





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


lowercase_		:				List[str]				=						logging.get_logger(__name__)

lowercase_		:				List[str]				=						{
    '''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 			lowercase				(							a_     ):



						"""simple docstring"""

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







						def __init__(    self			:		str ,				lowerCamelCase_			:		Dict=5_02_57 ,				lowerCamelCase_			:		List[Any]=20_48 ,				lowerCamelCase_			:		Union[str, Any]=20_48 ,				lowerCamelCase_			:		List[Any]=24 ,				lowerCamelCase_			:		Union[str, Any]=[[["global", "local"], 12]] ,				lowerCamelCase_			:		Tuple=16 ,				lowerCamelCase_			:		List[Any]=None ,				lowerCamelCase_			:		Tuple=2_56 ,				lowerCamelCase_			:		List[Any]="gelu_new" ,				lowerCamelCase_			:		Tuple=0.0 ,				lowerCamelCase_			:		List[Any]=0.0 ,				lowerCamelCase_			:		Dict=0.0 ,				lowerCamelCase_			:		str=0.1 ,				lowerCamelCase_			:		Union[str, Any]=1e-5 ,				lowerCamelCase_			:		Dict=0.02 ,				lowerCamelCase_			:		Optional[Any]=True ,				lowerCamelCase_			:		List[Any]=5_02_56 ,				lowerCamelCase_			:		int=5_02_56 ,				**lowerCamelCase_			:		Any ,				):





										'''simple docstring'''







										_snake_case :			Any  						=   vocab_size
										_snake_case :			Any  						=   max_position_embeddings
										_snake_case :			Any  						=   hidden_size
										_snake_case :			Union[str, Any]  						=   num_layers
										_snake_case :			Union[str, Any]  						=   num_heads
										_snake_case :			Any  						=   intermediate_size
										_snake_case :			Union[str, Any]  						=   window_size
										_snake_case :			str  						=   activation_function
										_snake_case :			List[str]  						=   resid_dropout
										_snake_case :			Union[str, Any]  						=   embed_dropout
										_snake_case :			List[Any]  						=   attention_dropout
										_snake_case :			Dict  						=   classifier_dropout
										_snake_case :			Tuple  						=   layer_norm_epsilon
										_snake_case :			Tuple  						=   initializer_range
										_snake_case :			List[Any]  						=   use_cache

										_snake_case :			Optional[int]  						=   bos_token_id
										_snake_case :			Any  						=   eos_token_id

										_snake_case :			List[Any]  						=   attention_types
										_snake_case :			Tuple  						=   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 		__UpperCAmelCase       (    lowerCamelCase_			:		Tuple					):





										'''simple docstring'''







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



def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				import torch

				_snake_case :			List[Any]  						=   input.size()
				_snake_case :			Tuple  						=   len(__snake_case )
				_snake_case :			List[Any]  						=   shape[dimension]

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

				_snake_case :			Optional[Any]  						=   [slice(__snake_case )] * rank
				_snake_case :			Any  						=   indices
				_snake_case :			Optional[Any]  						=   input[s]

				_snake_case :			Optional[int]  						=   list(range(0	,			rank + 1 ) )
				perm.append(perm.pop(dimension + 1 ) )

				return sliced.permute(__snake_case )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				import torch

				_snake_case :			int  						=   torch.arange(1	,			__snake_case )
				_snake_case :			Optional[Any]  						=   torch.remainder(__snake_case	,			__snake_case )
				_snake_case :			Any  						=   remainders == 0
				_snake_case :			Tuple  						=   candidates[divisor_indices]
				_snake_case :			List[Any]  						=   torch.max(__snake_case )
				return largest_divisor, torch.div(__snake_case	,			__snake_case	,			rounding_mode='floor' )


class 			lowercase				(							a_     ):



						"""simple docstring"""

						@property
						def 		__UpperCAmelCase       (    self			:		Union[str, Any]					):





										'''simple docstring'''







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

										return common_inputs







						@property
						def 		__UpperCAmelCase       (    self			:		List[str]					):





										'''simple docstring'''







										return self._config.num_heads







						def 		__UpperCAmelCase       (    self			:		Tuple ,				lowerCamelCase_			:		Dict ,				lowerCamelCase_			:		int = -1 ,				lowerCamelCase_			:		int = -1 ,				lowerCamelCase_			:		Union[str, Any] = False ,				lowerCamelCase_			:		Optional[int] = None ,				):





										'''simple docstring'''







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

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

										return ordered_inputs







						@property
						def 		__UpperCAmelCase       (    self			:		Optional[int]					):





										'''simple docstring'''







										return 13













from __future__ import annotations

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Dict  						=   list(range(len(__lowerCAmelCase ) ) )
				_snake_case :			Optional[int]  						=   [v / w for v, w in zip(__lowerCAmelCase	,			__lowerCAmelCase )]
				index.sort(key=lambda __lowerCAmelCase : ratio[i]	,			reverse=__lowerCAmelCase )

				_snake_case :			float  						=   0
				_snake_case :			list[float]  						=   [0] * len(__lowerCAmelCase )
				for i in index:
								if weight[i] <= capacity:
												_snake_case :			List[Any]  						=   1
												max_value += value[i]
												capacity -= weight[i]
								else:
												_snake_case :			Any  						=   capacity / weight[i]
												max_value += value[i] * capacity / weight[i]
												break

				return max_value, fractions


if __name__ == "__main__":
		import doctest

		doctest.testmod()






