
import unittest

from transformers import EsmConfig, is_torch_available
from transformers.testing_utils import TestCasePlus, require_torch, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
      import torch

      from transformers import EsmForMaskedLM, EsmForSequenceClassification, EsmForTokenClassification, EsmModel
      from transformers.models.esm.modeling_esm import (
          ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
          EsmEmbeddings,
          create_position_ids_from_input_ids,
      )


class   lowerCamelCase					:
  """simple docstring"""







  def __init__(							self :       Tuple  ,     __magic_name__ :       Any  ,     __magic_name__ :       Optional[int]=13  ,     __magic_name__ :       str=7  ,     __magic_name__ :       str=False  ,     __magic_name__ :       Optional[int]=True  ,     __magic_name__ :       Optional[int]=False  ,     __magic_name__ :       List[str]=True  ,     __magic_name__ :       List[str]=33  ,     __magic_name__ :       List[Any]=32  ,     __magic_name__ :       Tuple=5  ,     __magic_name__ :       int=4  ,     __magic_name__ :       Tuple=37  ,     __magic_name__ :       Union[str, Any]="gelu"  ,     __magic_name__ :       int=0.1  ,     __magic_name__ :       Tuple=0.1  ,     __magic_name__ :       Union[str, Any]=512  ,     __magic_name__ :       Dict=16  ,     __magic_name__ :       Optional[Any]=2  ,     __magic_name__ :       str=0.02  ,     __magic_name__ :       int=3  ,     __magic_name__ :       Optional[Any]=4  ,     __magic_name__ :       List[str]=None  ,     )  ->	str:
        SCREAMING_SNAKE_CASE_          =  parent
        SCREAMING_SNAKE_CASE_          =  batch_size
        SCREAMING_SNAKE_CASE_          =  seq_length
        SCREAMING_SNAKE_CASE_          =  is_training
        SCREAMING_SNAKE_CASE_          =  use_input_mask
        SCREAMING_SNAKE_CASE_          =  use_token_type_ids
        SCREAMING_SNAKE_CASE_          =  use_labels
        SCREAMING_SNAKE_CASE_          =  vocab_size
        SCREAMING_SNAKE_CASE_          =  hidden_size
        SCREAMING_SNAKE_CASE_          =  num_hidden_layers
        SCREAMING_SNAKE_CASE_          =  num_attention_heads
        SCREAMING_SNAKE_CASE_          =  intermediate_size
        SCREAMING_SNAKE_CASE_          =  hidden_act
        SCREAMING_SNAKE_CASE_          =  hidden_dropout_prob
        SCREAMING_SNAKE_CASE_          =  attention_probs_dropout_prob
        SCREAMING_SNAKE_CASE_          =  max_position_embeddings
        SCREAMING_SNAKE_CASE_          =  type_vocab_size
        SCREAMING_SNAKE_CASE_          =  type_sequence_label_size
        SCREAMING_SNAKE_CASE_          =  initializer_range
        SCREAMING_SNAKE_CASE_          =  num_labels
        SCREAMING_SNAKE_CASE_          =  num_choices
        SCREAMING_SNAKE_CASE_          =  scope







  def    __A     (							self :       Optional[Any]   )  ->	Optional[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_          =  self.get_config()

        return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels







  def    __A     (							self :       Union[str, Any]   )  ->	Union[str, Any]:
        return EsmConfig(
            vocab_size=self.vocab_size  ,     hidden_size=self.hidden_size  ,     pad_token_id=1  ,     num_hidden_layers=self.num_hidden_layers  ,     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  ,     )







  def    __A     (							self :       str  ,     __magic_name__ :       int  ,     __magic_name__ :       Any  ,     __magic_name__ :       str  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Optional[Any]   )  ->	str:
        SCREAMING_SNAKE_CASE_          =  EsmModel(config=UpperCAmelCase__   )
        model.to(UpperCAmelCase__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__  ,     attention_mask=UpperCAmelCase__   )
        SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__   )
        SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__   )

