

'''simple docstring'''






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     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Union[str, Any]      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]=1_3      ,lowercase__     :  str=7      ,lowercase__     :  Optional[Any]=False      ,lowercase__     :  List[str]=True      ,lowercase__     :  Any=False      ,lowercase__     :  List[str]=True      ,lowercase__     :  Union[str, Any]=3_3      ,lowercase__     :  str=3_2      ,lowercase__     :  Any=5      ,lowercase__     :  str=4      ,lowercase__     :  List[Any]=3_7      ,lowercase__     :  Tuple="gelu"      ,lowercase__     :  str=0.1      ,lowercase__     :  Optional[int]=0.1      ,lowercase__     :  List[str]=5_1_2      ,lowercase__     :  Tuple=1_6      ,lowercase__     :  Dict=2      ,lowercase__     :  Dict=0.0_2      ,lowercase__     :  Optional[Any]=3      ,lowercase__     :  List[str]=4      ,lowercase__     :  Tuple=None      ,):
   __lowercase													=				parent
   __lowercase													=				batch_size
   __lowercase													=				seq_length
   __lowercase													=				is_training
   __lowercase													=				use_input_mask
   __lowercase													=				use_token_type_ids
   __lowercase													=				use_labels
   __lowercase													=				vocab_size
   __lowercase													=				hidden_size
   __lowercase													=				num_hidden_layers
   __lowercase													=				num_attention_heads
   __lowercase													=				intermediate_size
   __lowercase													=				hidden_act
   __lowercase													=				hidden_dropout_prob
   __lowercase													=				attention_probs_dropout_prob
   __lowercase													=				max_position_embeddings
   __lowercase													=				type_vocab_size
   __lowercase													=				type_sequence_label_size
   __lowercase													=				initializer_range
   __lowercase													=				num_labels
   __lowercase													=				num_choices
   __lowercase													=				scope






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.vocab_size    )

   __lowercase													=				None
   if self.use_input_mask:
    __lowercase													=				random_attention_mask([self.batch_size, self.seq_length]    )

   __lowercase													=				None
   __lowercase													=				None
   __lowercase													=				None
   if self.use_labels:
    __lowercase													=				ids_tensor([self.batch_size]      ,self.type_sequence_label_size    )
    __lowercase													=				ids_tensor([self.batch_size, self.seq_length]      ,self.num_labels    )
    __lowercase													=				ids_tensor([self.batch_size]      ,self.num_choices    )

   __lowercase													=				self.get_config()

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






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple    ):
   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       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Any      ,lowercase__     :  List[str]      ,lowercase__     :  Union[str, Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  str    ):
   __lowercase													=				EsmModel(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__    )
   __lowercase													=				model(lowercase__    )
   __lowercase													=				model(lowercase__    )

   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       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  Any      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Optional[int]      ,lowercase__     :  str      ,lowercase__     :  Any      ,lowercase__     :  Tuple    ):
   __lowercase													=				EsmForMaskedLM(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.vocab_size)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  str      ,lowercase__     :  Dict      ,lowercase__     :  Optional[Any]      ,lowercase__     :  Any      ,lowercase__     :  Tuple      ,lowercase__     :  List[str]      ,lowercase__     :  Optional[Any]    ):
   __lowercase													=				self.num_labels
   __lowercase													=				EsmForTokenClassification(config=lowercase__    )
   model.to(lowercase__    )
   model.eval()
   __lowercase													=				model(lowercase__      ,attention_mask=lowercase__      ,labels=lowercase__    )
   self.parent.assertEqual(result.logits.shape      ,(self.batch_size, self.seq_length, self.num_labels)    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.prepare_config_and_inputs()
   (
       (
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			(
       __lowercase
   )						,			
   )													=				config_and_inputs
   __lowercase													=				{'''input_ids''': input_ids, '''attention_mask''': input_mask}
   return config, inputs_dict






@require_torch
class     lowercase_			(lowerCamelCase__       , lowerCamelCase__       , unittest.TestCase   ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE					:						Tuple           =    False

  SCREAMING_SNAKE_CASE					:						Union[str, Any]           =    (
      (
          EsmForMaskedLM,
          EsmModel,
          EsmForSequenceClassification,
          EsmForTokenClassification,
      )
      if is_torch_available()
      else ()
  )
  SCREAMING_SNAKE_CASE					:						Any           =    ()
  SCREAMING_SNAKE_CASE					:						List[Any]           =    (
      {
          'feature-extraction': EsmModel,
          'fill-mask': EsmForMaskedLM,
          'text-classification': EsmForSequenceClassification,
          'token-classification': EsmForTokenClassification,
          'zero-shot': EsmForSequenceClassification,
      }
      if is_torch_available()
      else {}
  )
  SCREAMING_SNAKE_CASE					:						int           =    True






  def       SCREAMING_SNAKE_CASE   (   self     :  Union[str, Any]    ):
   __lowercase													=				EsmModelTester(self    )
   __lowercase													=				ConfigTester(self      ,config_class=lowercase__      ,hidden_size=3_7    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   self.config_tester.run_common_tests()






  def       SCREAMING_SNAKE_CASE   (   self     :  int    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   for type in ["absolute", "relative_key", "relative_key_query"]:
    __lowercase													=				type
    self.model_tester.create_and_check_model(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*lowercase__    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   for model_name in ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
    __lowercase													=				EsmModel.from_pretrained(lowercase__    )
    self.assertIsNotNone(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[Any]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()[0]
   __lowercase													=				EsmEmbeddings(config=lowercase__    )

