
"""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 			_SCREAMING_SNAKE_CASE					:






			def __init__(					self       ,   __A       ,   )			->     Any:
						lowerCAmelCase_    :int			     =  parent
						lowerCAmelCase_    :Tuple			     =  13
						lowerCAmelCase_    :Optional[Any]			     =  7
						lowerCAmelCase_    :List[str]			     =  True
						lowerCAmelCase_    :Union[str, Any]			     =  True
						lowerCAmelCase_    :Tuple			     =  True
						lowerCAmelCase_    :int			     =  99
						lowerCAmelCase_    :Optional[Any]			     =  32
						lowerCAmelCase_    :Optional[int]			     =  2
						lowerCAmelCase_    :Optional[Any]			     =  4
						lowerCAmelCase_    :Any			     =  37
						lowerCAmelCase_    :List[Any]			     =  """gelu"""
						lowerCAmelCase_    :Optional[Any]			     =  0.1
						lowerCAmelCase_    :Dict			     =  0.1
						lowerCAmelCase_    :Union[str, Any]			     =  512
						lowerCAmelCase_    :Union[str, Any]			     =  16
						lowerCAmelCase_    :Optional[int]			     =  2
						lowerCAmelCase_    :str			     =  0.0_2
						lowerCAmelCase_    :List[Any]			     =  3
						lowerCAmelCase_    :Union[str, Any]			     =  4
						lowerCAmelCase_    :int			     =  None






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :str			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size       )

						lowerCAmelCase_    :Any			     =  None
						if self.use_input_mask:
									lowerCAmelCase_    :Union[str, Any]			     =  random_attention_mask([self.batch_size, self.seq_length]       )

						lowerCAmelCase_    :Tuple			     =  None
						lowerCAmelCase_    :Tuple			     =  None
						lowerCAmelCase_    :Optional[Any]			     =  None
						if self.use_labels:
									lowerCAmelCase_    :Union[str, Any]			     =  ids_tensor([self.batch_size]       ,   self.type_sequence_label_size       )
									lowerCAmelCase_    :Optional[int]			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels       )
									lowerCAmelCase_    :str			     =  ids_tensor([self.batch_size]       ,   self.num_choices       )

						lowerCAmelCase_    :Optional[Any]			     =  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  __lowerCAmelCase (					self       )			->     List[str]:
						(
						    (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       
						)    :Tuple			     =  self.prepare_config_and_inputs()

						lowerCAmelCase_    :Union[str, Any]			     =  True
						lowerCAmelCase_    :Any			     =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
						lowerCAmelCase_    :int			     =  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  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     int:
						lowerCAmelCase_    :Optional[Any]			     =  TFEsmModel(config=__A       )
						lowerCAmelCase_    :Tuple			     =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
						lowerCAmelCase_    :List[str]			     =  model(__A       )

						lowerCAmelCase_    :Union[str, Any]			     =  [input_ids, input_mask]
						lowerCAmelCase_    :int			     =  model(__A       )

						lowerCAmelCase_    :int			     =  model(__A       )

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






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   )			->     Any:
						lowerCAmelCase_    :Optional[int]			     =  True

						lowerCAmelCase_    :Tuple			     =  TFEsmModel(config=__A       )
						lowerCAmelCase_    :List[Any]			     =  {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """encoder_hidden_states""": encoder_hidden_states,
						    """encoder_attention_mask""": encoder_attention_mask,
						}
						lowerCAmelCase_    :Dict			     =  model(__A       )

						lowerCAmelCase_    :Optional[int]			     =  [input_ids, input_mask]
						lowerCAmelCase_    :Optional[Any]			     =  model(__A       ,   encoder_hidden_states=__A       )

						# Also check the case where encoder outputs are not passed
						lowerCAmelCase_    :int			     =  model(__A       ,   attention_mask=__A       )

