

from typing import TYPE_CHECKING

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


lowercase_     =       {
    'configuration_utils': ['GenerationConfig'],
    'streamers': ['TextIteratorStreamer', 'TextStreamer'],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_     =       [
		    'Constraint',
		    'ConstraintListState',
		    'DisjunctiveConstraint',
		    'PhrasalConstraint',
		]
		lowercase_     =       [
		    'BeamHypotheses',
		    'BeamScorer',
		    'BeamSearchScorer',
		    'ConstrainedBeamSearchScorer',
		]
		lowercase_     =       [
		    'EpsilonLogitsWarper',
		    'EtaLogitsWarper',
		    'ForcedBOSTokenLogitsProcessor',
		    'ForcedEOSTokenLogitsProcessor',
		    'HammingDiversityLogitsProcessor',
		    'InfNanRemoveLogitsProcessor',
		    'LogitsProcessor',
		    'LogitsProcessorList',
		    'LogitsWarper',
		    'MinLengthLogitsProcessor',
		    'MinNewTokensLengthLogitsProcessor',
		    'NoBadWordsLogitsProcessor',
		    'NoRepeatNGramLogitsProcessor',
		    'PrefixConstrainedLogitsProcessor',
		    'RepetitionPenaltyLogitsProcessor',
		    'SequenceBiasLogitsProcessor',
		    'EncoderRepetitionPenaltyLogitsProcessor',
		    'TemperatureLogitsWarper',
		    'TopKLogitsWarper',
		    'TopPLogitsWarper',
		    'TypicalLogitsWarper',
		    'EncoderNoRepeatNGramLogitsProcessor',
		    'ExponentialDecayLengthPenalty',
		    'LogitNormalization',
		]
		lowercase_     =       [
		    'MaxNewTokensCriteria',
		    'MaxLengthCriteria',
		    'MaxTimeCriteria',
		    'StoppingCriteria',
		    'StoppingCriteriaList',
		    'validate_stopping_criteria',
		]
		lowercase_     =       [
		    'GenerationMixin',
		    'top_k_top_p_filtering',
		    'GreedySearchEncoderDecoderOutput',
		    'GreedySearchDecoderOnlyOutput',
		    'SampleEncoderDecoderOutput',
		    'SampleDecoderOnlyOutput',
		    'BeamSearchEncoderDecoderOutput',
		    'BeamSearchDecoderOnlyOutput',
		    'BeamSampleEncoderDecoderOutput',
		    'BeamSampleDecoderOnlyOutput',
		    'ContrastiveSearchEncoderDecoderOutput',
		    'ContrastiveSearchDecoderOnlyOutput',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_     =       [
		    'TFForcedBOSTokenLogitsProcessor',
		    'TFForcedEOSTokenLogitsProcessor',
		    'TFLogitsProcessor',
		    'TFLogitsProcessorList',
		    'TFLogitsWarper',
		    'TFMinLengthLogitsProcessor',
		    'TFNoBadWordsLogitsProcessor',
		    'TFNoRepeatNGramLogitsProcessor',
		    'TFRepetitionPenaltyLogitsProcessor',
		    'TFTemperatureLogitsWarper',
		    'TFTopKLogitsWarper',
		    'TFTopPLogitsWarper',
		    'TFForceTokensLogitsProcessor',
		    'TFSuppressTokensAtBeginLogitsProcessor',
		    'TFSuppressTokensLogitsProcessor',
		]
		lowercase_     =       [
		    'TFGenerationMixin',
		    'tf_top_k_top_p_filtering',
		    'TFGreedySearchDecoderOnlyOutput',
		    'TFGreedySearchEncoderDecoderOutput',
		    'TFSampleEncoderDecoderOutput',
		    'TFSampleDecoderOnlyOutput',
		    'TFBeamSearchEncoderDecoderOutput',
		    'TFBeamSearchDecoderOnlyOutput',
		    'TFBeamSampleEncoderDecoderOutput',
		    'TFBeamSampleDecoderOnlyOutput',
		    'TFContrastiveSearchEncoderDecoderOutput',
		    'TFContrastiveSearchDecoderOnlyOutput',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		lowercase_     =       [
		    'FlaxForcedBOSTokenLogitsProcessor',
		    'FlaxForcedEOSTokenLogitsProcessor',
		    'FlaxLogitsProcessor',
		    'FlaxLogitsProcessorList',
		    'FlaxLogitsWarper',
		    'FlaxMinLengthLogitsProcessor',
		    'FlaxTemperatureLogitsWarper',
		    'FlaxTopKLogitsWarper',
		    'FlaxTopPLogitsWarper',
		]
		lowercase_     =       [
		    'FlaxGenerationMixin',
		    'FlaxGreedySearchOutput',
		    'FlaxSampleOutput',
		    'FlaxBeamSearchOutput',
		]

