


'''simple docstring'''



from abc import ABC, abstractmethod
from typing import List, Optional



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Optional[Any]      )						->							int:
          # test for the above condition
          self.test()

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Optional[int]  							=      0
          __UpperCAmelCase     :			Any  							=      False
          while not completed:
               if counter == 1:
                    self.reset()
               __UpperCAmelCase     :			Optional[int]  							=      self.advance()
               if not self.does_advance(snake_case      ):
                    raise Exception(
                        '''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.'''      )

               __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			int  							=      self.update(snake_case      )
               counter += 1

               if counter > 1_0000:
                    raise Exception('''update() does not fulfill the constraint.'''      )

          if self.remaining() != 0:
               raise Exception('''Custom Constraint is not defined correctly.'''      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							List[str]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int      )						->							Optional[int]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		int      )						->							int:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[int]:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )

     @abstractmethod
     def 			lowerCamelCase__			(    self					:		int      )						->							Tuple:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )






     @abstractmethod
     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		List[Any]=False      )						->							Any:
          raise NotImplementedError(
              f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.'      )



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		int							,			snake_case					:		List[int]      )						->							Tuple:
          super(snake_case							,			self      ).__init__()

          if not isinstance(snake_case							,			snake_case      ) or len(snake_case      ) == 0:
               raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.'      )
          if any((not isinstance(snake_case							,			snake_case      ) or token_id < 0) for token_id in token_ids      ):
               raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.'      )

          __UpperCAmelCase     :			Dict  							=      token_ids

          __UpperCAmelCase     :			Tuple  							=      len(self.token_ids      )
          __UpperCAmelCase     :			List[str]  							=      -1  # the index of the currently fulfilled step
          __UpperCAmelCase     :			int  							=      False

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							str:
          if self.completed:
               return None
          return self.token_ids[self.fulfilled_idx + 1]

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Optional[int]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case      )}'      )

          if self.completed:
               return False

          return token_id == self.token_ids[self.fulfilled_idx + 1]

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		int      )						->							Optional[int]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			Any  							=      False
          __UpperCAmelCase     :			Tuple  							=      False
          __UpperCAmelCase     :			Tuple  							=      False

          if self.does_advance(snake_case      ):
               self.fulfilled_idx += 1
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               if self.fulfilled_idx == (self.seqlen - 1):
                    __UpperCAmelCase     :			List[Any]  							=      True
               __UpperCAmelCase     :			Union[str, Any]  							=      completed
          else:
               # failed to make progress.
               __UpperCAmelCase     :			List[str]  							=      True
               self.reset()
          return stepped, completed, reset

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      False
          __UpperCAmelCase     :			Union[str, Any]  							=      0

     def 			lowerCamelCase__			(    self					:		str      )						->							Optional[int]:
          return self.seqlen - (self.fulfilled_idx + 1)






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Dict=False      )						->							List[str]:
          __UpperCAmelCase     :			List[str]  							=      PhrasalConstraint(self.token_ids      )

          if stateful:
               __UpperCAmelCase     :			int  							=      self.seqlen
               __UpperCAmelCase     :			Optional[Any]  							=      self.fulfilled_idx
               __UpperCAmelCase     :			List[Any]  							=      self.completed

          return new_constraint



class      a    :


     """simple docstring"""






     def __init__(    self					:		List[str]							,			snake_case					:		List[List[int]]							,			snake_case					:		Dict=True      )						->							Any:
          __UpperCAmelCase     :			List[Any]  							=      max([len(snake_case      ) for one in nested_token_ids]      )

          __UpperCAmelCase     :			Union[str, Any]  							=      {}
          for token_ids in nested_token_ids:
               __UpperCAmelCase     :			List[str]  							=      root
               for tidx, token_id in enumerate(snake_case      ):
                    if token_id not in level:
                         __UpperCAmelCase     :			List[Any]  							=      {}

                    __UpperCAmelCase     :			Tuple  							=      level[token_id]

          if no_subsets and self.has_subsets(snake_case							,			snake_case      ):
               raise ValueError(
                   '''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
                   f' {nested_token_ids}.'      )