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






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Any:
						lowerCAmelCase_    :Optional[int]			     =  TFEsmForMaskedLM(config=__A       )
						lowerCAmelCase_    :Tuple			     =  model([input_ids, input_mask]       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.vocab_size)       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Optional[int]:
						lowerCAmelCase_    :Dict			     =  self.num_labels
						lowerCAmelCase_    :List[str]			     =  TFEsmForTokenClassification(config=__A       )
						lowerCAmelCase_    :str			     =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
						lowerCAmelCase_    :Tuple			     =  model(__A       )
						self.parent.assertEqual(result.logits.shape       ,   (self.batch_size, self.seq_length, self.num_labels)       )






			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :List[Any]			     =  self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       
						)    :Optional[int]			     =  config_and_inputs
						lowerCAmelCase_    :Tuple			     =  {"""input_ids""": input_ids, """attention_mask""": input_mask}
						return config, inputs_dict




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			A__	, A__	, unittest.TestCase							):
			UpperCAmelCase_  :Any				 =						(
			    (
			        TFEsmModel,
			        TFEsmForMaskedLM,
			        TFEsmForSequenceClassification,
			        TFEsmForTokenClassification,
			    )
			    if is_tf_available()
			    else ()
			)
			UpperCAmelCase_  :int				 =						(
			    {
			        "feature-extraction": TFEsmModel,
			        "fill-mask": TFEsmForMaskedLM,
			        "text-classification": TFEsmForSequenceClassification,
			        "token-classification": TFEsmForTokenClassification,
			        "zero-shot": TFEsmForSequenceClassification,
			    }
			    if is_tf_available()
			    else {}
			)
			UpperCAmelCase_  :Any				 =						False
			UpperCAmelCase_  :List[str]				 =						False






			def  __lowerCAmelCase (					self       )			->     List[Any]:
						lowerCAmelCase_    :str			     =  TFEsmModelTester(self       )
						lowerCAmelCase_    :List[Any]			     =  ConfigTester(self       ,   config_class=__A       ,   hidden_size=37       )






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






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :str			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__A       )






			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :Dict			     =  self.model_tester.prepare_config_and_inputs_for_decoder()
						self.model_tester.create_and_check_model_as_decoder(*__A       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Optional[Any]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__A       )






			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :Optional[int]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__A       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Optional[int]:
						for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :Union[str, Any]			     =  TFEsmModel.from_pretrained(__A       )
									self.assertIsNotNone(__A       )






			@unittest.skip("""Protein models do not support embedding resizing."""       )
			def  __lowerCAmelCase (					self       )			->     str:
						pass






			@unittest.skip("""Protein models do not support embedding resizing."""       )
			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						pass






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_     ,       lowerCAmelCase_    :List[str]			     =  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
									lowerCAmelCase_    :Union[str, Any]			     =  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
												lowerCAmelCase_    :Optional[int]			     =  model.get_bias()
												assert isinstance(__A       ,   __A       )
												for k, v in name.items():
															assert isinstance(__A       ,   tf.Variable       )
									else:
												lowerCAmelCase_    :Tuple			     =  model.get_output_embeddings()
												assert x is None
												lowerCAmelCase_    :Any			     =  model.get_bias()
												assert name is None




@require_tf
class 			_SCREAMING_SNAKE_CASE					(			unittest.TestCase							):






			@slow
			def  __lowerCAmelCase (					self       )			->     List[Any]:
						lowerCAmelCase_    :Any			     =  TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D"""       )

						lowerCAmelCase_    :Tuple			     =  tf.constant([[0, 1, 2, 3, 4, 5]]       )
						lowerCAmelCase_    :str			     =  model(__A       )[0]
						lowerCAmelCase_    :str			     =  [1, 6, 33]
						self.assertEqual(list(output.numpy().shape       )       ,   __A       )
						# compare the actual values for a slice.
						lowerCAmelCase_    :int			     =  tf.constant(
						    [
						        [
						            [8.9_2_1_5_1_8, -1_0.5_8_9_8_1_4, -6.4_6_7_1_3_0_7],
						            [-6.3_9_6_7_1_5_6, -1_3.9_1_1_3_7_7, -1.1_2_1_1_9_1_5],
						            [-7.7_8_1_2_4_7, -1_3.9_5_1_5_5_7, -3.7_4_0_5_9_2],
						        ]
						    ]       )
						self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()       ,   expected_slice.numpy()       ,   atol=1E-2       )       )






