






import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
				import torch

				from transformers import CvtForImageClassification, CvtModel
				from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from PIL import Image

				from transformers import AutoImageProcessor

class 		_snake_case  (  A__					):






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= self.config_class(**self.inputs_dict)
						self.parent.assertTrue(hasattr(a		,					'embed_dim'))
						self.parent.assertTrue(hasattr(a		,					'num_heads'))



class 		_snake_case  :






			def __init__(       self		,					a		,					a=13		,					a=64		,					a=3		,					a=[16, 48, 96]		,					a=[1, 3, 6]		,					a=[1, 2, 10]		,					a=[7, 3, 3]		,					a=[4, 2, 2]		,					a=[2, 1, 1]		,					a=[2, 2, 2]		,					a=[False, False, True]		,					a=[0.0, 0.0, 0.0]		,					a=0.02		,					a=1E-12		,					a=True		,					a=True		,					a=2		,					)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= parent
						SCREAMING_SNAKE_CASE						= batch_size
						SCREAMING_SNAKE_CASE						= image_size
						SCREAMING_SNAKE_CASE						= patch_sizes
						SCREAMING_SNAKE_CASE						= patch_stride
						SCREAMING_SNAKE_CASE						= patch_padding
						SCREAMING_SNAKE_CASE						= is_training
						SCREAMING_SNAKE_CASE						= use_labels
						SCREAMING_SNAKE_CASE						= num_labels
						SCREAMING_SNAKE_CASE						= num_channels
						SCREAMING_SNAKE_CASE						= embed_dim
						SCREAMING_SNAKE_CASE						= num_heads
						SCREAMING_SNAKE_CASE						= stride_kv
						SCREAMING_SNAKE_CASE						= depth
						SCREAMING_SNAKE_CASE						= cls_token
						SCREAMING_SNAKE_CASE						= attention_drop_rate
						SCREAMING_SNAKE_CASE						= initializer_range
						SCREAMING_SNAKE_CASE						= layer_norm_eps






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       int:
						SCREAMING_SNAKE_CASE						= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

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

						SCREAMING_SNAKE_CASE						= self.get_config()
						return config, pixel_values, labels






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						return CvtConfig(
						    image_size=self.image_size		,					num_labels=self.num_labels		,					num_channels=self.num_channels		,					embed_dim=self.embed_dim		,					num_heads=self.num_heads		,					patch_sizes=self.patch_sizes		,					patch_padding=self.patch_padding		,					patch_stride=self.patch_stride		,					stride_kv=self.stride_kv		,					depth=self.depth		,					cls_token=self.cls_token		,					attention_drop_rate=self.attention_drop_rate		,					initializer_range=self.initializer_range		,					)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       List[Any]:
						SCREAMING_SNAKE_CASE						= CvtModel(config=a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(a)
						SCREAMING_SNAKE_CASE						= (self.image_size, self.image_size)
						SCREAMING_SNAKE_CASE				,						SCREAMING_SNAKE_CASE						= image_size[0], image_size[1]
						for i in range(len(self.depth)):
									SCREAMING_SNAKE_CASE						= floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
									SCREAMING_SNAKE_CASE						= floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1)
						self.parent.assertEqual(result.last_hidden_state.shape		,					(self.batch_size, self.embed_dim[-1], height, width))






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       Optional[int]:
						SCREAMING_SNAKE_CASE						= self.num_labels
						SCREAMING_SNAKE_CASE						= CvtForImageClassification(a)
						model.to(a)
						model.eval()
						SCREAMING_SNAKE_CASE						= model(a		,					labels=a)
						self.parent.assertEqual(result.logits.shape		,					(self.batch_size, 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						= config_and_inputs
						SCREAMING_SNAKE_CASE						= {'pixel_values': pixel_values}
						return config, inputs_dict



