
from __future__ import annotations

import copy
import inspect
import unittest

import numpy as np

from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property

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 tensorflow as tf

		from transformers import (
		    TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    LayoutLMvaConfig,
		    TFLayoutLMvaForQuestionAnswering,
		    TFLayoutLMvaForSequenceClassification,
		    TFLayoutLMvaForTokenClassification,
		    TFLayoutLMvaModel,
		)

if is_vision_available():
		from PIL import Image

		from transformers import LayoutLMvaImageProcessor







class 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      Optional[Any]  ,A_     :      Optional[Any]  ,A_     :      int=2  ,A_     :      int=3  ,A_     :      int=4  ,A_     :      Optional[int]=2  ,A_     :      Dict=7  ,A_     :      Any=True  ,A_     :      Dict=True  ,A_     :      Tuple=True  ,A_     :      List[Any]=True  ,A_     :      Dict=99  ,A_     :      List[str]=36  ,A_     :      str=2  ,A_     :      Union[str, Any]=4  ,A_     :      Any=37  ,A_     :      int="gelu"  ,A_     :      Tuple=0.1  ,A_     :      Tuple=0.1  ,A_     :      Union[str, Any]=512  ,A_     :      List[Any]=16  ,A_     :      int=2  ,A_     :      Union[str, Any]=0.02  ,A_     :      Dict=6  ,A_     :      str=6  ,A_     :      List[str]=3  ,A_     :      Optional[Any]=4  ,A_     :      Optional[Any]=None  ,A_     :      Any=1000  ,)     ->   List[Any]:
							A						   =   parent
							A						   =   batch_size
							A						   =   num_channels
							A						   =   image_size
							A						   =   patch_size
							A						   =   is_training
							A						   =   use_input_mask
							A						   =   use_token_type_ids
							A						   =   use_labels
							A						   =   vocab_size
							A						   =   hidden_size
							A						   =   num_hidden_layers
							A						   =   num_attention_heads
							A						   =   intermediate_size
							A						   =   hidden_act
							A						   =   hidden_dropout_prob
							A						   =   attention_probs_dropout_prob
							A						   =   max_position_embeddings
							A						   =   type_vocab_size
							A						   =   type_sequence_label_size
							A						   =   initializer_range
							A						   =   coordinate_size
							A						   =   shape_size
							A						   =   num_labels
							A						   =   num_choices
							A						   =   scope
							A						   =   range_bbox

							# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
							A						   =   text_seq_length
							A						   =   (image_size // patch_size) ** 2 + 1
							A						   =   self.text_seq_length + self.image_seq_length


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Union[str, Any]:
							A						   =   ids_tensor([self.batch_size, self.text_seq_length]  ,self.vocab_size      )

							A						   =   ids_tensor([self.batch_size, self.text_seq_length, 4]  ,self.range_bbox      )
							A						   =   bbox.numpy()
							# Ensure that bbox is legal
							for i in range(bbox.shape[0]      ):
								for j in range(bbox.shape[1]      ):
									if bbox[i, j, 3] < bbox[i, j, 1]:
										A						   =   bbox[i, j, 3]
										A						   =   bbox[i, j, 1]
										A						   =   tmp_coordinate
									if bbox[i, j, 2] < bbox[i, j, 0]:
										A						   =   bbox[i, j, 2]
										A						   =   bbox[i, j, 0]
										A						   =   tmp_coordinate
							A						   =   tf.constant(A_      )

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

							A						   =   None
							if self.use_input_mask:
								A						   =   random_attention_mask([self.batch_size, self.text_seq_length]      )

							A						   =   None
							if self.use_token_type_ids:
								A						   =   ids_tensor([self.batch_size, self.text_seq_length]  ,self.type_vocab_size      )

							A						   =   None
							A						   =   None
							if self.use_labels:
								A						   =   ids_tensor([self.batch_size]  ,self.type_sequence_label_size      )
								A						   =   ids_tensor([self.batch_size, self.text_seq_length]  ,self.num_labels      )

