





"""simple docstring"""


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

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding, EncodedInput
from ...utils import PaddingStrategy, logging


lowerCamelCase__				  =   logging.get_logger(__name__)


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

# See all LED models at https://huggingface.co/models?filter=LED
lowerCamelCase__				  =   {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

lowerCamelCase__				  =   {
    "allenai/led-base-16384": 1_6384,
}




@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def 					lowercase__    (					)					->		Union[str, Any]:




						"""simple docstring"""

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




def 					lowercase__    (					lowercase_			)					->		str:




						"""simple docstring"""

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



class      __SCREAMING_SNAKE_CASE    (						_UpperCamelCase   ):







		'''simple docstring'''


		SCREAMING_SNAKE_CASE__							:Optional[int]	     =					VOCAB_FILES_NAMES
		SCREAMING_SNAKE_CASE__							:Union[str, Any]	     =					PRETRAINED_VOCAB_FILES_MAP
		SCREAMING_SNAKE_CASE__							:Tuple	     =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		SCREAMING_SNAKE_CASE__							:Optional[int]	     =					["input_ids", "attention_mask"]

		def __init__(		self   :       Optional[Any]					,   __a   :       int					,   __a   :       Optional[int]					,   __a   :       Optional[int]="replace"					,   __a   :       Any="<s>"					,   __a   :       Dict="</s>"					,   __a   :       List[str]="</s>"					,   __a   :       int="<s>"					,   __a   :       Any="<unk>"					,   __a   :       str="<pad>"					,   __a   :       Any="<mask>"					,   __a   :       List[Any]=False					,   **__a   :       List[str]					,   )		->				str:
								_UpperCamelCase      :	Tuple						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else bos_token
								_UpperCamelCase      :	List[str]						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else eos_token
								_UpperCamelCase      :	Tuple						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else sep_token
								_UpperCamelCase      :	str						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else cls_token
								_UpperCamelCase      :	Any						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else unk_token
								_UpperCamelCase      :	List[Any]						=       AddedToken(__a					,   lstrip=__a					,   rstrip=__a	) if isinstance(__a					,   __a	) else pad_token

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

								super().__init__(
								    errors=__a					,   bos_token=__a					,   eos_token=__a					,   unk_token=__a					,   sep_token=__a					,   cls_token=__a					,   pad_token=__a					,   mask_token=__a					,   add_prefix_space=__a					,   **__a					,   )

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

								# Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
								_UpperCamelCase      :	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
		# Copied from transformers.models.bart.tokenization_bart.BartTokenizer.vocab_size
		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict	)		->				Optional[Any]:
								return len(self.encoder	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]	)		->				Tuple:
								return dict(self.encoder					,   **self.added_tokens_encoder	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       Optional[Any]	)		->				Tuple:
								if token in self.cache:
														return self.cache[token]
								_UpperCamelCase      :	Optional[int]						=       tuple(__a	)
								_UpperCamelCase      :	List[Any]						=       get_pairs(__a	)

								if not pairs:
														return token

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

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

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       List[str]	)		->				Tuple:
								_UpperCamelCase      :	str						=       []
								for token in re.findall(self.pat					,   __a	):
														_UpperCamelCase      :	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(__a	).split(" "	)	)
								return bpe_tokens

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[str]					,   __a   :       int	)		->				Tuple:
								return self.encoder.get(__a					,   self.encoder.get(self.unk_token	)	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       Dict	)		->				Union[str, Any]:
								return self.decoder.get(__a	)

		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       Tuple	)		->				str:
								_UpperCamelCase      :	Tuple						=       "".join(__a	)
								_UpperCamelCase      :	List[Any]						=       bytearray([self.byte_decoder[c] for c in text]	).decode("utf-8"					,   errors=self.errors	)
								return text

		def    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       str					,   __a   :       Optional[str] = None	)		->				Tuple[str]:
								if not os.path.isdir(__a	):
														logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''	)
														return
								_UpperCamelCase      :	Tuple						=       os.path.join(
								    __a					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)
								_UpperCamelCase      :	Dict						=       os.path.join(
								    __a					,   (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]	)

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

								_UpperCamelCase      :	str						=       0
								with open(__a					,   "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 __a	: 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      :	Union[str, Any]						=       token_index
																				writer.write(" ".join(__a	) + "\n"	)
																				index += 1

								return vocab_file, merge_file

		def    __SCREAMING_SNAKE_CASE				(		self   :       int					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:
								if token_ids_a is None:
														return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
								_UpperCamelCase      :	Tuple						=       [self.cls_token_id]
								_UpperCamelCase      :	List[Any]						=       [self.sep_token_id]
								return cls + token_ids_a + sep + sep + token_ids_a + sep

		def    __SCREAMING_SNAKE_CASE				(		self   :       List[Any]					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None					,   __a   :       bool = False	)		->				List[int]:
								if already_has_special_tokens:
														return super().get_special_tokens_mask(
														    token_ids_a=__a					,   token_ids_a=__a					,   already_has_special_tokens=__a	)

