




"""simple docstring"""


import os
import tempfile
import unittest

from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import (
         MODEL_FOR_PRETRAINING_MAPPING,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
         NezhaModel,
     )
     from transformers.models.nezha.modeling_nezha import NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST






class       lowercase:







 '''simple docstring'''





 def __init__(       self:	Tuple,   a_:	Any,   a_:	Optional[Any]=13,   a_:	List[str]=7,   a_:	str=True,   a_:	Union[str, Any]=True,   a_:	Optional[Any]=True,   a_:	int=True,   a_:	str=99,   a_:	List[Any]=32,   a_:	Optional[Any]=5,   a_:	int=4,   a_:	Optional[int]=37,   a_:	Dict="gelu",   a_:	List[Any]=0.1,   a_:	Dict=0.1,   a_:	List[str]=128,   a_:	str=32,   a_:	Optional[int]=16,   a_:	Optional[Any]=2,   a_:	int=0.02,   a_:	Tuple=3,   a_:	Any=4,   a_:	Dict=None,   ):




   '''simple docstring'''



   _snake_case							:	str			=						parent
   _snake_case							:	Dict			=						batch_size
   _snake_case							:	List[Any]			=						seq_length
   _snake_case							:	List[Any]			=						is_training
   _snake_case							:	Union[str, Any]			=						use_input_mask
   _snake_case							:	Optional[int]			=						use_token_type_ids
   _snake_case							:	int			=						use_labels
   _snake_case							:	Any			=						vocab_size
   _snake_case							:	Dict			=						hidden_size
   _snake_case							:	Union[str, Any]			=						num_hidden_layers
   _snake_case							:	List[str]			=						num_attention_heads
   _snake_case							:	List[Any]			=						intermediate_size
   _snake_case							:	Union[str, Any]			=						hidden_act
   _snake_case							:	List[str]			=						hidden_dropout_prob
   _snake_case							:	Union[str, Any]			=						attention_probs_dropout_prob
   _snake_case							:	Tuple			=						max_position_embeddings
   _snake_case							:	Union[str, Any]			=						type_vocab_size
   _snake_case							:	Optional[int]			=						type_sequence_label_size
   _snake_case							:	Optional[Any]			=						initializer_range
   _snake_case							:	List[str]			=						num_labels
   _snake_case							:	List[Any]			=						num_choices
   _snake_case							:	Optional[int]			=						scope


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	str			=						ids_tensor([self.batch_size, self.seq_length],   self.vocab_size	)

   _snake_case							:	List[str]			=						None
   if self.use_input_mask:
     _snake_case							:	Optional[Any]			=						random_attention_mask([self.batch_size, self.seq_length]	)

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

   _snake_case							:	Optional[int]			=						None
   _snake_case							:	int			=						None
   _snake_case							:	Dict			=						None
   if self.use_labels:
     _snake_case							:	List[str]			=						ids_tensor([self.batch_size],   self.type_sequence_label_size	)
     _snake_case							:	List[str]			=						ids_tensor([self.batch_size, self.seq_length],   self.num_labels	)
     _snake_case							:	Tuple			=						ids_tensor([self.batch_size],   self.num_choices	)

   _snake_case							:	List[str]			=						self.get_config()

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


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   return NezhaConfig(
       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,   is_decoder=a_,   initializer_range=self.initializer_range,   )


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   (
       (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      
   )							:	Union[str, Any]			=						self.prepare_config_and_inputs()

   _snake_case							:	int			=						True
   _snake_case							:	Optional[Any]			=						floats_tensor([self.batch_size, self.seq_length, self.hidden_size]	)
   _snake_case							:	int			=						ids_tensor([self.batch_size, self.seq_length],   vocab_size=2	)

   return (
       config,
       input_ids,
       token_type_ids,
       input_mask,
       sequence_labels,
       token_labels,
       choice_labels,
       encoder_hidden_states,
       encoder_attention_mask,
   )


 def 				UpperCamelCase_     (       self:	Tuple,   a_:	str,   a_:	Optional[int],   a_:	int,   a_:	Dict,   a_:	int,   a_:	Any,   a_:	str	):




   '''simple docstring'''



