



'''simple docstring'''
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

_UpperCamelCase									=      logging.get_logger(__name__)

_UpperCamelCase									=      {
    """vocab_file""": """vocab.json""",
    """merges_file""": """merges.txt""",
}

_UpperCamelCase									=      {
    """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""",
    },
}

_UpperCamelCase									=      {
    """gpt2""": 1024,
    """gpt2-medium""": 1024,
    """gpt2-large""": 1024,
    """gpt2-xl""": 1024,
    """distilgpt2""": 1024,
}


@lru_cache()
def 	lowercase_						( ):

			"""simple docstring"""





			__UpperCAmelCase :   Dict    					=  (
			    list(range(ord("""!"""			)      ,				ord("""~"""			) + 1			)			) + list(range(ord("""¡"""			)      ,				ord("""¬"""			) + 1			)			) + list(range(ord("""®"""			)      ,				ord("""ÿ"""			) + 1			)			)
			)
			__UpperCAmelCase :   Tuple    					=  bs[:]
			__UpperCAmelCase :   str    					=  0
			for b in range(2**8			):
						if b not in bs:
									bs.append(UpperCamelCase_			)
									cs.append(2**8 + n			)
									n += 1
			__UpperCAmelCase :   Optional[Any]    					=  [chr(UpperCamelCase_			) for n in cs]
			return dict(zip(UpperCamelCase_      ,				UpperCamelCase_			)			)


def 	lowercase_						( lowerCAmelCase__						:	Dict			):

			"""simple docstring"""





			__UpperCAmelCase :   Dict    					=  set()
			__UpperCAmelCase :   Dict    					=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)			)
						__UpperCAmelCase :   Any    					=  char
			return pairs






class _A	(     snake_case_     ):
		_SCREAMING_SNAKE_CASE       :     Dict       =      VOCAB_FILES_NAMES
		_SCREAMING_SNAKE_CASE       :     str       =      PRETRAINED_VOCAB_FILES_MAP
		_SCREAMING_SNAKE_CASE       :     int       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		_SCREAMING_SNAKE_CASE       :     Optional[Any]       =      ['input_ids', 'attention_mask']





		def __init__(  self			,							__UpperCAmelCase			,							__UpperCAmelCase			,							__UpperCAmelCase="replace"			,							__UpperCAmelCase="<|endoftext|>"			,							__UpperCAmelCase="<|endoftext|>"			,							__UpperCAmelCase="<|endoftext|>"			,							__UpperCAmelCase=None			,							__UpperCAmelCase=False			,							__UpperCAmelCase=False			,							**__UpperCAmelCase			,							)							->  Any:

					'''simple docstring'''
					__UpperCAmelCase :   Union[str, Any]    					=  AddedToken(lowercase_			,							lstrip=lowercase_			,							rstrip=lowercase_					) if isinstance(lowercase_			,							lowercase_					) else bos_token
					__UpperCAmelCase :   Union[str, Any]    					=  AddedToken(lowercase_			,							lstrip=lowercase_			,							rstrip=lowercase_					) if isinstance(lowercase_			,							lowercase_					) else eos_token
					__UpperCAmelCase :   int    					=  AddedToken(lowercase_			,							lstrip=lowercase_			,							rstrip=lowercase_					) if isinstance(lowercase_			,							lowercase_					) else unk_token
					__UpperCAmelCase :   Optional[Any]    					=  AddedToken(lowercase_			,							lstrip=lowercase_			,							rstrip=lowercase_					) if isinstance(lowercase_			,							lowercase_					) else pad_token
					super().__init__(
					    errors=lowercase_			,							unk_token=lowercase_			,							bos_token=lowercase_			,							eos_token=lowercase_			,							pad_token=lowercase_			,							add_prefix_space=lowercase_			,							add_bos_token=lowercase_			,							**lowercase_			,							)
					__UpperCAmelCase :   int    					=  add_bos_token

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

					# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
					__UpperCAmelCase :   Optional[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 				__A							(  self					)							->  int:

					'''simple docstring'''
					return len(self.encoder					)





		def 				__A							(  self					)							->  str:

					'''simple docstring'''
					return dict(self.encoder			,							**self.added_tokens_encoder					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  List[Any]:

					'''simple docstring'''
					if token in self.cache:
								return self.cache[token]
					__UpperCAmelCase :   Optional[int]    					=  tuple(lowercase_					)
					__UpperCAmelCase :   int    					=  get_pairs(lowercase_					)

					if not pairs:
								return token

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

											if word[i] == first and i < len(lowercase_					) - 1 and word[i + 1] == second:
														new_word.append(first + second					)
														i += 2
											else:
														new_word.append(word[i]					)
														i += 1
								__UpperCAmelCase :   Any    					=  tuple(lowercase_					)
								__UpperCAmelCase :   Union[str, Any]    					=  new_word
								if len(lowercase_					) == 1:
											break
								else:
											__UpperCAmelCase :   Optional[int]    					=  get_pairs(lowercase_					)
					__UpperCAmelCase :   Optional[int]    					=  ' '.join(lowercase_					)
					__UpperCAmelCase :   Dict    					=  word
					return word





