





import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
 from transformers.pipelines.conversational import Conversation

_lowerCAmelCase							:      List[str]						=	logging.get_logger(__name__)

_lowerCAmelCase							:      Dict						=	{
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
}

_lowerCAmelCase							:      Tuple						=	{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
}

_lowerCAmelCase							:      Optional[Any]						=	{
    '''gpt2''': 1_024,
    '''gpt2-medium''': 1_024,
    '''gpt2-large''': 1_024,
    '''gpt2-xl''': 1_024,
    '''distilgpt2''': 1_024,
}

@lru_cache()
def      __snake_case						(					) ->						Tuple:
   A_					:				List[str]          =			(
       list(range(ord("!"	)				,				ord("~"	) + 1	)	) + list(range(ord("¡"	)				,				ord("¬"	) + 1	)	) + list(range(ord("®"	)				,				ord("ÿ"	) + 1	)	)
   )
   A_					:				Dict          =			bs[:]
   A_					:				int          =			0
   for b in range(2**8	):
      if b not in bs:
         bs.append(_lowerCAmelCase	)
         cs.append(2**8 + n	)
         n += 1
   A_					:				List[str]          =			[chr(_lowerCAmelCase	) for n in cs]
   return dict(zip(_lowerCAmelCase				,				_lowerCAmelCase	)	)

def      __snake_case						(					_lowerCAmelCase : Union[str, Any]	) ->						Dict:
   A_					:				List[Any]          =			set()
   A_					:				List[Any]          =			word[0]
   for char in word[1:]:
      pairs.add((prev_char, char)	)
      A_					:				int          =			char
   return pairs





class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     __UpperCamelCase					    =     VOCAB_FILES_NAMES
     __UpperCamelCase					    =     PRETRAINED_VOCAB_FILES_MAP
     __UpperCamelCase					    =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __UpperCamelCase					    =     ['''input_ids''', '''attention_mask''']



     def __init__(							self       :Any			,      snake_case       :Optional[Any]			,      snake_case       :Optional[int]			,      snake_case       :Union[str, Any]="replace"			,      snake_case       :List[str]="<|endoftext|>"			,      snake_case       :Any="<|endoftext|>"			,      snake_case       :List[str]="<|endoftext|>"			,      snake_case       :str=None			,      snake_case       :List[str]=False			,      snake_case       :Any=False			,      **snake_case       :Optional[int]			,      ):





        '''simple docstring'''




        A_					:				List[str]          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else bos_token
        A_					:				Any          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else eos_token
        A_					:				int          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else unk_token
        A_					:				List[str]          =			AddedToken(snake_case			,      lstrip=snake_case			,      rstrip=snake_case  ) if isinstance(snake_case			,      snake_case  ) else pad_token
        super().__init__(
            errors=snake_case			,      unk_token=snake_case			,      bos_token=snake_case			,      eos_token=snake_case			,      pad_token=snake_case			,      add_prefix_space=snake_case			,      add_bos_token=snake_case			,      **snake_case			,      )
        A_					:				Dict          =			add_bos_token

        with open(snake_case			,      encoding="utf-8"  ) as vocab_handle:
           A_					:				int          =			json.load(snake_case  )
        A_					:				int          =			{v: k for k, v in self.encoder.items()}
        A_					:				str          =			errors  # how to handle errors in decoding
        A_					:				Union[str, Any]          =			bytes_to_unicode()
        A_					:				str          =			{v: k for k, v in self.byte_encoder.items()}
        with open(snake_case			,      encoding="utf-8"  ) as merges_handle:
           A_					:				Any          =			merges_handle.read().split("\n"  )[1:-1]
        A_					:				Tuple          =			[tuple(merge.split()  ) for merge in bpe_merges]
        A_					:				Any          =			dict(zip(snake_case			,      range(len(snake_case  )  )  )  )
        A_					:				Any          =			{}
        A_					:				Tuple          =			add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        A_					:				int          =			re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"  )



     @property
     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]  ):





