_size, self.seq_length]     ,		self.vocab_size				)

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

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

							__lowerCamelCase									=       self.get_config()

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


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       EsmConfig(
							    vocab_size=33     ,		hidden_size=self.hidden_size     ,		pad_token_id=1     ,		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     ,		max_position_embeddings=self.max_position_embeddings     ,		type_vocab_size=self.type_vocab_size     ,		initializer_range=self.initializer_range     ,		is_folding_model=lowerCamelCase__     ,		esmfold_config={'trunk': {'num_blocks': 2}, 'fp16_esm': False}     ,		)
							return config


			def  lowercase_       ( self     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__     ,		lowerCamelCase__				)       -> Optional[Any]:



							'''simple docstring'''
							__lowerCamelCase									=       EsmForProteinFolding(config=lowerCamelCase__				).float()
							model.to(lowerCamelCase__				)
							model.eval()
							__lowerCamelCase									=       model(lowerCamelCase__     ,		attention_mask=lowerCamelCase__				)
							__lowerCamelCase									=       model(lowerCamelCase__				)
							__lowerCamelCase									=       model(lowerCamelCase__				)

							self.parent.assertEqual(result.positions.shape     ,		(8, self.batch_size, self.seq_length, 14, 3)				)
							self.parent.assertEqual(result.angles.shape     ,		(8, self.batch_size, self.seq_length, 7, 2)				)






			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							__lowerCamelCase									=       self.prepare_config_and_inputs()
							(
							    (
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		(
							    __lowerCamelCase
							)			,		
							)									=       config_and_inputs
							__lowerCamelCase									=       {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_torch
class        __lowerCAmelCase					(			__magic_name__     ,							__magic_name__     ,							unittest.TestCase					):

			"""simple docstring"""







			snake_case_  						=			False

			snake_case_  						=			(EsmForProteinFolding,) if is_torch_available() else ()
			snake_case_  						=			()
			snake_case_  						=			{} if is_torch_available() else {}
			snake_case_  						=			False


			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							__lowerCamelCase									=       EsmFoldModelTester(self				)
							__lowerCamelCase									=       ConfigTester(self     ,		config_class=lowerCamelCase__     ,		hidden_size=37				)


			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							self.config_tester.run_common_tests()


			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*lowerCamelCase__				)


			@unittest.skip('Does not support attention outputs'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							pass


			@unittest.skip('Esm does not support embedding resizing'				)
			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							pass


			@unittest.skip('Esm does not support embedding resizing'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support passing input embeds!'				)
			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support head pruning.'				)
			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support head pruning.'				)
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support head pruning.'				)
			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support head pruning.'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support head pruning.'				)
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not output hidden states in the normal way.'				)
			def  lowercase_       ( self				)       -> List[str]:



							'''simple docstring'''
							pass


			@unittest.skip('ESMfold does not output hidden states in the normal way.'				)
			def  lowercase_       ( self				)       -> Optional[int]:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold only has one output format.'				)
			def  lowercase_       ( self				)       -> Tuple:



							'''simple docstring'''
							pass


			@unittest.skip('This test doesn\'t work for ESMFold and doesn\'t test core functionality'				)
			def  lowercase_       ( self				)       -> int:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold does not support input chunking.'				)
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t respect you and it certainly doesn\'t respect your initialization arguments.'				)
			def  lowercase_       ( self				)       -> Any:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support torchscript compilation.'				)
			def  lowercase_       ( self				)       -> Union[str, Any]:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support torchscript compilation.'				)
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support torchscript compilation.'				)
			def  lowercase_       ( self				)       -> Dict:



							'''simple docstring'''
							pass


			@unittest.skip('ESMFold doesn\'t support data parallel.'				)
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							pass






			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'				)
			def  lowercase_       ( self				)       -> List[Any]:



							'''simple docstring'''
							pass







@require_torch
class        __lowerCAmelCase					(			__magic_name__					):

			"""simple docstring"""







			@slow
			def  lowercase_       ( self				)       -> str:



							'''simple docstring'''
							__lowerCamelCase									=       EsmForProteinFolding.from_pretrained('facebook/esmfold_v1'				).float()
							model.eval()
							__lowerCamelCase									=       torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]				)
							__lowerCamelCase									=       model(lowerCamelCase__				)['positions']
							__lowerCamelCase									=       torch.tensor([2.58_28, 0.79_93, -10.93_34]     ,		dtype=torch.floataa				)
							self.assertTrue(torch.allclose(position_outputs[0, 0, 0, 0]     ,		lowerCamelCase__     ,		atol=1e-4				)				)


