'''simple docstring'''

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

import regex as re

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


_A      :				Tuple 				=    logging.get_logger(__name__)


_A      :				List[Any] 				=    {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt'''}

# See all MVP models at https://huggingface.co/models?filter=mvp
_A      :				List[Any] 				=    {
    '''vocab_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
    },
    '''added_tokens.json''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
    },
    '''merges_file''': {
        '''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
    },
}

_A      :				List[str] 				=    {
    '''RUCAIBox/mvp''': 1024,
}







@lru_cache()
def 					UpperCamelCase_						( ) ->					Any:






 '''simple docstring'''

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







def 					UpperCamelCase_						( snake_case_	:	Optional[int]				) ->					int:






 '''simple docstring'''

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







class     _lowercase					(    UpperCAmelCase__      ):


 '''simple docstring'''






 _SCREAMING_SNAKE_CASE					:	Tuple				     = VOCAB_FILES_NAMES
 _SCREAMING_SNAKE_CASE					:	List[Any]				     = PRETRAINED_VOCAB_FILES_MAP
 _SCREAMING_SNAKE_CASE					:	List[str]				     = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _SCREAMING_SNAKE_CASE					:	Optional[Any]				     = ["""input_ids""", """attention_mask"""]
 def __init__(				self       :     Optional[int]				,	SCREAMING_SNAKE_CASE__       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     List[str]="replace"				,	SCREAMING_SNAKE_CASE__       :     int="<s>"				,	SCREAMING_SNAKE_CASE__       :     Union[str, Any]="</s>"				,	SCREAMING_SNAKE_CASE__       :     Tuple="</s>"				,	SCREAMING_SNAKE_CASE__       :     List[Any]="<s>"				,	SCREAMING_SNAKE_CASE__       :     Dict="<unk>"				,	SCREAMING_SNAKE_CASE__       :     Any="<pad>"				,	SCREAMING_SNAKE_CASE__       :     Optional[int]="<mask>"				,	SCREAMING_SNAKE_CASE__       :     int=False				,	**SCREAMING_SNAKE_CASE__       :     Optional[Any]				,	)  ->   Optional[Any]:
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else bos_token
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else eos_token
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else sep_token
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else cls_token
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else unk_token
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else pad_token

  # Mask token behave like a normal word, i.e. include the space before it
  __lowerCAmelCase    							=						AddedToken(SCREAMING_SNAKE_CASE__				,	lstrip=SCREAMING_SNAKE_CASE__				,	rstrip=SCREAMING_SNAKE_CASE__							) if isinstance(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							) else mask_token

  super().__init__(
      errors=SCREAMING_SNAKE_CASE__				,	bos_token=SCREAMING_SNAKE_CASE__				,	eos_token=SCREAMING_SNAKE_CASE__				,	unk_token=SCREAMING_SNAKE_CASE__				,	sep_token=SCREAMING_SNAKE_CASE__				,	cls_token=SCREAMING_SNAKE_CASE__				,	pad_token=SCREAMING_SNAKE_CASE__				,	mask_token=SCREAMING_SNAKE_CASE__				,	add_prefix_space=SCREAMING_SNAKE_CASE__				,	**SCREAMING_SNAKE_CASE__				,	)
  with open(SCREAMING_SNAKE_CASE__				,	encoding="""utf-8"""							) as vocab_handle:
   __lowerCAmelCase    							=						json.load(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						{v: k for k, v in self.encoder.items()}
  __lowerCAmelCase    							=						errors  # how to handle errors in decoding
  __lowerCAmelCase    							=						bytes_to_unicode()
  __lowerCAmelCase    							=						{v: k for k, v in self.byte_encoder.items()}
  with open(SCREAMING_SNAKE_CASE__				,	encoding="""utf-8"""							) as merges_handle:
   __lowerCAmelCase    							=						merges_handle.read().split("""\n"""							)[1:-1]
  __lowerCAmelCase    							=						[tuple(merge.split()							) for merge in bpe_merges]
  __lowerCAmelCase    							=						dict(zip(SCREAMING_SNAKE_CASE__				,	range(len(SCREAMING_SNAKE_CASE__							)							)							)							)
  __lowerCAmelCase    							=						{}
  __lowerCAmelCase    							=						add_prefix_space

  # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
  __lowerCAmelCase    							=						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       :     Optional[int]							)  ->   Union[str, Any]:
  return len(self.encoder							)
 def a (				self       :     Optional[int]							)  ->   List[str]:
  return dict(self.encoder				,	**self.added_tokens_encoder							)
 def a (				self       :     Optional[Any]				,	SCREAMING_SNAKE_CASE__       :     Any							)  ->   List[str]:
  if token in self.cache:
   return self.cache[token]
  __lowerCAmelCase    							=						tuple(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						get_pairs(SCREAMING_SNAKE_CASE__							)

  if not pairs:
   return token

  while True:
   __lowerCAmelCase    							=						min(SCREAMING_SNAKE_CASE__				,	key=lambda SCREAMING_SNAKE_CASE__							: self.bpe_ranks.get(SCREAMING_SNAKE_CASE__				,	float("""inf"""							)							)							)
   if bigram not in self.bpe_ranks:
    break
   __lowerCAmelCase							,	__lowerCAmelCase    							=						bigram
   __lowerCAmelCase    							=						[]
   __lowerCAmelCase    							=						0
   while i < len(SCREAMING_SNAKE_CASE__							):
    try:
     __lowerCAmelCase    							=						word.index(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
    except ValueError:
     new_word.extend(word[i:]							)
     break
    else:
     new_word.extend(word[i:j]							)
     __lowerCAmelCase    							=						j

    if word[i] == first and i < len(SCREAMING_SNAKE_CASE__							) - 1 and word[i + 1] == second:
     new_word.append(first + second							)
     i += 2
    else:
     new_word.append(word[i]							)
     i += 1
   __lowerCAmelCase    							=						tuple(SCREAMING_SNAKE_CASE__							)
   __lowerCAmelCase    							=						new_word
   if len(SCREAMING_SNAKE_CASE__							) == 1:
    break
   else:
    __lowerCAmelCase    							=						get_pairs(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						""" """.join(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						word
  return word
 def a (				self       :     str				,	SCREAMING_SNAKE_CASE__       :     Dict							)  ->   str:
  __lowerCAmelCase    							=						[]
  for token in re.findall(self.pat				,	SCREAMING_SNAKE_CASE__							):
   __lowerCAmelCase    							=						"""""".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(SCREAMING_SNAKE_CASE__							).split(""" """							)							)
  return bpe_tokens
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     Any							)  ->   List[str]:
  return self.encoder.get(SCREAMING_SNAKE_CASE__				,	self.encoder.get(self.unk_token							)							)
 def a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     List[str]							)  ->   Any:
  return self.decoder.get(SCREAMING_SNAKE_CASE__							)
 def a (				self       :     List[Any]				,	SCREAMING_SNAKE_CASE__       :     List[str]							)  ->   List[Any]:
  __lowerCAmelCase    							=						"""""".join(SCREAMING_SNAKE_CASE__							)
  __lowerCAmelCase    							=						bytearray([self.byte_decoder[c] for c in text]							).decode("""utf-8"""				,	errors=self.errors							)
  return text
 def a (				self       :     Tuple				,	SCREAMING_SNAKE_CASE__       :     str				,	SCREAMING_SNAKE_CASE__       :     Optional[str] = None							)  ->   Tuple[str]:
  if not os.path.isdir(SCREAMING_SNAKE_CASE__							):
   logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
   return
  __lowerCAmelCase    							=						os.path.join(
      SCREAMING_SNAKE_CASE__				,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]							)
  __lowerCAmelCase    							=						os.path.join(
      SCREAMING_SNAKE_CASE__				,	(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)

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

  __lowerCAmelCase    							=						0
  with open(SCREAMING_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 SCREAMING_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!"""							)
     __lowerCAmelCase    							=						token_index
    writer.write(""" """.join(SCREAMING_SNAKE_CASE__							) + """\n"""							)
    index += 1

  return vocab_file, merge_file
 def a (				self       :     Union[str, Any]				,	SCREAMING_SNAKE_CASE__       :     List[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[List[int]] = None							)  ->   List[int]:
  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  __lowerCAmelCase    							=						[self.cls_token_id]
  __lowerCAmelCase    							=						[self.sep_token_id]
  return cls + token_ids_a + sep + sep + token_ids_a + sep
 def a (				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     List[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[List[int]] = None				,	SCREAMING_SNAKE_CASE__       :     bool = False							)  ->   List[int]:
  if already_has_special_tokens:
   return super().get_special_tokens_mask(
       token_ids_a=SCREAMING_SNAKE_CASE__				,	token_ids_a=SCREAMING_SNAKE_CASE__				,	already_has_special_tokens=SCREAMING_SNAKE_CASE__							)