        '''simple docstring'''




        return len(self.encoder  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]  ):





        '''simple docstring'''




        return dict(self.encoder			,      **self.added_tokens_encoder  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Tuple			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        if token in self.cache:
           return self.cache[token]
        A_					:				List[str]          =			tuple(snake_case  )
        A_					:				str          =			get_pairs(snake_case  )

        if not pairs:
           return token

        while True:
           A_					:				List[str]          =			min(snake_case			,      key=lambda snake_case  : self.bpe_ranks.get(snake_case			,      float("inf"  )  )  )
           if bigram not in self.bpe_ranks:
              break
           A_      ,						A_					:				Optional[int]          =			bigram
           A_					:				Union[str, Any]          =			[]
           A_					:				Optional[Any]          =			0
           while i < len(snake_case  ):
              try:
                 A_					:				Dict          =			word.index(snake_case			,      snake_case  )
              except ValueError:
                 new_word.extend(word[i:]  )
                 break
              else:
                 new_word.extend(word[i:j]  )
                 A_					:				str          =			j

              if word[i] == first and i < len(snake_case  ) - 1 and word[i + 1] == second:
                 new_word.append(first + second  )
                 i += 2
              else:
                 new_word.append(word[i]  )
                 i += 1
           A_					:				int          =			tuple(snake_case  )
           A_					:				Any          =			new_word
           if len(snake_case  ) == 1:
              break
           else:
              A_					:				int          =			get_pairs(snake_case  )
        A_					:				List[Any]          =			" ".join(snake_case  )
        A_					:				Optional[int]          =			word
        return word



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :Optional[int]			,      snake_case       :Dict=None  ):





        '''simple docstring'''




        if self.add_bos_token:
           A_					:				List[str]          =			[self.bos_token_id]
        else:
           A_					:				List[Any]          =			[]

        A_					:				Tuple          =			bos_token_ids + token_ids_a

        if token_ids_a is None:
           return output

        return output + bos_token_ids + token_ids_a



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :List[int]			,      snake_case       :Optional[List[int]] = None			,      snake_case       :bool = False  ):





        '''simple docstring'''




        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case			,      token_ids_a=snake_case			,      already_has_special_tokens=snake_case  )

        if not self.add_bos_token:
           return super().get_special_tokens_mask(
               token_ids_a=snake_case			,      token_ids_a=snake_case			,      already_has_special_tokens=snake_case  )

        if token_ids_a is None:
           return [1] + ([0] * len(snake_case  ))
        return [1] + ([0] * len(snake_case  )) + [1] + ([0] * len(snake_case  ))



     def 						SCREAMING_SNAKE_CASE		(							self       :List[Any]			,      snake_case       :Tuple  ):





        '''simple docstring'''




        A_					:				List[Any]          =			[]
        for token in re.findall(self.pat			,      snake_case  ):
           A_					:				str          =			"".join(
               self.byte_encoder[b] for b in token.encode("utf-8"  )  )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
           bpe_tokens.extend(bpe_token for bpe_token in self.bpe(snake_case  ).split(" "  )  )
        return bpe_tokens



     def 						SCREAMING_SNAKE_CASE		(							self       :List[str]			,      snake_case       :str  ):





        '''simple docstring'''




        return self.encoder.get(snake_case			,      self.encoder.get(self.unk_token  )  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :Any  ):





        '''simple docstring'''




        return self.decoder.get(snake_case  )



     def 						SCREAMING_SNAKE_CASE		(							self       :Dict			,      snake_case       :Any  ):





        '''simple docstring'''




        A_					:				str          =			"".join(snake_case  )
        A_					:				Tuple          =			bytearray([self.byte_decoder[c] for c in text]  ).decode("utf-8"			,      errors=self.errors  )
        return text



     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :str			,      snake_case       :Optional[str] = None  ):





