






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


__a					:				Optional[int]      =      {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

# See all MVP models at https://huggingface.co/models?filter=mvp
__a					:				Optional[int]      =      {
    "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					:				Union[str, Any]      =      {
    "RUCAIBox/mvp": 1024,
}



@lru_cache()
def 	_SCREAMING_SNAKE_CASE  (       )  ->					Tuple:





    """simple docstring"""

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



def 	_SCREAMING_SNAKE_CASE  (       __lowercase      :	Tuple			)  ->					Any:





    """simple docstring"""

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






class 					__lowercase					(		_UpperCAmelCase     ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE        = VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE        = PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE        = ["input_ids", "attention_mask"]




     def __init__( self				:	List[Any]       ,		UpperCamelCase_				:	List[str]       ,		UpperCamelCase_				:	Optional[Any]       ,		UpperCamelCase_				:	int="replace"       ,		UpperCamelCase_				:	Optional[int]="<s>"       ,		UpperCamelCase_				:	str="</s>"       ,		UpperCamelCase_				:	List[Any]="</s>"       ,		UpperCamelCase_				:	List[str]="<s>"       ,		UpperCamelCase_				:	List[str]="<unk>"       ,		UpperCamelCase_				:	Union[str, Any]="<pad>"       ,		UpperCamelCase_				:	Optional[int]="<mask>"       ,		UpperCamelCase_				:	List[Any]=False       ,		**UpperCamelCase_				:	str       ,		):


         """simple docstring"""



         __A   			=		AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__ ) if isinstance(lowercase__       ,		lowercase__ ) else bos_token
         __A   			=		AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__ ) if isinstance(lowercase__       ,		lowercase__ ) else eos_token
         __A   			=		AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__ ) if isinstance(lowercase__       ,		lowercase__ ) else sep_token
         __A   			=		AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__ ) if isinstance(lowercase__       ,		lowercase__ ) else cls_token
         __A   			=		AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__ ) if isinstance(lowercase__       ,		lowercase__ ) else unk_token
         __A   			=		AddedToken(lowercase__       ,		lstrip=lowercase__       ,		rstrip=lowercase__ ) if isinstance(lowercase__       ,		lowercase__ ) else pad_token

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

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

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




     @property
     def 						lowerCAmelCase_   ( self				:	int ):


         """simple docstring"""



         return len(self.encoder )




     def 						lowerCAmelCase_   ( self				:	int ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	Optional[int] ):


         """simple docstring"""



         if token in self.cache:
             return self.cache[token]
         __A   			=		tuple(lowercase__ )
         __A   			=		get_pairs(lowercase__ )

         if not pairs:
             return token

         while True:
             __A   			=		min(lowercase__       ,		key=lambda UpperCamelCase_ : self.bpe_ranks.get(lowercase__       ,		float("""inf""" ) ) )
             if bigram not in self.bpe_ranks:
                 break
             __A   			=		bigram
             __A   			=		[]
             __A   			=		0
             while i < len(lowercase__ ):
                 try:
                     __A   			=		word.index(lowercase__       ,		lowercase__ )
                 except ValueError:
                     new_word.extend(word[i:] )
                     break
                 else:
                     new_word.extend(word[i:j] )
                     __A   			=		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
             __A   			=		tuple(lowercase__ )
             __A   			=		new_word
             if len(lowercase__ ) == 1:
                 break
             else:
                 __A   			=		get_pairs(lowercase__ )
         __A   			=		" ".join(lowercase__ )
         __A   			=		word
         return word




     def 						lowerCAmelCase_   ( self				:	Tuple       ,		UpperCamelCase_				:	Tuple ):


         """simple docstring"""



         __A   			=		[]
         for token in re.findall(self.pat       ,		lowercase__ ):
             __A   			=		"".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 						lowerCAmelCase_   ( self				:	Optional[Any]       ,		UpperCamelCase_				:	str ):


         """simple docstring"""



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




     def 						lowerCAmelCase_   ( self				:	Optional[int]       ,		UpperCamelCase_				:	List[Any] ):


         """simple docstring"""



         return self.decoder.get(lowercase__ )




     def 						lowerCAmelCase_   ( self				:	Union[str, Any]       ,		UpperCamelCase_				:	List[str] ):


         """simple docstring"""



         __A   			=		"".join(lowercase__ )
         __A   			=		bytearray([self.byte_decoder[c] for c in text] ).decode("""utf-8"""       ,		errors=self.errors )
         return text




     def 						lowerCAmelCase_   ( self				:	int       ,		UpperCamelCase_				:	Any       ,		UpperCamelCase_				:	Tuple = None ):


         """simple docstring"""



         if not os.path.isdir(lowercase__ ):
             logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
             return
         __A   			=		os.path.join(
             lowercase__       ,		(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
         __A   			=		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""" )

