"""simple docstring"""


from __future__ import annotations

import unittest

from transformers import DebertaVaConfig, 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 (
				    TFDebertaVaForMaskedLM,
				    TFDebertaVaForQuestionAnswering,
				    TFDebertaVaForSequenceClassification,
				    TFDebertaVaForTokenClassification,
				    TFDebertaVaModel,
				)


class        a				:


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


				def      lowerCAmelCase_  (					self	:  str					):
						_UpperCAmelCase						 =      ids_tensor([self.batch_size, self.seq_length]   ,				self.vocab_size					)

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

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

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

						_UpperCAmelCase						 =      DebertaVaConfig(
						    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   ,				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      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict					):
						_UpperCAmelCase						 =      TFDebertaVaModel(config=__lowerCAmelCase					)
						_UpperCAmelCase						 =      {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}

						_UpperCAmelCase						 =      [input_ids, input_mask]
						_UpperCAmelCase						 =      model(__lowerCAmelCase					)

						_UpperCAmelCase						 =      model(__lowerCAmelCase					)

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


				def      lowerCAmelCase_  (					self	:  Dict   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[str]					):
						_UpperCAmelCase						 =      TFDebertaVaForMaskedLM(config=__lowerCAmelCase					)
						_UpperCAmelCase						 =      {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}
						_UpperCAmelCase						 =      model(__lowerCAmelCase					)
						self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.vocab_size)					)


				def      lowerCAmelCase_  (					self	:  str   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  List[str]   ,				__lowerCAmelCase	:  Optional[Any]					):
						_UpperCAmelCase						 =      self.num_labels
						_UpperCAmelCase						 =      TFDebertaVaForSequenceClassification(config=__lowerCAmelCase					)
						_UpperCAmelCase						 =      {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}

						_UpperCAmelCase						 =      model(__lowerCAmelCase					)
						self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.num_labels)					)


				def      lowerCAmelCase_  (					self	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]					):
						_UpperCAmelCase						 =      self.num_labels
						_UpperCAmelCase						 =      TFDebertaVaForTokenClassification(config=__lowerCAmelCase					)
						_UpperCAmelCase						 =      {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}
						_UpperCAmelCase						 =      model(__lowerCAmelCase					)
						self.parent.assertEqual(result.logits.shape   ,				(self.batch_size, self.seq_length, self.num_labels)					)


				def      lowerCAmelCase_  (					self	:  int   ,				__lowerCAmelCase	:  str   ,				__lowerCAmelCase	:  Any   ,				__lowerCAmelCase	:  Optional[Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Union[str, Any]   ,				__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Tuple					):
						_UpperCAmelCase						 =      TFDebertaVaForQuestionAnswering(config=__lowerCAmelCase					)
						_UpperCAmelCase						 =      {
						    """input_ids""": input_ids,
						    """attention_mask""": input_mask,
						    """token_type_ids""": token_type_ids,
						}

						_UpperCAmelCase						 =      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      lowerCAmelCase_  (					self	:  List[str]					):
						_UpperCAmelCase						 =      self.prepare_config_and_inputs()
						(
						    (
						    _UpperCAmelCase				
						) ,				(
						    _UpperCAmelCase				
						) ,				(
						    _UpperCAmelCase				
						) ,				(
						    _UpperCAmelCase				
						) ,				(
						    _UpperCAmelCase				
						) ,				(
						    _UpperCAmelCase				
						) ,				(
						    _UpperCAmelCase				
						) ,								
						)						 =      config_and_inputs
						_UpperCAmelCase						 =      {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict



@require_tf
class        a				(		lowerCAmelCase_	,							lowerCAmelCase_	,							unittest.TestCase   ):
				_snake_case     :  str     							=							(
				    (
				        TFDebertaVaModel,
				        TFDebertaVaForMaskedLM,
				        TFDebertaVaForQuestionAnswering,
				        TFDebertaVaForSequenceClassification,
				        TFDebertaVaForTokenClassification,
				    )
				    if is_tf_available()
				    else ()
				)
				_snake_case     :  List[str]     							=							(
				    {
				        'feature-extraction': TFDebertaVaModel,
				        'fill-mask': TFDebertaVaForMaskedLM,
				        'question-answering': TFDebertaVaForQuestionAnswering,
				        'text-classification': TFDebertaVaForSequenceClassification,
				        'token-classification': TFDebertaVaForTokenClassification,
				        'zero-shot': TFDebertaVaForSequenceClassification,
				    }
				    if is_tf_available()
				    else {}
				)

