




'''simple docstring'''


import unittest

from transformers import DebertaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device

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


if is_torch_available():
  import torch

  from transformers import (
      DebertaForMaskedLM,
      DebertaForQuestionAnswering,
      DebertaForSequenceClassification,
      DebertaForTokenClassification,
      DebertaModel,
  )
  from transformers.models.deberta.modeling_deberta import DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST





class    __lowercase						(  __magic_name__ ):







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase=13      ,							UpperCamelCase=7      ,							UpperCamelCase=True      ,							UpperCamelCase=True      ,							UpperCamelCase=True      ,							UpperCamelCase=True      ,							UpperCamelCase=99      ,							UpperCamelCase=32      ,							UpperCamelCase=5      ,							UpperCamelCase=4      ,							UpperCamelCase=37      ,							UpperCamelCase="gelu"      ,							UpperCamelCase=0.1      ,							UpperCamelCase=0.1      ,							UpperCamelCase=512      ,							UpperCamelCase=16      ,							UpperCamelCase=2      ,							UpperCamelCase=0.02      ,							UpperCamelCase=False      ,							UpperCamelCase=True      ,							UpperCamelCase="None"      ,							UpperCamelCase=3      ,							UpperCamelCase=4      ,							UpperCamelCase=None      ,							)				->    List[str]:
     __a			=					parent
     __a			=					batch_size
     __a			=					seq_length
     __a			=					is_training
     __a			=					use_input_mask
     __a			=					use_token_type_ids
     __a			=					use_labels
     __a			=					vocab_size
     __a			=					hidden_size
     __a			=					num_hidden_layers
     __a			=					num_attention_heads
     __a			=					intermediate_size
     __a			=					hidden_act
     __a			=					hidden_dropout_prob
     __a			=					attention_probs_dropout_prob
     __a			=					max_position_embeddings
     __a			=					type_vocab_size
     __a			=					type_sequence_label_size
     __a			=					initializer_range
     __a			=					num_labels
     __a			=					num_choices
     __a			=					relative_attention
     __a			=					position_biased_input
     __a			=					pos_att_type
     __a			=					scope







   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     __a			=					ids_tensor([self.batch_size, self.seq_length]      ,							self.vocab_size      )

     __a			=					None
     if self.use_input_mask:
       __a			=					ids_tensor([self.batch_size, self.seq_length]      ,							vocab_size=2      )

     __a			=					None
     if self.use_token_type_ids:
       __a			=					ids_tensor([self.batch_size, self.seq_length]      ,							self.type_vocab_size      )

     __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			=					self.get_config()

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







   def 		UpperCamelCase__			(	self      )				->    Any:
     return DebertaConfig(
         vocab_size=self.vocab_size      ,							hidden_size=self.hidden_size      ,							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      ,							relative_attention=self.relative_attention      ,							position_biased_input=self.position_biased_input      ,							pos_att_type=self.pos_att_type      ,							)







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.get_config()
     __a			=					300
     return config







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    int:
     self.parent.assertListEqual(list(result.loss.size()      )      ,							[]      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Dict:
     __a			=					DebertaModel(config=UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.eval()
     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							token_type_ids=UpperCamelCase      )[0]
     __a			=					model(UpperCamelCase      ,							token_type_ids=UpperCamelCase      )[0]
     __a			=					model(UpperCamelCase      )[0]

     self.parent.assertListEqual(list(sequence_output.size()      )      ,							[self.batch_size, self.seq_length, self.hidden_size]      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Tuple:
     __a			=					DebertaForMaskedLM(config=UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.eval()
     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							token_type_ids=UpperCamelCase      ,							labels=UpperCamelCase      )

     self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.vocab_size)      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    List[Any]:
     __a			=					self.num_labels
     __a			=					DebertaForSequenceClassification(UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.eval()
     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							token_type_ids=UpperCamelCase      ,							labels=UpperCamelCase      )
     self.parent.assertListEqual(list(result.logits.size()      )      ,							[self.batch_size, self.num_labels]      )
     self.check_loss_output(UpperCamelCase      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    int:
     __a			=					self.num_labels
     __a			=					DebertaForTokenClassification(config=UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.eval()
     __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							token_type_ids=UpperCamelCase      ,							labels=UpperCamelCase      )
     self.parent.assertEqual(result.logits.shape      ,							(self.batch_size, self.seq_length, self.num_labels)      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      ,							UpperCamelCase      )				->    Dict:
     __a			=					DebertaForQuestionAnswering(config=UpperCamelCase      )
     model.to(UpperCamelCase      )
     model.eval()
     __a			=					model(
         UpperCamelCase      ,							attention_mask=UpperCamelCase      ,							token_type_ids=UpperCamelCase      ,							start_positions=UpperCamelCase      ,							end_positions=UpperCamelCase      ,							)
     self.parent.assertEqual(result.start_logits.shape      ,							(self.batch_size, self.seq_length)      )
     self.parent.assertEqual(result.end_logits.shape      ,							(self.batch_size, self.seq_length)      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.prepare_config_and_inputs()
     (
         (
         __a
     )	,       (
         __a
     )	,       (
         __a
     )	,       (
         __a
     )	,       (
         __a
     )	,       (
         __a
     )	,       (
         __a
     )	,       
     )			=					config_and_inputs
     __a			=					{'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
     return config, inputs_dict