  if token_ids_a is None:
   return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
  return [1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1, 1] + ([0] * len(SCREAMING_SNAKE_CASE__							)) + [1]
 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     List[int]				,	SCREAMING_SNAKE_CASE__       :     Optional[List[int]] = None							)  ->   List[int]:
  __lowerCAmelCase    							=						[self.sep_token_id]
  __lowerCAmelCase    							=						[self.cls_token_id]

  if token_ids_a is None:
   return len(cls + token_ids_a + sep							) * [0]
  return len(cls + token_ids_a + sep + sep + token_ids_a + sep							) * [0]





 def a (				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     Dict				,	SCREAMING_SNAKE_CASE__       :     List[str]=False				,	**SCREAMING_SNAKE_CASE__       :     Optional[Any]							)  ->   Optional[Any]:
  __lowerCAmelCase    							=						kwargs.pop("""add_prefix_space"""				,	self.add_prefix_space							)
  if (is_split_into_words or add_prefix_space) and (len(SCREAMING_SNAKE_CASE__							) > 0 and not text[0].isspace()):
   __lowerCAmelCase    							=						""" """ + text
  return (text, kwargs)







'''simple docstring'''

import math

import torch
from torch import nn
from torch.nn import init

from .binarizer import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer







class     _lowercase					(    nn.Linear      ):


 '''simple docstring'''






 def __init__(				self       :     List[str]				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     int				,	SCREAMING_SNAKE_CASE__       :     bool = True				,	SCREAMING_SNAKE_CASE__       :     str = "constant"				,	SCREAMING_SNAKE_CASE__       :     float = 0.0				,	SCREAMING_SNAKE_CASE__       :     str = "topK"				,	)  ->   Any:
  super(SCREAMING_SNAKE_CASE__				,	self							).__init__(in_features=SCREAMING_SNAKE_CASE__				,	out_features=SCREAMING_SNAKE_CASE__				,	bias=SCREAMING_SNAKE_CASE__							)
  assert pruning_method in ["topK", "threshold", "sigmoied_threshold", "magnitude", "l0"]
  __lowerCAmelCase    							=						pruning_method

  if self.pruning_method in ["topK", "threshold", "sigmoied_threshold", "l0"]:
   __lowerCAmelCase    							=						mask_scale
   __lowerCAmelCase    							=						mask_init
   __lowerCAmelCase    							=						nn.Parameter(torch.empty(self.weight.size()							)							)
   self.init_mask()
 def a (				self       :     Union[str, Any]							)  ->   int:
  if self.mask_init == "constant":
   init.constant_(self.mask_scores				,	val=self.mask_scale							)
  elif self.mask_init == "uniform":
   init.uniform_(self.mask_scores				,	a=-self.mask_scale				,	b=self.mask_scale							)
  elif self.mask_init == "kaiming":
   init.kaiming_uniform_(self.mask_scores				,	a=math.sqrt(5							)							)





 def a (				self       :     Any				,	SCREAMING_SNAKE_CASE__       :     torch.tensor				,	SCREAMING_SNAKE_CASE__       :     float							)  ->   Tuple:
  # Get the mask
  if self.pruning_method == "topK":
   __lowerCAmelCase    							=						TopKBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method in ["threshold", "sigmoied_threshold"]:
   __lowerCAmelCase    							=						"""sigmoied""" in self.pruning_method
   __lowerCAmelCase    							=						ThresholdBinarizer.apply(self.mask_scores				,	SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "magnitude":
   __lowerCAmelCase    							=						MagnitudeBinarizer.apply(self.weight				,	SCREAMING_SNAKE_CASE__							)
  elif self.pruning_method == "l0":
   __lowerCAmelCase							,	__lowerCAmelCase							,	__lowerCAmelCase    							=						-0.1, 1.1, 2 / 3
   if self.training:
    __lowerCAmelCase    							=						torch.zeros_like(self.mask_scores							).uniform_().clamp(0.0_0_0_1				,	0.9_9_9_9							)
    __lowerCAmelCase    							=						torch.sigmoid((u.log() - (1 - u).log() + self.mask_scores) / b							)
   else:
    __lowerCAmelCase    							=						torch.sigmoid(self.mask_scores							)
   __lowerCAmelCase    							=						s * (r - l) + l
   __lowerCAmelCase    							=						s_bar.clamp(min=0.0				,	max=1.0							)
  # Mask weights with computed mask
  __lowerCAmelCase    							=						mask * self.weight
  # Compute output (linear layer) with masked weights
  return nn.functional.linear(SCREAMING_SNAKE_CASE__				,	SCREAMING_SNAKE_CASE__				,	self.bias							)






