

'''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


snake_case_       =       logging.get_logger(__name__)

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

snake_case_       =       {
    'vocab_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/vocab.json',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/vocab.json',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/vocab.json',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/vocab.json',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/vocab.json',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'roberta-base': 'https://huggingface.co/roberta-base/resolve/main/merges.txt',
        'roberta-large': 'https://huggingface.co/roberta-large/resolve/main/merges.txt',
        'roberta-large-mnli': 'https://huggingface.co/roberta-large-mnli/resolve/main/merges.txt',
        'distilroberta-base': 'https://huggingface.co/distilroberta-base/resolve/main/merges.txt',
        'roberta-base-openai-detector': 'https://huggingface.co/roberta-base-openai-detector/resolve/main/merges.txt',
        'roberta-large-openai-detector': (
            'https://huggingface.co/roberta-large-openai-detector/resolve/main/merges.txt'
        ),
    },
}

snake_case_       =       {
    'roberta-base': 5_1_2,
    'roberta-large': 5_1_2,
    'roberta-large-mnli': 5_1_2,
    'distilroberta-base': 5_1_2,
    'roberta-base-openai-detector': 5_1_2,
    'roberta-large-openai-detector': 5_1_2,
}


@lru_cache()
def 					__lowerCamelCase			(      )							->   str:
      """simple docstring"""

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


def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	int    )							->   Tuple:
      """simple docstring"""

      SCREAMING_SNAKE_CASE_      :			Tuple     =       set()
      SCREAMING_SNAKE_CASE_      :			int     =       word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)    )
            SCREAMING_SNAKE_CASE_      :			List[str]     =       char
      return pairs





class 	SCREAMING_SNAKE_CASE__			(				_UpperCAmelCase      ):
    _A			    =							VOCAB_FILES_NAMES
    _A			    =							PRETRAINED_VOCAB_FILES_MAP
    _A			    =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A			    =							["input_ids", "attention_mask"]




    def __init__(					self				,     lowercase__				,     lowercase__				,     lowercase__="replace"				,     lowercase__="<s>"				,     lowercase__="</s>"				,     lowercase__="</s>"				,     lowercase__="<s>"				,     lowercase__="<unk>"				,     lowercase__="<pad>"				,     lowercase__="<mask>"				,     lowercase__=False				,     **lowercase__				,     ):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__			) if isinstance(lowercase__				,     lowercase__			) else bos_token
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__			) if isinstance(lowercase__				,     lowercase__			) else eos_token
          SCREAMING_SNAKE_CASE_      :			Tuple     =       AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__			) if isinstance(lowercase__				,     lowercase__			) else sep_token
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__			) if isinstance(lowercase__				,     lowercase__			) else cls_token
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       AddedToken(lowercase__				,     lstrip=lowercase__				,     rstrip=lowercase__			) if isinstance(lowercase__				,     lowercase__			) else unk_token
          SCREAMING_SNAKE_CASE_      :			List[str]     =       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
          SCREAMING_SNAKE_CASE_      :			int     =       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:
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       json.load(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Any     =       {v: k for k, v in self.encoder.items()}
          SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       errors  # how to handle errors in decoding
          SCREAMING_SNAKE_CASE_      :			Any     =       bytes_to_unicode()
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       {v: k for k, v in self.byte_encoder.items()}
          with open(lowercase__				,     encoding="utf-8"			) as merges_handle:
                SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       merges_handle.read().split("\n"			)[1:-1]
          SCREAMING_SNAKE_CASE_      :			int     =       [tuple(merge.split()			) for merge in bpe_merges]
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       dict(zip(lowercase__				,     range(len(lowercase__			)			)			)			)
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       {}
          SCREAMING_SNAKE_CASE_      :			Dict     =       add_prefix_space

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       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			):






          """simple docstring"""






          return len(self.encoder			)




    def   __lowerCamelCase      (					self			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






          if token in self.cache:
                return self.cache[token]
          SCREAMING_SNAKE_CASE_      :			str     =       tuple(lowercase__			)
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       get_pairs(lowercase__			)

          if not pairs:
                return token

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




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			int     =       []
          for token in re.findall(self.pat				,     lowercase__			):
                SCREAMING_SNAKE_CASE_      :			List[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(lowercase__			).split(" "			)			)
          return bpe_tokens




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






          return self.decoder.get(lowercase__			)




    def   __lowerCamelCase      (					self				,     lowercase__			):






          """simple docstring"""






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




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






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

          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       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 lowercase__			: 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!"			)
                            SCREAMING_SNAKE_CASE_      :			Dict     =       token_index
                      writer.write(" ".join(lowercase__			) + "\n"			)
                      index += 1

          return vocab_file, merge_file




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          SCREAMING_SNAKE_CASE_      :			Optional[int]     =       [self.cls_token_id]
          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep




    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__ = None				,     lowercase__ = 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				,     lowercase__				,     lowercase__ = None			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       [self.sep_token_id]
          SCREAMING_SNAKE_CASE_      :			Dict     =       [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				,     lowercase__				,     lowercase__=False				,     **lowercase__			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			List[str]     =       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()):
                SCREAMING_SNAKE_CASE_      :			str     =       " " + 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)

