'''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 		__A       :






       def __init__(      self				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Optional[int]       				=		parent
              __UpperCAmelCase  :				Tuple       				=		13
              __UpperCAmelCase  :				Any       				=		7
              __UpperCAmelCase  :				Optional[int]       				=		True
              __UpperCAmelCase  :				Dict       				=		True
              __UpperCAmelCase  :				List[Any]       				=		True
              __UpperCAmelCase  :				str       				=		99
              __UpperCAmelCase  :				Any       				=		32
              __UpperCAmelCase  :				Dict       				=		2
              __UpperCAmelCase  :				List[str]       				=		4
              __UpperCAmelCase  :				Optional[int]       				=		37
              __UpperCAmelCase  :				int       				=		"gelu"
              __UpperCAmelCase  :				List[Any]       				=		0.1
              __UpperCAmelCase  :				int       				=		0.1
              __UpperCAmelCase  :				List[str]       				=		5_12
              __UpperCAmelCase  :				Tuple       				=		16
              __UpperCAmelCase  :				Optional[Any]       				=		2
              __UpperCAmelCase  :				Optional[int]       				=		0.0_2
              __UpperCAmelCase  :				Union[str, Any]       				=		3
              __UpperCAmelCase  :				List[str]       				=		4
              __UpperCAmelCase  :				List[Any]       				=		None






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		ids_tensor([self.batch_size, self.seq_length]				,     self.vocab_size      )

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

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

              __UpperCAmelCase  :				List[str]       				=		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      ):
              (
                  (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, 
              )  :				List[Any]       				=		self.prepare_config_and_inputs()

              __UpperCAmelCase  :				Optional[int]       				=		True
              __UpperCAmelCase  :				str       				=		floats_tensor([self.batch_size, self.seq_length, self.hidden_size]      )
              __UpperCAmelCase  :				List[Any]       				=		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				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		TFEsmModel(config=UpperCamelCase_      )
              __UpperCAmelCase  :				Any       				=		{"input_ids": input_ids, "attention_mask": input_mask}
              __UpperCAmelCase  :				List[str]       				=		model(UpperCamelCase_      )

              __UpperCAmelCase  :				List[str]       				=		[input_ids, input_mask]
              __UpperCAmelCase  :				List[Any]       				=		model(UpperCamelCase_      )

              __UpperCAmelCase  :				Tuple       				=		model(UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     ):
              __UpperCAmelCase  :				Optional[Any]       				=		True

              __UpperCAmelCase  :				Tuple       				=		TFEsmModel(config=UpperCamelCase_      )
              __UpperCAmelCase  :				Dict       				=		{
                  "input_ids": input_ids,
                  "attention_mask": input_mask,
                  "encoder_hidden_states": encoder_hidden_states,
                  "encoder_attention_mask": encoder_attention_mask,
              }
              __UpperCAmelCase  :				Union[str, Any]       				=		model(UpperCamelCase_      )

              __UpperCAmelCase  :				Dict       				=		[input_ids, input_mask]
              __UpperCAmelCase  :				List[str]       				=		model(UpperCamelCase_				,     encoder_hidden_states=UpperCamelCase_      )

              # Also check the case where encoder outputs are not passed
              __UpperCAmelCase  :				Dict       				=		model(UpperCamelCase_				,     attention_mask=UpperCamelCase_      )

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






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				Any       				=		TFEsmForMaskedLM(config=UpperCamelCase_      )
              __UpperCAmelCase  :				List[str]       				=		model([input_ids, input_mask]      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.vocab_size)      )






       def 			_snake_case						(      self				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_				,     UpperCamelCase_      ):
              __UpperCAmelCase  :				List[Any]       				=		self.num_labels
              __UpperCAmelCase  :				Union[str, Any]       				=		TFEsmForTokenClassification(config=UpperCamelCase_      )
              __UpperCAmelCase  :				Tuple       				=		{"input_ids": input_ids, "attention_mask": input_mask}
              __UpperCAmelCase  :				Union[str, Any]       				=		model(UpperCamelCase_      )
              self.parent.assertEqual(result.logits.shape				,     (self.batch_size, self.seq_length, self.num_labels)      )







