






"""simple docstring"""


from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


lowercase_    					=					logging.get_logger(__name__)

lowercase_    					=					{
    'gpt2': 'https://huggingface.co/gpt2/resolve/main/config.json',
    'gpt2-medium': 'https://huggingface.co/gpt2-medium/resolve/main/config.json',
    'gpt2-large': 'https://huggingface.co/gpt2-large/resolve/main/config.json',
    'gpt2-xl': 'https://huggingface.co/gpt2-xl/resolve/main/config.json',
    'distilgpt2': 'https://huggingface.co/distilgpt2/resolve/main/config.json',
}


class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   A_						:     str									=			"gpt2"
   A_						:     Optional[Any]									=			["past_key_values"]
   A_						:     Any									=			{
       "hidden_size": "n_embd",
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }


   def __init__(     self			:					Tuple,	_lowerCamelCase			:					Dict=5_02_57,	_lowerCamelCase			:					Tuple=10_24,	_lowerCamelCase			:					str=7_68,	_lowerCamelCase			:					str=12,	_lowerCamelCase			:					List[Any]=12,	_lowerCamelCase			:					str=None,	_lowerCamelCase			:					Dict="gelu_new",	_lowerCamelCase			:					Any=0.1,	_lowerCamelCase			:					Union[str, Any]=0.1,	_lowerCamelCase			:					Union[str, Any]=0.1,	_lowerCamelCase			:					Any=1e-5,	_lowerCamelCase			:					List[str]=0.02,	_lowerCamelCase			:					int="cls_index",	_lowerCamelCase			:					str=True,	_lowerCamelCase			:					Tuple=None,	_lowerCamelCase			:					int=True,	_lowerCamelCase			:					int=0.1,	_lowerCamelCase			:					Dict=True,	_lowerCamelCase			:					List[Any]=True,	_lowerCamelCase			:					List[Any]=5_02_56,	_lowerCamelCase			:					List[Any]=5_02_56,	_lowerCamelCase			:					Any=False,	_lowerCamelCase			:					Dict=False,	**_lowerCamelCase			:					Optional[int],	):

     '''simple docstring'''
     __A						      =			vocab_size
     __A						      =			n_positions
     __A						      =			n_embd
     __A						      =			n_layer
     __A						      =			n_head
     __A						      =			n_inner
     __A						      =			activation_function
     __A						      =			resid_pdrop
     __A						      =			embd_pdrop
     __A						      =			attn_pdrop
     __A						      =			layer_norm_epsilon
     __A						      =			initializer_range
     __A						      =			summary_type
     __A						      =			summary_use_proj
     __A						      =			summary_activation
     __A						      =			summary_first_dropout
     __A						      =			summary_proj_to_labels
     __A						      =			scale_attn_weights
     __A						      =			use_cache
     __A						      =			scale_attn_by_inverse_layer_idx
     __A						      =			reorder_and_upcast_attn

     __A						      =			bos_token_id
     __A						      =			eos_token_id

     super().__init__(bos_token_id=_lowerCamelCase,	eos_token_id=_lowerCamelCase,	**_lowerCamelCase  )





class  snake_case		(							_lowerCAmelCase						):


   '''simple docstring'''

   def __init__(     self			:					Optional[int],	_lowerCamelCase			:					PretrainedConfig,	_lowerCamelCase			:					str = "default",	_lowerCamelCase			:					List[PatchingSpec] = None,	_lowerCamelCase			:					bool = False,	):

     '''simple docstring'''
     super().__init__(_lowerCamelCase,	task=_lowerCamelCase,	patching_specs=_lowerCamelCase,	use_past=_lowerCamelCase  )
     if not getattr(self._config,	'''pad_token_id''',	_lowerCamelCase  ):
       # TODO: how to do that better?
       __A						      =			0


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

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

     return common_inputs


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Dict  ):

     '''simple docstring'''
     return self._config.n_layer


   @property
   def     _SCREAMING_SNAKE_CASE    (     self			:					Tuple  ):

     '''simple docstring'''
     return self._config.n_head


   def     _SCREAMING_SNAKE_CASE    (     self			:					Optional[Any],	_lowerCamelCase			:					PreTrainedTokenizer,	_lowerCamelCase			:					int = -1,	_lowerCamelCase			:					int = -1,	_lowerCamelCase			:					bool = False,	_lowerCamelCase			:					Optional[TensorType] = None,	):

     '''simple docstring'''
     __A						      =			super(_lowerCamelCase,	self  ).generate_dummy_inputs(
         _lowerCamelCase,	batch_size=_lowerCamelCase,	seq_length=_lowerCamelCase,	is_pair=_lowerCamelCase,	framework=_lowerCamelCase  )

     # We need to order the input in the way they appears in the forward()
     __A						      =			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						      =			common_inputs['''input_ids'''].shape
         # Not using the same length for past_key_values
         __A						      =			seqlen + 2
         __A						      =			(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         __A						      =			[
             (torch.zeros(_lowerCamelCase  ), torch.zeros(_lowerCamelCase  )) for _ in range(self.num_layers  )
         ]

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

     return ordered_inputs






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

     '''simple docstring'''
     return 13












"""simple docstring"""


def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  if len(__UpperCamelCase			) == 1:
    return True
  try:
    __A						      =			series[1] / series[0]
    for index in range(len(__UpperCamelCase			) - 1			):
      if series[index + 1] / series[index] != common_ratio:
        return False
  except ZeroDivisionError:
    return False
  return True



def  lowerCAmelCase    (   __UpperCamelCase			):




  """simple docstring"""







  if not isinstance(__UpperCamelCase		,	__UpperCamelCase			):
    raise ValueError('''Input series is not valid, valid series - [2, 4, 8]'''			)
  if len(__UpperCamelCase			) == 0:
    raise ValueError('''Input list must be a non empty list'''			)
  __A						      =			1
  for value in series:
    answer *= value
  return pow(__UpperCamelCase		,	1 / len(__UpperCamelCase			)			)


if __name__ == "__main__":
       import doctest

       doctest.testmod()




