





'''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        _lowerCAmelCase    (			UpperCamelCase_							):


  '''simple docstring'''
  a_							:       str      					="""gpt_neo"""
  a_							:       List[str]      					=["""past_key_values"""]
  a_							:       Dict      					={"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}







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


    '''simple docstring'''





    _snake_case   :				Tuple														=	vocab_size
    _snake_case   :				Tuple														=	max_position_embeddings
    _snake_case   :				List[Any]														=	hidden_size
    _snake_case   :				int														=	num_layers
    _snake_case   :				Dict														=	num_heads
    _snake_case   :				Optional[int]														=	intermediate_size
    _snake_case   :				List[Any]														=	window_size
    _snake_case   :				Optional[Any]														=	activation_function
    _snake_case   :				str														=	resid_dropout
    _snake_case   :				str														=	embed_dropout
    _snake_case   :				Union[str, Any]														=	attention_dropout
    _snake_case   :				Any														=	classifier_dropout
    _snake_case   :				Tuple														=	layer_norm_epsilon
    _snake_case   :				Any														=	initializer_range
    _snake_case   :				Optional[int]														=	use_cache

    _snake_case   :				List[str]														=	bos_token_id
    _snake_case   :				int														=	eos_token_id

    _snake_case   :				Any														=	attention_types
    _snake_case   :				Tuple														=	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 UpperCamelCase_ (      UpperCamelCase      :					Tuple							):


    '''simple docstring'''





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




def     lowerCamelCase_	(      lowerCAmelCase:	Any    ,							lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	Dict    ,							lowerCAmelCase:	List[str]				)->		Optional[int]:
  import torch

  _snake_case   :				str														=	input.size()
  _snake_case   :				str														=	len(lowerCAmelCase				)
  _snake_case   :				List[Any]														=	shape[dimension]

  _snake_case   :				int														=	torch.arange(0    ,							lowerCAmelCase    ,							lowerCAmelCase				)
  _snake_case   :				List[Any]														=	torch.div(sizedim - size    ,							lowerCAmelCase    ,							rounding_mode='floor'				) + 1
  _snake_case   :				Dict														=	torch.arange(lowerCAmelCase				) + low_indices[:min_length][:, None]

  _snake_case   :				int														=	[slice(lowerCAmelCase				)] * rank
  _snake_case   :				Dict														=	indices
  _snake_case   :				List[str]														=	input[s]

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

  return sliced.permute(lowerCAmelCase				)


def     lowerCamelCase_	(      lowerCAmelCase:	Tuple    ,							lowerCAmelCase:	List[Any]				)->		Union[str, Any]:
  import torch

  _snake_case   :				Tuple														=	torch.arange(1    ,							lowerCAmelCase				)
  _snake_case   :				Tuple														=	torch.remainder(lowerCAmelCase    ,							lowerCAmelCase				)
  _snake_case   :				Optional[Any]														=	remainders == 0
  _snake_case   :				Optional[int]														=	candidates[divisor_indices]
  _snake_case   :				Tuple														=	torch.max(lowerCAmelCase				)
  return largest_divisor, torch.div(lowerCAmelCase    ,							lowerCAmelCase    ,							rounding_mode='floor'				)







class        _lowerCAmelCase    (			UpperCamelCase_							):


  '''simple docstring'''







  @property
  def UpperCamelCase_ (      self      :					Optional[Any]							):


    '''simple docstring'''





    _snake_case   :				str														=	OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}							)
    if self.use_past:
      self.fill_with_past_key_values_(__a      ,				direction='inputs'							)
      _snake_case   :				List[str]														=	{0: 'batch', 1: 'past_sequence + sequence'}
    else:
      _snake_case   :				Dict														=	{0: 'batch', 1: 'sequence'}

    return common_inputs







  @property
  def UpperCamelCase_ (      self      :					Any							):


    '''simple docstring'''





    return self._config.num_heads







  def UpperCamelCase_ (      self      :					List[Any]      ,				UpperCamelCase      :					Tuple      ,				UpperCamelCase      :					str = -1      ,				UpperCamelCase      :					Any = -1      ,				UpperCamelCase      :					Tuple = False      ,				UpperCamelCase      :					str = None      ,				):


    '''simple docstring'''





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

    _snake_case   :				Any														=	common_inputs['attention_mask']
    if self.use_past:
      _snake_case   :				Any														=	ordered_inputs['attention_mask'].dtype
      _snake_case   :				Optional[Any]														=	torch.cat(
          [ordered_inputs['attention_mask'], torch.ones(__a      ,				__a      ,				dtype=__a							)]      ,				dim=1							)

    return ordered_inputs







  @property
  def UpperCamelCase_ (      self      :					str							):


    '''simple docstring'''





    return 13











def     lowerCamelCase_	(      lowerCAmelCase:	int				)->		int:

  if not isinstance(lowerCAmelCase    ,							lowerCAmelCase				):
    _snake_case   :				Union[str, Any]														=	F"""Input value of [number={number}] must be an integer"""
    raise TypeError(lowerCAmelCase				)

  if number < 1:
    _snake_case   :				int														=	F"""Input value of [number={number}] must be > 0"""
    raise ValueError(lowerCAmelCase				)

  _snake_case   :				int														=	1

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

  return current_number


if __name__ == "__main__":
      import doctest

      doctest.testmod()