							A						   =   LayoutLMvaConfig(
							    vocab_size=self.vocab_size  ,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  ,max_position_embeddings=self.max_position_embeddings  ,type_vocab_size=self.type_vocab_size  ,initializer_range=self.initializer_range  ,coordinate_size=self.coordinate_size  ,shape_size=self.shape_size  ,input_size=self.image_size  ,patch_size=self.patch_size  ,)

							return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Optional[Any]  ,A_     :      Tuple  ,A_     :      Optional[int]  ,A_     :      Optional[int]  ,A_     :      str  ,A_     :      Optional[int]      )     ->   Dict:
							A						   =   TFLayoutLMvaModel(config=A_      )

							# text + image
							A						   =   model(A_  ,pixel_values=A_  ,training=A_      )
							A						   =   model(
							    A_  ,bbox=A_  ,pixel_values=A_  ,attention_mask=A_  ,token_type_ids=A_  ,training=A_  ,)
							A						   =   model(A_  ,bbox=A_  ,pixel_values=A_  ,training=A_      )

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

							# text only
							A						   =   model(A_  ,training=A_      )
							self.parent.assertEqual(
							    result.last_hidden_state.shape  ,(self.batch_size, self.text_seq_length, self.hidden_size)      )

							# image only
							A						   =   model({'pixel_values': pixel_values}  ,training=A_      )
							self.parent.assertEqual(
							    result.last_hidden_state.shape  ,(self.batch_size, self.image_seq_length, self.hidden_size)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      int  ,A_     :      Tuple  ,A_     :      int  ,A_     :      str  ,A_     :      List[str]  ,A_     :      Any  ,A_     :      List[str]  ,A_     :      Optional[int]      )     ->   Dict:
							A						   =   self.num_labels
							A						   =   TFLayoutLMvaForSequenceClassification(config=A_      )
							A						   =   model(
							    A_  ,bbox=A_  ,pixel_values=A_  ,attention_mask=A_  ,token_type_ids=A_  ,labels=A_  ,training=A_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.num_labels)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str  ,A_     :      Optional[Any]  ,A_     :      Optional[int]  ,A_     :      Any  ,A_     :      Any  ,A_     :      List[Any]  ,A_     :      List[str]  ,A_     :      Tuple      )     ->   Tuple:
							A						   =   self.num_labels
							A						   =   TFLayoutLMvaForTokenClassification(config=A_      )
							A						   =   model(
							    A_  ,bbox=A_  ,pixel_values=A_  ,attention_mask=A_  ,token_type_ids=A_  ,labels=A_  ,training=A_  ,)
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.text_seq_length, self.num_labels)      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Dict  ,A_     :      Union[str, Any]  ,A_     :      List[str]  ,A_     :      int  ,A_     :      Any  ,A_     :      int  ,A_     :      Union[str, Any]  ,A_     :      Any      )     ->   str:
							A						   =   2
							A						   =   TFLayoutLMvaForQuestionAnswering(config=A_      )
							A						   =   model(
							    A_  ,bbox=A_  ,pixel_values=A_  ,attention_mask=A_  ,token_type_ids=A_  ,start_positions=A_  ,end_positions=A_  ,training=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     :      List[Any]      )     ->   Tuple:
							A						   =   self.prepare_config_and_inputs()
							((A)   ,      (A)   ,      (A)   ,      (A)   ,      (A)   ,      (A)   ,      (A)   ,      (A))						   =   config_and_inputs
							A						   =   {
							    'input_ids': input_ids,
							    'bbox': bbox,
							    'pixel_values': pixel_values,
							    'token_type_ids': token_type_ids,
							    'attention_mask': input_mask,
							}
							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Tuple      =    (
						    (
						        TFLayoutLMvaModel,
						        TFLayoutLMvaForQuestionAnswering,
						        TFLayoutLMvaForSequenceClassification,
						        TFLayoutLMvaForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:    Optional[int]      =    (
						    {'''document-question-answering''': TFLayoutLMvaForQuestionAnswering, '''feature-extraction''': TFLayoutLMvaModel}
						    if is_tf_available()
						    else {}
						)

						_lowerCamelCase:    List[str]      =    False
						_lowerCamelCase:    str      =    False
						_lowerCamelCase:    int      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      str  ,A_     :      Any  ,A_     :      Optional[Any]  ,A_     :      List[Any]  ,A_     :      str      )     ->   Dict:
							return True