        '''simple docstring'''




        if not os.path.isdir(snake_case  ):
           logger.error(f"Vocabulary path ({save_directory}) should be a directory"  )
           return
        A_					:				str          =			os.path.join(
            snake_case			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )
        A_					:				List[Any]          =			os.path.join(
            snake_case			,      (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]  )

        with open(snake_case			,      "w"			,      encoding="utf-8"  ) as f:
           f.write(json.dumps(self.encoder			,      indent=2			,      sort_keys=snake_case			,      ensure_ascii=snake_case  ) + "\n"  )

        A_					:				Optional[Any]          =			0
        with open(snake_case			,      "w"			,      encoding="utf-8"  ) as writer:
           writer.write("#version: 0.2\n"  )
           for bpe_tokens, token_index in sorted(self.bpe_ranks.items()			,      key=lambda snake_case  : kv[1]  ):
              if index != token_index:
                 logger.warning(
                     f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                     " Please check that the tokenizer is not corrupted!"  )
                 A_					:				Any          =			token_index
              writer.write(" ".join(snake_case  ) + "\n"  )
              index += 1

        return vocab_file, merge_file



     def 						SCREAMING_SNAKE_CASE		(							self       :Union[str, Any]			,      snake_case       :Tuple			,      snake_case       :Optional[Any]=False			,      **snake_case       :Dict  ):





        '''simple docstring'''




        A_					:				List[Any]          =			kwargs.pop("add_prefix_space"			,      self.add_prefix_space  )
        if is_split_into_words or add_prefix_space:
           A_					:				Optional[Any]          =			" " + text
        return (text, kwargs)





     def 						SCREAMING_SNAKE_CASE		(							self       :Optional[int]			,      snake_case       :"Conversation"  ):





        '''simple docstring'''




        A_					:				int          =			[]
        for is_user, text in conversation.iter_texts():
           input_ids.extend(self.encode(snake_case			,      add_special_tokens=snake_case  ) + [self.eos_token_id]  )
        if len(snake_case  ) > self.model_max_length:
           A_					:				Tuple          =			input_ids[-self.model_max_length :]
        return input_ids







import torch
from torch import autograd


class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Any			,      snake_case       :torch.tensor			,      snake_case       :float			,      snake_case       :bool  ):





        '''simple docstring'''




        A_					:				int          =			inputs.numel()
        A_					:				str          =			int(0.005 * nb_elems  ) + 1
        if sigmoid:
           A_					:				List[Any]          =			(torch.sigmoid(snake_case  ) > threshold).type(inputs.type()  )
        else:
           A_					:				Optional[Any]          =			(inputs > threshold).type(inputs.type()  )
        if mask.sum() < nb_min:
           # We limit the pruning so that at least 0.5% (half a percent) of the weights are remaining
           A_					:				Tuple          =			inputs.flatten().kthvalue(max(nb_elems - nb_min			,      1  )  ).values
           A_					:				Optional[int]          =			(inputs > k_threshold).type(inputs.type()  )
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Optional[int]			,      snake_case       :int  ):





        '''simple docstring'''




        return gradOutput, None, None




class 					__magic_name__  (							autograd.Function							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :Tuple			,      snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				Tuple          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.flatten().sort(descending=snake_case  )
        A_					:				Dict          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				str          =			mask.flatten()
        A_					:				List[Any]          =			0
        A_					:				List[str]          =			1
        return mask





     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :List[Any]			,      snake_case       :Optional[Any]  ):





        '''simple docstring'''




        return gradOutput, None




class 					__magic_name__  (							lowerCamelCase__							):





     """simple docstring"""



     @staticmethod
     def 						SCREAMING_SNAKE_CASE		(							snake_case       :torch.tensor			,      snake_case       :float  ):





        '''simple docstring'''




        A_					:				str          =			inputs.clone()
        A_      ,						A_					:				Optional[int]          =			inputs.abs().flatten().sort(descending=snake_case  )
        A_					:				int          =			int(threshold * inputs.numel()  )

        # flat_out and mask access the same memory.
        A_					:				List[str]          =			mask.flatten()
        A_					:				List[str]          =			0
        A_					:				Union[str, Any]          =			1
        return mask