if TYPE_CHECKING:
		from .configuration_utils import GenerationConfig
		from .streamers import TextIteratorStreamer, TextStreamer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .beam_constraints import Constraint, ConstraintListState, DisjunctiveConstraint, PhrasalConstraint
				from .beam_search import BeamHypotheses, BeamScorer, BeamSearchScorer, ConstrainedBeamSearchScorer
				from .logits_process import (
				    EncoderNoRepeatNGramLogitsProcessor,
				    EncoderRepetitionPenaltyLogitsProcessor,
				    EpsilonLogitsWarper,
				    EtaLogitsWarper,
				    ExponentialDecayLengthPenalty,
				    ForcedBOSTokenLogitsProcessor,
				    ForcedEOSTokenLogitsProcessor,
				    HammingDiversityLogitsProcessor,
				    InfNanRemoveLogitsProcessor,
				    LogitNormalization,
				    LogitsProcessor,
				    LogitsProcessorList,
				    LogitsWarper,
				    MinLengthLogitsProcessor,
				    MinNewTokensLengthLogitsProcessor,
				    NoBadWordsLogitsProcessor,
				    NoRepeatNGramLogitsProcessor,
				    PrefixConstrainedLogitsProcessor,
				    RepetitionPenaltyLogitsProcessor,
				    SequenceBiasLogitsProcessor,
				    TemperatureLogitsWarper,
				    TopKLogitsWarper,
				    TopPLogitsWarper,
				    TypicalLogitsWarper,
				)
				from .stopping_criteria import (
				    MaxLengthCriteria,
				    MaxNewTokensCriteria,
				    MaxTimeCriteria,
				    StoppingCriteria,
				    StoppingCriteriaList,
				    validate_stopping_criteria,
				)
				from .utils import (
				    BeamSampleDecoderOnlyOutput,
				    BeamSampleEncoderDecoderOutput,
				    BeamSearchDecoderOnlyOutput,
				    BeamSearchEncoderDecoderOutput,
				    ContrastiveSearchDecoderOnlyOutput,
				    ContrastiveSearchEncoderDecoderOutput,
				    GenerationMixin,
				    GreedySearchDecoderOnlyOutput,
				    GreedySearchEncoderDecoderOutput,
				    SampleDecoderOnlyOutput,
				    SampleEncoderDecoderOutput,
				    top_k_top_p_filtering,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tf_logits_process import (
				    TFForcedBOSTokenLogitsProcessor,
				    TFForcedEOSTokenLogitsProcessor,
				    TFForceTokensLogitsProcessor,
				    TFLogitsProcessor,
				    TFLogitsProcessorList,
				    TFLogitsWarper,
				    TFMinLengthLogitsProcessor,
				    TFNoBadWordsLogitsProcessor,
				    TFNoRepeatNGramLogitsProcessor,
				    TFRepetitionPenaltyLogitsProcessor,
				    TFSuppressTokensAtBeginLogitsProcessor,
				    TFSuppressTokensLogitsProcessor,
				    TFTemperatureLogitsWarper,
				    TFTopKLogitsWarper,
				    TFTopPLogitsWarper,
				)
				from .tf_utils import (
				    TFBeamSampleDecoderOnlyOutput,
				    TFBeamSampleEncoderDecoderOutput,
				    TFBeamSearchDecoderOnlyOutput,
				    TFBeamSearchEncoderDecoderOutput,
				    TFContrastiveSearchDecoderOnlyOutput,
				    TFContrastiveSearchEncoderDecoderOutput,
				    TFGenerationMixin,
				    TFGreedySearchDecoderOnlyOutput,
				    TFGreedySearchEncoderDecoderOutput,
				    TFSampleDecoderOnlyOutput,
				    TFSampleEncoderDecoderOutput,
				    tf_top_k_top_p_filtering,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .flax_logits_process import (
				    FlaxForcedBOSTokenLogitsProcessor,
				    FlaxForcedEOSTokenLogitsProcessor,
				    FlaxLogitsProcessor,
				    FlaxLogitsProcessorList,
				    FlaxLogitsWarper,
				    FlaxMinLengthLogitsProcessor,
				    FlaxTemperatureLogitsWarper,
				    FlaxTopKLogitsWarper,
				    FlaxTopPLogitsWarper,
				)
				from .flax_utils import FlaxBeamSearchOutput, FlaxGenerationMixin, FlaxGreedySearchOutput, FlaxSampleOutput
else:
		import sys

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





from __future__ import annotations

import unittest

from transformers import DebertaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
  import tensorflow as tf

  from transformers import (
      TFDebertaForMaskedLM,
      TFDebertaForQuestionAnswering,
      TFDebertaForSequenceClassification,
      TFDebertaForTokenClassification,
      TFDebertaModel,
  )





class 			__lowerCAmelCase					:




  def __init__(    self							,       lowerCAmelCase							,       lowerCAmelCase=13							,       lowerCAmelCase=7							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=True							,       lowerCAmelCase=99							,       lowerCAmelCase=32							,       lowerCAmelCase=2							,       lowerCAmelCase=4							,       lowerCAmelCase=37							,       lowerCAmelCase="gelu"							,       lowerCAmelCase=0.1							,       lowerCAmelCase=0.1							,       lowerCAmelCase=512							,       lowerCAmelCase=16							,       lowerCAmelCase=2							,       lowerCAmelCase=0.02							,       lowerCAmelCase=3							,       lowerCAmelCase=4							,       lowerCAmelCase=None							,       )  ->			Tuple:
        '''simple docstring'''