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]  ,A_     :      str  ,A_     :      List[str]  ,A_     :      Optional[int]=False      )     ->   dict:
							A						   =   copy.deepcopy(A_      )

							if model_class in get_values(A_      ):
								A						   =   {
								    k: tf.tile(tf.expand_dims(A_  ,1      )  ,(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)      )
								    if isinstance(A_  ,tf.Tensor      ) and v.ndim > 0
								    else v
								    for k, v in inputs_dict.items()
								}

							if return_labels:
								if model_class in get_values(A_      ):
									A						   =   tf.ones(self.model_tester.batch_size  ,dtype=tf.intaa      )
								elif model_class in get_values(A_      ):
									A						   =   tf.zeros(self.model_tester.batch_size  ,dtype=tf.intaa      )
									A						   =   tf.zeros(self.model_tester.batch_size  ,dtype=tf.intaa      )
								elif model_class in get_values(A_      ):
									A						   =   tf.zeros(self.model_tester.batch_size  ,dtype=tf.intaa      )
								elif model_class in get_values(A_      ):
									A						   =   tf.zeros(
									    (self.model_tester.batch_size, self.model_tester.text_seq_length)  ,dtype=tf.intaa      )

							return inputs_dict


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[str]:
							A						   =   TFLayoutLMvaModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,hidden_size=37      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Union[str, Any]:
							self.config_tester.run_common_tests()


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Tuple:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes:
								A						   =   model_class(A_      )
								if getattr(A_  ,'hf_compute_loss'  ,A_      ):
									# The number of elements in the loss should be the same as the number of elements in the label
									A						   =   self._prepare_for_class(inputs_dict.copy()  ,A_  ,return_labels=A_      )
									A						   =   prepared_for_class[
									    sorted(prepared_for_class.keys() - inputs_dict.keys()  ,reverse=A_      )[0]
									]
									A						   =   added_label.shape.as_list()[:1]

									# Test that model correctly compute the loss with kwargs
									A						   =   self._prepare_for_class(inputs_dict.copy()  ,A_  ,return_labels=A_      )
									A						   =   prepared_for_class.pop('input_ids'      )

									A						   =   model(A_  ,**A_      )[0]
									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

									# Test that model correctly compute the loss when we mask some positions
									A						   =   self._prepare_for_class(inputs_dict.copy()  ,A_  ,return_labels=A_      )
									A						   =   prepared_for_class.pop('input_ids'      )
									if "labels" in prepared_for_class:
										A						   =   prepared_for_class['labels'].numpy()
										if len(labels.shape      ) > 1 and labels.shape[1] != 1:
											A						   =   -100
											A						   =   tf.convert_to_tensor(A_      )
											A						   =   model(A_  ,**A_      )[0]
											self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )
											self.assertTrue(not np.any(np.isnan(loss.numpy()      )      )      )

                # Test that model correctly compute the loss with a dict
									A						   =   self._prepare_for_class(inputs_dict.copy()  ,A_  ,return_labels=A_      )
									A						   =   model(A_      )[0]
									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )

									# Test that model correctly compute the loss with a tuple
									A						   =   self._prepare_for_class(inputs_dict.copy()  ,A_  ,return_labels=A_      )

									# Get keys that were added with the _prepare_for_class function
									A						   =   prepared_for_class.keys() - inputs_dict.keys()
									A						   =   inspect.signature(model.call      ).parameters
									A						   =   list(signature.keys()      )

									# Create a dictionary holding the location of the tensors in the tuple
									A						   =   {0: 'input_ids'}
									for label_key in label_keys:
										A						   =   signature_names.index(A_      )
										A						   =   label_key
									A						   =   sorted(tuple_index_mapping.items()      )
									# Initialize a list with their default values, update the values and convert to a tuple
									A						   =   []

									for name in signature_names:
										if name != "kwargs":
											list_input.append(signature[name].default      )

									for index, value in sorted_tuple_index_mapping:
										A						   =   prepared_for_class[value]

									A						   =   tuple(A_      )