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

		def    __SCREAMING_SNAKE_CASE				(		self   :       str					,   __a   :       List[int]					,   __a   :       Optional[List[int]] = None	)		->				List[int]:
								_UpperCamelCase      :	Optional[int]						=       [self.sep_token_id]
								_UpperCamelCase      :	List[Any]						=       [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    __SCREAMING_SNAKE_CASE				(		self   :       Any					,   __a   :       str					,   __a   :       int=False					,   **__a   :       List[Any]	)		->				List[Any]:
								_UpperCamelCase      :	Optional[Any]						=       kwargs.pop("add_prefix_space"					,   self.add_prefix_space	)
								if (is_split_into_words or add_prefix_space) and (len(__a	) > 0 and not text[0].isspace()):
														_UpperCamelCase      :	List[Any]						=       " " + text
								return (text, kwargs)


		def    __SCREAMING_SNAKE_CASE				(		self   :       Dict					,   __a   :       Union[Dict[str, EncodedInput], BatchEncoding]					,   __a   :       Optional[int] = None					,   __a   :       PaddingStrategy = PaddingStrategy.DO_NOT_PAD					,   __a   :       Optional[int] = None					,   __a   :       Optional[bool] = None					,   )		->				dict:
								_UpperCamelCase      :	Any						=       super()._pad(
								    encoded_inputs=__a					,   max_length=__a					,   padding_strategy=__a					,   pad_to_multiple_of=__a					,   return_attention_mask=__a					,   )

								# Load from model defaults
								if return_attention_mask is None:
														_UpperCamelCase      :	str						=       "attention_mask" in self.model_input_names

								if return_attention_mask and "global_attention_mask" in encoded_inputs:
														_UpperCamelCase      :	Dict						=       encoded_inputs[self.model_input_names[0]]
														# `global_attention_mask` need to have the same length as other (sequential) inputs.
														_UpperCamelCase      :	str						=       len(encoded_inputs["global_attention_mask"]	) != len(__a	)

														if needs_to_be_padded:
																				_UpperCamelCase      :	List[str]						=       len(__a	) - len(encoded_inputs["global_attention_mask"]	)

																				if self.padding_side == "right":
																										# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
																										_UpperCamelCase      :	str						=       (
																										    encoded_inputs["global_attention_mask"] + [-1] * difference
																										)
																				elif self.padding_side == "left":
																										_UpperCamelCase      :	List[Any]						=       [-1] * difference + encoded_inputs[
																										    "global_attention_mask"
																										]
																				else:
																										raise ValueError("Invalid padding strategy:" + str(self.padding_side	)	)

								return encoded_inputs







"""simple docstring"""


import argparse
import collections
import os
import re

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
lowerCamelCase__				  =   "src/transformers"
lowerCamelCase__				  =   "docs/source/en"
lowerCamelCase__				  =   "."




def 					lowercase__    (					lowercase_		,lowercase_		,lowercase_			)					->		List[str]:




      """simple docstring"""

      with open(lowercase_		,"r"		,encoding="utf-8"		,newline="\n"			) as f:
            _UpperCamelCase      :	Union[str, Any]						=       f.readlines()
      # Find the start prompt.
      _UpperCamelCase      :	Dict						=       0
      while not lines[start_index].startswith(lowercase_			):
            start_index += 1
      start_index += 1

      _UpperCamelCase      :	Optional[int]						=       start_index
      while not lines[end_index].startswith(lowercase_			):
            end_index += 1
      end_index -= 1

      while len(lines[start_index]			) <= 1:
            start_index += 1
      while len(lines[end_index]			) <= 1:
            end_index -= 1
      end_index += 1
      return "".join(lines[start_index:end_index]			), start_index, end_index, lines


# Add here suffixes that are used to identify models, separated by |
lowerCamelCase__				  =   "Model|Encoder|Decoder|ForConditionalGeneration"
# Regexes that match TF/Flax/PT model names.
lowerCamelCase__				  =   re.compile(R"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
lowerCamelCase__				  =   re.compile(R"Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCamelCase__				  =   re.compile(R"(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")


# This is to make sure the transformers module imported is the one in the repo.
lowerCamelCase__				  =   direct_transformers_import(TRANSFORMERS_PATH)




def 					lowercase__    (					lowercase_			)					->		Any:




      """simple docstring"""

      _UpperCamelCase      :	Tuple						=       re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"		,lowercase_			)
      return [m.group(0			) for m in matches]




def 					lowercase__    (					lowercase_		,lowercase_			)					->		Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase      :	str						=       2 if text == "✅" or text == "❌" else len(lowercase_			)
      _UpperCamelCase      :	Union[str, Any]						=       (width - text_length) // 2
      _UpperCamelCase      :	Dict						=       width - text_length - left_indent
      return " " * left_indent + text + " " * right_indent




def 					lowercase__    (					)					->		str:




      """simple docstring"""

      _UpperCamelCase      :	Optional[Any]						=       transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
      _UpperCamelCase      :	str						=       {
          name: config_maping_names[code]
          for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
          if code in config_maping_names
      }
      _UpperCamelCase      :	Dict						=       {name: config.replace("Config"		,""			) for name, config in model_name_to_config.items()}

      # Dictionaries flagging if each model prefix has a slow/fast tokenizer, backend in PT/TF/Flax.
      _UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	Dict						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	int						=       collections.defaultdict(lowercase_			)
      _UpperCamelCase      :	str						=       collections.defaultdict(lowercase_			)

      # Let's lookup through all transformers object (once).
      for attr_name in dir(lowercase_			):
            _UpperCamelCase      :	List[str]						=       None
            if attr_name.endswith("Tokenizer"			):
                  _UpperCamelCase      :	Tuple						=       slow_tokenizers
                  _UpperCamelCase      :	Any						=       attr_name[:-9]
            elif attr_name.endswith("TokenizerFast"			):
                  _UpperCamelCase      :	Optional[Any]						=       fast_tokenizers
                  _UpperCamelCase      :	List[str]						=       attr_name[:-13]
            elif _re_tf_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	List[Any]						=       tf_models
                  _UpperCamelCase      :	Dict						=       _re_tf_models.match(lowercase_			).groups()[0]
            elif _re_flax_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	Dict						=       flax_models
                  _UpperCamelCase      :	Union[str, Any]						=       _re_flax_models.match(lowercase_			).groups()[0]
            elif _re_pt_models.match(lowercase_			) is not None:
                  _UpperCamelCase      :	Optional[int]						=       pt_models
                  _UpperCamelCase      :	Any						=       _re_pt_models.match(lowercase_			).groups()[0]

            if lookup_dict is not None:
                  while len(lowercase_			) > 0:
                        if attr_name in model_name_to_prefix.values():
                              _UpperCamelCase      :	Dict						=       True
                              break
                        # Try again after removing the last word in the name
                        _UpperCamelCase      :	List[str]						=       "".join(camel_case_split(lowercase_			)[:-1]			)

    # Let's build that table!
      _UpperCamelCase      :	Any						=       list(model_name_to_config.keys()			)
      model_names.sort(key=str.lower			)
      _UpperCamelCase      :	List[str]						=       ["Model", "Tokenizer slow", "Tokenizer fast", "PyTorch support", "TensorFlow support", "Flax Support"]
      # We'll need widths to properly display everything in the center (+2 is to leave one extra space on each side).
      _UpperCamelCase      :	Union[str, Any]						=       [len(lowercase_			) + 2 for c in columns]
      _UpperCamelCase      :	Any						=       max([len(lowercase_			) for name in model_names]			) + 2

      # Build the table per se
      _UpperCamelCase      :	Tuple						=       "|" + "|".join([_center_text(lowercase_		,lowercase_			) for c, w in zip(lowercase_		,lowercase_			)]			) + "|\n"
      # Use ":-----:" format to center-aligned table cell texts
      table += "|" + "|".join([":" + "-" * (w - 2) + ":" for w in widths]			) + "|\n"

      _UpperCamelCase      :	Union[str, Any]						=       {True: "✅", False: "❌"}
      for name in model_names:
            _UpperCamelCase      :	Optional[int]						=       model_name_to_prefix[name]
            _UpperCamelCase      :	Tuple						=       [
                name,
                check[slow_tokenizers[prefix]],
                check[fast_tokenizers[prefix]],
                check[pt_models[prefix]],
                check[tf_models[prefix]],
                check[flax_models[prefix]],
            ]
            table += "|" + "|".join([_center_text(lowercase_		,lowercase_			) for l, w in zip(lowercase_		,lowercase_			)]			) + "|\n"
      return table




def 					lowercase__    (					lowercase_=False			)					->		List[Any]:




      """simple docstring"""

      _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase,  _UpperCamelCase      :	str						=       _find_text_in_file(
          filename=os.path.join(lowercase_		,"index.md"			)		,start_prompt="<!--This table is updated automatically from the auto modules"		,end_prompt="<!-- End table-->"		,)
      _UpperCamelCase      :	Any						=       get_model_table_from_auto_modules()

      if current_table != new_table:
            if overwrite:
                  with open(os.path.join(lowercase_		,"index.md"			)		,"w"		,encoding="utf-8"		,newline="\n"			) as f:
                        f.writelines(lines[:start_index] + [new_table] + lines[end_index:]			)
            else:
                  raise ValueError(
                      "The model table in the `index.md` has not been updated. Run `make fix-copies` to fix this."			)


if __name__ == "__main__":
  lowerCamelCase__				  =   argparse.ArgumentParser()
  parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
  lowerCamelCase__				  =   parser.parse_args()

  check_model_table(args.fix_and_overwrite)