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=None					)							->  List[Any]:

					'''simple docstring'''
					if self.add_bos_token:
								__UpperCAmelCase :   Optional[Any]    					=  [self.bos_token_id]
					else:
								__UpperCAmelCase :   Union[str, Any]    					=  []

					__UpperCAmelCase :   Optional[int]    					=  bos_token_ids + token_ids_a

					if token_ids_a is None:
								return output

					return output + bos_token_ids + token_ids_a





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = None			,							__UpperCAmelCase = False					)							->  List[int]:

					'''simple docstring'''
					if already_has_special_tokens:
								return super().get_special_tokens_mask(
								    token_ids_a=lowercase_			,							token_ids_a=lowercase_			,							already_has_special_tokens=lowercase_					)

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

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





		def 				__A							(  self			,							__UpperCAmelCase					)							->  List[str]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[Any]    					=  []
					for token in re.findall(self.pat			,							lowercase_					):
								__UpperCAmelCase :   Optional[Any]    					=  ''.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(lowercase_					).split(""" """					)					)
					return bpe_tokens





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					return self.encoder.get(lowercase_			,							self.encoder.get(self.unk_token					)					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Union[str, Any]:

					'''simple docstring'''
					return self.decoder.get(lowercase_					)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  Optional[int]:

					'''simple docstring'''
					__UpperCAmelCase :   Optional[int]    					=  ''.join(lowercase_					)
					__UpperCAmelCase :   List[Any]    					=  bytearray([self.byte_decoder[c] for c in text]					).decode("""utf-8"""			,							errors=self.errors					)
					return text





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase = None					)							->  Tuple[str]:

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

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

					__UpperCAmelCase :   List[Any]    					=  0
					with open(lowercase_			,							"""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 __UpperCAmelCase					: 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!"""					)
														__UpperCAmelCase :   Optional[Any]    					=  token_index
											writer.write(""" """.join(lowercase_					) + """\n"""					)
											index += 1

					return vocab_file, merge_file





		def 				__A							(  self			,							__UpperCAmelCase			,							__UpperCAmelCase=False			,							**__UpperCAmelCase					)							->  Union[str, Any]:

					'''simple docstring'''
					__UpperCAmelCase :   Dict    					=  kwargs.pop("""add_prefix_space"""			,							self.add_prefix_space					)
					if is_split_into_words or add_prefix_space:
								__UpperCAmelCase :   Optional[Any]    					=  ' ' + text
					return (text, kwargs)





		def 				__A							(  self			,							__UpperCAmelCase					)							->  List[int]:

					'''simple docstring'''
					__UpperCAmelCase :   List[str]    					=  []
					for is_user, text in conversation.iter_texts():
								input_ids.extend(self.encode(lowercase_			,							add_special_tokens=lowercase_					) + [self.eos_token_id]					)
					if len(lowercase_					) > self.model_max_length:
								__UpperCAmelCase :   Optional[int]    					=  input_ids[-self.model_max_length :]
					return input_ids












"""simple docstring"""


import torch
from torch import autograd






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		Optional[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float					,      lowercase_	:		bool )		->				List[str]:
   __A						:							str      =						inputs.numel()
   __A						:							str      =						int(0.005 * nb_elems ) + 1
   if sigmoid:
    __A						:							Dict      =						(torch.sigmoid(lowercase_ ) > threshold).type(inputs.type() )
   else:
    __A						:							Tuple      =						(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						:							Any      =						inputs.flatten().kthvalue(max(nb_elems - nb_min					,      1 ) ).values
    __A						:							Union[str, Any]      =						(inputs > k_threshold).type(inputs.type() )
   return mask



  @staticmethod
  def     A_					(lowercase_	:		str					,      lowercase_	:		Any )		->				int:
   return gradOutput, None, None






class __a ( autograd.Function					):



  @staticmethod
  def     A_					(lowercase_	:		List[Any]					,      lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				Dict:
   __A						:							List[Any]      =						inputs.clone()
   __A     ,       __A						:							Optional[int]      =						inputs.flatten().sort(descending=lowercase_ )
   __A						:							Tuple      =						int(threshold * inputs.numel() )

   # flat_out and mask access the same memory.
   __A						:							Optional[Any]      =						mask.flatten()
   __A						:							Dict      =						0
   __A						:							Any      =						1
   return mask



  @staticmethod
  def     A_					(lowercase_	:		Tuple					,      lowercase_	:		Optional[Any] )		->				str:
   return gradOutput, None






class __a ( snake_case_					):



  @staticmethod
  def     A_					(lowercase_	:		torch.tensor					,      lowercase_	:		float )		->				List[str]:
   __A						:							List[str]      =						inputs.clone()
   __A     ,       __A						:							List[Any]      =						inputs.abs().flatten().sort(descending=lowercase_ )
   __A						:							Dict      =						int(threshold * inputs.numel() )

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