       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[Any]       				=		self.prepare_config_and_inputs()
              (
                  (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, (
                  __UpperCAmelCase
              )				, 
              )  :				List[str]       				=		config_and_inputs
              __UpperCAmelCase  :				Optional[int]       				=		{"input_ids": input_ids, "attention_mask": input_mask}
              return config, inputs_dict






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






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				str       				=		TFEsmModelTester(self      )
              __UpperCAmelCase  :				int       				=		ConfigTester(self				,     config_class=UpperCamelCase_				,     hidden_size=37      )






       def 			_snake_case						(      self      ):
              self.config_tester.run_common_tests()






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				List[str]       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Union[str, Any]       				=		self.model_tester.prepare_config_and_inputs_for_decoder()
              self.model_tester.create_and_check_model_as_decoder(*UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_masked_lm(*UpperCamelCase_      )






       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Any       				=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_for_token_classification(*UpperCamelCase_      )






       @slow
       def 			_snake_case						(      self      ):
              for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                     __UpperCAmelCase  :				List[str]       				=		TFEsmModel.from_pretrained(UpperCamelCase_      )
                     self.assertIsNotNone(UpperCamelCase_      )






       @unittest.skip("Protein models do not support embedding resizing."      )
       def 			_snake_case						(      self      ):
              pass






       @unittest.skip("Protein models do not support embedding resizing."      )
       def 			_snake_case						(      self      ):
              pass







       def 			_snake_case						(      self      ):
              __UpperCAmelCase				, __UpperCAmelCase  :				Union[str, Any]       				=		self.model_tester.prepare_config_and_inputs_for_common()

              for model_class in self.all_model_classes:
                     __UpperCAmelCase  :				str       				=		model_class(UpperCamelCase_      )
                     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  :				Any       				=		model.get_bias()
                            assert isinstance(UpperCamelCase_				,     UpperCamelCase_      )
                            for k, v in name.items():
                                   assert isinstance(UpperCamelCase_				,     tf.Variable      )
                     else:
                            __UpperCAmelCase  :				str       				=		model.get_output_embeddings()
                            assert x is None
                            __UpperCAmelCase  :				List[str]       				=		model.get_bias()
                            assert name is None






@require_tf
class 		__A       (unittest.TestCase   ):






       @slow
       def 			_snake_case						(      self      ):
              __UpperCAmelCase  :				int       				=		TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"      )

              __UpperCAmelCase  :				List[str]       				=		tf.constant([[0, 1, 2, 3, 4, 5]]      )
              __UpperCAmelCase  :				List[str]       				=		model(UpperCamelCase_      )[0]
              __UpperCAmelCase  :				Any       				=		[1, 6, 33]
              self.assertEqual(list(output.numpy().shape      )				,     UpperCamelCase_      )
              # compare the actual values for a slice.
              __UpperCAmelCase  :				Optional[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 			_snake_case						(      self      ):
              __UpperCAmelCase  :				Optional[int]       				=		TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"      )

              __UpperCAmelCase  :				int       				=		tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]      )
              __UpperCAmelCase  :				List[Any]       				=		model(UpperCamelCase_      )[0]
              # compare the actual values for a slice.
              __UpperCAmelCase  :				List[str]       				=		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 typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


_a   :	str 							=   {"configuration_vit": ["VIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTConfig", "ViTOnnxConfig"]}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	str 							=   ["ViTFeatureExtractor"]
   _a   :	Dict 							=   ["ViTImageProcessor"]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	int 							=   [
       "VIT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "ViTForImageClassification",
       "ViTForMaskedImageModeling",
       "ViTModel",
       "ViTPreTrainedModel",
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	List[str] 							=   [
       "TFViTForImageClassification",
       "TFViTModel",
       "TFViTPreTrainedModel",
   ]

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _a   :	Dict 							=   [
       "FlaxViTForImageClassification",
       "FlaxViTModel",
       "FlaxViTPreTrainedModel",
   ]

if TYPE_CHECKING:
   from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_vit import ViTFeatureExtractor
      from .image_processing_vit import ViTImageProcessor

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_vit import (
          VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
          ViTForImageClassification,
          ViTForMaskedImageModeling,
          ViTModel,
          ViTPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel


else:
   import sys

   _a   :	Dict 							=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






