


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     __UpperCAmelCase							:


     """simple docstring"""


     def __init__(	self  ,       __A  ,       ):
        __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 	snake_case_  (	self    ):
        __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 	snake_case_  (	self    ):
        (
            (
            __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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       ):
        __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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __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 	snake_case_  (	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __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 	snake_case_  (	self    ):
        __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     __UpperCAmelCase							(       __A ,					__A ,					unittest.TestCase							):


     """simple docstring"""
     _lowerCamelCase   =      (
         (
             TFEsmModel,
             TFEsmForMaskedLM,
             TFEsmForSequenceClassification,
             TFEsmForTokenClassification,
         )
         if is_tf_available()
         else ()
     )
     _lowerCamelCase   =      (
         {
             """feature-extraction""": TFEsmModel,
             """fill-mask""": TFEsmForMaskedLM,
             """text-classification""": TFEsmForSequenceClassification,
             """token-classification""": TFEsmForTokenClassification,
             """zero-shot""": TFEsmForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     _lowerCamelCase   =      False
     _lowerCamelCase   =      False


     def 	snake_case_  (	self    ):
        __a					=  TFEsmModelTester(self    )
        __a					=  ConfigTester(self  ,       config_class=__A  ,       hidden_size=37    )


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


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs_for_decoder()
        self.model_tester.create_and_check_model_as_decoder(*__A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*__A    )


     def 	snake_case_  (	self    ):
        __a					=  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*__A    )


     @slow
     def 	snake_case_  (	self    ):
        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 	snake_case_  (	self    ):
        pass


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


     def 	snake_case_  (	self    ):
        __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     __UpperCAmelCase							(       unittest.TestCase							):


     """simple docstring"""


     @slow
     def 	snake_case_  (	self    ):
        __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(
            [
                [
                    [8.921518, -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    ):
        __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(
            [
                [
                    [0.14443092, 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    )    )







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


SCREAMING_SNAKE_CASE							    =							{
    'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
    'tokenization_tapas': ['TapasTokenizer'],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TapasForMaskedLM',
      'TapasForQuestionAnswering',
      'TapasForSequenceClassification',
      'TapasModel',
      'TapasPreTrainedModel',
      'load_tf_weights_in_tapas',
  ]
try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFTapasForMaskedLM',
      'TFTapasForQuestionAnswering',
      'TFTapasForSequenceClassification',
      'TFTapasModel',
      'TFTapasPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
  from .tokenization_tapas import TapasTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tapas import (
        TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TapasForMaskedLM,
        TapasForQuestionAnswering,
        TapasForSequenceClassification,
        TapasModel,
        TapasPreTrainedModel,
        load_tf_weights_in_tapas,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_tapas import (
        TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTapasForMaskedLM,
        TFTapasForQuestionAnswering,
        TFTapasForSequenceClassification,
        TFTapasModel,
        TFTapasPreTrainedModel,
    )


else:
  import sys

  SCREAMING_SNAKE_CASE							    =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



