


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					:		Dict            =						logging.get_logger(__name__)

lowerCamelCase					:		Union[str, 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 snake_case__			(    _UpperCAmelCase					):
       _lowerCAmelCase   ='gpt_neo'
       _lowerCAmelCase   =['past_key_values']
       _lowerCAmelCase   ={'num_attention_heads': 'num_heads', 'num_hidden_layers': 'num_layers'}




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

              snake_case__ :       Dict	   =							bos_token_id
              snake_case__ :       List[Any]	   =							eos_token_id

              snake_case__ :       List[str]	   =							attention_types
              snake_case__ :       Dict	   =							self.expand_attention_types_params(SCREAMING_SNAKE_CASE_			)

              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=SCREAMING_SNAKE_CASE_    ,				eos_token_id=SCREAMING_SNAKE_CASE_    ,				**SCREAMING_SNAKE_CASE_			)




       @staticmethod
       def     UpperCAmelCase__    ( _lowerCamelCase				:    Dict			):
              snake_case__ :       Dict	   =							[]
              for item in attention_types:
                     for _ in range(item[1]			):
                            attentions.extend(item[0]			)
              return attentions





def 	lowercase__(		A     ,			A     ,			A     ,			A      ):
       import torch

       snake_case__ :       Optional[Any]	   =							input.size()
       snake_case__ :       Dict	   =							len(UpperCAmelCase_      )
       snake_case__ :       Any	   =							shape[dimension]

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

       snake_case__ :       Tuple	   =							[slice(UpperCAmelCase_      )] * rank
       snake_case__ :       Union[str, Any]	   =							indices
       snake_case__ :       Optional[int]	   =							input[s]

       snake_case__ :       Optional[Any]	   =							list(range(0     ,			rank + 1      )      )
       perm.append(perm.pop(dimension + 1      )      )

       return sliced.permute(UpperCAmelCase_      )





def 	lowercase__(		A     ,			A      ):
       import torch

       snake_case__ :       Optional[Any]	   =							torch.arange(1     ,			UpperCAmelCase_      )
       snake_case__ :       List[Any]	   =							torch.remainder(UpperCAmelCase_     ,			UpperCAmelCase_      )
       snake_case__ :       List[Any]	   =							remainders == 0
       snake_case__ :       Dict	   =							candidates[divisor_indices]
       snake_case__ :       Dict	   =							torch.max(UpperCAmelCase_      )
       return largest_divisor, torch.div(UpperCAmelCase_     ,			UpperCAmelCase_     ,			rounding_mode='floor'      )


class snake_case__			(    _UpperCAmelCase					):




       @property
       def     UpperCAmelCase__    ( self				:    int			):
              snake_case__ :       List[Any]	   =							OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
              if self.use_past:
                     self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE_    ,				direction='inputs'			)
                     snake_case__ :       Optional[int]	   =							{0: 'batch', 1: 'past_sequence + sequence'}
              else:
                     snake_case__ :       Any	   =							{0: 'batch', 1: 'sequence'}

              return common_inputs




       @property
       def     UpperCAmelCase__    ( self				:    Optional[Any]			):
              return self._config.num_heads




       def     UpperCAmelCase__    ( self				:    Optional[Any]    ,				_lowerCamelCase				:    int    ,				_lowerCamelCase				:    str = -1    ,				_lowerCamelCase				:    Dict = -1    ,				_lowerCamelCase				:    Optional[Any] = False    ,				_lowerCamelCase				:    Any = None    ,				):
              snake_case__ :       List[Any]	   =							super(SCREAMING_SNAKE_CASE_    ,				self			).generate_dummy_inputs(
                  SCREAMING_SNAKE_CASE_    ,				batch_size=SCREAMING_SNAKE_CASE_    ,				seq_length=SCREAMING_SNAKE_CASE_    ,				is_pair=SCREAMING_SNAKE_CASE_    ,				framework=SCREAMING_SNAKE_CASE_			)

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

              snake_case__ :       List[Any]	   =							common_inputs['attention_mask']
              if self.use_past:
                     snake_case__ :       Any	   =							ordered_inputs['attention_mask'].dtype
                     snake_case__ :       Union[str, Any]	   =							torch.cat(
                         [ordered_inputs['attention_mask'], torch.ones(SCREAMING_SNAKE_CASE_    ,				SCREAMING_SNAKE_CASE_    ,				dtype=SCREAMING_SNAKE_CASE_			)]    ,				dim=1			)

              return ordered_inputs




       @property
       def     UpperCAmelCase__    ( self				:    List[Any]			):
              return 1_3









'''simple docstring'''



from collections.abc import Generator
from math import sin



def      UpperCAmelCase__   (      UpperCAmelCase_       :							bytes				)	->						bytes:
     if len(UpperCAmelCase_				) != 32:
          raise ValueError('Input must be of length 32'				)

     __lowerCamelCase  :      Dict														=						B''
     for i in [3, 2, 1, 0]:
          little_endian += string_aa[8 * i : 8 * i + 8]
     return little_endian



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int				)	->						bytes:
     if i < 0:
          raise ValueError('Input must be non-negative'				)

     __lowerCamelCase  :      Union[str, Any]														=						format(UpperCAmelCase_ ,       '08x'				)[-8:]
     __lowerCamelCase  :      str														=						B''
     for i in [3, 2, 1, 0]:
          little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode('utf-8'				)
     return little_endian_hex



def      UpperCAmelCase__   (      UpperCAmelCase_       :							bytes				)	->						bytes:
     __lowerCamelCase  :      Optional[Any]														=						B''
     for char in message:
          bit_string += format(UpperCAmelCase_ ,       '08b'				).encode('utf-8'				)
     __lowerCamelCase  :      List[str]														=						format(len(UpperCAmelCase_				) ,       '064b'				).encode('utf-8'				)