          __UpperCAmelCase     :			Tuple  							=      root

     def 			lowerCamelCase__			(    self					:		Dict							,			snake_case					:		List[str]      )						->							List[Any]:
          __UpperCAmelCase     :			Dict  							=      self.trie

          for current_token in current_seq:
               __UpperCAmelCase     :			List[str]  							=      start[current_token]

          __UpperCAmelCase     :			str  							=      list(start.keys()      )

          return next_tokens

     def 			lowerCamelCase__			(    self					:		Optional[Any]							,			snake_case					:		List[str]      )						->							Any:
          __UpperCAmelCase     :			Optional[Any]  							=      self.next_tokens(snake_case      )

          return len(snake_case      ) == 0

     def 			lowerCamelCase__			(    self					:		Union[str, Any]							,			snake_case					:		int      )						->							Optional[int]:
          __UpperCAmelCase     :			str  							=      list(root.values()      )
          if len(snake_case      ) == 0:
               return 1
          else:
               return sum([self.count_leaves(snake_case      ) for nn in next_nodes]      )






     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		Dict      )						->							str:
          __UpperCAmelCase     :			Dict  							=      self.count_leaves(snake_case      )
          return len(snake_case      ) != leaf_count



class      a    (		_a	):


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[List[int]]      )						->							str:
          super(snake_case							,			self      ).__init__()

          if not isinstance(snake_case							,			snake_case      ) or len(snake_case      ) == 0:
               raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.'      )
          if any(not isinstance(snake_case							,			snake_case      ) for token_ids in nested_token_ids      ):
               raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.'      )
          if any(
              any((not isinstance(snake_case							,			snake_case      ) or token_id < 0) for token_id in token_ids      )
              for token_ids in nested_token_ids      ):
               raise ValueError(
                   f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.'      )

          __UpperCAmelCase     :			Optional[int]  							=      DisjunctiveTrie(snake_case      )
          __UpperCAmelCase     :			Tuple  							=      nested_token_ids

          __UpperCAmelCase     :			List[Any]  							=      self.trie.max_height
          __UpperCAmelCase     :			List[Any]  							=      []
          __UpperCAmelCase     :			Union[str, Any]  							=      False

     def 			lowerCamelCase__			(    self					:		int      )						->							List[Any]:
          __UpperCAmelCase     :			Optional[int]  							=      self.trie.next_tokens(self.current_seq      )

          if len(snake_case      ) == 0:
               return None
          else:
               return token_list

     def 			lowerCamelCase__			(    self					:		Tuple							,			snake_case					:		int      )						->							Dict:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			List[str]  							=      self.trie.next_tokens(self.current_seq      )

          return token_id in next_tokens

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Tuple:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(snake_case      )}'      )

          __UpperCAmelCase     :			Dict  							=      False
          __UpperCAmelCase     :			Optional[Any]  							=      False
          __UpperCAmelCase     :			str  							=      False

          if self.does_advance(snake_case      ):
               self.current_seq.append(snake_case      )
               __UpperCAmelCase     :			int  							=      True
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      True
               self.reset()

          __UpperCAmelCase     :			Optional[Any]  							=      self.trie.reached_leaf(self.current_seq      )
          __UpperCAmelCase     :			Tuple  							=      completed

          return stepped, completed, reset

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			str  							=      False
          __UpperCAmelCase     :			Tuple  							=      []

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							Any:
          if self.completed:
               # since this can be completed without reaching max height
               return 0
          else:
               return self.seqlen - len(self.current_seq      )






     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		Optional[int]=False      )						->							Tuple:
          __UpperCAmelCase     :			str  							=      DisjunctiveConstraint(self.token_ids      )

          if stateful:
               __UpperCAmelCase     :			Tuple  							=      self.seqlen
               __UpperCAmelCase     :			Dict  							=      self.current_seq
               __UpperCAmelCase     :			str  							=      self.completed

          return new_constraint



class      a    :


     """simple docstring"""






     def __init__(    self					:		Union[str, Any]							,			snake_case					:		List[Constraint]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Optional[Any]  							=      constraints

          # max # of steps required to fulfill a given constraint
          __UpperCAmelCase     :			int  							=      max([c.seqlen for c in constraints]      )
          __UpperCAmelCase     :			int  							=      len(snake_case      )
          __UpperCAmelCase     :			Optional[int]  							=      False

          self.init_state()

