






'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


a_		:						Any             =    {'configuration_fnet': ['FNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FNetConfig']}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Union[str, Any]             =    ['FNetTokenizer']

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Union[str, Any]             =    ['FNetTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Tuple             =    [
				    'FNET_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'FNetForMaskedLM',
				    'FNetForMultipleChoice',
				    'FNetForNextSentencePrediction',
				    'FNetForPreTraining',
				    'FNetForQuestionAnswering',
				    'FNetForSequenceClassification',
				    'FNetForTokenClassification',
				    'FNetLayer',
				    'FNetModel',
				    'FNetPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet import FNetTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_fnet_fast import FNetTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_fnet import (
								    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    FNetForMaskedLM,
								    FNetForMultipleChoice,
								    FNetForNextSentencePrediction,
								    FNetForPreTraining,
								    FNetForQuestionAnswering,
								    FNetForSequenceClassification,
								    FNetForTokenClassification,
								    FNetLayer,
								    FNetModel,
								    FNetPreTrainedModel,
								)


else:
				import sys

				a_		:						List[str]             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












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))




