
"""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


lowerCAmelCase__							=				logging.get_logger(__name__)

lowerCAmelCase__							=				{
    '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  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      '''gpt_neo'''
							UpperCAmelCase__						     =      ['''past_key_values''']
							UpperCAmelCase__						     =      {'''num_attention_heads''': '''num_heads''', '''num_hidden_layers''': '''num_layers'''}

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=5_02_57       ,       SCREAMING_SNAKE_CASE_=20_48       ,       SCREAMING_SNAKE_CASE_=20_48       ,       SCREAMING_SNAKE_CASE_=24       ,       SCREAMING_SNAKE_CASE_=[[["global", "local"], 12]]       ,       SCREAMING_SNAKE_CASE_=16       ,       SCREAMING_SNAKE_CASE_=None       ,       SCREAMING_SNAKE_CASE_=2_56       ,       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_=0.02       ,       SCREAMING_SNAKE_CASE_=True       ,       SCREAMING_SNAKE_CASE_=5_02_56       ,       SCREAMING_SNAKE_CASE_=5_02_56       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







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

									SCREAMING_SNAKE_CASE_						 = bos_token_id
									SCREAMING_SNAKE_CASE_						 = eos_token_id

									SCREAMING_SNAKE_CASE_						 = attention_types
									SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







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







def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		import torch

		SCREAMING_SNAKE_CASE_						 = input.size()
		SCREAMING_SNAKE_CASE_						 = len(__a    )
		SCREAMING_SNAKE_CASE_						 = shape[dimension]

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

		SCREAMING_SNAKE_CASE_						 = [slice(__a    )] * rank
		SCREAMING_SNAKE_CASE_						 = indices
		SCREAMING_SNAKE_CASE_						 = input[s]

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

		return sliced.permute(__a    )



def        _lowerCamelCase ( __a,							__a    ):
		import torch

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





class  snake_case     (							__lowercase   ):

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







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

									return common_inputs

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self._config.num_heads

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_ = -1       ,       SCREAMING_SNAKE_CASE_ = -1       ,       SCREAMING_SNAKE_CASE_ = False       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = 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()
									SCREAMING_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

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

									SCREAMING_SNAKE_CASE_						 = common_inputs['''attention_mask''']
									if self.use_past:
											SCREAMING_SNAKE_CASE_						 = ordered_inputs['''attention_mask'''].dtype
											SCREAMING_SNAKE_CASE_						 = 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 _lowercase					(self    ):

									"""simple docstring"""







									return 13

"""simple docstring"""




def        _lowerCamelCase ( __a    ):

		if not isinstance(__a,							__a    ):
				SCREAMING_SNAKE_CASE_						 = F'Input value of [number={number}] must be an integer'
				raise TypeError(__a    )

		if number < 1:
				SCREAMING_SNAKE_CASE_						 = F'Input value of [number={number}] must be > 0'
				raise ValueError(__a    )

		SCREAMING_SNAKE_CASE_						 = 1

		for i in range(1,							__a    ):
				current_number *= 4 * i - 2
				current_number //= i + 1

		return current_number


if __name__ == "__main__":
						import doctest

						doctest.testmod()