			@slow
			def  __lowerCAmelCase (					self       )			->     int:
						lowerCAmelCase_    :List[str]			     =  TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D"""       )

						lowerCAmelCase_    :Dict			     =  tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]       )
						lowerCAmelCase_    :Tuple			     =  model(__A       )[0]
						# compare the actual values for a slice.
						lowerCAmelCase_    :int			     =  tf.constant(
						    [
						        [
						            [0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
						            [0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
						            [0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
						        ]
						    ]       )
						self.assertTrue(numpy.allclose(output[:, :3, :3].numpy()       ,   expected_slice.numpy()       ,   atol=1E-4       )       )










'''simple docstring'''







from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


A__:    List[str]					    =       logging.get_logger(__name__)

A__:    Union[str, Any]					    =       {
    '''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/resolve/main/config.json''',
}







class A__       (						UpperCAmelCase__      ):
      __UpperCamelCase       :  int	       =							"data2vec-text"






      def __init__( self			:str     ,						SCREAMING_SNAKE_CASE			:Optional[Any]=3_0_5_2_2     ,						SCREAMING_SNAKE_CASE			:Any=7_6_8     ,						SCREAMING_SNAKE_CASE			:List[Any]=1_2     ,						SCREAMING_SNAKE_CASE			:List[str]=1_2     ,						SCREAMING_SNAKE_CASE			:Dict=3_0_7_2     ,						SCREAMING_SNAKE_CASE			:List[str]="gelu"     ,						SCREAMING_SNAKE_CASE			:Any=0.1     ,						SCREAMING_SNAKE_CASE			:List[str]=0.1     ,						SCREAMING_SNAKE_CASE			:int=5_1_2     ,						SCREAMING_SNAKE_CASE			:int=2     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=0.02     ,						SCREAMING_SNAKE_CASE			:Dict=1e-12     ,						SCREAMING_SNAKE_CASE			:int=1     ,						SCREAMING_SNAKE_CASE			:Dict=0     ,						SCREAMING_SNAKE_CASE			:List[Any]=2     ,						SCREAMING_SNAKE_CASE			:str="absolute"     ,						SCREAMING_SNAKE_CASE			:Tuple=True     ,						SCREAMING_SNAKE_CASE			:Union[str, Any]=None     ,						**SCREAMING_SNAKE_CASE			:Union[str, Any]     ,						)       ->					List[str]:




             '''simple docstring'''
             super().__init__(pad_token_id=SCREAMING_SNAKE_CASE     ,						bos_token_id=SCREAMING_SNAKE_CASE     ,						eos_token_id=SCREAMING_SNAKE_CASE     ,						**SCREAMING_SNAKE_CASE	)

             _a    : Optional[Any]     					=vocab_size
             _a    : Optional[Any]     					=hidden_size
             _a    : Any     					=num_hidden_layers
             _a    : List[str]     					=num_attention_heads
             _a    : Union[str, Any]     					=hidden_act
             _a    : Any     					=intermediate_size
             _a    : str     					=hidden_dropout_prob
             _a    : Optional[Any]     					=attention_probs_dropout_prob
             _a    : Optional[Any]     					=max_position_embeddings
             _a    : Union[str, Any]     					=type_vocab_size
             _a    : Tuple     					=initializer_range
             _a    : Optional[int]     					=layer_norm_eps
             _a    : Tuple     					=position_embedding_type
             _a    : int     					=use_cache
             _a    : List[str]     					=classifier_dropout







class A__       (						UpperCAmelCase__      ):






      @property
      def __UpperCAmelCase       ( self			:int	)       ->					Mapping[str, Mapping[int, str]]:




             '''simple docstring'''
             if self.task == "multiple-choice":
                    _a    : Tuple     					={0: """batch""", 1: """choice""", 2: """sequence"""}
             else:
                    _a    : List[Any]     					={0: """batch""", 1: """sequence"""}
             return OrderedDict(
                 [
                     ("""input_ids""", dynamic_axis),
                     ("""attention_mask""", dynamic_axis),
                 ]	)


