



'''simple docstring'''




import unittest

from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
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.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM







@require_torch
class 	_lowerCAmelCase							:


	def __init__(self       ,      lowercase       ,      lowercase=99       ,      lowercase=13       ,      lowercase=16       ,      lowercase=7       ,      lowercase=True       ,      lowercase=True       ,      lowercase=True       ,      lowercase=False       ,      lowercase=True       ,      lowercase=2       ,      lowercase=32       ,      lowercase=4       ,      lowercase=4       ,      lowercase=30       ,      lowercase=0       ,      lowercase=1       ,      lowercase=2       ,      lowercase=None       ,      ):
				A_	:						Optional[int]	=     parent
				A_	:						List[Any]	=     batch_size
				A_	:						Any	=     decoder_seq_length
				# For common tests
				A_	:						str	=     self.decoder_seq_length
				A_	:						Optional[int]	=     is_training
				A_	:						Union[str, Any]	=     use_attention_mask
				A_	:						Union[str, Any]	=     use_labels

				A_	:						int	=     vocab_size
				A_	:						int	=     d_model
				A_	:						List[str]	=     d_model
				A_	:						Optional[int]	=     decoder_layers
				A_	:						Union[str, Any]	=     decoder_layers
				A_	:						Union[str, Any]	=     decoder_ffn_dim
				A_	:						Dict	=     decoder_attention_heads
				A_	:						str	=     decoder_attention_heads
				A_	:						Union[str, Any]	=     eos_token_id
				A_	:						Any	=     bos_token_id
				A_	:						Any	=     pad_token_id
				A_	:						Optional[int]	=     decoder_start_token_id
				A_	:						str	=     use_cache
				A_	:						List[str]	=     max_position_embeddings

				A_	:						Optional[Any]	=     None
				A_	:						int	=     decoder_seq_length
				A_	:						List[str]	=     2
				A_	:						int	=     1


	def     _a	(self							):
				A_	:						Union[str, Any]	=     ids_tensor([self.batch_size, self.decoder_seq_length]       ,      self.vocab_size							)

				A_	:						Optional[Any]	=     None
				if self.use_attention_mask:
							A_	:						List[Any]	=     ids_tensor([self.batch_size, self.decoder_seq_length]       ,      vocab_size=2							)

				A_	:						Any	=     None
				if self.use_labels:
							A_	:						Dict	=     ids_tensor([self.batch_size, self.decoder_seq_length]       ,      self.vocab_size							)

				A_	:						str	=     TrOCRConfig(
				    vocab_size=self.vocab_size       ,      d_model=self.d_model       ,      decoder_layers=self.decoder_layers       ,      decoder_ffn_dim=self.decoder_ffn_dim       ,      decoder_attention_heads=self.decoder_attention_heads       ,      eos_token_id=self.eos_token_id       ,      bos_token_id=self.bos_token_id       ,      use_cache=self.use_cache       ,      pad_token_id=self.pad_token_id       ,      decoder_start_token_id=self.decoder_start_token_id       ,      max_position_embeddings=self.max_position_embeddings       ,      )

				return (config, input_ids, attention_mask, lm_labels)


	def     _a	(self       ,      lowercase       ,      lowercase       ,      lowercase       ,      lowercase       ,      ):
				A_	:						int	=     True
				A_	:						Tuple	=     TrOCRDecoder(config=__SCREAMING_SNAKE_CASE							).to(__SCREAMING_SNAKE_CASE							).eval()
				A_	:						Optional[int]	=     input_ids[:2]

				input_ids[input_ids == 0] += 1
				# first forward pass
				A_	:						int	=     model(__SCREAMING_SNAKE_CASE       ,      use_cache=__SCREAMING_SNAKE_CASE							)
				A_	:						Dict	=     model(__SCREAMING_SNAKE_CASE							)
				A_	:						List[str]	=     model(__SCREAMING_SNAKE_CASE       ,      use_cache=__SCREAMING_SNAKE_CASE							)

				self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							)							)
				self.parent.assertTrue(len(__SCREAMING_SNAKE_CASE							) == len(__SCREAMING_SNAKE_CASE							) + 1							)

