

'''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      lowerCAmelCase   (					__a      ):







       def __init__(							self	:       Tuple  ,					__lowercase	:       str  ,					__lowercase	:       Union[str, Any]=13  ,					__lowercase	:       Union[str, Any]=7  ,					__lowercase	:       int=True  ,					__lowercase	:       str=True  ,					__lowercase	:       Any=True  ,					__lowercase	:       str=True  ,					__lowercase	:       str=99  ,					__lowercase	:       Any=32  ,					__lowercase	:       List[str]=5  ,					__lowercase	:       List[str]=4  ,					__lowercase	:       str=37  ,					__lowercase	:       int="gelu"  ,					__lowercase	:       int=0.1  ,					__lowercase	:       List[Any]=0.1  ,					__lowercase	:       Union[str, Any]=512  ,					__lowercase	:       Union[str, Any]=16  ,					__lowercase	:       Dict=2  ,					__lowercase	:       int=0.0_2  ,					__lowercase	:       int=False  ,					__lowercase	:       Optional[int]=True  ,					__lowercase	:       Union[str, Any]="None"  ,					__lowercase	:       Optional[Any]=3  ,					__lowercase	:       Optional[int]=4  ,					__lowercase	:       List[Any]=None  ,					):







            """simple docstring"""







            __lowercase							=parent
            __lowercase							=batch_size
            __lowercase							=seq_length
            __lowercase							=is_training
            __lowercase							=use_input_mask
            __lowercase							=use_token_type_ids
            __lowercase							=use_labels
            __lowercase							=vocab_size
            __lowercase							=hidden_size
            __lowercase							=num_hidden_layers
            __lowercase							=num_attention_heads
            __lowercase							=intermediate_size
            __lowercase							=hidden_act
            __lowercase							=hidden_dropout_prob
            __lowercase							=attention_probs_dropout_prob
            __lowercase							=max_position_embeddings
            __lowercase							=type_vocab_size
            __lowercase							=type_sequence_label_size
            __lowercase							=initializer_range
            __lowercase							=num_labels
            __lowercase							=num_choices
            __lowercase							=relative_attention
            __lowercase							=position_biased_input
            __lowercase							=pos_att_type
            __lowercase							=scope







       def 	snake_case      (							self	:       List[Any]					):







            """simple docstring"""







            __lowercase							=ids_tensor([self.batch_size, self.seq_length]  ,					self.vocab_size					)

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

            __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							=ids_tensor([self.batch_size]  ,					self.num_choices					)

            __lowercase							=self.get_config()

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







       def 	snake_case      (							self	:       int					):







            """simple docstring"""







            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 	snake_case      (							self	:       Tuple					):







            """simple docstring"""







            __lowercase							=self.get_config()
            __lowercase							=300
            return config







       def 	snake_case      (							self	:       Optional[int]  ,					__lowercase	:       List[Any]					):







            """simple docstring"""







            self.parent.assertListEqual(list(result.loss.size()					)  ,					[]					)







       def 	snake_case      (							self	:       str  ,					__lowercase	:       Optional[int]  ,					__lowercase	:       str  ,					__lowercase	:       Any  ,					__lowercase	:       Tuple  ,					__lowercase	:       List[str]  ,					__lowercase	:       Optional[Any]  ,					__lowercase	:       int					):







            """simple docstring"""







            __lowercase							=DebertaModel(config=lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.eval()
            __lowercase							=model(lowerCAmelCase__  ,					attention_mask=lowerCAmelCase__  ,					token_type_ids=lowerCAmelCase__					)[0]
            __lowercase							=model(lowerCAmelCase__  ,					token_type_ids=lowerCAmelCase__					)[0]
            __lowercase							=model(lowerCAmelCase__					)[0]

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







       def 	snake_case      (							self	:       Optional[int]  ,					__lowercase	:       Union[str, Any]  ,					__lowercase	:       Optional[Any]  ,					__lowercase	:       List[str]  ,					__lowercase	:       Dict  ,					__lowercase	:       Any  ,					__lowercase	:       Any  ,					__lowercase	:       Any					):







