
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_6_3_8_4,
}





@lru_cache()
# Copied from transformers.models.bart.tokenization_bart.bytes_to_unicode
def     _a					(						):
 lowerCamelCase		:	int							=      (
     list(range(ord("""!"""							),	ord("""~"""							) + 1							)							) + list(range(ord("""¡"""							),	ord("""¬"""							) + 1							)							) + list(range(ord("""®"""							),	ord("""ÿ"""							) + 1							)							)
 )
 lowerCamelCase		:	str							=      bs[:]
 lowerCamelCase		:	int							=      0
 for b in range(2**8							):
  if b not in bs:
   bs.append(lowerCamelCase							)
   cs.append(2**8 + n							)
   n += 1
 lowerCamelCase		:	Tuple							=      [chr(lowerCamelCase							) for n in cs]
 return dict(zip(lowerCamelCase,	lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	List[Any]							=      set()
 lowerCamelCase		:	str							=      word[0]
 for char in word[1:]:
  pairs.add((prev_char, char)							)
  lowerCamelCase		:	int							=      char
 return pairs





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			VOCAB_FILES_NAMES
      _UpperCAmelCase				:    Optional[Any]           =			PRETRAINED_VOCAB_FILES_MAP
      _UpperCAmelCase				:    List[Any]           =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _UpperCAmelCase				:    int           =			["""input_ids""", """attention_mask"""]
      def __init__(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__="replace"    ,  __magic_name__="<s>"    ,  __magic_name__="</s>"    ,  __magic_name__="</s>"    ,  __magic_name__="<s>"    ,  __magic_name__="<unk>"    ,  __magic_name__="<pad>"    ,  __magic_name__="<mask>"    ,  __magic_name__=False    ,  **__magic_name__    ,  ):
       lowerCamelCase		:	str							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else bos_token
       lowerCamelCase		:	int							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else eos_token
       lowerCamelCase		:	str							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else sep_token
       lowerCamelCase		:	List[str]							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else cls_token
       lowerCamelCase		:	str							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else unk_token
       lowerCamelCase		:	int							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else pad_token

       # Mask token behave like a normal word, i.e. include the space before it
       lowerCamelCase		:	List[str]							=      AddedToken(__magic_name__    ,  lstrip=__magic_name__    ,  rstrip=__magic_name__      ) if isinstance(__magic_name__    ,  __magic_name__      ) else mask_token

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

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

       # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
       lowerCamelCase		:	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 						UpperCamelCase__							(      self      ):
       return len(self.encoder      )
      def 						UpperCamelCase__							(      self      ):
       return dict(self.encoder    ,  **self.added_tokens_encoder      )
      def 						UpperCamelCase__							(      self    ,  __magic_name__      ):
       if token in self.cache:
        return self.cache[token]
       lowerCamelCase		:	int							=      tuple(__magic_name__      )
       lowerCamelCase		:	List[str]							=      get_pairs(__magic_name__      )

       if not pairs:
        return token

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

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

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

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

       return vocab_file, merge_file
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       lowerCamelCase		:	int							=      [self.cls_token_id]
       lowerCamelCase		:	List[Any]							=      [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = False      ):
       if already_has_special_tokens:
        return super().get_special_tokens_mask(
            token_ids_a=__magic_name__    ,  token_ids_a=__magic_name__    ,  already_has_special_tokens=__magic_name__      )

       if token_ids_a is None:
        return [1] + ([0] * len(__magic_name__      )) + [1]
       return [1] + ([0] * len(__magic_name__      )) + [1, 1] + ([0] * len(__magic_name__      )) + [1]
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None      ):
       lowerCamelCase		:	int							=      [self.sep_token_id]
       lowerCamelCase		:	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 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__=False    ,  **__magic_name__      ):
       lowerCamelCase		:	Optional[int]							=      kwargs.pop("""add_prefix_space"""    ,  self.add_prefix_space      )
       if (is_split_into_words or add_prefix_space) and (len(__magic_name__      ) > 0 and not text[0].isspace()):
        lowerCamelCase		:	Union[str, Any]							=      """ """ + text
       return (text, kwargs)






      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__ = None    ,  __magic_name__ = PaddingStrategy.DO_NOT_PAD    ,  __magic_name__ = None    ,  __magic_name__ = None    ,  ):
       lowerCamelCase		:	List[str]							=      super()._pad(
           encoded_inputs=__magic_name__    ,  max_length=__magic_name__    ,  padding_strategy=__magic_name__    ,  pad_to_multiple_of=__magic_name__    ,  return_attention_mask=__magic_name__    ,  )

       # Load from model defaults
       if return_attention_mask is None:
        lowerCamelCase		:	List[Any]							=      """attention_mask""" in self.model_input_names

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

        if needs_to_be_padded:
         lowerCamelCase		:	Optional[Any]							=      len(__magic_name__      ) - 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`
          lowerCamelCase		:	Optional[Any]							=      (
              encoded_inputs["""global_attention_mask"""] + [-1] * difference
          )
         elif self.padding_side == "left":
          lowerCamelCase		:	Union[str, Any]							=      [-1] * difference + encoded_inputs[
              """global_attention_mask"""
          ]
         else:
          raise ValueError("""Invalid padding strategy:""" + str(self.padding_side      )      )

       return encoded_inputs






from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)