   __lowercase													=				torch.as_tensor([[1_2, 3_1, 1_3, model.padding_idx]]    )
   __lowercase													=				torch.as_tensor(
       [
           [
               0 + model.padding_idx + 1,
               1 + model.padding_idx + 1,
               2 + model.padding_idx + 1,
               model.padding_idx,
           ]
       ]    )
   __lowercase													=				create_position_ids_from_input_ids(lowercase__      ,model.padding_idx    )
   self.assertEqual(position_ids.shape      ,expected_positions.shape    )
   self.assertTrue(torch.all(torch.eq(lowercase__      ,lowercase__    )    )    )






  def       SCREAMING_SNAKE_CASE   (   self     :  List[str]    ):
   __lowercase													=				self.model_tester.prepare_config_and_inputs()[0]
   __lowercase													=				EsmEmbeddings(config=lowercase__    )

   __lowercase													=				torch.empty(2      ,4      ,3_0    )
   __lowercase													=				[
       0 + embeddings.padding_idx + 1,
       1 + embeddings.padding_idx + 1,
       2 + embeddings.padding_idx + 1,
       3 + embeddings.padding_idx + 1,
   ]
   __lowercase													=				torch.as_tensor([expected_single_positions, expected_single_positions]    )
   __lowercase													=				embeddings.create_position_ids_from_inputs_embeds(lowercase__    )
   self.assertEqual(position_ids.shape      ,expected_positions.shape    )
   self.assertTrue(torch.all(torch.eq(lowercase__      ,lowercase__    )    )    )






  @unittest.skip('''Esm does not support embedding resizing'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   pass






  @unittest.skip('''Esm does not support embedding resizing'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






  @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''    )
  def       SCREAMING_SNAKE_CASE   (   self     :  Dict    ):
   pass






@require_torch
class     lowercase_			(lowerCamelCase__   ):




  """simple docstring"""




  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[Any]    ):
   with torch.no_grad():
    __lowercase													=				EsmForMaskedLM.from_pretrained('''facebook/esm2_t6_8M_UR50D'''    )
    model.eval()
    __lowercase													=				torch.tensor([[0, 1, 2, 3, 4, 5]]    )
    __lowercase													=				model(lowercase__    )[0]

    __lowercase													=				3_3

    __lowercase													=				torch.Size((1, 6, vocab_size)    )
    self.assertEqual(output.shape      ,lowercase__    )

    __lowercase													=				torch.tensor(
        [[[8.9_2_1_5, -1_0.5_8_9_8, -6.4_6_7_1], [-6.3_9_6_7, -1_3.9_1_1_4, -1.1_2_1_2], [-7.7_8_1_2, -1_3.9_5_1_6, -3.7_4_0_6]]]    )
    self.assertTrue(torch.allclose(output[:, :3, :3]      ,lowercase__      ,atol=1e-4    )    )






  @slow
  def       SCREAMING_SNAKE_CASE   (   self     :  str    ):
   with torch.no_grad():
    __lowercase													=				EsmModel.from_pretrained('''facebook/esm2_t6_8M_UR50D'''    )
    model.eval()

    __lowercase													=				torch.tensor([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]]    )
    __lowercase													=				model(lowercase__    )[0]
    # compare the actual values for a slice.
    __lowercase													=				torch.tensor(
        [[[0.1_4_4_4, 0.5_4_1_3, 0.3_2_4_8], [0.3_0_3_4, 0.0_0_5_3, 0.3_1_0_8], [0.3_2_2_8, -0.2_4_9_9, 0.3_4_1_5]]]    )
    self.assertTrue(torch.allclose(output[:, :3, :3]      ,lowercase__      ,atol=1e-4    )    )




'''simple docstring'''






from typing import TYPE_CHECKING

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


lowerCAmelCase__      		=			{'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''ViTMAEForPreTraining''',
     '''ViTMAELayer''',
     '''ViTMAEModel''',
     '''ViTMAEPreTrainedModel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 lowerCAmelCase__      		=			[
     '''TFViTMAEForPreTraining''',
     '''TFViTMAEModel''',
     '''TFViTMAEPreTrainedModel''',
 ]

if TYPE_CHECKING:
 from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_vit_mae import (
      VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
      ViTMAEForPreTraining,
      ViTMAELayer,
      ViTMAEModel,
      ViTMAEPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel


else:
 import sys

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