   _snake_case							:	Dict			=						NezhaModel(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Any			=						model(a_,   attention_mask=a_,   token_type_ids=a_	)
   _snake_case							:	Union[str, Any]			=						model(a_,   token_type_ids=a_	)
   _snake_case							:	List[str]			=						model(a_	)
   self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)	)
   self.parent.assertEqual(result.pooler_output.shape,   (self.batch_size, self.hidden_size)	)


 def 				UpperCamelCase_     (       self:	List[str],   a_:	Optional[Any],   a_:	List[str],   a_:	str,   a_:	Any,   a_:	Any,   a_:	Tuple,   a_:	Any,   a_:	Tuple,   a_:	Any,   ):




   '''simple docstring'''



   _snake_case							:	str			=						True
   _snake_case							:	Dict			=						NezhaModel(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Optional[Any]			=						model(
       a_,   attention_mask=a_,   token_type_ids=a_,   encoder_hidden_states=a_,   encoder_attention_mask=a_,   )
   _snake_case							:	Dict			=						model(
       a_,   attention_mask=a_,   token_type_ids=a_,   encoder_hidden_states=a_,   )
   _snake_case							:	Tuple			=						model(a_,   attention_mask=a_,   token_type_ids=a_	)
   self.parent.assertEqual(result.last_hidden_state.shape,   (self.batch_size, self.seq_length, self.hidden_size)	)
   self.parent.assertEqual(result.pooler_output.shape,   (self.batch_size, self.hidden_size)	)


 def 				UpperCamelCase_     (       self:	Optional[Any],   a_:	Union[str, Any],   a_:	int,   a_:	Optional[Any],   a_:	str,   a_:	Dict,   a_:	Optional[int],   a_:	Optional[Any]	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						NezhaForMaskedLM(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Dict			=						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 				UpperCamelCase_     (       self:	Tuple,   a_:	int,   a_:	Optional[Any],   a_:	Any,   a_:	int,   a_:	Union[str, Any],   a_:	Optional[int],   a_:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						NezhaForNextSentencePrediction(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	Optional[int]			=						model(
       a_,   attention_mask=a_,   token_type_ids=a_,   labels=a_,   )
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, 2)	)


 def 				UpperCamelCase_     (       self:	Any,   a_:	List[str],   a_:	Union[str, Any],   a_:	List[str],   a_:	Optional[Any],   a_:	int,   a_:	int,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						NezhaForPreTraining(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						model(
       a_,   attention_mask=a_,   token_type_ids=a_,   labels=a_,   next_sentence_label=a_,   )
   self.parent.assertEqual(result.prediction_logits.shape,   (self.batch_size, self.seq_length, self.vocab_size)	)
   self.parent.assertEqual(result.seq_relationship_logits.shape,   (self.batch_size, 2)	)


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	Optional[Any],   a_:	int,   a_:	List[Any],   a_:	List[str],   a_:	str,   a_:	Any,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						NezhaForQuestionAnswering(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						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 				UpperCamelCase_     (       self:	int,   a_:	Union[str, Any],   a_:	str,   a_:	Optional[Any],   a_:	str,   a_:	List[str],   a_:	List[str],   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.num_labels
   _snake_case							:	Union[str, Any]			=						NezhaForSequenceClassification(a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						model(a_,   attention_mask=a_,   token_type_ids=a_,   labels=a_	)
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_labels)	)


 def 				UpperCamelCase_     (       self:	Dict,   a_:	Any,   a_:	Dict,   a_:	Tuple,   a_:	List[Any],   a_:	int,   a_:	str,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	int			=						self.num_labels
   _snake_case							:	List[Any]			=						NezhaForTokenClassification(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	List[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 				UpperCamelCase_     (       self:	Any,   a_:	Dict,   a_:	Union[str, Any],   a_:	str,   a_:	int,   a_:	int,   a_:	Tuple,   a_:	Dict	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.num_choices
   _snake_case							:	Optional[Any]			=						NezhaForMultipleChoice(config=a_	)
   model.to(a_	)
   model.eval()
   _snake_case							:	int			=						input_ids.unsqueeze(1	).expand(-1,   self.num_choices,   -1	).contiguous()
   _snake_case							:	List[Any]			=						token_type_ids.unsqueeze(1	).expand(-1,   self.num_choices,   -1	).contiguous()
   _snake_case							:	List[str]			=						input_mask.unsqueeze(1	).expand(-1,   self.num_choices,   -1	).contiguous()
   _snake_case							:	int			=						model(
       a_,   attention_mask=a_,   token_type_ids=a_,   labels=a_,   )
   self.parent.assertEqual(result.logits.shape,   (self.batch_size, self.num_choices)	)


 def 				UpperCamelCase_     (       self:	Dict	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.prepare_config_and_inputs()
   (
       (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      
   )							:	Tuple			=						config_and_inputs
   _snake_case							:	List[Any]			=						{"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
   return config, inputs_dict







