
"""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 			_SCREAMING_SNAKE_CASE					(			A__							):






			def __init__(					self       ,   __A       ,   __A=13       ,   __A=7       ,   __A=True       ,   __A=True       ,   __A=True       ,   __A=True       ,   __A=99       ,   __A=32       ,   __A=5       ,   __A=4       ,   __A=37       ,   __A="gelu"       ,   __A=0.1       ,   __A=0.1       ,   __A=512       ,   __A=16       ,   __A=2       ,   __A=0.0_2       ,   __A=False       ,   __A=True       ,   __A="None"       ,   __A=3       ,   __A=4       ,   __A=None       ,   )			->     List[Any]:
						lowerCAmelCase_    :List[Any]			     =  parent
						lowerCAmelCase_    :List[str]			     =  batch_size
						lowerCAmelCase_    :List[str]			     =  seq_length
						lowerCAmelCase_    :List[Any]			     =  is_training
						lowerCAmelCase_    :Any			     =  use_input_mask
						lowerCAmelCase_    :str			     =  use_token_type_ids
						lowerCAmelCase_    :Tuple			     =  use_labels
						lowerCAmelCase_    :Optional[Any]			     =  vocab_size
						lowerCAmelCase_    :Tuple			     =  hidden_size
						lowerCAmelCase_    :List[Any]			     =  num_hidden_layers
						lowerCAmelCase_    :Union[str, Any]			     =  num_attention_heads
						lowerCAmelCase_    :List[str]			     =  intermediate_size
						lowerCAmelCase_    :List[str]			     =  hidden_act
						lowerCAmelCase_    :str			     =  hidden_dropout_prob
						lowerCAmelCase_    :Dict			     =  attention_probs_dropout_prob
						lowerCAmelCase_    :Any			     =  max_position_embeddings
						lowerCAmelCase_    :Dict			     =  type_vocab_size
						lowerCAmelCase_    :Optional[Any]			     =  type_sequence_label_size
						lowerCAmelCase_    :int			     =  initializer_range
						lowerCAmelCase_    :int			     =  num_labels
						lowerCAmelCase_    :List[Any]			     =  num_choices
						lowerCAmelCase_    :List[str]			     =  relative_attention
						lowerCAmelCase_    :List[str]			     =  position_biased_input
						lowerCAmelCase_    :str			     =  pos_att_type
						lowerCAmelCase_    :Tuple			     =  scope






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :Any			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.vocab_size       )

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

						lowerCAmelCase_    :List[Any]			     =  None
						if self.use_token_type_ids:
									lowerCAmelCase_    :int			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.type_vocab_size       )

						lowerCAmelCase_    :Dict			     =  None
						lowerCAmelCase_    :List[str]			     =  None
						lowerCAmelCase_    :List[Any]			     =  None
						if self.use_labels:
									lowerCAmelCase_    :str			     =  ids_tensor([self.batch_size]       ,   self.type_sequence_label_size       )
									lowerCAmelCase_    :List[str]			     =  ids_tensor([self.batch_size, self.seq_length]       ,   self.num_labels       )
									lowerCAmelCase_    :List[Any]			     =  ids_tensor([self.batch_size]       ,   self.num_choices       )

						lowerCAmelCase_    :Optional[int]			     =  self.get_config()

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






			def  __lowerCAmelCase (					self       )			->     int:
						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       )			->     Tuple:
						lowerCAmelCase_    :Optional[int]			     =  self.get_config()
						lowerCAmelCase_    :Optional[Any]			     =  300
						return config






			def  __lowerCAmelCase (					self       ,   __A       )			->     List[Any]:
						self.parent.assertListEqual(list(result.loss.size()       )       ,   []       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     List[Any]:
						lowerCAmelCase_    :Union[str, Any]			     =  DebertaModel(config=__A       )
						model.to(__A       )
						model.eval()
						lowerCAmelCase_    :Tuple			     =  model(__A       ,   attention_mask=__A       ,   token_type_ids=__A       )[0]
						lowerCAmelCase_    :Dict			     =  model(__A       ,   token_type_ids=__A       )[0]
						lowerCAmelCase_    :str			     =  model(__A       )[0]

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






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Union[str, Any]:
						lowerCAmelCase_    :str			     =  DebertaForMaskedLM(config=__A       )
						model.to(__A       )
						model.eval()
						lowerCAmelCase_    :Any			     =  model(__A       ,   attention_mask=__A       ,   token_type_ids=__A       ,   labels=__A       )

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






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     Dict:
						lowerCAmelCase_    :Any			     =  self.num_labels
						lowerCAmelCase_    :Dict			     =  DebertaForSequenceClassification(__A       )
						model.to(__A       )
						model.eval()
						lowerCAmelCase_    :Dict			     =  model(__A       ,   attention_mask=__A       ,   token_type_ids=__A       ,   labels=__A       )
						self.parent.assertListEqual(list(result.logits.size()       )       ,   [self.batch_size, self.num_labels]       )
						self.check_loss_output(__A       )






