





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast
from ...utils import logging


UpperCAmelCase_      							=     logging.get_logger(__name__)

UpperCAmelCase_      							=     {
    """EleutherAI/gpt-neo-1.3B""": """https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json""",
    # See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class 			UpperCamelCase_		(  UpperCamelCase_					):
					lowerCAmelCase_							=	'''gpt_neo'''
					lowerCAmelCase_							=	['''past_key_values''']
					lowerCAmelCase_							=	{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}




					def __init__(		self				,						lowerCAmelCase_=5_0257				,						lowerCAmelCase_=2048				,						lowerCAmelCase_=2048				,						lowerCAmelCase_=24				,						lowerCAmelCase_=[[["global", "local"], 12]]				,						lowerCAmelCase_=16				,						lowerCAmelCase_=None				,						lowerCAmelCase_=256				,						lowerCAmelCase_="gelu_new"				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.0				,						lowerCAmelCase_=0.1				,						lowerCAmelCase_=1E-5				,						lowerCAmelCase_=0.02				,						lowerCAmelCase_=True				,						lowerCAmelCase_=5_0256				,						lowerCAmelCase_=5_0256				,						**lowerCAmelCase_				,						)		->							Dict:
										_snake_case									=				vocab_size
										_snake_case									=				max_position_embeddings
										_snake_case									=				hidden_size
										_snake_case									=				num_layers
										_snake_case									=				num_heads
										_snake_case									=				intermediate_size
										_snake_case									=				window_size
										_snake_case									=				activation_function
										_snake_case									=				resid_dropout
										_snake_case									=				embed_dropout
										_snake_case									=				attention_dropout
										_snake_case									=				classifier_dropout
										_snake_case									=				layer_norm_epsilon
										_snake_case									=				initializer_range
										_snake_case									=				use_cache

										_snake_case									=				bos_token_id
										_snake_case									=				eos_token_id

										_snake_case									=				attention_types
										_snake_case									=				self.expand_attention_types_params(__a  )

										if len(self.attention_layers  ) != self.num_layers:
															raise ValueError(
															    'Configuration for convolutional module is incorrect. '
															    'It is required that `len(config.attention_layers)` == `config.num_layers` '
															    F'''but is `len(config.attention_layers) = {len(self.attention_layers  )}`, '''
															    F'''`config.num_layers = {self.num_layers}`. '''
															    '`config.attention_layers` is prepared using `config.attention_types`. '
															    'Please verify the value of `config.attention_types` argument.'  )

										super().__init__(bos_token_id=__a				,						eos_token_id=__a				,						**__a  )






					@staticmethod
					def 	lowerCAmelCase			(		lowerCAmelCase_  )		->							Tuple:
										_snake_case									=				[]
										for item in attention_types:
															for _ in range(item[1]  ):
																				attentions.extend(item[0]  )
										return attentions





def 						lowerCamelCase__     (     UpperCamelCase__			:       Any       ,    UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       Dict       ,    UpperCamelCase__			:       List[str]						)			-> Optional[int]:



					'''simple docstring'''




					import torch

					_snake_case									=				input.size()
					_snake_case									=				len(UpperCamelCase__						)
					_snake_case									=				shape[dimension]

					_snake_case									=				torch.arange(0       ,    UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				torch.div(sizedim - size       ,    UpperCamelCase__       ,    rounding_mode='floor'						) + 1
					_snake_case									=				torch.arange(UpperCamelCase__						) + low_indices[:min_length][:, None]

					_snake_case									=				[slice(UpperCamelCase__						)] * rank
					_snake_case									=				indices
					_snake_case									=				input[s]

					_snake_case									=				list(range(0       ,    rank + 1						)						)
					perm.append(perm.pop(dimension + 1						)						)

					return sliced.permute(UpperCamelCase__						)





def 						lowerCamelCase__     (     UpperCamelCase__			:       Tuple       ,    UpperCamelCase__			:       List[Any]						)			-> Union[str, Any]:



					'''simple docstring'''




					import torch

					_snake_case									=				torch.arange(1       ,    UpperCamelCase__						)
					_snake_case									=				torch.remainder(UpperCamelCase__       ,    UpperCamelCase__						)
					_snake_case									=				remainders == 0
					_snake_case									=				candidates[divisor_indices]
					_snake_case									=				torch.max(UpperCamelCase__						)
					return largest_divisor, torch.div(UpperCamelCase__       ,    UpperCamelCase__       ,    rounding_mode='floor'						)




class 			UpperCamelCase_		(  UpperCamelCase_					):




					@property
					def 	lowerCAmelCase			(		self  )		->							Mapping[str, Mapping[int, str]]:
										_snake_case									=				OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
										if self.use_past:
															self.fill_with_past_key_values_(__a				,						direction='inputs'  )
															_snake_case									=				{0: 'batch', 1: 'past_sequence + sequence'}
										else:
															_snake_case									=				{0: 'batch', 1: 'sequence'}

										return common_inputs




					@property
					def 	lowerCAmelCase			(		self  )		->							int:
										return self._config.num_heads




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_ = -1				,						lowerCAmelCase_ = -1				,						lowerCAmelCase_ = False				,						lowerCAmelCase_ = None				,						)		->							Mapping[str, Any]:
										_snake_case									=				super(__a				,						self  ).generate_dummy_inputs(
										    __a				,						batch_size=__a				,						seq_length=__a				,						is_pair=__a				,						framework=__a  )

										# We need to order the input in the way they appears in the forward()
										_snake_case									=				OrderedDict({'input_ids': common_inputs['input_ids']}  )

										# Need to add the past_keys
										if self.use_past:
															if not is_torch_available():
																				raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.'  )
															else:
																				import torch

																				_snake_case									=				common_inputs['input_ids'].shape
																				# Not using the same length for past_key_values
																				_snake_case									=				seqlen + 2
																				_snake_case									=				(
																				    batch,
																				    self.num_attention_heads,
																				    past_key_values_length,
																				    self._config.hidden_size // self.num_attention_heads,
																				)
																				_snake_case									=				[
																				    (torch.zeros(__a  ), torch.zeros(__a  )) for _ in range(self.num_layers  )
																				]

										_snake_case									=				common_inputs['attention_mask']
										if self.use_past:
															_snake_case									=				ordered_inputs['attention_mask'].dtype
															_snake_case									=				torch.cat(
															    [ordered_inputs['attention_mask'], torch.ones(__a				,						__a				,						dtype=__a  )]				,						dim=1  )

										return ordered_inputs






					@property
					def 	lowerCAmelCase			(		self  )		->							int:
										return 13










def 						lowerCamelCase__     (     UpperCamelCase__			:       list						)			-> list:



					'''simple docstring'''




					_snake_case									=				len(UpperCamelCase__						)
					for i in range(1       ,    UpperCamelCase__						):
										_snake_case									=				collection[i]
										_snake_case									=				0
										_snake_case									=				i - 1

										while low <= high:
															_snake_case									=				(low + high) // 2
															if val < collection[mid]:
																				_snake_case									=				mid - 1
															else:
																				_snake_case									=				mid + 1
										for j in range(UpperCamelCase__       ,    UpperCamelCase__       ,    -1						):
															_snake_case									=				collection[j - 1]
										_snake_case									=				val
					return collection


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