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							List[str]:
          __UpperCAmelCase     :			List[Any]  							=      []
          __UpperCAmelCase     :			List[str]  							=      None
          __UpperCAmelCase     :			str  							=      [constraint.copy(stateful=snake_case      ) for constraint in self.constraints]

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Dict  							=      0
          if self.inprogress_constraint:
               # extra points for having a constraint mid-fulfilled
               add += self.max_seqlen - self.inprogress_constraint.remaining()

          return (len(self.complete_constraints      ) * self.max_seqlen) + add

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							int:
          __UpperCAmelCase     :			int  							=      []
          if self.inprogress_constraint is None:
               for constraint in self.pending_constraints:  # "pending" == "unfulfilled yet"
                    __UpperCAmelCase     :			Optional[int]  							=      constraint.advance()
                    if isinstance(snake_case							,			snake_case      ):
                         token_list.append(snake_case      )
                    elif isinstance(snake_case							,			snake_case      ):
                         token_list.extend(snake_case      )
          else:
               __UpperCAmelCase     :			Optional[Any]  							=      self.inprogress_constraint.advance()
               if isinstance(snake_case							,			snake_case      ):
                    token_list.append(snake_case      )
               elif isinstance(snake_case							,			snake_case      ):
                    token_list.extend(snake_case      )

          if len(snake_case      ) == 0:
               return None
          else:
               return token_list

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		Optional[List[int]]      )						->							Optional[int]:
          self.init_state()

          if token_ids is not None:
               for token in token_ids:
                    # completes or steps **one** constraint
                    __UpperCAmelCase							,       __UpperCAmelCase     :			Dict  							=      self.add(snake_case      )

                    # the entire list of constraints are fulfilled
                    if self.completed:
                         break

     def 			lowerCamelCase__			(    self					:		List[str]							,			snake_case					:		int      )						->							List[str]:
          if not isinstance(snake_case							,			snake_case      ):
               raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.'      )

          __UpperCAmelCase							,       __UpperCAmelCase     :			str  							=      False, False

          if self.completed:
               __UpperCAmelCase     :			Union[str, Any]  							=      True
               __UpperCAmelCase     :			str  							=      False
               return complete, stepped

          if self.inprogress_constraint is not None:
               # In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
               # job, simply update the state

               __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Any  							=      self.inprogress_constraint.update(snake_case      )
               if reset:
                    # 1. If the next token breaks the progress, then we must restart.
                    #     e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".

                    #     But that doesn't mean we self.init_state(), since we only reset the state for this particular
                    #     constraint, not the full list of constraints.

                    self.pending_constraints.append(self.inprogress_constraint.copy(stateful=snake_case      )      )
                    __UpperCAmelCase     :			Optional[Any]  							=      None

               if complete:
                    # 2. If the next token completes the constraint, move it to completed list, set
                    #     inprogress to None. If there are no pending constraints either, then this full list of constraints
                    #     is complete.

                    self.complete_constraints.append(self.inprogress_constraint      )
                    __UpperCAmelCase     :			str  							=      None

                    if len(self.pending_constraints      ) == 0:
                         # we're done!
                         __UpperCAmelCase     :			Optional[int]  							=      True

          else:
               # Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
               # of constraints?

               for cidx, pending_constraint in enumerate(self.pending_constraints      ):
                    if pending_constraint.does_advance(snake_case      ):
                         __UpperCAmelCase							,       __UpperCAmelCase							,       __UpperCAmelCase     :			Union[str, Any]  							=      pending_constraint.update(snake_case      )

                         if not stepped:
                              raise Exception(
                                  '''`constraint.update(token_id)` is not yielding incremental progress, '''
                                  '''even though `constraint.does_advance(token_id)` is true.'''      )

                         if complete:
                              self.complete_constraints.append(snake_case      )
                              __UpperCAmelCase     :			Tuple  							=      None

                         if not complete and stepped:
                              __UpperCAmelCase     :			List[Any]  							=      pending_constraint

                         if complete or stepped:
                              # If we made any progress at all, then it's at least not a "pending constraint".

                              __UpperCAmelCase     :			Optional[int]  							=      (
                                  self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
                              )

                              if len(self.pending_constraints      ) == 0 and self.inprogress_constraint is None:
                                   # If there's no longer any pending after this and no inprogress either, then we must be
                                   # complete.