				_snake_case     :  Optional[Any]     							=							False
				_snake_case     :  Tuple     							=							False


				def      lowerCAmelCase_  (					self	:  int					):
						_UpperCAmelCase						 =      TFDebertaVaModelTester(self					)
						_UpperCAmelCase						 =      ConfigTester(self   ,				config_class=__lowerCAmelCase   ,				hidden_size=37					)


				def      lowerCAmelCase_  (					self	:  Any					):
						self.config_tester.run_common_tests()


				def      lowerCAmelCase_  (					self	:  List[Any]					):
						_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_model(*__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Tuple					):
						_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_masked_lm(*__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Any					):
						_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_question_answering(*__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  Optional[int]					):
						_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_sequence_classification(*__lowerCAmelCase					)


				def      lowerCAmelCase_  (					self	:  List[Any]					):
						_UpperCAmelCase						 =      self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_token_classification(*__lowerCAmelCase					)


				@slow
				def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
						_UpperCAmelCase						 =      TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""					)
						self.assertIsNotNone(__lowerCAmelCase					)



@require_tf
class        a				(		unittest.TestCase   ):


				@unittest.skip(reason="""Model not available yet"""					)
				def      lowerCAmelCase_  (					self	:  Any					):
						pass


				@slow
				def      lowerCAmelCase_  (					self	:  Tuple					):
						_UpperCAmelCase						 =      TFDebertaVaModel.from_pretrained("""kamalkraj/deberta-v2-xlarge"""					)
						_UpperCAmelCase						 =      tf.constant([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]					)
						_UpperCAmelCase						 =      tf.constant([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
						_UpperCAmelCase						 =      model(__lowerCAmelCase   ,				attention_mask=__lowerCAmelCase					)[0]

						_UpperCAmelCase						 =      tf.constant(
						    [[[0.2_356, 0.1_948, 0.0_369], [-0.1_063, 0.3_586, -0.5_152], [-0.6_399, -0.0_259, -0.2_525]]]					)
						tf.debugging.assert_near(output[:, 1:4, 1:4]   ,				__lowerCAmelCase   ,				atol=1e-4					)

"""simple docstring"""


import copy
import re


class        a				:
    _snake_case     :  Any     							=							'hp'
    _snake_case     :  Union[str, Any]     							=							{}
    _snake_case     :  Optional[Any]     							=							None


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Union[str, Any]   ,				__lowerCAmelCase	:  List[Any]   ,				__lowerCAmelCase	:  str					):
      _UpperCAmelCase						 =      prefix
      _UpperCAmelCase						 =      defaults
      cls.build_naming_info()


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Dict   ,				__lowerCAmelCase	:  Optional[Any]					):
      if len(__lowerCAmelCase					) == 0:
        return ""
      _UpperCAmelCase						 =      None
      if any(char.isdigit() for char in word					):
        raise Exception(f'''Parameters should not contain numbers: \'{word}\' contains a number'''					)
      if word in info["short_word"]:
        return info["short_word"][word]
      for prefix_len in range(1   ,				len(__lowerCAmelCase					) + 1					):
        _UpperCAmelCase						 =      word[:prefix_len]
        if prefix in info["reverse_short_word"]:
          continue
        else:
          _UpperCAmelCase						 =      prefix
          break

      if short_word is None:
        # Paranoid fallback
        def int_to_alphabetic(__lowerCAmelCase	:  List[Any]					):
          _UpperCAmelCase						 =      """"""
          while integer != 0:
            _UpperCAmelCase						 =      chr(ord("""A"""					) + integer % 10					) + s
            integer //= 10
          return s

