




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__							:	Tuple											=		logging.get_logger(__name__)

lowercase__							:	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 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						lowerCAmelCase_							=  '''gpt_neo'''
						lowerCAmelCase_							=  ['''past_key_values''']
						lowerCAmelCase_							=  {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






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






							"""simple docstring"""






							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(__lowercase		)

							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=__lowercase			,      eos_token_id=__lowercase			,      **__lowercase		)






						@staticmethod
						def 			snake_case__		(		__lowercase				:	Dict		):






							"""simple docstring"""






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







def 		lowerCamelCase__       (						_A	,      _A	,      _A	,      _A				):







	'''simple docstring'''





	import torch

	snake_case_						       =	input.size()
	snake_case_						       =	len(_A				)
	snake_case_						       =	shape[dimension]

	snake_case_						       =	torch.arange(0	,      _A	,      _A				)
	snake_case_						       =	torch.div(sizedim - size	,      _A	,      rounding_mode="floor"				) + 1
	snake_case_						       =	torch.arange(_A				) + low_indices[:min_length][:, None]

	snake_case_						       =	[slice(_A				)] * rank
	snake_case_						       =	indices
	snake_case_						       =	input[s]

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

	return sliced.permute(_A				)


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	import torch

	snake_case_						       =	torch.arange(1	,      _A				)
	snake_case_						       =	torch.remainder(_A	,      _A				)
	snake_case_						       =	remainders == 0
	snake_case_						       =	candidates[divisor_indices]
	snake_case_						       =	torch.max(_A				)
	return largest_divisor, torch.div(_A	,      _A	,      rounding_mode="floor"				)






class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							snake_case_						       =	OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
							if self.use_past:
								self.fill_with_past_key_values_(__lowercase			,      direction="inputs"		)
								snake_case_						       =	{0: "batch", 1: "past_sequence + sequence"}
							else:
								snake_case_						       =	{0: "batch", 1: "sequence"}

							return common_inputs






						@property
						def 			snake_case__		(		self				:	int		):






							"""simple docstring"""






							return self._config.num_heads






						def 			snake_case__		(		self				:	int			,      __lowercase				:	PreTrainedTokenizer			,      __lowercase				:	int = -1			,      __lowercase				:	int = -1			,      __lowercase				:	bool = False			,      __lowercase				:	Optional[TensorType] = None			,      ):






							"""simple docstring"""






							snake_case_						       =	super(__lowercase			,      self		).generate_dummy_inputs(
							    __lowercase			,      batch_size=__lowercase			,      seq_length=__lowercase			,      is_pair=__lowercase			,      framework=__lowercase		)

							# 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_       , 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(__lowercase		), torch.zeros(__lowercase		)) 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(__lowercase			,      __lowercase			,      dtype=__lowercase		)]			,      dim=1		)

							return ordered_inputs






						@property
						def 			snake_case__		(		self				:	List[Any]		):






							"""simple docstring"""






							return 13






from abc import ABC, abstractmethod
from argparse import ArgumentParser
class 						UpperCAmelCase  (							UpperCAmelCase__     ):







						'''simple docstring'''





						@staticmethod
						@abstractmethod
						def 			snake_case__		(		__lowercase				:	ArgumentParser		):






							"""simple docstring"""






							raise NotImplementedError()






						@abstractmethod
						def 			snake_case__		(		self				:	Optional[Any]		):






							"""simple docstring"""






							raise NotImplementedError()
