






import os
import tempfile
import unittest

from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, 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 import (
				    FlaubertForMultipleChoice,
				    FlaubertForQuestionAnswering,
				    FlaubertForQuestionAnsweringSimple,
				    FlaubertForSequenceClassification,
				    FlaubertForTokenClassification,
				    FlaubertModel,
				    FlaubertWithLMHeadModel,
				)
				from transformers.models.flaubert.modeling_flaubert import FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST

class 		_snake_case  (  A__					):






			def __init__(       self		,					a		,					a=13		,					a=7		,					a=True		,					a=True		,					a=True		,					a=True		,					a=True		,					a=False		,					a=False		,					a=False		,					a=2		,					a=99		,					a=0		,					a=32		,					a=5		,					a=4		,					a=0.1		,					a=0.1		,					a=512		,					a=12		,					a=2		,					a=0.02		,					a=3		,					a=4		,					a="last"		,					a=None		,					a=None		,					)      ->       Dict:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= seq_length
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_input_lengths
						SCREAMING_SNAKE_CASE						= use_token_type_ids
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= gelu_activation
						SCREAMING_SNAKE_CASE						= sinusoidal_embeddings
						SCREAMING_SNAKE_CASE						= causal
						SCREAMING_SNAKE_CASE						= asm
						SCREAMING_SNAKE_CASE						= n_langs
						SCREAMING_SNAKE_CASE						= vocab_size
						SCREAMING_SNAKE_CASE						= n_special
						SCREAMING_SNAKE_CASE						= hidden_size
						SCREAMING_SNAKE_CASE						= num_hidden_layers
						SCREAMING_SNAKE_CASE						= num_attention_heads
						SCREAMING_SNAKE_CASE						= hidden_dropout_prob
						SCREAMING_SNAKE_CASE						= attention_probs_dropout_prob
						SCREAMING_SNAKE_CASE						= max_position_embeddings
						SCREAMING_SNAKE_CASE						= type_vocab_size
						SCREAMING_SNAKE_CASE						= type_sequence_label_size
						SCREAMING_SNAKE_CASE						= initializer_range
						SCREAMING_SNAKE_CASE						= num_labels
						SCREAMING_SNAKE_CASE						= num_choices
						SCREAMING_SNAKE_CASE						= summary_type
						SCREAMING_SNAKE_CASE						= use_proj
						SCREAMING_SNAKE_CASE						= scope






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size)
						SCREAMING_SNAKE_CASE						= random_attention_mask([self.batch_size, self.seq_length])

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

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

						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						SCREAMING_SNAKE_CASE						= None
						if self.use_labels:
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.type_sequence_label_size)
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.num_labels)
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					2).float()
									SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size]		,					self.num_choices)

						SCREAMING_SNAKE_CASE						= self.get_config()

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						return 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		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= FlaubertModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(a		,					lengths=a		,					langs=a)
						SCREAMING_SNAKE_CASE						= model(a		,					langs=a)
						SCREAMING_SNAKE_CASE						= model(a)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.seq_length, self.hidden_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       int:
						SCREAMING_SNAKE_CASE						= FlaubertWithLMHeadModel(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a		,					token_type_ids=a		,					labels=a)
						self.parent.assertEqual(result.loss.shape		,					())
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= FlaubertForQuestionAnsweringSimple(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= model(a		,					start_positions=a		,					end_positions=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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       int:
						SCREAMING_SNAKE_CASE						= FlaubertForQuestionAnswering(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= model(
						    a		,					start_positions=a		,					end_positions=a		,					cls_index=a		,					is_impossible=a		,					p_mask=a		,					)

						SCREAMING_SNAKE_CASE						= model(
						    a		,					start_positions=a		,					end_positions=a		,					cls_index=a		,					is_impossible=a		,					)

						((SCREAMING_SNAKE_CASE)				,						)						= result_with_labels.to_tuple()

						SCREAMING_SNAKE_CASE						= model(a		,					start_positions=a		,					end_positions=a)