         __A   			=		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!""" )
                     __A   			=		token_index
                 writer.write(""" """.join(lowercase__ ) + """\n""" )
                 index += 1

         return vocab_file, merge_file




     def 						lowerCAmelCase_   ( self				:	Optional[int]       ,		UpperCamelCase_				:	str       ,		UpperCamelCase_				:	Tuple = None ):


         """simple docstring"""



         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         __A   			=		[self.cls_token_id]
         __A   			=		[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep




     def 						lowerCAmelCase_   ( self				:	Tuple       ,		UpperCamelCase_				:	List[str]       ,		UpperCamelCase_				:	List[Any] = None       ,		UpperCamelCase_				:	Union[str, Any] = False ):


         """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 token_ids_a is None:
             return [1] + ([0] * len(lowercase__ )) + [1]
         return [1] + ([0] * len(lowercase__ )) + [1, 1] + ([0] * len(lowercase__ )) + [1]




     def 						lowerCAmelCase_   ( self				:	str       ,		UpperCamelCase_				:	Tuple       ,		UpperCamelCase_				:	List[str] = None ):


         """simple docstring"""



         __A   			=		[self.sep_token_id]
         __A   			=		[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 						lowerCAmelCase_   ( self				:	Tuple       ,		UpperCamelCase_				:	List[str]       ,		UpperCamelCase_				:	Dict=False       ,		**UpperCamelCase_				:	Optional[Any] ):


         """simple docstring"""



         __A   			=		kwargs.pop("""add_prefix_space"""       ,		self.add_prefix_space )
         if (is_split_into_words or add_prefix_space) and (len(lowercase__ ) > 0 and not text[0].isspace()):
             __A   			=		" " + text
         return (text, kwargs)







'''simple docstring'''






import argparse
import os

import torch
from emmental.modules import ThresholdBinarizer, TopKBinarizer


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	Optional[int]    )							->   str:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Any     =       args.serialization_dir
      SCREAMING_SNAKE_CASE_      :			List[str]     =       args.pruning_method
      SCREAMING_SNAKE_CASE_      :			str     =       args.threshold

      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       torch.load(os.path.join(SCREAMING_SNAKE_CASE_  ,  "pytorch_model.bin"    )  ,  map_location="cpu"    )

      SCREAMING_SNAKE_CASE_      :			List[Any]     =       0  # Number of remaining (not pruned) params in the encoder
      SCREAMING_SNAKE_CASE_      :			Optional[int]     =       0  # Number of params in the encoder

      print("name".ljust(6_0  ,  " "    )  ,  "Remaining Weights %"  ,  "Remaining Weight"    )
      for name, param in st.items():
            if "encoder" not in name:
                  continue

            if "mask_scores" in name:
                  if pruning_method == "topK":
                        SCREAMING_SNAKE_CASE_      :			int     =       TopKBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "sigmoied_threshold":
                        SCREAMING_SNAKE_CASE_      :			Tuple     =       ThresholdBinarizer.apply(SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_  ,  SCREAMING_SNAKE_CASE_    ).sum().item()
                  elif pruning_method == "l0":
                        SCREAMING_SNAKE_CASE_,						SCREAMING_SNAKE_CASE_      :			Dict     =       -0.1, 1.1
                        SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       torch.sigmoid(SCREAMING_SNAKE_CASE_    )
                        SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       s * (r - l) + l
                        SCREAMING_SNAKE_CASE_      :			str     =       s_bar.clamp(min=0.0  ,  max=1.0    )
                        SCREAMING_SNAKE_CASE_      :			Dict     =       (mask > 0.0).sum().item()
                  else:
                        raise ValueError("Unknown pruning method"    )
                  remaining_count += mask_ones
                  print(name.ljust(6_0  ,  " "    )  ,  str(round(1_0_0 * mask_ones / param.numel()  ,  3    )    ).ljust(2_0  ,  " "    )  ,  str(SCREAMING_SNAKE_CASE_    )    )
            else:
                  encoder_count += param.numel()
                  if "bias" in name or "LayerNorm" in name:
                        remaining_count += param.numel()

      print(""    )
      print("Remaining Weights (global) %: "  ,  1_0_0 * remaining_count / encoder_count    )


if __name__ == "__main__":
      snake_case_       =       argparse.ArgumentParser()

      parser.add_argument(
          '--pruning_method',
          choices=['l0', 'topK', 'sigmoied_threshold'],
          type=str,
          required=True,
          help=(
              'Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'
              ' pruning)'
          ),
      )
      parser.add_argument(
          '--threshold',
          type=float,
          required=False,
          help=(
              'For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'
              'For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'
              'Not needed for `l0`'
          ),
      )
      parser.add_argument(
          '--serialization_dir',
          type=str,
          required=True,
          help='Folder containing the model that was previously fine-pruned',
      )

      snake_case_       =       parser.parse_args()

      main(args)