@require_torch
class 		_snake_case  (  A__   ,    A__   ,    unittest.TestCase					):
			_lowercase		:    Tuple			     =							(CvtModel, CvtForImageClassification) if is_torch_available() else ()
			_lowercase		:    Tuple			     =							(
			    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
			    if is_torch_available()
			    else {}
			)

			_lowercase		:    Union[str, Any]			     =							False
			_lowercase		:    Any			     =							False
			_lowercase		:    str			     =							False
			_lowercase		:    List[str]			     =							False
			_lowercase		:    Union[str, Any]			     =							False






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						SCREAMING_SNAKE_CASE						= CvtModelTester(self)
						SCREAMING_SNAKE_CASE						= ConfigTester(self		,					config_class=a		,					has_text_modality=a		,					hidden_size=37)






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Tuple:
						self.create_and_test_config_common_properties()
						self.config_tester.create_and_test_config_to_json_string()
						self.config_tester.create_and_test_config_to_json_file()
						self.config_tester.create_and_test_config_from_and_save_pretrained()
						self.config_tester.create_and_test_config_with_num_labels()
						self.config_tester.check_config_can_be_init_without_params()
						self.config_tester.check_config_arguments_init()






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






			@unittest.skip(reason='Cvt does not output attentions')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						pass






			@unittest.skip(reason='Cvt does not use inputs_embeds')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						pass






			@unittest.skip(reason='Cvt does not support input and output embeddings')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[str]:
						pass






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Dict:
						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)
									SCREAMING_SNAKE_CASE						= inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									SCREAMING_SNAKE_CASE						= [*signature.parameters.keys()]

									SCREAMING_SNAKE_CASE						= ['pixel_values']
									self.assertListEqual(arg_names[:1]		,					a)






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






			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Any:
						def check_hidden_states_output(a		,					a		,					a):
									SCREAMING_SNAKE_CASE						= model_class(a)
									model.to(a)
									model.eval()

									with torch.no_grad():
												SCREAMING_SNAKE_CASE						= model(**self._prepare_for_class(a		,					a))

									SCREAMING_SNAKE_CASE						= outputs.hidden_states

									SCREAMING_SNAKE_CASE						= len(self.model_tester.depth)
									self.assertEqual(len(a)		,					a)

									# verify the first hidden states (first block)
									self.assertListEqual(
									    list(hidden_states[0].shape[-3:])		,					[
									        self.model_tester.embed_dim[0],
									        self.model_tester.image_size // 4,
									        self.model_tester.image_size // 4,
									    ]		,					)

						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						= True
									check_hidden_states_output(a		,					a		,					a)

									# check that output_hidden_states also work using config
									del inputs_dict["output_hidden_states"]
									SCREAMING_SNAKE_CASE						= True

									check_hidden_states_output(a		,					a		,					a)






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






			@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.')
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       str:
						pass







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									SCREAMING_SNAKE_CASE						= CvtModel.from_pretrained(a)
									self.assertIsNotNone(a)






def 				lowerCamelCase__				():
			SCREAMING_SNAKE_CASE						= Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
			return image







@require_torch
@require_vision
class 		_snake_case  (  unittest.TestCase					):






			@cached_property
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       List[Any]:
						return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0])







			@slow
			def    SCREAMING_SNAKE_CASE__    (       self)      ->       Union[str, Any]:
						SCREAMING_SNAKE_CASE						= CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to(a)

						SCREAMING_SNAKE_CASE						= self.default_image_processor
						SCREAMING_SNAKE_CASE						= prepare_img()
						SCREAMING_SNAKE_CASE						= image_processor(images=a		,					return_tensors='pt').to(a)

						# forward pass
						with torch.no_grad():
									SCREAMING_SNAKE_CASE						= model(**a)

						# verify the logits
						SCREAMING_SNAKE_CASE						= torch.Size((1, 1000))
						self.assertEqual(outputs.logits.shape		,					a)

						SCREAMING_SNAKE_CASE						= torch.tensor([0.92_85, 0.90_15, -0.31_50]).to(a)

						self.assertTrue(torch.allclose(outputs.logits[0, :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))