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






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       ,   __A       )			->     List[Any]:
						lowerCAmelCase_    :Union[str, Any]			     =  DebertaForQuestionAnswering(config=__A       )
						model.to(__A       )
						model.eval()
						lowerCAmelCase_    :Optional[Any]			     =  model(
						    __A       ,   attention_mask=__A       ,   token_type_ids=__A       ,   start_positions=__A       ,   end_positions=__A       ,   )
						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       )			->     Union[str, Any]:
						lowerCAmelCase_    :Optional[int]			     =  self.prepare_config_and_inputs()
						(
						    (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       (
						    lowerCAmelCase_
						)     ,       
						)    :int			     =  config_and_inputs
						lowerCAmelCase_    :Dict			     =  {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
						return config, inputs_dict




@require_torch
class 			_SCREAMING_SNAKE_CASE					(			A__	, A__	, unittest.TestCase							):
			UpperCAmelCase_  :Dict				 =						(
			    (
			        DebertaModel,
			        DebertaForMaskedLM,
			        DebertaForSequenceClassification,
			        DebertaForTokenClassification,
			        DebertaForQuestionAnswering,
			    )
			    if is_torch_available()
			    else ()
			)
			UpperCAmelCase_  :List[Any]				 =						(
			    {
			        "feature-extraction": DebertaModel,
			        "fill-mask": DebertaForMaskedLM,
			        "question-answering": DebertaForQuestionAnswering,
			        "text-classification": DebertaForSequenceClassification,
			        "token-classification": DebertaForTokenClassification,
			        "zero-shot": DebertaForSequenceClassification,
			    }
			    if is_torch_available()
			    else {}
			)

			UpperCAmelCase_  :int				 =						True
			UpperCAmelCase_  :str				 =						False
			UpperCAmelCase_  :Tuple				 =						False
			UpperCAmelCase_  :List[Any]				 =						False
			UpperCAmelCase_  :List[Any]				 =						False






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Tuple			     =  DebertaModelTester(self       )
						lowerCAmelCase_    :List[Any]			     =  ConfigTester(self       ,   config_class=__A       ,   hidden_size=37       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						self.config_tester.run_common_tests()






			def  __lowerCAmelCase (					self       )			->     List[Any]:
						lowerCAmelCase_    :List[str]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_model(*__A       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :int			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_sequence_classification(*__A       )






			def  __lowerCAmelCase (					self       )			->     Optional[Any]:
						lowerCAmelCase_    :Optional[Any]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_masked_lm(*__A       )






			def  __lowerCAmelCase (					self       )			->     Optional[int]:
						lowerCAmelCase_    :Any			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_question_answering(*__A       )






			def  __lowerCAmelCase (					self       )			->     str:
						lowerCAmelCase_    :Union[str, Any]			     =  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_deberta_for_token_classification(*__A       )






			@slow
			def  __lowerCAmelCase (					self       )			->     Dict:
						for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									lowerCAmelCase_    :str			     =  DebertaModel.from_pretrained(__A       )
									self.assertIsNotNone(__A       )




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






			@unittest.skip(reason="""Model not available yet"""       )
			def  __lowerCAmelCase (					self       )			->     Tuple:
						pass






			@slow
			def  __lowerCAmelCase (					self       )			->     List[str]:
						lowerCAmelCase_    :List[str]			     =  DebertaModel.from_pretrained("""microsoft/deberta-base"""       )

						lowerCAmelCase_    :Dict			     =  torch.tensor([[0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2]]       )
						lowerCAmelCase_    :int			     =  torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]       )
						with torch.no_grad():
									lowerCAmelCase_    :List[str]			     =  model(__A       ,   attention_mask=__A       )[0]
						# compare the actual values for a slice.
						lowerCAmelCase_    :Union[str, Any]			     =  torch.tensor(
						    [[[-0.5_9_8_6, -0.8_0_5_5, -0.8_4_6_2], [1.4_4_8_4, -0.9_3_4_8, -0.8_0_5_9], [0.3_1_2_3, 0.0_0_3_2, -1.4_1_3_1]]]       )
						self.assertTrue(torch.allclose(output[:, 1:4, 1:4]       ,   __A       ,   atol=1E-4       )       ,   f"""{output[:, 1:4, 1:4]}"""       )













from __future__ import annotations



def _a				(	lowerCamelCase:   list[int | str] )	->						None:







      '''simple docstring'''






      create_state_space_tree(lowerCamelCase					,				[]					,				0					,				[0 for i in range(len(lowerCamelCase ) )] )



def _a				(	lowerCamelCase:   list[int | str]					,				lowerCamelCase:   list[int | str]					,				lowerCamelCase:   int					,				lowerCamelCase:   list[int]					,				)	->						None:







      '''simple docstring'''






      if index == len(lowerCamelCase ):
            print(lowerCamelCase )
            return

      for i in range(len(lowerCamelCase ) ):
            if not index_used[i]:
                  current_sequence.append(sequence[i] )
                  __A          =				True
                  create_state_space_tree(lowerCamelCase					,				lowerCamelCase					,				index + 1					,				lowerCamelCase )
                  current_sequence.pop()
                  __A          =				False
snake_case__					:							list[int | str]													=     [3, 1, 2, 4]
generate_all_permutations(sequence)

snake_case__					:							list[int | str]													=     ["A", "B", "C"]







generate_all_permutations(sequence_a)