						((SCREAMING_SNAKE_CASE)				,						)						= result_with_labels.to_tuple()

						self.parent.assertEqual(result_with_labels.loss.shape		,					())
						self.parent.assertEqual(result.start_top_log_probs.shape		,					(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(result.start_top_index.shape		,					(self.batch_size, model.config.start_n_top))
						self.parent.assertEqual(
						    result.end_top_log_probs.shape		,					(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(
						    result.end_top_index.shape		,					(self.batch_size, model.config.start_n_top * model.config.end_n_top))
						self.parent.assertEqual(result.cls_logits.shape		,					(self.batch_size,))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= FlaubertForSequenceClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a)
						SCREAMING_SNAKE_CASE						= model(a		,					labels=a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= FlaubertForTokenClassification(a)
						model.to(a)
						model.eval()

						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a		,					labels=a)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.num_choices
						SCREAMING_SNAKE_CASE						= FlaubertForMultipleChoice(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= input_ids.unsqueeze(1).expand(-1		,					self.num_choices		,					-1).contiguous()
						SCREAMING_SNAKE_CASE						= token_type_ids.unsqueeze(1).expand(-1		,					self.num_choices		,					-1).contiguous()
						SCREAMING_SNAKE_CASE						= input_mask.unsqueeze(1).expand(-1		,					self.num_choices		,					-1).contiguous()
						SCREAMING_SNAKE_CASE						= model(
						    a		,					attention_mask=a		,					token_type_ids=a		,					labels=a		,					)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.num_choices))







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						
						)						= config_and_inputs
						SCREAMING_SNAKE_CASE						= {
						    'input_ids': input_ids,
						    'token_type_ids': token_type_ids,
						    'lengths': input_lengths,
						    'attention_mask': input_mask,
						}
						return config, inputs_dict



@require_torch
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    List[str]			     =							(
			    (
			        FlaubertModel,
			        FlaubertWithLMHeadModel,
			        FlaubertForQuestionAnswering,
			        FlaubertForQuestionAnsweringSimple,
			        FlaubertForSequenceClassification,
			        FlaubertForTokenClassification,
			        FlaubertForMultipleChoice,
			    )
			    if is_torch_available()
			    else ()
			)
			_lowercase		:    str			     =							(
			    {
			        '''feature-extraction''': FlaubertModel,
			        '''fill-mask''': FlaubertWithLMHeadModel,
			        '''question-answering''': FlaubertForQuestionAnsweringSimple,
			        '''text-classification''': FlaubertForSequenceClassification,
			        '''token-classification''': FlaubertForTokenClassification,
			        '''zero-shot''': FlaubertForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a)      ->       str:
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a=False)      ->       int:
						SCREAMING_SNAKE_CASE						= super()._prepare_for_class(a		,					a		,					return_labels=a)

						if return_labels:
									if model_class.__name__ == "FlaubertForQuestionAnswering":
												SCREAMING_SNAKE_CASE						= torch.zeros(
												    self.model_tester.batch_size		,					dtype=torch.long		,					device=a)
												SCREAMING_SNAKE_CASE						= torch.zeros(
												    self.model_tester.batch_size		,					dtype=torch.long		,					device=a)

						return inputs_dict






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= FlaubertModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					emb_dim=37)






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_model(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_lm_head(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_simple_qa(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_qa(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_sequence_classif(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_token_classif(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_flaubert_multiple_choice(*a)






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						for model_name in FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= FlaubertModel.from_pretrained(a)
									self.assertIsNotNone(a)







			@slow
			@require_torch_gpu
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()
						for model_class in self.all_model_classes:
									# FlauBertForMultipleChoice behaves incorrectly in JIT environments.
									if model_class == FlaubertForMultipleChoice:
												return

									SCREAMING_SNAKE_CASE						= True
									SCREAMING_SNAKE_CASE						= model_class(config=a)