@require_torch
class       lowercase(  __a ,    __a ,    __a ,    unittest.TestCase				):







 '''simple docstring'''





 lowercase__				      =      (
     (
         NezhaModel,
         NezhaForMaskedLM,
         NezhaForMultipleChoice,
         NezhaForNextSentencePrediction,
         NezhaForPreTraining,
         NezhaForQuestionAnswering,
         NezhaForSequenceClassification,
         NezhaForTokenClassification,
     )
     if is_torch_available()
     else ()
 )
 lowercase__				      =      (
     {
         "feature-extraction": NezhaModel,
         "fill-mask": NezhaForMaskedLM,
         "question-answering": NezhaForQuestionAnswering,
         "text-classification": NezhaForSequenceClassification,
         "token-classification": NezhaForTokenClassification,
         "zero-shot": NezhaForSequenceClassification,
     }
     if is_torch_available()
     else {}
 )
 lowercase__				      =      True


 def 				UpperCamelCase_     (       self:	Union[str, Any],   a_:	int,   a_:	List[Any],   a_:	List[str]=False	):




   '''simple docstring'''



   _snake_case							:	Any			=						super()._prepare_for_class(a_,   a_,   return_labels=a_	)

   if return_labels:
     if model_class in get_values(a_	):
       _snake_case							:	Union[str, Any]			=						torch.zeros(
           (self.model_tester.batch_size, self.model_tester.seq_length),   dtype=torch.long,   device=a_	)
       _snake_case							:	str			=						torch.zeros(
           self.model_tester.batch_size,   dtype=torch.long,   device=a_	)
   return inputs_dict


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	int			=						NezhaModelTester(self	)
   _snake_case							:	List[Any]			=						ConfigTester(self,   config_class=a_,   hidden_size=37	)


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




   '''simple docstring'''



   self.config_tester.run_common_tests()


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*a_	)


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




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.model_tester.prepare_config_and_inputs_for_decoder()
   self.model_tester.create_and_check_model_as_decoder(*a_	)


 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   (
       (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      (
       _snake_case
   )	,      
   )							:	List[str]			=						self.model_tester.prepare_config_and_inputs_for_decoder()

   _snake_case							:	List[Any]			=						None

   self.model_tester.create_and_check_model_as_decoder(
       a_,   a_,   a_,   a_,   a_,   a_,   a_,   a_,   a_,   )


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_masked_lm(*a_	)


 def 				UpperCamelCase_     (       self:	List[Any]	):




   '''simple docstring'''



   _snake_case							:	Any			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_multiple_choice(*a_	)


 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_next_sequence_prediction(*a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Optional[int]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_pretraining(*a_	)


 def 				UpperCamelCase_     (       self:	int	):




   '''simple docstring'''



   _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_question_answering(*a_	)


 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_sequence_classification(*a_	)


 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case							:	Union[str, Any]			=						self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_token_classification(*a_	)


 @slow
 def 				UpperCamelCase_     (       self:	Any	):




   '''simple docstring'''



   for model_name in NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _snake_case							:	List[Any]			=						NezhaModel.from_pretrained(a_	)
     self.assertIsNotNone(a_	)


 @slow
 @require_torch_gpu
 def 				UpperCamelCase_     (       self:	Optional[int]	):




   '''simple docstring'''



   _snake_case	,      _snake_case							:	List[Any]			=						self.model_tester.prepare_config_and_inputs_for_common()
   for model_class in self.all_model_classes:
     # NezhaForMultipleChoice behaves incorrectly in JIT environments.
     if model_class == NezhaForMultipleChoice:
       return

     _snake_case							:	str			=						True
     _snake_case							:	Tuple			=						model_class(config=a_	)

