
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__							:							Optional[Any]									=			logging.get_logger(__name__)

lowercase__							:							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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






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



							def __init__(  self     ,	SCREAMING_SNAKE_CASE_=50257     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=2048     ,	SCREAMING_SNAKE_CASE_=24     ,	SCREAMING_SNAKE_CASE_=[[["global", "local"], 12]]     ,	SCREAMING_SNAKE_CASE_=16     ,	SCREAMING_SNAKE_CASE_=None     ,	SCREAMING_SNAKE_CASE_=256     ,	SCREAMING_SNAKE_CASE_="gelu_new"     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.0     ,	SCREAMING_SNAKE_CASE_=0.1     ,	SCREAMING_SNAKE_CASE_=1E-5     ,	SCREAMING_SNAKE_CASE_=0.0_2     ,	SCREAMING_SNAKE_CASE_=True     ,	SCREAMING_SNAKE_CASE_=50256     ,	SCREAMING_SNAKE_CASE_=50256     ,	**SCREAMING_SNAKE_CASE_     ,	)->   List[Any]:





													'''simple docstring'''






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

													__UpperCamelCase       =			bos_token_id
													__UpperCamelCase       =			eos_token_id

													__UpperCamelCase       =			attention_types
													__UpperCamelCase       =			self.expand_attention_types_params(SCREAMING_SNAKE_CASE_			)

													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=SCREAMING_SNAKE_CASE_     ,	eos_token_id=SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)



							@staticmethod
							def     A__		(  SCREAMING_SNAKE_CASE_			)->   Tuple:





													'''simple docstring'''






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





def 	A_ (							snake_case							:   Tuple ,      snake_case							:   Any ,      snake_case							:   Any ,      snake_case							:   Union[str, Any] )      ->    Optional[int]:



						'''simple docstring'''

						import torch

						__UpperCamelCase       =			input.size()
						__UpperCamelCase       =			len(snake_case )
						__UpperCamelCase       =			shape[dimension]

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

						__UpperCamelCase       =			[slice(snake_case )] * rank
						__UpperCamelCase       =			indices
						__UpperCamelCase       =			input[s]

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

						return sliced.permute(snake_case )




def 	A_ (							snake_case							:   str ,      snake_case							:   Optional[int] )      ->    Tuple:



						'''simple docstring'''

						import torch

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






class        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							@property
							def     A__		(  self			)->   Mapping[str, Mapping[int, str]]:





													'''simple docstring'''






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

													return common_inputs



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return self._config.num_heads



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = -1     ,	SCREAMING_SNAKE_CASE_ = False     ,	SCREAMING_SNAKE_CASE_ = None     ,	)->   Mapping[str, Any]:





													'''simple docstring'''






													__UpperCamelCase       =			super(SCREAMING_SNAKE_CASE_     ,	self			).generate_dummy_inputs(
													    SCREAMING_SNAKE_CASE_     ,	batch_size=SCREAMING_SNAKE_CASE_     ,	seq_length=SCREAMING_SNAKE_CASE_     ,	is_pair=SCREAMING_SNAKE_CASE_     ,	framework=SCREAMING_SNAKE_CASE_			)

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

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

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

													return ordered_inputs



							@property
							def     A__		(  self			)->   int:





													'''simple docstring'''






													return 13






from .configuration_bert_masked import MaskedBertConfig
from .modeling_bert_masked import (
    MaskedBertForMultipleChoice,
    MaskedBertForQuestionAnswering,
    MaskedBertForSequenceClassification,
    MaskedBertForTokenClassification,
    MaskedBertModel,
)
from .modules import *




