

def 		UpperCamelCase   (lowercase_:					list       , lowercase_:					int       , lowercase_:					int = 0       , lowercase_:					int = 0   )	-> int:
	A__				:       List[str]	  =						right or len(lowercase_   ) - 1
	if left > right:
		return -1
	elif list_data[left] == key:
		return left
	elif list_data[right] == key:
		return right
	else:
		return search(lowercase_       , lowercase_       , left + 1       , right - 1   )


if __name__ == "__main__":
	import doctest

	doctest.testmod()






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


A_			:					List[Any]    			=     logging.get_logger(__name__)

A_			:					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        _a		(__magic_name__							):


					'''simple docstring'''






					UpperCAmelCase__:  List[str]    				=			'''gpt_neo'''
					UpperCAmelCase__:  str    				=			['''past_key_values''']
					UpperCAmelCase__:  Dict    				=			{'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}






					def __init__(					self       ,			A__=5_0257       ,			A__=2048       ,			A__=2048       ,			A__=24       ,			A__=[[["global", "local"], 12]]       ,			A__=16       ,			A__=None       ,			A__=256       ,			A__="gelu_new"       ,			A__=0.0       ,			A__=0.0       ,			A__=0.0       ,			A__=0.1       ,			A__=1e-5       ,			A__=0.0_2       ,			A__=True       ,			A__=5_0256       ,			A__=5_0256       ,			**A__       ,			):
						A__				:       Optional[Any]	  =						vocab_size
						A__				:       Optional[int]	  =						max_position_embeddings
						A__				:       Optional[int]	  =						hidden_size
						A__				:       Dict	  =						num_layers
						A__				:       List[str]	  =						num_heads
						A__				:       Optional[int]	  =						intermediate_size
						A__				:       Union[str, Any]	  =						window_size
						A__				:       Optional[int]	  =						activation_function
						A__				:       Optional[Any]	  =						resid_dropout
						A__				:       Optional[int]	  =						embed_dropout
						A__				:       Any	  =						attention_dropout
						A__				:       Optional[Any]	  =						classifier_dropout
						A__				:       int	  =						layer_norm_epsilon
						A__				:       Tuple	  =						initializer_range
						A__				:       str	  =						use_cache

						A__				:       List[str]	  =						bos_token_id
						A__				:       List[Any]	  =						eos_token_id

						A__				:       Any	  =						attention_types
						A__				:       List[str]	  =						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     __A     (					A__     ):
						A__				:       int	  =						[]
						for item in attention_types:
							for _ in range(item[1]     ):
								attentions.extend(item[0]     )
						return attentions



def 		UpperCamelCase   (lowercase_:					Dict       , lowercase_:					Any       , lowercase_:					Union[str, Any]       , lowercase_:					Optional[Any]   )	-> Dict:
	import torch

	A__				:       List[Any]	  =						input.size()
	A__				:       List[str]	  =						len(lowercase_   )
	A__				:       Union[str, Any]	  =						shape[dimension]

	A__				:       Optional[int]	  =						torch.arange(0       , lowercase_       , lowercase_   )
	A__				:       str	  =						torch.div(sizedim - size       , lowercase_       , rounding_mode="""floor"""   ) + 1
	A__				:       Any	  =						torch.arange(lowercase_   ) + low_indices[:min_length][:, None]

	A__				:       Union[str, Any]	  =						[slice(lowercase_   )] * rank
	A__				:       Any	  =						indices
	A__				:       List[str]	  =						input[s]

	A__				:       Dict	  =						list(range(0       , rank + 1   )   )
	perm.append(perm.pop(dimension + 1   )   )

	return sliced.permute(lowercase_   )

def 		UpperCamelCase   (lowercase_:					str       , lowercase_:					int   )	-> int:
	import torch

	A__				:       Union[str, Any]	  =						torch.arange(1       , lowercase_   )
	A__				:       str	  =						torch.remainder(lowercase_       , lowercase_   )
	A__				:       Optional[Any]	  =						remainders == 0
	A__				:       Any	  =						candidates[divisor_indices]
	A__				:       Optional[Any]	  =						torch.max(lowercase_   )
	return largest_divisor, torch.div(lowercase_       , lowercase_       , rounding_mode="""floor"""   )



class        _a		(__magic_name__							):


					'''simple docstring'''






					@property
					def     __A     (					self     ):
						A__				:       Any	  =						OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}     )
						if self.use_past:
							self.fill_with_past_key_values_(A__       ,			direction="""inputs"""     )
							A__				:       Optional[Any]	  =						{0: """batch""", 1: """past_sequence + sequence"""}
						else:
							A__				:       List[str]	  =						{0: """batch""", 1: """sequence"""}

						return common_inputs






					@property
					def     __A     (					self     ):
						return self._config.num_heads






					def     __A     (					self       ,			A__       ,			A__ = -1       ,			A__ = -1       ,			A__ = False       ,			A__ = None       ,			):
						A__				:       int	  =						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()
						A__				:       Optional[int]	  =						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

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

						A__				:       List[Any]	  =						common_inputs["""attention_mask"""]
						if self.use_past:
							A__				:       int	  =						ordered_inputs["""attention_mask"""].dtype
							A__				:       str	  =						torch.cat(
							    [ordered_inputs["""attention_mask"""], torch.ones(A__       ,			A__       ,			dtype=A__     )]       ,			dim=1     )

						return ordered_inputs






					@property
					def     __A     (					self     ):
						return 13