     _snake_case							:	Optional[int]			=						self._prepare_for_class(a_,   a_	)
     _snake_case							:	Optional[Any]			=						torch.jit.trace(
         a_,   (inputs_dict["""input_ids"""].to("""cpu"""	), inputs_dict["""attention_mask"""].to("""cpu"""	))	)

     with tempfile.TemporaryDirectory() as tmp:
       torch.jit.save(a_,   os.path.join(a_,   """bert.pt"""	)	)
       _snake_case							:	List[str]			=						torch.jit.load(os.path.join(a_,   """bert.pt"""	),   map_location=a_	)
       loaded(inputs_dict["""input_ids"""].to(a_	),   inputs_dict["""attention_mask"""].to(a_	)	)







@require_torch
class       lowercase(  unittest.TestCase				):







 '''simple docstring'''





 @slow
 def 				UpperCamelCase_     (       self:	str	):




   '''simple docstring'''



   _snake_case							:	int			=						NezhaModel.from_pretrained("""sijunhe/nezha-cn-base"""	)
   _snake_case							:	int			=						torch.tensor([[0, 1, 2, 3, 4, 5]]	)
   _snake_case							:	Tuple			=						torch.tensor([[0, 1, 1, 1, 1, 1]]	)
   with torch.no_grad():
     _snake_case							:	List[Any]			=						model(a_,   attention_mask=a_	)[0]
   _snake_case							:	str			=						torch.Size((1, 6, 768)	)
   self.assertEqual(output.shape,   a_	)
   _snake_case							:	List[Any]			=						torch.tensor([[[0.0_685, 0.2_441, 0.1_102], [0.0_600, 0.1_906, 0.1_349], [0.0_221, 0.0_819, 0.0_586]]]	)

   self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   a_,   atol=1E-4	)	)


 @slow
 def 				UpperCamelCase_     (       self:	Tuple	):




   '''simple docstring'''



   _snake_case							:	Tuple			=						NezhaForMaskedLM.from_pretrained("""sijunhe/nezha-cn-base"""	)
   _snake_case							:	Any			=						torch.tensor([[0, 1, 2, 3, 4, 5]]	)
   _snake_case							:	int			=						torch.tensor([[1, 1, 1, 1, 1, 1]]	)
   with torch.no_grad():
     _snake_case							:	Any			=						model(a_,   attention_mask=a_	)[0]
   _snake_case							:	Optional[int]			=						torch.Size((1, 6, 21_128)	)
   self.assertEqual(output.shape,   a_	)
   _snake_case							:	int			=						torch.tensor(
       [[-2.7_939, -1.7_902, -2.2_189], [-2.8_585, -1.8_908, -2.3_723], [-2.6_499, -1.7_750, -2.2_558]]	)

   self.assertTrue(torch.allclose(output[:, 1:4, 1:4],   a_,   atol=1E-4	)	)








"""simple docstring"""


def       UpperCAmelCase__ (snake_case__							:      list[int]	,     snake_case__							:      str					):
  """simple docstring"""

  _snake_case							:	str			=						int(snake_case__					)

  # Initialize Result
  _snake_case							:	str			=						[]

  # Traverse through all denomination
  for denomination in reversed(snake_case__					):
    # Find denominations
    while int(snake_case__					) >= int(snake_case__					):
      total_value -= int(snake_case__					)
      answer.append(snake_case__					)  # Append the "answers" array

  return answer


# Driver Code
if __name__ == "__main__":
     A_      			=       []
     A_      			=       '''0'''

     if (
         input('''Do you want to enter your denominations ? (yY/n): ''').strip().lower()
         == "y"
     ):
          A_      			=       int(input('''Enter the number of denominations you want to add: ''').strip())

          for i in range(0, n):
               denominations.append(int(input(F'''Denomination {i}: ''').strip()))
          A_      			=       input('''Enter the change you want to make in Indian Currency: ''').strip()
     else:
          # All denominations of Indian Currency if user does not enter
          A_      			=       [1, 2, 5, 10, 20, 50, 1_00, 5_00, 20_00]
          A_      			=       input('''Enter the change you want to make: ''').strip()

     if int(value) == 0 or int(value) < 0:
          print('''The total value cannot be zero or negative.''')

     else:
          print(F'''Following is minimal change for {value}: ''')
          A_      			=       find_minimum_change(denominations, value)
          # Print result
          for i in range(len(answer)):
               print(answer[i], end=''' ''')