                                   __UpperCAmelCase     :			Any  							=      True

                              break  # prevent accidentally stepping through multiple constraints with just one token.

          return complete, stepped






     def 			lowerCamelCase__			(    self					:		int							,			snake_case					:		Optional[int]=True      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      ConstraintListState(self.constraints      )  # we actually never though self.constraints objects
          # throughout this process. So it's at initialization state.

          if stateful:
               __UpperCAmelCase     :			str  							=      [
                   constraint.copy(stateful=snake_case      ) for constraint in self.complete_constraints
               ]
               if self.inprogress_constraint is not None:
                    __UpperCAmelCase     :			Union[str, Any]  							=      self.inprogress_constraint.copy(stateful=snake_case      )
               __UpperCAmelCase     :			Tuple  							=      [constraint.copy() for constraint in self.pending_constraints]

          return new_state



'''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      a    (		_a	):


     """simple docstring"""






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

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[int]:
          __UpperCAmelCase     :			int  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.vocab_size      )

          __UpperCAmelCase     :			Optional[Any]  							=      None
          if self.use_input_mask:
               __UpperCAmelCase     :			str  							=      ids_tensor([self.batch_size, self.seq_length]							,			vocab_size=2      )

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

          __UpperCAmelCase     :			List[str]  							=      None
          __UpperCAmelCase     :			int  							=      None
          __UpperCAmelCase     :			str  							=      None
          if self.use_labels:
               __UpperCAmelCase     :			List[str]  							=      ids_tensor([self.batch_size]							,			self.type_sequence_label_size      )
               __UpperCAmelCase     :			Union[str, Any]  							=      ids_tensor([self.batch_size, self.seq_length]							,			self.num_labels      )
               __UpperCAmelCase     :			Optional[Any]  							=      ids_tensor([self.batch_size]							,			self.num_choices      )

          __UpperCAmelCase     :			str  							=      self.get_config()

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

     def 			lowerCamelCase__			(    self					:		Tuple      )						->							List[str]:
          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 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							str:
          __UpperCAmelCase     :			Optional[int]  							=      self.get_config()
          __UpperCAmelCase     :			Dict  							=      300
          return config

     def 			lowerCamelCase__			(    self					:		Any							,			snake_case					:		int      )						->							Any:
          self.parent.assertListEqual(list(result.loss.size()      )							,			[]      )

     def 			lowerCamelCase__			(    self					:		List[Any]							,			snake_case					:		Optional[int]							,			snake_case					:		Any							,			snake_case					:		List[Any]							,			snake_case					:		Any							,			snake_case					:		Union[str, Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		Union[str, Any]      )						->							Optional[Any]:
          __UpperCAmelCase     :			List[str]  							=      DebertaModel(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			str  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case      )[0]
          __UpperCAmelCase     :			Tuple  							=      model(snake_case							,			token_type_ids=snake_case      )[0]
          __UpperCAmelCase     :			Optional[int]  							=      model(snake_case      )[0]

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

     def 			lowerCamelCase__			(    self					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		Tuple							,			snake_case					:		Any							,			snake_case					:		Tuple							,			snake_case					:		Union[str, Any]							,			snake_case					:		Optional[int]							,			snake_case					:		List[str]      )						->							Optional[int]:
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForMaskedLM(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			int  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )

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

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		Union[str, Any]							,			snake_case					:		Tuple							,			snake_case					:		Optional[Any]							,			snake_case					:		Tuple							,			snake_case					:		List[Any]							,			snake_case					:		str							,			snake_case					:		Tuple      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Tuple  							=      self.num_labels
          __UpperCAmelCase     :			List[Any]  							=      DebertaForSequenceClassification(snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Any  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertListEqual(list(result.logits.size()      )							,			[self.batch_size, self.num_labels]      )
          self.check_loss_output(snake_case      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[str]							,			snake_case					:		Union[str, Any]							,			snake_case					:		List[str]							,			snake_case					:		Dict							,			snake_case					:		Optional[Any]							,			snake_case					:		Dict							,			snake_case					:		Optional[int]      )						->							int:
          __UpperCAmelCase     :			List[Any]  							=      self.num_labels
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForTokenClassification(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Any  							=      model(snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			labels=snake_case      )
          self.parent.assertEqual(result.logits.shape							,			(self.batch_size, self.seq_length, self.num_labels)      )

