





'''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]			,					lowerCAmelCase__  :						Optional[Any]			,					)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			parent
            _UpperCamelCase							       =			13
            _UpperCamelCase							       =			7
            _UpperCamelCase							       =			True
            _UpperCamelCase							       =			True
            _UpperCamelCase							       =			True
            _UpperCamelCase							       =			99
            _UpperCamelCase							       =			32
            _UpperCamelCase							       =			2
            _UpperCamelCase							       =			4
            _UpperCamelCase							       =			37
            _UpperCamelCase							       =			'''gelu'''
            _UpperCamelCase							       =			0.1
            _UpperCamelCase							       =			0.1
            _UpperCamelCase							       =			512
            _UpperCamelCase							       =			16
            _UpperCamelCase							       =			2
            _UpperCamelCase							       =			0.02
            _UpperCamelCase							       =			3
            _UpperCamelCase							       =			4
            _UpperCamelCase							       =			None





       def snake_case__		(				self  :						Tuple	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			ids_tensor([self.batch_size, self.seq_length]			,					self.vocab_size	)

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

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

            _UpperCamelCase							       =			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 snake_case__		(				self  :						Dict	)       ->	int:

            '''simple docstring'''




            (
                (
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							(
                _UpperCamelCase
            )       ,							
            )							       =			self.prepare_config_and_inputs()

            _UpperCamelCase							       =			True
            _UpperCamelCase							       =			floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
            _UpperCamelCase							       =			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 snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						int			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Tuple	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			TFEsmModel(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
            _UpperCamelCase							       =			model(lowerCAmelCase__	)

            _UpperCamelCase							       =			[input_ids, input_mask]
            _UpperCamelCase							       =			model(lowerCAmelCase__	)

            _UpperCamelCase							       =			model(lowerCAmelCase__	)

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





       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						List[Any]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						List[Any]			,					)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			True

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

            _UpperCamelCase							       =			[input_ids, input_mask]
            _UpperCamelCase							       =			model(lowerCAmelCase__			,					encoder_hidden_states=lowerCAmelCase__	)

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

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





       def snake_case__		(				self  :						List[str]			,					lowerCAmelCase__  :						Tuple			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						str			,					lowerCAmelCase__  :						Optional[int]	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			TFEsmForMaskedLM(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			model([input_ids, input_mask]	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.vocab_size)	)





       def snake_case__		(				self  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[int]			,					lowerCAmelCase__  :						Optional[Any]			,					lowerCAmelCase__  :						Any			,					lowerCAmelCase__  :						List[str]			,					lowerCAmelCase__  :						Union[str, Any]			,					lowerCAmelCase__  :						Optional[int]	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			self.num_labels
            _UpperCamelCase							       =			TFEsmForTokenClassification(config=lowerCAmelCase__	)
            _UpperCamelCase							       =			{'''input_ids''': input_ids, '''attention_mask''': input_mask}
            _UpperCamelCase							       =			model(lowerCAmelCase__	)
            self.parent.assertEqual(result.logits.shape			,					(self.batch_size, self.seq_length, self.num_labels)	)







       def snake_case__		(				self  :						int	)       ->	Union[str, Any]:

            '''simple docstring'''




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

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






       """simple docstring"""
       _snake_case      :	Optional[int]									=       (
           (
               TFEsmModel,
               TFEsmForMaskedLM,
               TFEsmForSequenceClassification,
               TFEsmForTokenClassification,
           )
           if is_tf_available()
           else ()
       )
       _snake_case      :	Optional[Any]									=       (
           {
               'feature-extraction': TFEsmModel,
               'fill-mask': TFEsmForMaskedLM,
               'text-classification': TFEsmForSequenceClassification,
               'token-classification': TFEsmForTokenClassification,
               'zero-shot': TFEsmForSequenceClassification,
           }
           if is_tf_available()
           else {}
       )
       _snake_case      :	Dict									=       False
       _snake_case      :	int									=       False





       def snake_case__		(				self  :						Dict	)       ->	Dict:

            '''simple docstring'''




            _UpperCamelCase							       =			TFEsmModelTester(self	)
            _UpperCamelCase							       =			ConfigTester(self			,					config_class=lowerCAmelCase__			,					hidden_size=37	)





       def snake_case__		(				self  :						List[Any]	)       ->	Dict:

            '''simple docstring'''




            self.config_tester.run_common_tests()





       def snake_case__		(				self  :						Any	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_model(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Dict	)       ->	Optional[int]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_decoder()
            self.model_tester.create_and_check_model_as_decoder(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Union[str, Any]	)       ->	Union[str, Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase__	)





       def snake_case__		(				self  :						Optional[Any]	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase__	)





       @slow
       def snake_case__		(				self  :						int	)       ->	Optional[Any]:

            '''simple docstring'''




            for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 _UpperCamelCase							       =			TFEsmModel.from_pretrained(lowerCAmelCase__	)
                 self.assertIsNotNone(lowerCAmelCase__	)





       @unittest.skip('''Protein models do not support embedding resizing.'''	)
       def snake_case__		(				self  :						Union[str, Any]	)       ->	List[str]:

            '''simple docstring'''




            pass





       @unittest.skip('''Protein models do not support embedding resizing.'''	)
       def snake_case__		(				self  :						int	)       ->	List[str]:

            '''simple docstring'''




            pass







       def snake_case__		(				self  :						Tuple	)       ->	str:

            '''simple docstring'''




            _UpperCamelCase       ,							_UpperCamelCase							       =			self.model_tester.prepare_config_and_inputs_for_common()

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

@require_tf
class 							__lowerCAmelCase	(       unittest.TestCase					):






       """simple docstring"""





       @slow
       def snake_case__		(				self  :						Tuple	)       ->	Optional[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			TFEsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''	)

            _UpperCamelCase							       =			tf.constant([[0, 1, 2, 3, 4, 5]]	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)[0]
            _UpperCamelCase							       =			[1, 6, 33]
            self.assertEqual(list(output.numpy().shape	)			,					lowerCAmelCase__	)
            # compare the actual values for a slice.
            _UpperCamelCase							       =			tf.constant(
                [
                    [
                        [8.921518, -10.589814, -6.4671307],
                        [-6.3967156, -13.911377, -1.1211915],
                        [-7.781247, -13.951557, -3.740592],
                    ]
                ]	)
            self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,					expected_slice.numpy()			,					atol=1e-2	)	)







       @slow
       def snake_case__		(				self  :						int	)       ->	List[Any]:

            '''simple docstring'''




            _UpperCamelCase							       =			TFEsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''	)

            _UpperCamelCase							       =			tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]	)
            _UpperCamelCase							       =			model(lowerCAmelCase__	)[0]
            # compare the actual values for a slice.
            _UpperCamelCase							       =			tf.constant(
                [
                    [
                        [0.14443092, 0.54125327, 0.3247739],
                        [0.30340484, 0.00526676, 0.31077722],
                        [0.32278043, -0.24987096, 0.3414628],
                    ]
                ]	)
            self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()			,					expected_slice.numpy()			,					atol=1e-4	)	)











"""simple docstring"""
import os
from typing import Dict, List, Tuple, TypeVar, Union


lowerCamelCase_							      = TypeVar('''T''')

lowerCamelCase_							      = Union[List[T], Tuple[T, ...]]
lowerCamelCase_							      = Union[T, List[T], Dict[str, T]]
lowerCamelCase_							      = Union[str, bytes, os.PathLike]



