


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


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

_SCREAMING_SNAKE_CASE			:       List[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 			UpperCamelCase__			(			__lowerCamelCase      ):
      a__ :  str		    =    """gpt_neo"""
      a__ :  str		    =    ["""past_key_values"""]
      a__ :  str		    =    {"""num_attention_heads""": """num_heads""", """num_hidden_layers""": """num_layers"""}






      def __init__(  self       : Dict,		__lowerCamelCase       : int=5_02_57,		__lowerCamelCase       : List[Any]=20_48,		__lowerCamelCase       : Any=20_48,		__lowerCamelCase       : Optional[Any]=24,		__lowerCamelCase       : List[Any]=[[["global", "local"], 12]],		__lowerCamelCase       : Union[str, Any]=16,		__lowerCamelCase       : Optional[Any]=None,		__lowerCamelCase       : Any=2_56,		__lowerCamelCase       : Optional[Any]="gelu_new",		__lowerCamelCase       : Optional[Any]=0.0,		__lowerCamelCase       : Any=0.0,		__lowerCamelCase       : Dict=0.0,		__lowerCamelCase       : int=0.1,		__lowerCamelCase       : List[Any]=1e-5,		__lowerCamelCase       : str=0.02,		__lowerCamelCase       : Any=True,		__lowerCamelCase       : int=5_02_56,		__lowerCamelCase       : Any=5_02_56,		**__lowerCamelCase       : Union[str, Any],		)	->			List[str]:
           UpperCamelCase__				:	Union[str, Any]										=  vocab_size
           UpperCamelCase__				:	int										=  max_position_embeddings
           UpperCamelCase__				:	Dict										=  hidden_size
           UpperCamelCase__				:	Union[str, Any]										=  num_layers
           UpperCamelCase__				:	str										=  num_heads
           UpperCamelCase__				:	List[str]										=  intermediate_size
           UpperCamelCase__				:	Union[str, Any]										=  window_size
           UpperCamelCase__				:	Union[str, Any]										=  activation_function
           UpperCamelCase__				:	str										=  resid_dropout
           UpperCamelCase__				:	int										=  embed_dropout
           UpperCamelCase__				:	Tuple										=  attention_dropout
           UpperCamelCase__				:	int										=  classifier_dropout
           UpperCamelCase__				:	Tuple										=  layer_norm_epsilon
           UpperCamelCase__				:	Optional[Any]										=  initializer_range
           UpperCamelCase__				:	Any										=  use_cache

           UpperCamelCase__				:	Optional[Any]										=  bos_token_id
           UpperCamelCase__				:	Tuple										=  eos_token_id

           UpperCamelCase__				:	Any										=  attention_types
           UpperCamelCase__				:	str										=  self.expand_attention_types_params(_lowercase )

           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=_lowercase,		eos_token_id=_lowercase,		**_lowercase )






      @staticmethod
      def 						__lowercase(  __lowerCamelCase       : List[Any] )	->			List[str]:
           UpperCamelCase__				:	Tuple										=  []
           for item in attention_types:
                for _ in range(item[1] ):
                     attentions.extend(item[0] )
           return attentions







def     _lowercase							(  __lowerCamelCase      :       Any		,__lowerCamelCase      :       int		,__lowerCamelCase      :       Optional[int]		,__lowerCamelCase      :       List[str]      )	-> int:





     '''simple docstring'''





     import torch

     UpperCamelCase__				:	int										=  input.size()
     UpperCamelCase__				:	Any										=  len(SCREAMING_SNAKE_CASE_      )
     UpperCamelCase__				:	Optional[Any]										=  shape[dimension]

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

     UpperCamelCase__				:	List[Any]										=  [slice(SCREAMING_SNAKE_CASE_      )] * rank
     UpperCamelCase__				:	Any										=  indices
     UpperCamelCase__				:	int										=  input[s]