									# Send to model
									A						   =   model(tuple_input[:-1]      )[0]

									self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							(
							    (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,            
							)						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(A_  ,A_  ,A_  ,A_  ,A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   List[str]:
							(
							    (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,            
							)						   =   self.model_tester.prepare_config_and_inputs()
							for type in ["absolute", "relative_key", "relative_key_query"]:
								A						   =   type
								self.model_tester.create_and_check_model(A_  ,A_  ,A_  ,A_  ,A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Union[str, Any]:
							(
							    (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,            
							)						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_sequence_classification(
							    A_  ,A_  ,A_  ,A_  ,A_  ,A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   List[str]:
							(
							    (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,            
							)						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(
							    A_  ,A_  ,A_  ,A_  ,A_  ,A_  ,A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Tuple:
							(
							    (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,      (
							    A      
							)   ,            
							)						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_question_answering(
							    A_  ,A_  ,A_  ,A_  ,A_  ,A_  ,A_      )




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   Optional[Any]:
							for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   TFLayoutLMvaModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )






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







@require_tf
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


						@cached_property
						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							return LayoutLMvaImageProcessor(apply_ocr=A_      ) if is_vision_available() else None




						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[Any]      )     ->   Optional[Any]:
							A						   =   TFLayoutLMvaModel.from_pretrained('microsoft/layoutlmv3-base'      )

							A						   =   self.default_image_processor
							A						   =   prepare_img()
							A						   =   image_processor(images=A_  ,return_tensors='tf'      ).pixel_values

							A						   =   tf.constant([[1, 2]]      )
							A						   =   tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]      )  ,axis=0      )

							# forward pass
							A						   =   model(input_ids=A_  ,bbox=A_  ,pixel_values=A_  ,training=A_      )

							# verify the logits
							A						   =   (1, 199, 768)
							self.assertEqual(outputs.last_hidden_state.shape  ,A_      )

							A						   =   tf.constant(
							    [[-0.05_29, 0.36_18, 0.16_32], [-0.15_87, -0.16_67, -0.04_00], [-0.15_57, -0.16_71, -0.05_05]]      )

							self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3]  ,A_  ,atol=1e-4      )      )

"""simple docstring"""


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 				lowerCAmelCase_				:



						'''simple docstring'''


						def __init__( self     :      List[Any]  ,A_     :      Optional[Any]  ,)     ->   Optional[int]:
							A						   =   parent
							A						   =   13
							A						   =   7
							A						   =   True
							A						   =   True
							A						   =   True
							A						   =   99
							A						   =   32
							A						   =   2
							A						   =   4
							A						   =   37
							A						   =   'gelu'
							A						   =   0.1
							A						   =   0.1
							A						   =   512
							A						   =   16
							A						   =   2
							A						   =   0.02
							A						   =   3
							A						   =   4
							A						   =   None


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   Union[str, Any]:
							A						   =   ids_tensor([self.batch_size, self.seq_length]  ,self.vocab_size      )

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

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

							A						   =   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     :      Union[str, Any]      )     ->   Dict:
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   self.prepare_config_and_inputs()

							A						   =   True
							A						   =   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
							A						   =   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     :      Optional[int]  ,A_     :      int  ,A_     :      List[str]  ,A_     :      Optional[int]  ,A_     :      List[Any]  ,A_     :      Any  ,A_     :      Any      )     ->   Dict:
							A						   =   TFEsmModel(config=A_      )
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							A						   =   model(A_      )

							A						   =   [input_ids, input_mask]
							A						   =   model(A_      )

							A						   =   model(A_      )

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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]  ,A_     :      Any  ,A_     :      Union[str, Any]  ,A_     :      Tuple  ,A_     :      int  ,A_     :      List[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[str]  ,)     ->   Optional[int]:
							A						   =   True

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

							A						   =   [input_ids, input_mask]
							A						   =   model(A_  ,encoder_hidden_states=A_      )

