
"""simple docstring"""



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


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''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        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							List[Any]							=					"gpt_neo"
				SCREAMING_SNAKE_CASE_:							Optional[int]							=					["past_key_values"]
				SCREAMING_SNAKE_CASE_:							str							=					{"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}


				def __init__(					self	:			Dict				,     UpperCAmelCase_	:			Union[str, Any]=50_257				,     UpperCAmelCase_	:			str=2_048				,     UpperCAmelCase_	:			Tuple=2_048				,     UpperCAmelCase_	:			Any=24				,     UpperCAmelCase_	:			str=[[["global", "local"], 12]]				,     UpperCAmelCase_	:			Tuple=16				,     UpperCAmelCase_	:			Union[str, Any]=None				,     UpperCAmelCase_	:			Any=256				,     UpperCAmelCase_	:			Any="gelu_new"				,     UpperCAmelCase_	:			Union[str, Any]=0.0				,     UpperCAmelCase_	:			Union[str, Any]=0.0				,     UpperCAmelCase_	:			Optional[Any]=0.0				,     UpperCAmelCase_	:			int=0.1				,     UpperCAmelCase_	:			Tuple=1E-5				,     UpperCAmelCase_	:			List[str]=0.02				,     UpperCAmelCase_	:			Dict=True				,     UpperCAmelCase_	:			List[Any]=50_256				,     UpperCAmelCase_	:			Union[str, Any]=50_256				,     **UpperCAmelCase_	:			str				,     )		->  Union[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_layers
											_lowerCAmelCase  	=			num_heads
											_lowerCAmelCase  	=			intermediate_size
											_lowerCAmelCase  	=			window_size
											_lowerCAmelCase  	=			activation_function
											_lowerCAmelCase  	=			resid_dropout
											_lowerCAmelCase  	=			embed_dropout
											_lowerCAmelCase  	=			attention_dropout
											_lowerCAmelCase  	=			classifier_dropout
											_lowerCAmelCase  	=			layer_norm_epsilon
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			use_cache

											_lowerCAmelCase  	=			bos_token_id
											_lowerCAmelCase  	=			eos_token_id

											_lowerCAmelCase  	=			attention_types
											_lowerCAmelCase  	=			self.expand_attention_types_params(UpperCAmelCase_  )

											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=UpperCAmelCase_				,     eos_token_id=UpperCAmelCase_				,     **UpperCAmelCase_  )







				@staticmethod
				def 				__lowerCamelCase	(					UpperCAmelCase_	:			int  )		->  List[Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			[]
											for item in attention_types:
																		for _ in range(item[1]  ):
																									attentions.extend(item[0]  )
											return attentions






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Optional[Any]     ,  SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					Union[str, Any]						):






							"""simple docstring"""



							import torch

							_lowerCAmelCase  	=			input.size()
							_lowerCAmelCase  	=			len(SCREAMING_SNAKE_CASE						)
							_lowerCAmelCase  	=			shape[dimension]

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

							_lowerCAmelCase  	=			[slice(SCREAMING_SNAKE_CASE						)] * rank
							_lowerCAmelCase  	=			indices
							_lowerCAmelCase  	=			input[s]

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

							return sliced.permute(SCREAMING_SNAKE_CASE						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any     ,  SCREAMING_SNAKE_CASE:					List[Any]						):






							"""simple docstring"""



							import torch

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

class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				@property
				def 				__lowerCamelCase	(					self	:			Union[str, Any]  )		->  Mapping[str, Mapping[int, str]]:







											"""simple docstring"""




											_lowerCAmelCase  	=			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}  )
											if self.use_past:
																		self.fill_with_past_key_values_(UpperCAmelCase_				,     direction='inputs'  )
																		_lowerCAmelCase  	=			{0: 'batch', 1: 'past_sequence + sequence'}
											else:
																		_lowerCAmelCase  	=			{0: 'batch', 1: 'sequence'}

											return common_inputs


				@property
				def 				__lowerCamelCase	(					self	:			Optional[Any]  )		->  int:







											"""simple docstring"""




											return self._config.num_heads


				def 				__lowerCamelCase	(					self	:			Optional[Any]				,     UpperCAmelCase_	:			PreTrainedTokenizer				,     UpperCAmelCase_	:			int = -1				,     UpperCAmelCase_	:			int = -1				,     UpperCAmelCase_	:			bool = False				,     UpperCAmelCase_	:			Optional[TensorType] = None				,     )		->  Mapping[str, Any]:







											"""simple docstring"""




											_lowerCAmelCase  	=			super(UpperCAmelCase_				,     self  ).generate_dummy_inputs(
											    UpperCAmelCase_				,     batch_size=UpperCAmelCase_				,     seq_length=UpperCAmelCase_				,     is_pair=UpperCAmelCase_				,     framework=UpperCAmelCase_  )

											# We need to order the input in the way they appears in the forward()
											_lowerCAmelCase  	=			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

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

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

											return ordered_inputs







				@property
				def 				__lowerCamelCase	(					self	:			Tuple  )		->  int:







											"""simple docstring"""




											return 13








"""simple docstring"""



def 	__snake_case  (    SCREAMING_SNAKE_CASE:					list[int]						):






							"""simple docstring"""



							_lowerCAmelCase  	=			[]
							if len(SCREAMING_SNAKE_CASE						) == 1:
														return [nums.copy()]
							for _ in range(len(SCREAMING_SNAKE_CASE						)						):
														_lowerCAmelCase  	=			nums.pop(0						)
														_lowerCAmelCase  	=			permute(SCREAMING_SNAKE_CASE						)
														for perm in permutations:
																					perm.append(SCREAMING_SNAKE_CASE						)
														result.extend(SCREAMING_SNAKE_CASE						)
														nums.append(SCREAMING_SNAKE_CASE						)
							return result






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Any						):






							"""simple docstring"""



							def backtrack(SCREAMING_SNAKE_CASE:					Tuple						):
														if start == len(SCREAMING_SNAKE_CASE						) - 1:
																					output.append(nums[:]						)
														else:
																					for i in range(SCREAMING_SNAKE_CASE     ,  len(SCREAMING_SNAKE_CASE						)						):
																												_lowerCAmelCase	,		_lowerCAmelCase  	=			nums[i], nums[start]
																												backtrack(start + 1						)
																												_lowerCAmelCase	,		_lowerCAmelCase  	=			nums[i], nums[start]  # backtrack

							_lowerCAmelCase  	=			[]
							backtrack(0						)
							return output


if __name__ == "__main__":
	import doctest

	# use res to print the data in permute2 function
	_snake_case          =						permutea([1, 2, 3])
	print(res)
	doctest.testmod()