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

     return sliced.permute(SCREAMING_SNAKE_CASE_      )







def     _lowercase							(  __lowerCamelCase      :       Any		,__lowerCamelCase      :       List[Any]      )	-> Optional[int]:





     '''simple docstring'''





     import torch

     UpperCamelCase__				:	List[Any]										=  torch.arange(1		,SCREAMING_SNAKE_CASE_      )
     UpperCamelCase__				:	Optional[int]										=  torch.remainder(SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_      )
     UpperCamelCase__				:	Union[str, Any]										=  remainders == 0
     UpperCamelCase__				:	Dict										=  candidates[divisor_indices]
     UpperCamelCase__				:	Optional[int]										=  torch.max(SCREAMING_SNAKE_CASE_      )
     return largest_divisor, torch.div(SCREAMING_SNAKE_CASE_		,SCREAMING_SNAKE_CASE_		,rounding_mode='''floor'''      )







class 			UpperCamelCase__			(			__lowerCamelCase      ):






      @property
      def 						__lowercase(  self       : Tuple )	->			Mapping[str, Mapping[int, str]]:
           UpperCamelCase__				:	Tuple										=  OrderedDict({'''input_ids''': {0: '''batch''', 1: '''sequence'''}} )
           if self.use_past:
                self.fill_with_past_key_values_(_lowercase,		direction='''inputs''' )
                UpperCamelCase__				:	Dict										=  {0: '''batch''', 1: '''past_sequence + sequence'''}
           else:
                UpperCamelCase__				:	Optional[Any]										=  {0: '''batch''', 1: '''sequence'''}

           return common_inputs






      @property
      def 						__lowercase(  self       : Tuple )	->			int:
           return self._config.num_heads






      def 						__lowercase(  self       : Any,		__lowerCamelCase       : int,		__lowerCamelCase       : Any = -1,		__lowerCamelCase       : str = -1,		__lowerCamelCase       : Dict = False,		__lowerCamelCase       : List[str] = None,		)	->			Mapping[str, Any]:
           UpperCamelCase__				:	str										=  super(_lowercase,		self ).generate_dummy_inputs(
               _lowercase,		batch_size=_lowercase,		seq_length=_lowercase,		is_pair=_lowercase,		framework=_lowercase )

           # We need to order the input in the way they appears in the forward()
           UpperCamelCase__				:	Dict										=  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__				:	Any										=  common_inputs['''input_ids'''].shape
                     # Not using the same length for past_key_values
                     UpperCamelCase__				:	List[str]										=  seqlen + 2
                     UpperCamelCase__				:	Optional[Any]										=  (
                         batch,
                         self.num_attention_heads,
                         past_key_values_length,
                         self._config.hidden_size // self.num_attention_heads,
                     )
                     UpperCamelCase__				:	Tuple										=  [
                         (torch.zeros(_lowercase ), torch.zeros(_lowercase )) for _ in range(self.num_layers )
                     ]

           UpperCamelCase__				:	Dict										=  common_inputs['''attention_mask''']
           if self.use_past:
                UpperCamelCase__				:	str										=  ordered_inputs['''attention_mask'''].dtype
                UpperCamelCase__				:	str										=  torch.cat(
                    [ordered_inputs['''attention_mask'''], torch.ones(_lowercase,		_lowercase,		dtype=_lowercase )],		dim=1 )

           return ordered_inputs






      @property
      def 						__lowercase(  self       : Optional[int] )	->			int:
           return 13







def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			)->						str:



    """simple docstring"""






    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'float' object cannot be interpreted as an integer"			)
    if isinstance(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
        raise TypeError("'str' object cannot be interpreted as an integer"			)

    if num == 0:
        return "0b0"

    UpperCamelCase_			  =						False

    if num < 0:
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						-num

    UpperCamelCase_			  =						[]
    while num > 0:
        binary.insert(0			,						num % 2			)
        num >>= 1

    if negative:
        return "-0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)

    return "0b" + "".join(str(SCREAMING_SNAKE_CASE_			) for e in binary			)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