									SCREAMING_SNAKE_CASE						= self._prepare_for_class(a		,					a)
									SCREAMING_SNAKE_CASE						= torch.jit.trace(
									    a		,					(inputs_dict['input_ids'].to('cpu'), inputs_dict['attention_mask'].to('cpu')))

									with tempfile.TemporaryDirectory() as tmp:
												torch.jit.save(a		,					os.path.join(a		,					'traced_model.pt'))
												SCREAMING_SNAKE_CASE						= torch.jit.load(os.path.join(a		,					'traced_model.pt')		,					map_location=a)
												loaded(inputs_dict['input_ids'].to(a)		,					inputs_dict['attention_mask'].to(a))



@require_torch
class 		_snake_case  (  unittest.TestCase					):






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= FlaubertModel.from_pretrained('flaubert/flaubert_base_cased')
						SCREAMING_SNAKE_CASE						= torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= model(a)[0]
						SCREAMING_SNAKE_CASE						= torch.Size((1, 11, 768))
						self.assertEqual(output.shape		,					a)
						SCREAMING_SNAKE_CASE						= torch.tensor(
						    [[[-2.62_51, -1.42_98, -0.02_27], [-2.85_10, -1.63_87, 0.22_58], [-2.81_14, -1.18_32, -0.30_66]]])

						self.assertTrue(torch.allclose(output[:, :3, :3]		,					a		,					atol=1E-4))












from __future__ import annotations

import unittest

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

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


if is_tf_available():
				import numpy
				import tensorflow as tf

				from transformers.models.esm.modeling_tf_esm import (
				    TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFEsmForMaskedLM,
				    TFEsmForSequenceClassification,
				    TFEsmForTokenClassification,
				    TFEsmModel,
				)

class 		_snake_case  :






			def __init__(       self		,					a		,					)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= 13
						SCREAMING_SNAKE_CASE						= 7
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= 99
						SCREAMING_SNAKE_CASE						= 32
						SCREAMING_SNAKE_CASE						= 2
						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						= 512
						SCREAMING_SNAKE_CASE						= 16
						SCREAMING_SNAKE_CASE						= 2
						SCREAMING_SNAKE_CASE						= 0.02
						SCREAMING_SNAKE_CASE						= 3
						SCREAMING_SNAKE_CASE						= 4
						SCREAMING_SNAKE_CASE						= None






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					self.vocab_size)

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

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

						SCREAMING_SNAKE_CASE						= EsmConfig(
						    vocab_size=self.vocab_size		,					hidden_size=self.hidden_size		,					num_hidden_layers=self.num_hidden_layers		,					pad_token_id=1		,					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		,					)

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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						(
						    (
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						
						)						= self.prepare_config_and_inputs()

						SCREAMING_SNAKE_CASE						= True
						SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
						SCREAMING_SNAKE_CASE						= ids_tensor([self.batch_size, self.seq_length]		,					vocab_size=2)

						return (
						    config,
						    input_ids,
						    input_mask,
						    sequence_labels,
						    token_labels,
						    choice_labels,
						    encoder_hidden_states,
						    encoder_attention_mask,
						)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       List[str]:
						SCREAMING_SNAKE_CASE						= TFEsmModel(config=a)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask}
						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= model(a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					a		,					)      ->       str:
						SCREAMING_SNAKE_CASE						= True

						SCREAMING_SNAKE_CASE						= TFEsmModel(config=a)
						SCREAMING_SNAKE_CASE						= {
						    'input_ids': input_ids,
						    'attention_mask': input_mask,
						    'encoder_hidden_states': encoder_hidden_states,
						    'encoder_attention_mask': encoder_attention_mask,
						}
						SCREAMING_SNAKE_CASE						= model(a)

						SCREAMING_SNAKE_CASE						= [input_ids, input_mask]
						SCREAMING_SNAKE_CASE						= model(a		,					encoder_hidden_states=a)

						# Also check the case where encoder outputs are not passed
						SCREAMING_SNAKE_CASE						= model(a		,					attention_mask=a)