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







  def    __A     (							self :       Optional[Any]  ,     __magic_name__ :       int  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[Any]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[Any]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  EsmForMaskedLM(config=UpperCAmelCase__   )
        model.to(UpperCAmelCase__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__  ,     attention_mask=UpperCAmelCase__  ,     labels=UpperCAmelCase__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.vocab_size)   )







  def    __A     (							self :       Union[str, Any]  ,     __magic_name__ :       List[str]  ,     __magic_name__ :       Dict  ,     __magic_name__ :       Union[str, Any]  ,     __magic_name__ :       Tuple  ,     __magic_name__ :       Dict  ,     __magic_name__ :       List[Any]   )  ->	List[Any]:
        SCREAMING_SNAKE_CASE_          =  self.num_labels
        SCREAMING_SNAKE_CASE_          =  EsmForTokenClassification(config=UpperCAmelCase__   )
        model.to(UpperCAmelCase__   )
        model.eval()
        SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__  ,     attention_mask=UpperCAmelCase__  ,     labels=UpperCAmelCase__   )
        self.parent.assertEqual(result.logits.shape  ,     (self.batch_size, self.seq_length, self.num_labels)   )







  def    __A     (							self :       int   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.prepare_config_and_inputs()
        (
            SCREAMING_SNAKE_CASE_	
        )          =  config_and_inputs
        SCREAMING_SNAKE_CASE_          =  {'''input_ids''': input_ids, '''attention_mask''': input_mask}
        return config, inputs_dict



@require_torch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__  ,      SCREAMING_SNAKE_CASE__  ,      unittest.TestCase					):
  """simple docstring"""







  lowerCamelCase__     =						False

  lowerCamelCase__     =						(
      (
          EsmForMaskedLM,
          EsmModel,
          EsmForSequenceClassification,
          EsmForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  lowerCamelCase__     =						()
  lowerCamelCase__     =						(
      {
          'feature-extraction': EsmModel,
          'fill-mask': EsmForMaskedLM,
          'text-classification': EsmForSequenceClassification,
          'token-classification': EsmForTokenClassification,
          'zero-shot': EsmForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  lowerCamelCase__     =						True







  def    __A     (							self :       Any   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  EsmModelTester(self   )
        SCREAMING_SNAKE_CASE_          =  ConfigTester(self  ,     config_class=UpperCAmelCase__  ,     hidden_size=37   )







  def    __A     (							self :       Optional[int]   )  ->	Union[str, Any]:
        self.config_tester.run_common_tests()







  def    __A     (							self :       int   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*UpperCAmelCase__   )







  def    __A     (							self :       Optional[int]   )  ->	Union[str, Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        for type in ["absolute", "relative_key", "relative_key_query"]:
              SCREAMING_SNAKE_CASE_          =  type
              self.model_tester.create_and_check_model(*UpperCAmelCase__   )







  def    __A     (							self :       List[str]   )  ->	Optional[int]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase__   )







  def    __A     (							self :       Optional[int]   )  ->	Optional[Any]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase__   )







  @slow
  def    __A     (							self :       Union[str, Any]   )  ->	Tuple:
        for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              SCREAMING_SNAKE_CASE_          =  EsmModel.from_pretrained(UpperCAmelCase__   )
              self.assertIsNotNone(UpperCAmelCase__   )







  def    __A     (							self :       Tuple   )  ->	Tuple:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()[0]
        SCREAMING_SNAKE_CASE_          =  EsmEmbeddings(config=UpperCAmelCase__   )

        SCREAMING_SNAKE_CASE_          =  torch.as_tensor([[12, 31, 13, model.padding_idx]]   )
        SCREAMING_SNAKE_CASE_          =  torch.as_tensor(
            [
                [
                    0 + model.padding_idx + 1,
                    1 + model.padding_idx + 1,
                    2 + model.padding_idx + 1,
                    model.padding_idx,
                ]
            ]   )
        SCREAMING_SNAKE_CASE_          =  create_position_ids_from_input_ids(UpperCAmelCase__  ,     model.padding_idx   )
        self.assertEqual(position_ids.shape  ,     expected_positions.shape   )
        self.assertTrue(torch.all(torch.eq(UpperCAmelCase__  ,     UpperCAmelCase__   )   )   )







  def    __A     (							self :       int   )  ->	List[str]:
        SCREAMING_SNAKE_CASE_          =  self.model_tester.prepare_config_and_inputs()[0]
        SCREAMING_SNAKE_CASE_          =  EsmEmbeddings(config=UpperCAmelCase__   )