     def 			lowerCamelCase__			(    self					:		str							,			snake_case					:		List[Any]							,			snake_case					:		Optional[Any]							,			snake_case					:		str							,			snake_case					:		Optional[int]							,			snake_case					:		int							,			snake_case					:		int							,			snake_case					:		str      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Union[str, Any]  							=      DebertaForQuestionAnswering(config=snake_case      )
          model.to(snake_case      )
          model.eval()
          __UpperCAmelCase     :			Optional[int]  							=      model(
              snake_case							,			attention_mask=snake_case							,			token_type_ids=snake_case							,			start_positions=snake_case							,			end_positions=snake_case							,			)
          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					:		str      )						->							int:
          __UpperCAmelCase     :			int  							=      self.prepare_config_and_inputs()
          (
              (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       (
              __UpperCAmelCase
          )							,       
          )     :			Any  							=      config_and_inputs
          __UpperCAmelCase     :			str  							=      {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
          return config, inputs_dict



@require_torch
class      a    (		_a     ,  _a     ,  unittest.TestCase	):


     """simple docstring"""






     SCREAMING_SNAKE_CASE	:			Optional[int]							       =       (
         (
             DebertaModel,
             DebertaForMaskedLM,
             DebertaForSequenceClassification,
             DebertaForTokenClassification,
             DebertaForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     SCREAMING_SNAKE_CASE	:			Dict							       =       (
         {
             "feature-extraction": DebertaModel,
             "fill-mask": DebertaForMaskedLM,
             "question-answering": DebertaForQuestionAnswering,
             "text-classification": DebertaForSequenceClassification,
             "token-classification": DebertaForTokenClassification,
             "zero-shot": DebertaForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )

     SCREAMING_SNAKE_CASE	:			Union[str, Any]							       =       True
     SCREAMING_SNAKE_CASE	:			Dict							       =       False
     SCREAMING_SNAKE_CASE	:			List[Any]							       =       False
     SCREAMING_SNAKE_CASE	:			List[str]							       =       False
     SCREAMING_SNAKE_CASE	:			Any							       =       False

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Union[str, Any]:
          __UpperCAmelCase     :			Dict  							=      DebertaModelTester(self      )
          __UpperCAmelCase     :			List[Any]  							=      ConfigTester(self							,			config_class=snake_case							,			hidden_size=37      )

     def 			lowerCamelCase__			(    self					:		Optional[int]      )						->							Dict:
          self.config_tester.run_common_tests()

     def 			lowerCamelCase__			(    self					:		List[str]      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_model(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							List[str]:
          __UpperCAmelCase     :			Tuple  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_sequence_classification(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							str:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_masked_lm(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Union[str, Any]      )						->							Any:
          __UpperCAmelCase     :			Optional[int]  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_question_answering(*snake_case      )

     def 			lowerCamelCase__			(    self					:		Dict      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_deberta_for_token_classification(*snake_case      )






     @slow
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Tuple:
          for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __UpperCAmelCase     :			str  							=      DebertaModel.from_pretrained(snake_case      )
               self.assertIsNotNone(snake_case      )



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


     """simple docstring"""






     @unittest.skip(reason='''Model not available yet'''      )
     def 			lowerCamelCase__			(    self					:		Dict      )						->							Tuple:
          pass






     @slow
     def 			lowerCamelCase__			(    self					:		int      )						->							Optional[Any]:
          __UpperCAmelCase     :			Any  							=      DebertaModel.from_pretrained('''microsoft/deberta-base'''      )

          __UpperCAmelCase     :			Any  							=      torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]      )
          __UpperCAmelCase     :			Dict  							=      torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]      )
          with torch.no_grad():
               __UpperCAmelCase     :			int  							=      model(snake_case							,			attention_mask=snake_case      )[0]
          # compare the actual values for a slice.
          __UpperCAmelCase     :			Optional[Any]  							=      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]							,			snake_case							,			atol=1E-4      )							,			f'{output[:, 1:4, 1:4]}'      )