@require_torch
class    __lowercase						(  __magic_name__       ,							__magic_name__       ,							unittest.TestCase ):
   _a							=      (
       (
           DebertaModel,
           DebertaForMaskedLM,
           DebertaForSequenceClassification,
           DebertaForTokenClassification,
           DebertaForQuestionAnswering,
       )
       if is_torch_available()
       else ()
   )
   _a							=      (
       {
           """feature-extraction""": DebertaModel,
           """fill-mask""": DebertaForMaskedLM,
           """question-answering""": DebertaForQuestionAnswering,
           """text-classification""": DebertaForSequenceClassification,
           """token-classification""": DebertaForTokenClassification,
           """zero-shot""": DebertaForSequenceClassification,
       }
       if is_torch_available()
       else {}
   )

   _a							=      True
   _a							=      False
   _a							=      False
   _a							=      False
   _a							=      False







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					DebertaModelTester(self      )
     __a			=					ConfigTester(self      ,							config_class=UpperCamelCase      ,							hidden_size=37      )







   def 		UpperCamelCase__			(	self      )				->    List[Any]:
     self.config_tester.run_common_tests()







   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_model(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Optional[int]:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    str:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    List[str]:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_question_answering(*UpperCamelCase      )







   def 		UpperCamelCase__			(	self      )				->    Tuple:
     __a			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_deberta_for_token_classification(*UpperCamelCase      )







   @slow
   def 		UpperCamelCase__			(	self      )				->    Tuple:
     for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       __a			=					DebertaModel.from_pretrained(UpperCamelCase      )
       self.assertIsNotNone(UpperCamelCase      )





@require_torch
@require_sentencepiece
@require_tokenizers
class    __lowercase						(  unittest.TestCase ):







   @unittest.skip(reason='Model not available yet'      )
   def 		UpperCamelCase__			(	self      )				->    List[str]:
     pass







   @slow
   def 		UpperCamelCase__			(	self      )				->    int:
     __a			=					DebertaModel.from_pretrained('microsoft/deberta-base'      )

     __a			=					torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
     __a			=					torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
     with torch.no_grad():
       __a			=					model(UpperCamelCase      ,							attention_mask=UpperCamelCase      )[0]
     # compare the actual values for a slice.
     __a			=					torch.tensor(
         [[[-0.5_986, -0.8_055, -0.8_462], [1.4_484, -0.9_348, -0.8_059], [0.3_123, 0.0_032, -1.4_131]]]      )
     self.assertTrue(torch.allclose(output[:, 1:4, 1:4]      ,							UpperCamelCase      ,							atol=1e-4      )      ,							f"{output[:, 1:4, 1:4]}"      )









'''simple docstring'''


from typing import TYPE_CHECKING

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


UpperCAmelCase_ 		=      {
    "configuration_roformer": ["ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "RoFormerConfig", "RoFormerOnnxConfig"],
    "tokenization_roformer": ["RoFormerTokenizer"],
}

try:
  if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      ["RoFormerTokenizerFast"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "RoFormerForCausalLM",
      "RoFormerForMaskedLM",
      "RoFormerForMultipleChoice",
      "RoFormerForQuestionAnswering",
      "RoFormerForSequenceClassification",
      "RoFormerForTokenClassification",
      "RoFormerLayer",
      "RoFormerModel",
      "RoFormerPreTrainedModel",
      "load_tf_weights_in_roformer",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFRoFormerForCausalLM",
      "TFRoFormerForMaskedLM",
      "TFRoFormerForMultipleChoice",
      "TFRoFormerForQuestionAnswering",
      "TFRoFormerForSequenceClassification",
      "TFRoFormerForTokenClassification",
      "TFRoFormerLayer",
      "TFRoFormerModel",
      "TFRoFormerPreTrainedModel",
  ]


try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  UpperCAmelCase_ 		=      [
      "FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "FlaxRoFormerForMaskedLM",
      "FlaxRoFormerForMultipleChoice",
      "FlaxRoFormerForQuestionAnswering",
      "FlaxRoFormerForSequenceClassification",
      "FlaxRoFormerForTokenClassification",
      "FlaxRoFormerModel",
      "FlaxRoFormerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_roformer import ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, RoFormerConfig, RoFormerOnnxConfig
  from .tokenization_roformer import RoFormerTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_roformer_fast import RoFormerTokenizerFast

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_roformer import (
        ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        RoFormerForCausalLM,
        RoFormerForMaskedLM,
        RoFormerForMultipleChoice,
        RoFormerForQuestionAnswering,
        RoFormerForSequenceClassification,
        RoFormerForTokenClassification,
        RoFormerLayer,
        RoFormerModel,
        RoFormerPreTrainedModel,
        load_tf_weights_in_roformer,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_roformer import (
        TF_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFRoFormerForCausalLM,
        TFRoFormerForMaskedLM,
        TFRoFormerForMultipleChoice,
        TFRoFormerForQuestionAnswering,
        TFRoFormerForSequenceClassification,
        TFRoFormerForTokenClassification,
        TFRoFormerLayer,
        TFRoFormerModel,
        TFRoFormerPreTrainedModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_roformer import (
        FLAX_ROFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaxRoFormerForMaskedLM,
        FlaxRoFormerForMultipleChoice,
        FlaxRoFormerForQuestionAnswering,
        FlaxRoFormerForSequenceClassification,
        FlaxRoFormerForTokenClassification,
        FlaxRoFormerModel,
        FlaxRoFormerPreTrainedModel,
    )


else:
  import sys

  UpperCAmelCase_ 		=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



