


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


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


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

# See all LED models at https://huggingface.co/models?filter=LED
UpperCamelCase					:       List[Any]			    =     {
    """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""",
    },
}

UpperCamelCase					:       Optional[Any]			    =     {
    """allenai/led-base-16384""": 1_6384,
}



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



def UpperCamelCase_				(				__a      )	->   Optional[int]:
      a__	:  Tuple     =   set()
      a__	:  List[str]     =   word[0]
      for char in word[1:]:
            pairs.add((prev_char, char)      )
            a__	:  Dict     =   char
      return pairs



class A__     ( A__  ):



  """simple docstring"""



  _lowercase									=		VOCAB_FILES_NAMES
  _lowercase									=		PRETRAINED_VOCAB_FILES_MAP
  _lowercase									=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _lowercase									=		['input_ids', 'attention_mask']

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

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

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

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

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        a__	:  Dict     =   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			:  Dict       ):
        return len(self.encoder       )

  def    _UpperCamelCase( self			:  Tuple       ):
        return dict(self.encoder				,				**self.added_tokens_encoder       )

  def    _UpperCamelCase( self			:  Union[str, Any]				,				lowerCamelCase__			:  Union[str, Any]       ):
        if token in self.cache:
              return self.cache[token]
        a__	:  str     =   tuple(lowerCamelCase__       )
        a__	:  Dict     =   get_pairs(lowerCamelCase__       )

        if not pairs:
              return token

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

                    if word[i] == first and i < len(lowerCamelCase__       ) - 1 and word[i + 1] == second:
                          new_word.append(first + second       )
                          i += 2
                    else:
                          new_word.append(word[i]       )
                          i += 1
              a__	:  Tuple     =   tuple(lowerCamelCase__       )
              a__	:  Union[str, Any]     =   new_word
              if len(lowerCamelCase__       ) == 1:
                    break
              else:
                    a__	:  str     =   get_pairs(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   " ".join(lowerCamelCase__       )
        a__	:  Union[str, Any]     =   word
        return word

  def    _UpperCamelCase( self			:  int				,				lowerCamelCase__			:  Tuple       ):
        a__	:  Optional[int]     =   []
        for token in re.findall(self.pat				,				lowerCamelCase__       ):
              a__	:  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(lowerCamelCase__       ).split(" "       )       )
        return bpe_tokens

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  Any       ):
        return self.encoder.get(lowerCamelCase__				,				self.encoder.get(self.unk_token       )       )

  def    _UpperCamelCase( self			:  Tuple				,				lowerCamelCase__			:  str       ):
        return self.decoder.get(lowerCamelCase__       )

  def    _UpperCamelCase( self			:  Dict				,				lowerCamelCase__			:  Any       ):
        a__	:  Optional[Any]     =   "".join(lowerCamelCase__       )
        a__	:  Optional[int]     =   bytearray([self.byte_decoder[c] for c in text]       ).decode("utf-8"				,				errors=self.errors       )
        return text

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

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

        a__	:  Dict     =   0
        with open(lowerCamelCase__				,				"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 lowerCamelCase__       : 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__	:  List[Any]     =   token_index
                    writer.write(" ".join(lowerCamelCase__       ) + "\n"       )
                    index += 1

        return vocab_file, merge_file

  def    _UpperCamelCase( self			:  Optional[Any]				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None       ):
        if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        a__	:  str     =   [self.cls_token_id]
        a__	:  List[str]     =   [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep

  def    _UpperCamelCase( self			:  Any				,				lowerCamelCase__			:  List[int]				,				lowerCamelCase__			:  Optional[List[int]] = None				,				lowerCamelCase__			:  bool = False       ):
        if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=lowerCamelCase__				,				token_ids_a=lowerCamelCase__				,				already_has_special_tokens=lowerCamelCase__       )

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

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

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

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

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

              if needs_to_be_padded:
                    a__	:  int     =   len(lowerCamelCase__       ) - 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`
                          a__	:  Tuple     =   (
                              encoded_inputs["global_attention_mask"] + [-1] * difference
                          )
                    elif self.padding_side == "left":
                          a__	:  Dict     =   [-1] * difference + encoded_inputs[
                              "global_attention_mask"
                          ]
                    else:
                          raise ValueError("Invalid padding strategy:" + str(self.padding_side       )       )

        return encoded_inputs






"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_							=    {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTConfig"""]}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """OPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """OPTForCausalLM""",
	    """OPTModel""",
	    """OPTPreTrainedModel""",
	    """OPTForSequenceClassification""",
	    """OPTForQuestionAnswering""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    ["""TFOPTForCausalLM""", """TFOPTModel""", """TFOPTPreTrainedModel"""]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a_							=    [
	    """FlaxOPTForCausalLM""",
	    """FlaxOPTModel""",
	    """FlaxOPTPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_opt import (
		    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    OPTForCausalLM,
		    OPTForQuestionAnswering,
		    OPTForSequenceClassification,
		    OPTModel,
		    OPTPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
	import sys

	a_							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