        _lowercase     	=parent
        _lowercase     	=13
        _lowercase     	=7
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=True
        _lowercase     	=99
        _lowercase     	=32
        _lowercase     	=2
        _lowercase     	=4
        _lowercase     	=37
        _lowercase     	='gelu'
        _lowercase     	=0.1
        _lowercase     	=0.1
        _lowercase     	=512
        _lowercase     	=16
        _lowercase     	=False
        _lowercase     	=-1
        _lowercase     	=True
        _lowercase     	=2
        _lowercase     	=0.02
        _lowercase     	=3
        _lowercase     	=4
        _lowercase     	=None




  def   A__					(    self )  ->			Tuple:
        '''simple docstring'''






        _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
        if self.use_token_type_ids:
              _lowercase     	=ids_tensor([self.batch_size, self.seq_length]							,       self.type_vocab_size )

        _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     	=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							,       relative_attention=self.relative_attention							,       max_relative_positions=self.max_relative_positions							,       position_biased_input=self.position_biased_input							,       initializer_range=self.initializer_range							,       return_dict=lowerCAmelCase							,       )

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel(config=lowerCAmelCase )
        _lowercase     	={'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}

        _lowercase     	=[input_ids, input_mask]
        _lowercase     	=model(lowerCAmelCase )

        _lowercase     	=model(lowerCAmelCase )

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




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForMaskedLM(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.vocab_size) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForSequenceClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.num_labels
        _lowercase     	=TFDebertaForTokenClassification(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }
        _lowercase     	=model(lowerCAmelCase )
        self.parent.assertEqual(result.logits.shape							,       (self.batch_size, self.seq_length, self.num_labels) )




  def   A__					(    self							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase							,       lowerCAmelCase )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaForQuestionAnswering(config=lowerCAmelCase )
        _lowercase     	={
            'input_ids': input_ids,
            'attention_mask': input_mask,
            'token_type_ids': token_type_ids,
        }

        _lowercase     	=model(lowerCAmelCase )
        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   A__					(    self )  ->			str:
        '''simple docstring'''






        _lowercase     	=self.prepare_config_and_inputs()
        (
            (
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	(
            _lowercase
        )	,	
        )     	=config_and_inputs
        _lowercase     	={'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
        return config, inputs_dict





@require_tf
class 			__lowerCAmelCase					( SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      unittest.TestCase							):
  _a						      =  (
      (
          TFDebertaModel,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
      )
      if is_tf_available()
      else ()
  )
  _a						      =  (
      {
          """feature-extraction""": TFDebertaModel,
          """fill-mask""": TFDebertaForMaskedLM,
          """question-answering""": TFDebertaForQuestionAnswering,
          """text-classification""": TFDebertaForSequenceClassification,
          """token-classification""": TFDebertaForTokenClassification,
          """zero-shot""": TFDebertaForSequenceClassification,
      }
      if is_tf_available()
      else {}
  )

  _a						      =  False
  _a						      =  False




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModelTester(self )
        _lowercase     	=ConfigTester(self							,       config_class=lowerCAmelCase							,       hidden_size=37 )




  def   A__					(    self )  ->			Any:
        '''simple docstring'''






        self.config_tester.run_common_tests()




  def   A__					(    self )  ->			int:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*lowerCAmelCase )




  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_masked_lm(*lowerCAmelCase )




  def   A__					(    self )  ->			List[Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_question_answering(*lowerCAmelCase )




  def   A__					(    self )  ->			Union[str, Any]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_sequence_classification(*lowerCAmelCase )




  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        _lowercase     	=self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_token_classification(*lowerCAmelCase )




  @slow
  def   A__					(    self )  ->			Optional[Any]:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        self.assertIsNotNone(lowerCAmelCase )





@require_tf
class 			__lowerCAmelCase					( unittest.TestCase							):




  @unittest.skip(reason='Model not available yet' )
  def   A__					(    self )  ->			Optional[int]:
        '''simple docstring'''






        pass




  @slow
  def   A__					(    self )  ->			Dict:
        '''simple docstring'''






        _lowercase     	=TFDebertaModel.from_pretrained('kamalkraj/deberta-base' )
        _lowercase     	=tf.constant([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]] )
        _lowercase     	=tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
        _lowercase     	=model(lowerCAmelCase							,       attention_mask=lowerCAmelCase )[0]

        _lowercase     	=tf.constant(
            [
                [
                    [-0.59855896, -0.80552566, -0.8462135],
                    [1.4484025, -0.93483794, -0.80593085],
                    [0.3122741, 0.00316059, -1.4131377],
                ]
            ] )
        tf.debugging.assert_near(output[:, 1:4, 1:4]							,       lowerCAmelCase							,       atol=1e-4 )