							# Also check the case where encoder outputs are not passed
							A						   =   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     :      str  ,A_     :      List[Any]  ,A_     :      List[Any]  ,A_     :      Optional[Any]  ,A_     :      Optional[int]  ,A_     :      Optional[Any]  ,A_     :      List[Any]      )     ->   Dict:
							A						   =   TFEsmForMaskedLM(config=A_      )
							A						   =   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     :      Any  ,A_     :      Optional[Any]  ,A_     :      str  ,A_     :      List[Any]  ,A_     :      int  ,A_     :      Tuple  ,A_     :      Optional[int]      )     ->   Union[str, Any]:
							A						   =   self.num_labels
							A						   =   TFEsmForTokenClassification(config=A_      )
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							A						   =   model(A_      )
							self.parent.assertEqual(result.logits.shape  ,(self.batch_size, self.seq_length, self.num_labels)      )




						def 						_SCREAMING_SNAKE_CASE      ( self     :      Any      )     ->   List[Any]:
							A						   =   self.prepare_config_and_inputs()
							(
							    (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      (
							    A
							)   ,      
							)						   =   config_and_inputs
							A						   =   {'input_ids': input_ids, 'attention_mask': input_mask}
							return config, inputs_dict







@require_tf
class 				lowerCAmelCase_				(      _lowercase		,					_lowercase		,					unittest.TestCase    ):



						'''simple docstring'''


						_lowerCamelCase:    Dict      =    (
						    (
						        TFEsmModel,
						        TFEsmForMaskedLM,
						        TFEsmForSequenceClassification,
						        TFEsmForTokenClassification,
						    )
						    if is_tf_available()
						    else ()
						)
						_lowerCamelCase:    List[str]      =    (
						    {
						        '''feature-extraction''': TFEsmModel,
						        '''fill-mask''': TFEsmForMaskedLM,
						        '''text-classification''': TFEsmForSequenceClassification,
						        '''token-classification''': TFEsmForTokenClassification,
						        '''zero-shot''': TFEsmForSequenceClassification,
						    }
						    if is_tf_available()
						    else {}
						)
						_lowerCamelCase:    Union[str, Any]      =    False
						_lowerCamelCase:    List[Any]      =    False


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   int:
							A						   =   TFEsmModelTester(self      )
							A						   =   ConfigTester(self  ,config_class=A_  ,hidden_size=37      )


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


						def 						_SCREAMING_SNAKE_CASE      ( self     :      Union[str, Any]      )     ->   List[str]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_model(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs_for_decoder()
							self.model_tester.create_and_check_model_as_decoder(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_masked_lm(*A_      )


						def 						_SCREAMING_SNAKE_CASE      ( self     :      List[Any]      )     ->   List[Any]:
							A						   =   self.model_tester.prepare_config_and_inputs()
							self.model_tester.create_and_check_for_token_classification(*A_      )


						@slow
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Tuple      )     ->   Tuple:
							for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								A						   =   TFEsmModel.from_pretrained(A_      )
								self.assertIsNotNone(A_      )


						@unittest.skip('Protein models do not support embedding resizing.'      )
						def 						_SCREAMING_SNAKE_CASE      ( self     :      Optional[int]      )     ->   Optional[int]:
							pass


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




						def 						_SCREAMING_SNAKE_CASE      ( self     :      str      )     ->   Dict:
							A   ,      A						   =   self.model_tester.prepare_config_and_inputs_for_common()

							for model_class in self.all_model_classes:
								A						   =   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
									A						   =   model.get_bias()
									assert isinstance(A_  ,A_      )
									for k, v in name.items():
										assert isinstance(A_  ,tf.Variable      )
								else:
									A						   =   model.get_output_embeddings()
									assert x is None
									A						   =   model.get_bias()
									assert name is None







@require_tf
class 				lowerCAmelCase_				(      unittest.TestCase    ):



						'''simple docstring'''


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

							A						   =   tf.constant([[0, 1, 2, 3, 4, 5]]      )
							A						   =   model(A_      )[0]
							A						   =   [1, 6, 33]
							self.assertEqual(list(output.numpy().shape      )  ,A_      )
							# compare the actual values for a slice.
							A						   =   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     :      Optional[Any]      )     ->   int:
							A						   =   TFEsmModel.from_pretrained('facebook/esm2_t6_8M_UR50D'      )

							A						   =   tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
							A						   =   model(A_      )[0]
							# compare the actual values for a slice.
							A						   =   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      )      )