            """simple docstring"""







            __lowercase							=DebertaForMaskedLM(config=lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.eval()
            __lowercase							=model(lowerCAmelCase__  ,					attention_mask=lowerCAmelCase__  ,					token_type_ids=lowerCAmelCase__  ,					labels=lowerCAmelCase__					)

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







       def 	snake_case      (							self	:       int  ,					__lowercase	:       str  ,					__lowercase	:       List[Any]  ,					__lowercase	:       List[Any]  ,					__lowercase	:       int  ,					__lowercase	:       Dict  ,					__lowercase	:       int  ,					__lowercase	:       Optional[int]					):







            """simple docstring"""







            __lowercase							=self.num_labels
            __lowercase							=DebertaForSequenceClassification(lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.eval()
            __lowercase							=model(lowerCAmelCase__  ,					attention_mask=lowerCAmelCase__  ,					token_type_ids=lowerCAmelCase__  ,					labels=lowerCAmelCase__					)
            self.parent.assertListEqual(list(result.logits.size()					)  ,					[self.batch_size, self.num_labels]					)
            self.check_loss_output(lowerCAmelCase__					)







       def 	snake_case      (							self	:       Tuple  ,					__lowercase	:       int  ,					__lowercase	:       Tuple  ,					__lowercase	:       Dict  ,					__lowercase	:       Dict  ,					__lowercase	:       str  ,					__lowercase	:       List[Any]  ,					__lowercase	:       Tuple					):







            """simple docstring"""







            __lowercase							=self.num_labels
            __lowercase							=DebertaForTokenClassification(config=lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.eval()
            __lowercase							=model(lowerCAmelCase__  ,					attention_mask=lowerCAmelCase__  ,					token_type_ids=lowerCAmelCase__  ,					labels=lowerCAmelCase__					)
            self.parent.assertEqual(result.logits.shape  ,					(self.batch_size, self.seq_length, self.num_labels)					)







       def 	snake_case      (							self	:       Any  ,					__lowercase	:       List[Any]  ,					__lowercase	:       Tuple  ,					__lowercase	:       List[Any]  ,					__lowercase	:       Union[str, Any]  ,					__lowercase	:       int  ,					__lowercase	:       Dict  ,					__lowercase	:       Optional[int]					):







            """simple docstring"""







            __lowercase							=DebertaForQuestionAnswering(config=lowerCAmelCase__					)
            model.to(lowerCAmelCase__					)
            model.eval()
            __lowercase							=model(
                lowerCAmelCase__  ,					attention_mask=lowerCAmelCase__  ,					token_type_ids=lowerCAmelCase__  ,					start_positions=lowerCAmelCase__  ,					end_positions=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 	snake_case      (							self	:       List[Any]					):







            """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_torch
class      lowerCAmelCase   (					__a      ,	__a      ,	unittest.TestCase      ):
       lowerCAmelCase_				=       (
           (
               DebertaModel,
               DebertaForMaskedLM,
               DebertaForSequenceClassification,
               DebertaForTokenClassification,
               DebertaForQuestionAnswering,
           )
           if is_torch_available()
           else ()
       )
       lowerCAmelCase_				=       (
           {
               '''feature-extraction''': DebertaModel,
               '''fill-mask''': DebertaForMaskedLM,
               '''question-answering''': DebertaForQuestionAnswering,
               '''text-classification''': DebertaForSequenceClassification,
               '''token-classification''': DebertaForTokenClassification,
               '''zero-shot''': DebertaForSequenceClassification,
           }
           if is_torch_available()
           else {}
       )

       lowerCAmelCase_				=       True
       lowerCAmelCase_				=       False
       lowerCAmelCase_				=       False
       lowerCAmelCase_				=       False
       lowerCAmelCase_				=       False







       def 	snake_case      (							self	:       Dict					):







            """simple docstring"""