        _UpperCAmelCase						 =      0
        while True:
          _UpperCAmelCase						 =      word + """#""" + int_to_alphabetic(__lowerCAmelCase					)
          if sword in info["reverse_short_word"]:
            continue
          else:
            _UpperCAmelCase						 =      sword
            break

      _UpperCAmelCase						 =      short_word
      _UpperCAmelCase						 =      word
      return short_word


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Tuple   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      param_name.split("""_"""					)

      _UpperCAmelCase						 =      [TrialShortNamer.shortname_for_word(__lowerCAmelCase   ,				__lowerCAmelCase					) for word in words]

      # We try to create a separatorless short name, but if there is a collision we have to fallback
      # to a separated short name
      _UpperCAmelCase						 =      ["""""", """_"""]

      for separator in separators:
        _UpperCAmelCase						 =      separator.join(__lowerCAmelCase					)
        if shortname not in info["reverse_short_param"]:
          _UpperCAmelCase						 =      shortname
          _UpperCAmelCase						 =      param_name
          return shortname

      return param_name


    @staticmethod
    def      lowerCAmelCase_  (					__lowerCAmelCase	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[int]					):
      _UpperCAmelCase						 =      TrialShortNamer.shortname_for_key(__lowerCAmelCase   ,				__lowerCAmelCase					)
      _UpperCAmelCase						 =      short_name
      _UpperCAmelCase						 =      param_name


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[Any]					):
      if cls.NAMING_INFO is not None:
        return

      _UpperCAmelCase						 =      {
          """short_word""": {},
          """reverse_short_word""": {},
          """short_param""": {},
          """reverse_short_param""": {},
      }

      _UpperCAmelCase						 =      list(cls.DEFAULTS.keys()					)

      for k in field_keys:
        cls.add_new_param_name(__lowerCAmelCase   ,				__lowerCAmelCase					)

      _UpperCAmelCase						 =      info


    @classmethod
    def      lowerCAmelCase_  (					cls	:  List[str]   ,				__lowerCAmelCase	:  int					):
      cls.build_naming_info()
      assert cls.PREFIX is not None
      _UpperCAmelCase						 =      [copy.copy(cls.PREFIX					)]

      for k, v in params.items():
        if k not in cls.DEFAULTS:
          raise Exception(f'''You should provide a default value for the param name {k} with value {v}'''					)
        if v == cls.DEFAULTS[k]:
          # The default value is not added to the name
          continue

        _UpperCAmelCase						 =      cls.NAMING_INFO["""short_param"""][k]

        if isinstance(__lowerCAmelCase   ,				__lowerCAmelCase					):
          _UpperCAmelCase						 =      1 if v else 0

        _UpperCAmelCase						 =      """""" if isinstance(__lowerCAmelCase   ,				(int, float)					) else """-"""
        _UpperCAmelCase						 =      f'''{key}{sep}{v}'''
        name.append(__lowerCAmelCase					)

      return "_".join(__lowerCAmelCase					)


    @classmethod
    def      lowerCAmelCase_  (					cls	:  Optional[int]   ,				__lowerCAmelCase	:  Optional[Any]					):
      _UpperCAmelCase						 =      repr[len(cls.PREFIX					) + 1 :]
      if repr == "":
        _UpperCAmelCase						 =      []
      else:
        _UpperCAmelCase						 =      repr.split("""_"""					)

      _UpperCAmelCase						 =      {}

      for value in values:
        if "-" in value:
          _UpperCAmelCase ,				_UpperCAmelCase						 =      value.split("""-"""					)
        else:
          _UpperCAmelCase						 =      re.sub("""[0-9.]"""   ,				""""""   ,				__lowerCAmelCase					)
          _UpperCAmelCase						 =      float(re.sub("""[^0-9.]"""   ,				""""""   ,				__lowerCAmelCase					)					)

        _UpperCAmelCase						 =      cls.NAMING_INFO["""reverse_short_param"""][p_k]

        _UpperCAmelCase						 =      p_v

      for k in cls.DEFAULTS:
        if k not in parameters:
          _UpperCAmelCase						 =      cls.DEFAULTS[k]

      return parameters