     # Pad bit_string to a multiple of 512 chars
     bit_string += b"1"
     while len(UpperCAmelCase_				) % 5_12 != 4_48:
          bit_string += b"0"
     bit_string += to_little_endian(start_len[32:]				) + to_little_endian(start_len[:32]				)

     return bit_string



def      UpperCAmelCase__   (      UpperCAmelCase_       :							bytes				)	->						Generator[list[int], None, None]:
     if len(UpperCAmelCase_				) % 5_12 != 0:
          raise ValueError('Input must have length that\'s a multiple of 512'				)

     for pos in range(0 ,       len(UpperCAmelCase_				) ,       5_12				):
          __lowerCamelCase  :      Any														=						bit_string[pos : pos + 5_12]
          __lowerCamelCase  :      Optional[int]														=						[]
          for i in range(0 ,       5_12 ,       32				):
               block_words.append(int(to_little_endian(block[i : i + 32]				) ,       2				)				)
          yield block_words



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int				)	->						int:
     if i < 0:
          raise ValueError('Input must be non-negative'				)

     __lowerCamelCase  :      List[Any]														=						format(UpperCAmelCase_ ,       '032b'				)
     __lowerCamelCase  :      Optional[int]														=						''
     for c in i_str:
          new_str += "1" if c == "0" else "0"
     return int(UpperCAmelCase_ ,       2				)



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:
     return (a + b) % 2**32



def      UpperCAmelCase__   (      UpperCAmelCase_       :							int ,       UpperCAmelCase_       :							int				)	->						int:
     if i < 0:
          raise ValueError('Input must be non-negative'				)
     if shift < 0:
          raise ValueError('Shift must be non-negative'				)
     return ((i << shift) ^ (i >> (32 - shift))) % 2**32



def      UpperCAmelCase__   (      UpperCAmelCase_       :							bytes				)	->						bytes:
     __lowerCamelCase  :      Optional[Any]														=						preprocess(UpperCAmelCase_				)

     __lowerCamelCase  :      Union[str, Any]														=						[int(2**32 * abs(sin(i + 1				)				)				) for i in range(64				)]

     # Starting states
     __lowerCamelCase  :      Dict														=						0x67_45_23_01
     __lowerCamelCase  :      Union[str, Any]														=						0xef_cd_ab_89
     __lowerCamelCase  :      Optional[Any]														=						0x98_ba_dc_fe
     __lowerCamelCase  :      Union[str, Any]														=						0x10_32_54_76

     __lowerCamelCase  :      List[str]														=						[
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         7,
         12,
         17,
         22,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         5,
         9,
         14,
         20,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         4,
         11,
         16,
         23,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
         6,
         10,
         15,
         21,
     ]

     # Process bit string in chunks, each with 16 32-char words
     for block_words in get_block_words(UpperCAmelCase_				):
          __lowerCamelCase  :      Dict														=						aa
          __lowerCamelCase  :      Tuple														=						ba
          __lowerCamelCase  :      List[Any]														=						ca
          __lowerCamelCase  :      Dict														=						da

          # Hash current chunk
          for i in range(64				):
               if i <= 15:
                    # f = (b & c) | (not_32(b) & d)     # Alternate definition for f
                    __lowerCamelCase  :      List[str]														=						d ^ (b & (c ^ d))
                    __lowerCamelCase  :      Optional[int]														=						i
               elif i <= 31:
                    # f = (d & b) | (not_32(d) & c)     # Alternate definition for f
                    __lowerCamelCase  :      Optional[int]														=						c ^ (d & (b ^ c))
                    __lowerCamelCase  :      Tuple														=						(5 * i + 1) % 16
               elif i <= 47:
                    __lowerCamelCase  :      str														=						b ^ c ^ d
                    __lowerCamelCase  :      Any														=						(3 * i + 5) % 16
               else:
                    __lowerCamelCase  :      Union[str, Any]														=						c ^ (b | not_aa(UpperCAmelCase_				))
                    __lowerCamelCase  :      int														=						(7 * i) % 16
               __lowerCamelCase  :      Optional[int]														=						(f + a + added_consts[i] + block_words[g]) % 2**32
               __lowerCamelCase  :      Optional[Any]														=						d
               __lowerCamelCase  :      Tuple														=						c
               __lowerCamelCase  :      Optional[int]														=						b
               __lowerCamelCase  :      List[str]														=						sum_aa(UpperCAmelCase_ ,       left_rotate_aa(UpperCAmelCase_ ,       shift_amounts[i]				)				)

          # Add hashed chunk to running total
          __lowerCamelCase  :      int														=						sum_aa(UpperCAmelCase_ ,       UpperCAmelCase_				)
          __lowerCamelCase  :      str														=						sum_aa(UpperCAmelCase_ ,       UpperCAmelCase_				)
          __lowerCamelCase  :      List[Any]														=						sum_aa(UpperCAmelCase_ ,       UpperCAmelCase_				)
          __lowerCamelCase  :      List[str]														=						sum_aa(UpperCAmelCase_ ,       UpperCAmelCase_				)

     __lowerCamelCase  :      Dict														=						reformat_hex(UpperCAmelCase_				) + reformat_hex(UpperCAmelCase_				) + reformat_hex(UpperCAmelCase_				) + reformat_hex(UpperCAmelCase_				)
     return digest


if __name__ == "__main__":
      import doctest

      doctest.testmod()






