






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:     Union[str, Any]   					=					logging.get_logger(__name__)

__a:     Optional[int]   					=					{
    '''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__	(  UpperCAmelCase     ):

  '''simple docstring'''







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


  def __init__(      self		:			int       ,				lowerCamelCase		:			int=5_0257       ,				lowerCamelCase		:			str=2048       ,				lowerCamelCase		:			Dict=2048       ,				lowerCamelCase		:			List[str]=24       ,				lowerCamelCase		:			Any=[[["global", "local"], 12]]       ,				lowerCamelCase		:			str=16       ,				lowerCamelCase		:			List[str]=None       ,				lowerCamelCase		:			List[str]=256       ,				lowerCamelCase		:			Union[str, Any]="gelu_new"       ,				lowerCamelCase		:			str=0.0       ,				lowerCamelCase		:			Tuple=0.0       ,				lowerCamelCase		:			List[str]=0.0       ,				lowerCamelCase		:			Union[str, Any]=0.1       ,				lowerCamelCase		:			Optional[Any]=1E-5       ,				lowerCamelCase		:			Tuple=0.02       ,				lowerCamelCase		:			Union[str, Any]=True       ,				lowerCamelCase		:			Optional[int]=5_0256       ,				lowerCamelCase		:			int=5_0256       ,				**lowerCamelCase		:			Tuple       ,				)						->	Dict:


         """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(lowerCamelCase							)

         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=lowerCamelCase       ,				eos_token_id=lowerCamelCase       ,				**lowerCamelCase							)





  @staticmethod
  def  lowerCamelCase       (      lowerCamelCase		:			Any							)						->	int:


         """simple docstring"""
         _UpperCAmelCase							=    []
         for item in attention_types:
                for _ in range(item[1]							):
                       attentions.extend(item[0]							)
         return attentions




def       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case   ,      __snake_case   ,      __snake_case )				->						Dict:
       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       _SCREAMING_SNAKE_CASE (							__snake_case   ,      __snake_case )				->						Optional[int]:
       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__	(  UpperCAmelCase     ):

  '''simple docstring'''







  @property
  def  lowerCamelCase       (      self		:			List[Any]							)						->	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_(lowerCamelCase       ,				direction="""inputs"""							)
                _UpperCAmelCase							=    {0: """batch""", 1: """past_sequence + sequence"""}
         else:
                _UpperCAmelCase							=    {0: """batch""", 1: """sequence"""}

         return common_inputs


  @property
  def  lowerCamelCase       (      self		:			Any							)						->	int:


         """simple docstring"""
         return self._config.num_heads


  def  lowerCamelCase       (      self		:			Optional[int]       ,				lowerCamelCase		:			PreTrainedTokenizer       ,				lowerCamelCase		:			int = -1       ,				lowerCamelCase		:			int = -1       ,				lowerCamelCase		:			bool = False       ,				lowerCamelCase		:			Optional[TensorType] = None       ,				)						->	Mapping[str, Any]:


         """simple docstring"""
         _UpperCAmelCase							=    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()
         _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(lowerCamelCase							), torch.zeros(lowerCamelCase							)) 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(lowerCamelCase       ,				lowerCamelCase       ,				dtype=lowerCamelCase							)]       ,				dim=1							)

         return ordered_inputs





  @property
  def  lowerCamelCase       (      self		:			str							)						->	int:


         """simple docstring"""
         return 13

def   __lowerCAmelCase    (				UpperCAmelCase__					:   int							)	->     int:

							if not isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
														raise ValueError("""multiplicative_persistence() only accepts integral values"""							)
							if num < 0:
														raise ValueError("""multiplicative_persistence() does not accept negative values"""							)

							lowerCamelCase_					=       0
							lowerCamelCase_					=       str(UpperCAmelCase__							)

							while len(UpperCAmelCase__							) != 1:
														lowerCamelCase_					=       [int(UpperCAmelCase__							) for i in num_string]

														lowerCamelCase_					=       1
														for i in range(0      ,					len(UpperCAmelCase__							)							):
																					total *= numbers[i]

														lowerCamelCase_					=       str(UpperCAmelCase__							)

														steps += 1
							return steps



def   __lowerCAmelCase    (				UpperCAmelCase__					:   int							)	->     int:

							if not isinstance(UpperCAmelCase__      ,					UpperCAmelCase__							):
														raise ValueError("""additive_persistence() only accepts integral values"""							)
							if num < 0:
														raise ValueError("""additive_persistence() does not accept negative values"""							)

							lowerCamelCase_					=       0
							lowerCamelCase_					=       str(UpperCAmelCase__							)

							while len(UpperCAmelCase__							) != 1:
														lowerCamelCase_					=       [int(UpperCAmelCase__							) for i in num_string]

														lowerCamelCase_					=       0
														for i in range(0      ,					len(UpperCAmelCase__							)							):
																					total += numbers[i]

														lowerCamelCase_					=       str(UpperCAmelCase__							)

														steps += 1
							return steps


if __name__ == "__main__":
		import doctest

		doctest.testmod()





