
'''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


UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)

UpperCAmelCase_       :   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  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		'gpt_neo'
    lowerCAmelCase_				    =		['past_key_values']
    lowerCAmelCase_				    =		{'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}



    def __init__(	self      :       List[str],__A      :       Optional[Any]=5_0_2_5_7,__A      :       List[Any]=2_0_4_8,__A      :       List[str]=2_0_4_8,__A      :       List[str]=2_4,__A      :       Dict=[[["global", "local"], 1_2]],__A      :       Any=1_6,__A      :       List[Any]=None,__A      :       str=2_5_6,__A      :       Union[str, Any]="gelu_new",__A      :       Union[str, Any]=0.0,__A      :       str=0.0,__A      :       Any=0.0,__A      :       List[str]=0.1,__A      :       Union[str, Any]=1e-5,__A      :       Any=0.02,__A      :       str=True,__A      :       Tuple=5_0_2_5_6,__A      :       Dict=5_0_2_5_6,**__A      :       List[Any],):
          _lowerCamelCase				:   Any								=   vocab_size
          _lowerCamelCase				:   Tuple								=   max_position_embeddings
          _lowerCamelCase				:   List[str]								=   hidden_size
          _lowerCamelCase				:   Optional[Any]								=   num_layers
          _lowerCamelCase				:   str								=   num_heads
          _lowerCamelCase				:   Any								=   intermediate_size
          _lowerCamelCase				:   int								=   window_size
          _lowerCamelCase				:   Any								=   activation_function
          _lowerCamelCase				:   Optional[Any]								=   resid_dropout
          _lowerCamelCase				:   Any								=   embed_dropout
          _lowerCamelCase				:   str								=   attention_dropout
          _lowerCamelCase				:   Dict								=   classifier_dropout
          _lowerCamelCase				:   Tuple								=   layer_norm_epsilon
          _lowerCamelCase				:   Dict								=   initializer_range
          _lowerCamelCase				:   Dict								=   use_cache

          _lowerCamelCase				:   List[str]								=   bos_token_id
          _lowerCamelCase				:   Any								=   eos_token_id

          _lowerCamelCase				:   Tuple								=   attention_types
          _lowerCamelCase				:   Optional[Any]								=   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   lowerCamelCase_  (	__A      :       List[str]     ):
          _lowerCamelCase				:   List[str]								=   []
          for item in attention_types:
                for _ in range(item[1]     ):
                      attentions.extend(item[0]     )
          return attentions







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Optional[int]							):



      """simple docstring"""




      import torch

      _lowerCamelCase				:   Tuple								=   input.size()
      _lowerCamelCase				:   Any								=   len(_lowerCAmelCase							)
      _lowerCamelCase				:   str								=   shape[dimension]

      _lowerCamelCase				:   Union[str, Any]								=   torch.arange(0				,	_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase				:   Tuple								=   torch.div(sizedim - size				,	_lowerCAmelCase				,	rounding_mode="floor"							) + 1
      _lowerCamelCase				:   List[Any]								=   torch.arange(_lowerCAmelCase							) + low_indices[:min_length][:, None]

      _lowerCamelCase				:   Tuple								=   [slice(_lowerCAmelCase							)] * rank
      _lowerCamelCase				:   int								=   indices
      _lowerCamelCase				:   Dict								=   input[s]

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

      return sliced.permute(_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Dict							):



      """simple docstring"""




      import torch

      _lowerCamelCase				:   Any								=   torch.arange(1				,	_lowerCAmelCase							)
      _lowerCamelCase				:   Optional[Any]								=   torch.remainder(_lowerCAmelCase				,	_lowerCAmelCase							)
      _lowerCamelCase				:   List[Any]								=   remainders == 0
      _lowerCamelCase				:   Any								=   candidates[divisor_indices]
      _lowerCamelCase				:   Union[str, Any]								=   torch.max(_lowerCAmelCase							)
      return largest_divisor, torch.div(_lowerCAmelCase				,	_lowerCAmelCase				,	rounding_mode="floor"							)




class  UpperCAmelCase__						(       A ):



    @property
    def   lowerCamelCase_  (	self      :       str     ):
          _lowerCamelCase				:   Optional[Any]								=   OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}     )
          if self.use_past:
                self.fill_with_past_key_values_(__A,direction="inputs"     )
                _lowerCamelCase				:   List[Any]								=   {0: "batch", 1: "past_sequence + sequence"}
          else:
                _lowerCamelCase				:   Union[str, Any]								=   {0: "batch", 1: "sequence"}

          return common_inputs



    @property
    def   lowerCamelCase_  (	self      :       Dict     ):
          return self._config.num_heads



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Tuple,__A      :       int = -1,__A      :       List[str] = -1,__A      :       Optional[int] = False,__A      :       Optional[int] = None,):
          _lowerCamelCase				:   Any								=   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()
          _lowerCamelCase				:   List[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

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

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

          return ordered_inputs



    @property
    def   lowerCamelCase_  (	self      :       int     ):
          return 1_3

'''simple docstring'''






def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




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

      if num == 0:
            return "0b0"

      _lowerCamelCase				:   Tuple								=   False

      if num < 0:
            _lowerCamelCase				:   List[Any]								=   True
            _lowerCamelCase				:   int								=   -num

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

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

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


if __name__ == "__main__":
 import doctest

 doctest.testmod()