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






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= TFEsmForMaskedLM(config=a)
						SCREAMING_SNAKE_CASE						= model([input_ids, input_mask])
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.vocab_size))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a		,					a		,					a		,					a)      ->       Dict:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= TFEsmForTokenClassification(config=a)
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask}
						SCREAMING_SNAKE_CASE						= model(a)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, self.seq_length, self.num_labels))







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.prepare_config_and_inputs()
						(
						    (
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						(
						    SCREAMING_SNAKE_CASE
						)				,						
						)						= config_and_inputs
						SCREAMING_SNAKE_CASE						= {'input_ids': input_ids, 'attention_mask': input_mask}
						return config, inputs_dict



@require_tf
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    str			     =							(
			    (
			        TFEsmModel,
			        TFEsmForMaskedLM,
			        TFEsmForSequenceClassification,
			        TFEsmForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			_lowercase		:    int			     =							(
			    {
			        '''feature-extraction''': TFEsmModel,
			        '''fill-mask''': TFEsmForMaskedLM,
			        '''text-classification''': TFEsmForSequenceClassification,
			        '''token-classification''': TFEsmForTokenClassification,
			        '''zero-shot''': TFEsmForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			_lowercase		:    str			     =							False
			_lowercase		:    Tuple			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= TFEsmModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					hidden_size=37)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						self.config_tester.run_common_tests()






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*a)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*a)






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= TFEsmModel.from_pretrained(a)
									self.assertIsNotNone(a)






			@unittest.skip('Protein models do not support embedding resizing.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						pass






			@unittest.skip('Protein models do not support embedding resizing.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						pass







			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									SCREAMING_SNAKE_CASE						= model_class(a)
									assert isinstance(model.get_input_embeddings()		,					tf.keras.layers.Layer)
									if model_class is TFEsmForMaskedLM:
												# Output embedding test differs from the main test because they're a matrix, not a layer
												SCREAMING_SNAKE_CASE						= model.get_bias()
												assert isinstance(a		,					a)
												for k, v in name.items():
															assert isinstance(a		,					tf.Variable)
									else:
												SCREAMING_SNAKE_CASE						= model.get_output_embeddings()
												assert x is None
												SCREAMING_SNAKE_CASE						= model.get_bias()
												assert name is None



@require_tf
class 		_snake_case  (  unittest.TestCase					):






			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Optional[Any]:
						SCREAMING_SNAKE_CASE						= TFEsmForMaskedLM.from_pretrained('facebook/esm2_t6_8M_UR50D')

						SCREAMING_SNAKE_CASE						= tf.constant([[0, 1, 2, 3, 4, 5]])
						SCREAMING_SNAKE_CASE						= model(a)[0]
						SCREAMING_SNAKE_CASE						= [1, 6, 33]
						self.assertEqual(list(output.numpy().shape)		,					a)
						# compare the actual values for a slice.
						SCREAMING_SNAKE_CASE						= tf.constant(
						    [
						        [
						            [8.92_15_18, -10.58_98_14, -6.4_67_13_07],
						            [-6.3_96_71_56, -13.91_13_77, -1.1_21_19_15],
						            [-7.78_12_47, -13.95_15_57, -3.74_05_92],
						        ]
						    ])
						self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,					expected_slice.numpy()		,					atol=1E-2))







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						SCREAMING_SNAKE_CASE						= TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D')

						SCREAMING_SNAKE_CASE						= tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
						SCREAMING_SNAKE_CASE						= model(a)[0]
						# compare the actual values for a slice.
						SCREAMING_SNAKE_CASE						= tf.constant(
						    [
						        [
						            [0.14_44_30_92, 0.54_12_53_27, 0.3_24_77_39],
						            [0.30_34_04_84, 0.00_52_66_76, 0.31_07_77_22],
						            [0.32_27_80_43, -0.24_98_70_96, 0.3_41_46_28],
						        ]
						    ])
						self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()		,					expected_slice.numpy()		,					atol=1E-4))