            __lowercase							=DebertaModelTester(self					)
            __lowercase							=ConfigTester(self  ,					config_class=lowerCAmelCase__  ,					hidden_size=37					)







       def 	snake_case      (							self	:       List[Any]					):







            """simple docstring"""







            self.config_tester.run_common_tests()







       def 	snake_case      (							self	:       Any					):







            """simple docstring"""







            __lowercase							=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_model(*lowerCAmelCase__					)







       def 	snake_case      (							self	:       Union[str, Any]					):







            """simple docstring"""







            __lowercase							=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_sequence_classification(*lowerCAmelCase__					)







       def 	snake_case      (							self	:       Any					):







            """simple docstring"""







            __lowercase							=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_masked_lm(*lowerCAmelCase__					)







       def 	snake_case      (							self	:       List[str]					):







            """simple docstring"""







            __lowercase							=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_question_answering(*lowerCAmelCase__					)







       def 	snake_case      (							self	:       Union[str, Any]					):







            """simple docstring"""







            __lowercase							=self.model_tester.prepare_config_and_inputs()
            self.model_tester.create_and_check_deberta_for_token_classification(*lowerCAmelCase__					)







       @slow
       def 	snake_case      (							self	:       Optional[Any]					):







            """simple docstring"""







            for model_name in DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 __lowercase							=DebertaModel.from_pretrained(lowerCAmelCase__					)
                 self.assertIsNotNone(lowerCAmelCase__					)






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







       @unittest.skip(reason='Model not available yet'					)
       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







            pass







       @slow
       def 	snake_case      (							self	:       Optional[int]					):







            """simple docstring"""







            __lowercase							=DebertaModel.from_pretrained('microsoft/deberta-base'					)

            __lowercase							=torch.tensor([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]					)
            __lowercase							=torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]					)
            with torch.no_grad():
                 __lowercase							=model(lowerCAmelCase__  ,					attention_mask=lowerCAmelCase__					)[0]
            # compare the actual values for a slice.
            __lowercase							=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]  ,					lowerCAmelCase__  ,					atol=1E-4					)  ,					f'''{output[:, 1:4, 1:4]}'''					)




"""simple docstring"""





from __future__ import annotations

__magic_name__       =			[-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__magic_name__       =			[-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)

		for i in range(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for j in range(i + 1  ,				UpperCamelCase_				):
						if arr[i] < arr[j]:
								__SCREAMING_SNAKE_CASE            = arr[j]
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = []
		for i, outer in enumerate(UpperCamelCase_				):
				__SCREAMING_SNAKE_CASE            = -1
				for inner in arr[i + 1 :]:
						if outer < inner:
								__SCREAMING_SNAKE_CASE            = inner
								break
				result.append(UpperCamelCase_				)
		return result



def 						_lowerCAmelCase				(				UpperCamelCase_				):
		__SCREAMING_SNAKE_CASE            = len(UpperCamelCase_				)
		__SCREAMING_SNAKE_CASE            = []
		__SCREAMING_SNAKE_CASE            = [-1] * arr_size

		for index in reversed(range(UpperCamelCase_				)				):
				if stack:
						while stack[-1] <= arr[index]:
								stack.pop()
								if not stack:
										break
				if stack:
						__SCREAMING_SNAKE_CASE            = stack[-1]
				stack.append(arr[index]				)
		return result


if __name__ == "__main__":
							from doctest import testmod
							from timeit import timeit

							testmod()
							print(next_greatest_element_slow(arr))
							print(next_greatest_element_fast(arr))
							print(next_greatest_element(arr))

							__magic_name__       =			(
							    "from __main__ import arr, next_greatest_element_slow, "
							    "next_greatest_element_fast, next_greatest_element"
							)
							print(
							    "next_greatest_element_slow():",
							    timeit("next_greatest_element_slow(arr)", setup=setup),
							)
							print(
							    "next_greatest_element_fast():",
							    timeit("next_greatest_element_fast(arr)", setup=setup),
							)
							print(
							    "     next_greatest_element():",
							    timeit("next_greatest_element(arr)", setup=setup),
							)




