






'''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


_UpperCAmelCase			:		Any   				=    logging.get_logger(__name__)

_UpperCAmelCase			:		List[Any]   				=    {
    "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  lowerCAmelCase_       (				UpperCamelCase_					):
							UpperCamelCase_		:str		      =    'gpt_neo'
							UpperCamelCase_		:List[Any]		      =    ['past_key_values']
							UpperCamelCase_		:Dict		      =    {'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




							def __init__(   self     :   List[str] ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=50_257 ,							SCREAMING_SNAKE_CASE_     :   List[str]=2_048 ,							SCREAMING_SNAKE_CASE_     :   List[str]=2_048 ,							SCREAMING_SNAKE_CASE_     :   Optional[int]=24 ,							SCREAMING_SNAKE_CASE_     :   Tuple=[[["global", "local"], 12]] ,							SCREAMING_SNAKE_CASE_     :   List[Any]=16 ,							SCREAMING_SNAKE_CASE_     :   Tuple=None ,							SCREAMING_SNAKE_CASE_     :   List[str]=256 ,							SCREAMING_SNAKE_CASE_     :   int="gelu_new" ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=0.0 ,							SCREAMING_SNAKE_CASE_     :   List[str]=0.0 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=0.0 ,							SCREAMING_SNAKE_CASE_     :   Any=0.1 ,							SCREAMING_SNAKE_CASE_     :   List[str]=1e-5 ,							SCREAMING_SNAKE_CASE_     :   Optional[Any]=0.02 ,							SCREAMING_SNAKE_CASE_     :   Tuple=True ,							SCREAMING_SNAKE_CASE_     :   str=50_256 ,							SCREAMING_SNAKE_CASE_     :   Tuple=50_256 ,							**SCREAMING_SNAKE_CASE_     :   Any ,							):
											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(__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 						__snake_case					(   SCREAMING_SNAKE_CASE_     :   Union[str, Any]	):
											lowerCAmelCase__      			=						[]
											for item in attention_types:
															for _ in range(item[1]	):
																			attentions.extend(item[0]	)
											return attentions





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


				'''simple docstring'''




				import torch

				lowerCAmelCase__      			=						input.size()
				lowerCAmelCase__      			=						len(lowercase__       )
				lowerCAmelCase__      			=						shape[dimension]

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

				lowerCAmelCase__      			=						[slice(lowercase__       )] * rank
				lowerCAmelCase__      			=						indices
				lowerCAmelCase__      			=						input[s]

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

				return sliced.permute(lowercase__       )





def 	lowerCAmelCase_		(lowercase__						:       Tuple     ,					lowercase__						:       List[Any]       )		->    Union[str, Any]:


				'''simple docstring'''




				import torch

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




class  lowerCAmelCase_       (				UpperCamelCase_					):




							@property
							def 						__snake_case					(   self     :   str	):
											lowerCAmelCase__      			=						OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}}	)
											if self.use_past:
															self.fill_with_past_key_values_(__a ,							direction='''inputs'''	)
															lowerCAmelCase__      			=						{0: 'batch', 1: 'past_sequence + sequence'}
											else:
															lowerCAmelCase__      			=						{0: 'batch', 1: 'sequence'}

											return common_inputs




							@property
							def 						__snake_case					(   self     :   str	):
											return self._config.num_heads




							def 						__snake_case					(   self     :   Optional[int] ,							SCREAMING_SNAKE_CASE_     :   Dict ,							SCREAMING_SNAKE_CASE_     :   Tuple = -1 ,							SCREAMING_SNAKE_CASE_     :   Optional[int] = -1 ,							SCREAMING_SNAKE_CASE_     :   Dict = False ,							SCREAMING_SNAKE_CASE_     :   Any = None ,							):
											lowerCAmelCase__      			=						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()
											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__      			=						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(__a	), torch.zeros(__a	)) 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(__a ,							__a ,							dtype=__a	)] ,							dim=1	)

											return ordered_inputs




							@property
							def 						__snake_case					(   self     :   List[str]	):
											return 13













from __future__ import annotations





def 	lowerCAmelCase_		(lowercase__						:       list[int]       )		->    bool:


				'''simple docstring'''




				return len(set(lowercase__       )       ) == len(lowercase__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()





