





'''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	,   _lowerCAmelCase	,   _lowerCAmelCase=99	,   _lowerCAmelCase=13	,   _lowerCAmelCase=16	,   _lowerCAmelCase=7	,   _lowerCAmelCase=True	,   _lowerCAmelCase=True	,   _lowerCAmelCase=True	,   _lowerCAmelCase=False	,   _lowerCAmelCase=True	,   _lowerCAmelCase=2	,   _lowerCAmelCase=32	,   _lowerCAmelCase=4	,   _lowerCAmelCase=4	,   _lowerCAmelCase=30	,   _lowerCAmelCase=0	,   _lowerCAmelCase=1	,   _lowerCAmelCase=2	,   _lowerCAmelCase=None	,   )			->							List[Any]:
     _lowerCAmelCase			=					parent
     _lowerCAmelCase			=					batch_size
     _lowerCAmelCase			=					decoder_seq_length
     # For common tests
     _lowerCAmelCase			=					self.decoder_seq_length
     _lowerCAmelCase			=					is_training
     _lowerCAmelCase			=					use_attention_mask
     _lowerCAmelCase			=					use_labels

     _lowerCAmelCase			=					vocab_size
     _lowerCAmelCase			=					d_model
     _lowerCAmelCase			=					d_model
     _lowerCAmelCase			=					decoder_layers
     _lowerCAmelCase			=					decoder_layers
     _lowerCAmelCase			=					decoder_ffn_dim
     _lowerCAmelCase			=					decoder_attention_heads
     _lowerCAmelCase			=					decoder_attention_heads
     _lowerCAmelCase			=					eos_token_id
     _lowerCAmelCase			=					bos_token_id
     _lowerCAmelCase			=					pad_token_id
     _lowerCAmelCase			=					decoder_start_token_id
     _lowerCAmelCase			=					use_cache
     _lowerCAmelCase			=					max_position_embeddings

     _lowerCAmelCase			=					None
     _lowerCAmelCase			=					decoder_seq_length
     _lowerCAmelCase			=					2
     _lowerCAmelCase			=					1




 def 							_snake_case      (		self						)			->							Tuple:
     _lowerCAmelCase			=					ids_tensor([self.batch_size, self.decoder_seq_length]	,   self.vocab_size						)

     _lowerCAmelCase			=					None
     if self.use_attention_mask:
         _lowerCAmelCase			=					ids_tensor([self.batch_size, self.decoder_seq_length]	,   vocab_size=2						)

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

     _lowerCAmelCase			=					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 							_snake_case      (		self	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   _lowerCAmelCase	,   )			->							Optional[int]:
     _lowerCAmelCase			=					True
     _lowerCAmelCase			=					TrOCRDecoder(config=_lowerCAmelCase						).to(_lowerCAmelCase						).eval()
     _lowerCAmelCase			=					input_ids[:2]

     input_ids[input_ids == 0] += 1
     # first forward pass
     _lowerCAmelCase			=					model(_lowerCAmelCase	,   use_cache=_lowerCAmelCase						)
     _lowerCAmelCase			=					model(_lowerCAmelCase						)
     _lowerCAmelCase			=					model(_lowerCAmelCase	,   use_cache=_lowerCAmelCase						)

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

     _lowerCAmelCase			=					outputs["past_key_values"]

     # create hypothetical next token and extent to next_input_ids
     _lowerCAmelCase			=					ids_tensor((2, 1)	,   config.vocab_size - 1						) + 1

     # append to next input_ids and
     _lowerCAmelCase			=					torch.cat([input_ids, next_tokens]	,   dim=-1						)

     _lowerCAmelCase			=					model(_lowerCAmelCase						)["last_hidden_state"]
     _lowerCAmelCase			=					model(_lowerCAmelCase	,   past_key_values=_lowerCAmelCase						)["last_hidden_state"]

     # select random slice
     _lowerCAmelCase			=					ids_tensor((1,)	,   output_from_past.shape[-1]						).item()
     _lowerCAmelCase			=					output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
     _lowerCAmelCase			=					output_from_past[:, 0, random_slice_idx].detach()

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





 def 							_snake_case      (		self						)			->							Optional[Any]:
     _lowerCAmelCase			=					self.prepare_config_and_inputs()
     _lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase	,	_lowerCAmelCase			=					config_and_inputs

     _lowerCAmelCase			=					{"input_ids": input_ids, "attention_mask": attention_mask}
     return config, inputs_dict







@require_torch
class       lowerCAmelCase_						(						snake_case__  ,snake_case__  ,snake_case__  ,unittest.TestCase						):
 __lowerCamelCase  :							Dict				     =    (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
 __lowerCamelCase  :							Optional[int]				     =    (TrOCRForCausalLM,) if is_torch_available() else ()
 __lowerCamelCase  :							Any				     =    {"text-generation": TrOCRForCausalLM} if is_torch_available() else {}
 __lowerCamelCase  :							List[Any]				     =    True
 __lowerCamelCase  :							str				     =    False




 def 							_snake_case      (		self						)			->							Dict:
     _lowerCAmelCase			=					TrOCRStandaloneDecoderModelTester(self	,   is_training=_lowerCAmelCase						)
     _lowerCAmelCase			=					ConfigTester(self	,   config_class=_lowerCAmelCase						)




 def 							_snake_case      (		self						)			->							List[str]:
     pass




 def 							_snake_case      (		self						)			->							List[str]:
     pass




 def 							_snake_case      (		self						)			->							List[Any]:
     pass




 def 							_snake_case      (		self						)			->							Dict:
     self.config_tester.run_common_tests()




 def 							_snake_case      (		self						)			->							List[Any]:
     _lowerCAmelCase			=					self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_decoder_model_past(*_lowerCAmelCase						)




 def 							_snake_case      (		self						)			->							Any:
     return





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










def __A    (      __lowerCAmelCase	)->  list:





						"""simple docstring"""



						if len(__lowerCAmelCase	) < 2:
												return collection

						def circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	) -> bool:
												_UpperCAmelCase									=			False

												if low == high:
																		return swapped

												_UpperCAmelCase									=			low
												_UpperCAmelCase									=			high

												while left < right:
																		if collection[left] > collection[right]:
																								_UpperCAmelCase					, _UpperCAmelCase									=			(
																								    collection[right],
																								    collection[left],
																								)
																								_UpperCAmelCase									=			True

																		left += 1
																		right -= 1

												if left == right and collection[left] > collection[right + 1]:
																		_UpperCAmelCase					, _UpperCAmelCase									=			(
																		    collection[right + 1],
																		    collection[left],
																		)

																		_UpperCAmelCase									=			True

												_UpperCAmelCase									=			low + int((high - low) / 2	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		mid + 1 ,		__lowerCAmelCase	)

												return swapped or left_swap or right_swap

						_UpperCAmelCase									=			True

						while is_not_sorted is True:
												_UpperCAmelCase									=			circle_sort_util(__lowerCAmelCase ,		0 ,		len(__lowerCAmelCase	) - 1	)

						return collection


if __name__ == "__main__":
		_a      	=  input('''Enter numbers separated by a comma:\n''').strip()
		_a      	=  [int(item) for item in user_input.split(''',''')]
		print(circle_sort(unsorted))
