






"""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


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

_UpperCamelCase						:    Optional[Any]		=						{
    '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 			a	(  lowerCamelCase__      ):
    UpperCAmelCase_					:	Union[str, Any]      	="""gpt2"""
    UpperCAmelCase_					:	str      	=["""past_key_values"""]
    UpperCAmelCase_					:	Optional[int]      	={
        """hidden_size""": """n_embd""",
        """max_position_embeddings""": """n_positions""",
        """num_attention_heads""": """n_head""",
        """num_hidden_layers""": """n_layer""",
    }







    def __init__(     self				,						_lowerCamelCase=5_0_2_5_7				,						_lowerCamelCase=1_0_2_4				,						_lowerCamelCase=7_6_8				,						_lowerCamelCase=1_2				,						_lowerCamelCase=1_2				,						_lowerCamelCase=None				,						_lowerCamelCase="gelu_new"				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.1				,						_lowerCamelCase=0.1				,						_lowerCamelCase=1e-5				,						_lowerCamelCase=0.0_2				,						_lowerCamelCase="cls_index"				,						_lowerCamelCase=True				,						_lowerCamelCase=None				,						_lowerCamelCase=True				,						_lowerCamelCase=0.1				,						_lowerCamelCase=True				,						_lowerCamelCase=True				,						_lowerCamelCase=5_0_2_5_6				,						_lowerCamelCase=5_0_2_5_6				,						_lowerCamelCase=False				,						_lowerCamelCase=False				,						**_lowerCamelCase				,						):
      lowercase				       =			vocab_size
      lowercase				       =			n_positions
      lowercase				       =			n_embd
      lowercase				       =			n_layer
      lowercase				       =			n_head
      lowercase				       =			n_inner
      lowercase				       =			activation_function
      lowercase				       =			resid_pdrop
      lowercase				       =			embd_pdrop
      lowercase				       =			attn_pdrop
      lowercase				       =			layer_norm_epsilon
      lowercase				       =			initializer_range
      lowercase				       =			summary_type
      lowercase				       =			summary_use_proj
      lowercase				       =			summary_activation
      lowercase				       =			summary_first_dropout
      lowercase				       =			summary_proj_to_labels
      lowercase				       =			scale_attn_weights
      lowercase				       =			use_cache
      lowercase				       =			scale_attn_by_inverse_layer_idx
      lowercase				       =			reorder_and_upcast_attn

      lowercase				       =			bos_token_id
      lowercase				       =			eos_token_id

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


class 			a	(  lowerCamelCase__      ):







    def __init__(     self				,						_lowerCamelCase				,						_lowerCamelCase = "default"				,						_lowerCamelCase = None				,						_lowerCamelCase = False				,						):
      super().__init__(snake_case__				,						task=snake_case__				,						patching_specs=snake_case__				,						use_past=snake_case__			)
      if not getattr(self._config				,						'pad_token_id'				,						snake_case__			):
        # TODO: how to do that better?
        lowercase				       =			0







    @property
    def      UpperCamelCase_ (     self			):
      lowercase				       =			OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}}			)
      if self.use_past:
        self.fill_with_past_key_values_(snake_case__				,						direction='inputs'			)
        lowercase				       =			{0: '''batch''', 1: '''past_sequence + sequence'''}
      else:
        lowercase				       =			{0: '''batch''', 1: '''sequence'''}

      return common_inputs







    @property
    def      UpperCamelCase_ (     self			):
      return self._config.n_layer







    @property
    def      UpperCamelCase_ (     self			):
      return self._config.n_head







    def      UpperCamelCase_ (     self				,						_lowerCamelCase				,						_lowerCamelCase = -1				,						_lowerCamelCase = -1				,						_lowerCamelCase = False				,						_lowerCamelCase = None				,						):
      lowercase				       =			super(snake_case__				,						self			).generate_dummy_inputs(
          snake_case__				,						batch_size=snake_case__				,						seq_length=snake_case__				,						is_pair=snake_case__				,						framework=snake_case__			)

      # We need to order the input in the way they appears in the forward()
      lowercase				       =			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

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

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

      return ordered_inputs







    @property
    def      UpperCamelCase_ (     self			):
      return 1_3



from hashlib import mda, shaaaa

__snake_case         =     (shaaaa, mda)







class       __snake_case :
 def __init__(	self		, snake_case__ = 8    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=0b0
       UpperCAmelCase     :			str     	=size
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				None:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=self.hash_(snake_case__    )
       self.bitarray |= h
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.hash_(snake_case__    )
       return (h & self.bitarray) == h
 def __contains__(	self		, snake_case__    )						->				bool:
       '''simple docstring'''





       return self.exists(snake_case__    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=bin(snake_case__    )[2:]
       return res.zfill(self.size    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.bitarray    )
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				int:
       '''simple docstring'''





       UpperCAmelCase     :			Union[str, Any]     	=0b0
       for func in HASH_FUNCTIONS:
             UpperCAmelCase     :			Optional[Any]     	=(
                 int.from_bytes(func(value.encode()    ).digest()		, '''little'''    ) % self.size
             )
             res |= 2**position
       return res
 def 						UpperCAmelCase__							(	self		, snake_case__    )						->				str:
       '''simple docstring'''





       return self.format_bin(self.hash_(snake_case__    )    )






 @property
 def 						UpperCAmelCase__							(	self    )						->				float:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=bin(self.bitarray    ).count('''1'''    )
       return (n_ones / self.size) ** len(snake_case__    )