        SCREAMING_SNAKE_CASE_          =  torch.empty(2  ,     4  ,     30   )
        SCREAMING_SNAKE_CASE_          =  [
            0 + embeddings.padding_idx + 1,
            1 + embeddings.padding_idx + 1,
            2 + embeddings.padding_idx + 1,
            3 + embeddings.padding_idx + 1,
        ]
        SCREAMING_SNAKE_CASE_          =  torch.as_tensor([expected_single_positions, expected_single_positions]   )
        SCREAMING_SNAKE_CASE_          =  embeddings.create_position_ids_from_inputs_embeds(UpperCAmelCase__   )
        self.assertEqual(position_ids.shape  ,     expected_positions.shape   )
        self.assertTrue(torch.all(torch.eq(UpperCAmelCase__  ,     UpperCAmelCase__   )   )   )







  @unittest.skip("Esm does not support embedding resizing"   )
  def    __A     (							self :       int   )  ->	List[Any]:
        pass







  @unittest.skip("Esm does not support embedding resizing"   )
  def    __A     (							self :       str   )  ->	int:
        pass







  @unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."   )
  def    __A     (							self :       Optional[int]   )  ->	Union[str, Any]:
        pass



@require_torch
class   lowerCamelCase					(SCREAMING_SNAKE_CASE__					):
  """simple docstring"""







  @slow
  def    __A     (							self :       Optional[int]   )  ->	str:
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  EsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D"   )
              model.eval()
              SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 1, 2, 3, 4, 5]]   )
              SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__   )[0]

              SCREAMING_SNAKE_CASE_          =  33

              SCREAMING_SNAKE_CASE_          =  torch.Size((1, 6, vocab_size)   )
              self.assertEqual(output.shape  ,     UpperCAmelCase__   )

              SCREAMING_SNAKE_CASE_          =  torch.tensor(
                  [[[8.9215, -10.5898, -6.4671], [-6.3967, -13.9114, -1.1212], [-7.7812, -13.9516, -3.7406]]]   )
              self.assertTrue(torch.allclose(output[:, :3, :3]  ,     UpperCAmelCase__  ,     atol=1e-4   )   )







  @slow
  def    __A     (							self :       int   )  ->	Dict:
        with torch.no_grad():
              SCREAMING_SNAKE_CASE_          =  EsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D"   )
              model.eval()

              SCREAMING_SNAKE_CASE_          =  torch.tensor([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]]   )
              SCREAMING_SNAKE_CASE_          =  model(UpperCAmelCase__   )[0]
              # compare the actual values for a slice.
              SCREAMING_SNAKE_CASE_          =  torch.tensor(
                  [[[0.1444, 0.5413, 0.3248], [0.3034, 0.0053, 0.3108], [0.3228, -0.2499, 0.3415]]]   )
              self.assertTrue(torch.allclose(output[:, :3, :3]  ,     UpperCAmelCase__  ,     atol=1e-4   )   )



from typing import TYPE_CHECKING

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


A     :  str					     =       {
    "configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizer"]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       ["RemBertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  Tuple					     =       [
          "REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "RemBertForCausalLM",
          "RemBertForMaskedLM",
          "RemBertForMultipleChoice",
          "RemBertForQuestionAnswering",
          "RemBertForSequenceClassification",
          "RemBertForTokenClassification",
          "RemBertLayer",
          "RemBertModel",
          "RemBertPreTrainedModel",
          "load_tf_weights_in_rembert",
      ]


try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A     :  List[str]					     =       [
          "TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFRemBertForCausalLM",
          "TFRemBertForMaskedLM",
          "TFRemBertForMultipleChoice",
          "TFRemBertForQuestionAnswering",
          "TFRemBertForSequenceClassification",
          "TFRemBertForTokenClassification",
          "TFRemBertLayer",
          "TFRemBertModel",
          "TFRemBertPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig

      try:
            if not is_sentencepiece_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert import RemBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_rembert_fast import RemBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_rembert import (
                REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RemBertForCausalLM,
                RemBertForMaskedLM,
                RemBertForMultipleChoice,
                RemBertForQuestionAnswering,
                RemBertForSequenceClassification,
                RemBertForTokenClassification,
                RemBertLayer,
                RemBertModel,
                RemBertPreTrainedModel,
                load_tf_weights_in_rembert,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_rembert import (
                TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFRemBertForCausalLM,
                TFRemBertForMaskedLM,
                TFRemBertForMultipleChoice,
                TFRemBertForQuestionAnswering,
                TFRemBertForSequenceClassification,
                TFRemBertForTokenClassification,
                TFRemBertLayer,
                TFRemBertModel,
                TFRemBertPreTrainedModel,
            )


else:
      import sys

      A     :  Tuple					     =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