				A_	:						str	=     outputs["""past_key_values"""]

				# create hypothetical next token and extent to next_input_ids
				A_	:						Union[str, Any]	=     ids_tensor((2, 1)       ,      config.vocab_size - 1							) + 1

				# append to next input_ids and
				A_	:						List[Any]	=     torch.cat([input_ids, next_tokens]       ,      dim=-1							)

				A_	:						Optional[int]	=     model(__SCREAMING_SNAKE_CASE							)["""last_hidden_state"""]
				A_	:						int	=     model(__SCREAMING_SNAKE_CASE       ,      past_key_values=__SCREAMING_SNAKE_CASE							)["""last_hidden_state"""]

				# select random slice
				A_	:						int	=     ids_tensor((1,)       ,      output_from_past.shape[-1]							).item()
				A_	:						List[Any]	=     output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
				A_	:						Tuple	=     output_from_past[:, 0, random_slice_idx].detach()

				# test that outputs are equal for slice
				assert torch.allclose(__SCREAMING_SNAKE_CASE       ,      __SCREAMING_SNAKE_CASE       ,      atol=1E-3							)


	def     _a	(self							):
				A_	:						Union[str, Any]	=     self.prepare_config_and_inputs()
				A_,    A_,    A_,    A_	:						Optional[Any]	=     config_and_inputs

				A_	:						Optional[Any]	=     {"""input_ids""": input_ids, """attention_mask""": attention_mask}
				return config, inputs_dict







@require_torch
class 	_lowerCAmelCase							(			__UpperCAmelCase      ,			__UpperCAmelCase      ,			__UpperCAmelCase      ,			unittest.TestCase ):
	__SCREAMING_SNAKE_CASE   :		List[Any]					=					(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		Optional[Any]					=					(TrOCRForCausalLM,) if is_torch_available() else ()
	__SCREAMING_SNAKE_CASE   :		List[str]					=					{'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
	__SCREAMING_SNAKE_CASE   :		int					=					True
	__SCREAMING_SNAKE_CASE   :		Union[str, Any]					=					False


	def     _a	(self							):
				A_	:						Any	=     TrOCRStandaloneDecoderModelTester(self       ,      is_training=__SCREAMING_SNAKE_CASE							)
				A_	:						int	=     ConfigTester(self       ,      config_class=__SCREAMING_SNAKE_CASE							)


	def     _a	(self							):
				pass


	def     _a	(self							):
				pass


	def     _a	(self							):
				pass


	def     _a	(self							):
				self.config_tester.run_common_tests()


	def     _a	(self							):
				A_	:						int	=     self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_decoder_model_past(*__SCREAMING_SNAKE_CASE							)


	def     _a	(self							):
				return


	@unittest.skip("""The model doesn't support left padding"""							)  # and it's not used enough to be worth fixing :)
	def     _a	(self							):
				pass


'''simple docstring'''



def    a__       (  a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      int(a__						)

     # Initialize Result
     __SCREAMING_SNAKE_CASE     =      []

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

     return answer


# Driver Code
if __name__ == "__main__":
  UpperCAmelCase :       Dict			   =     []
  UpperCAmelCase :       List[str]			   =     '0'

  if (
      input('Do you want to enter your denominations ? (yY/n): ').strip().lower()
      == "y"
  ):
    UpperCAmelCase :       List[str]			   =     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()))
    UpperCAmelCase :       str			   =     input('Enter the change you want to make in Indian Currency: ').strip()
  else:
    # All denominations of Indian Currency if user does not enter
    UpperCAmelCase :       int			   =     [1, 2, 5, 1_0, 2_0, 5_0, 1_0_0, 5_0_0, 2_0_0_0]
    UpperCAmelCase :       Any			   =     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}: """)
    UpperCAmelCase :       Any			   =     find_minimum_change(denominations, value)
    # Print result
    for i in range(len(answer)):
      print(answer[i], end=' ')






