





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


SCREAMING_SNAKE_CASE__							:      Any         =    logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__							:      Union[str, Any]         =    {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all LED models at https://huggingface.co/models?filter=LED
SCREAMING_SNAKE_CASE__							:      Tuple         =    {
    """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""",
    },
}

SCREAMING_SNAKE_CASE__							:      Tuple         =    {
    """allenai/led-base-16384""": 1_6_3_8_4,
}






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






def    _A	(   lowerCamelCase ):
   a__						:    Tuple   		=	set()
   a__						:    int   		=	word[0]
   for char in word[1:]:
      pairs.add((prev_char, char) )
      a__						:    Dict   		=	char
   return pairs







class 	__lowerCAmelCase      (       _UpperCamelCase	):
   _UpperCamelCase			:    Tuple  		=					VOCAB_FILES_NAMES
   _UpperCamelCase			:    Tuple  		=					PRETRAINED_VOCAB_FILES_MAP
   _UpperCamelCase			:    Optional[int]  		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCamelCase			:    Optional[int]  		=					["""input_ids""", """attention_mask"""]





   def __init__(			self       ,      snake_case       ,      snake_case       ,      snake_case="replace"       ,      snake_case="<s>"       ,      snake_case="</s>"       ,      snake_case="</s>"       ,      snake_case="<s>"       ,      snake_case="<unk>"       ,      snake_case="<pad>"       ,      snake_case="<mask>"       ,      snake_case=False       ,      **snake_case       ,      )					->	str:
      """simple docstring"""





      a__						:    List[str]   		=	AddedToken(snake_case       ,      lstrip=snake_case       ,      rstrip=snake_case   ) if isinstance(snake_case       ,      snake_case   ) else bos_token
      a__						:    Union[str, Any]   		=	AddedToken(snake_case       ,      lstrip=snake_case       ,      rstrip=snake_case   ) if isinstance(snake_case       ,      snake_case   ) else eos_token
      a__						:    Tuple   		=	AddedToken(snake_case       ,      lstrip=snake_case       ,      rstrip=snake_case   ) if isinstance(snake_case       ,      snake_case   ) else sep_token
      a__						:    str   		=	AddedToken(snake_case       ,      lstrip=snake_case       ,      rstrip=snake_case   ) if isinstance(snake_case       ,      snake_case   ) else cls_token
      a__						:    Optional[int]   		=	AddedToken(snake_case       ,      lstrip=snake_case       ,      rstrip=snake_case   ) if isinstance(snake_case       ,      snake_case   ) else unk_token
      a__						:    Optional[Any]   		=	AddedToken(snake_case       ,      lstrip=snake_case       ,      rstrip=snake_case   ) if isinstance(snake_case       ,      snake_case   ) else pad_token

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

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

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

      # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
      a__						:    List[str]   		=	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 						_snake_case			(			self   )					->	Optional[Any]:
      """simple docstring"""





      return len(self.encoder   )





   def 						_snake_case			(			self   )					->	List[str]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	Dict:
      """simple docstring"""





      if token in self.cache:
         return self.cache[token]
      a__						:    Optional[Any]   		=	tuple(snake_case   )
      a__						:    Dict   		=	get_pairs(snake_case   )

      if not pairs:
         return token

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

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





   def 						_snake_case			(			self       ,      snake_case   )					->	Tuple:
      """simple docstring"""





      a__						:    List[Any]   		=	[]
      for token in re.findall(self.pat       ,      snake_case   ):
         a__						:    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(snake_case   ).split(" "   )   )
      return bpe_tokens





   def 						_snake_case			(			self       ,      snake_case   )					->	Union[str, Any]:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case   )					->	Optional[Any]:
      """simple docstring"""





      return self.decoder.get(snake_case   )





   def 						_snake_case			(			self       ,      snake_case   )					->	int:
      """simple docstring"""





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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	Tuple[str]:
      """simple docstring"""





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

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

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

      return vocab_file, merge_file





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	List[int]:
      """simple docstring"""





      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__						:    Optional[int]   		=	[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = False   )					->	List[int]:
      """simple docstring"""





      if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=snake_case       ,      token_ids_a=snake_case       ,      already_has_special_tokens=snake_case   )

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





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None   )					->	List[int]:
      """simple docstring"""





      a__						:    Optional[Any]   		=	[self.sep_token_id]
      a__						:    Optional[int]   		=	[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 						_snake_case			(			self       ,      snake_case       ,      snake_case=False       ,      **snake_case   )					->	Tuple:
      """simple docstring"""





      a__						:    Union[str, Any]   		=	kwargs.pop("add_prefix_space"       ,      self.add_prefix_space   )
      if (is_split_into_words or add_prefix_space) and (len(snake_case   ) > 0 and not text[0].isspace()):
         a__						:    str   		=	" " + text
      return (text, kwargs)





   def 						_snake_case			(			self       ,      snake_case       ,      snake_case = None       ,      snake_case = PaddingStrategy.DO_NOT_PAD       ,      snake_case = None       ,      snake_case = None       ,      )					->	dict:
      """simple docstring"""





      a__						:    str   		=	super()._pad(
          encoded_inputs=snake_case       ,      max_length=snake_case       ,      padding_strategy=snake_case       ,      pad_to_multiple_of=snake_case       ,      return_attention_mask=snake_case       ,      )

      # Load from model defaults
      if return_attention_mask is None:
         a__						:    Union[str, Any]   		=	"attention_mask" in self.model_input_names

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

         if needs_to_be_padded:
            a__						:    Tuple   		=	len(snake_case   ) - 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__						:    List[str]   		=	(
                   encoded_inputs["global_attention_mask"] + [-1] * difference
               )
            elif self.padding_side == "left":
               a__						:    List[Any]   		=	[-1] * difference + encoded_inputs[
                   "global_attention_mask"
               ]
            else:
               raise ValueError("Invalid padding strategy:" + str(self.padding_side   )   )

      return encoded_inputs








from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
  if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  from ...utils.dummy_torch_and_transformers_objects import (
      VersatileDiffusionDualGuidedPipeline,
      VersatileDiffusionImageVariationPipeline,
      VersatileDiffusionPipeline,
      VersatileDiffusionTextToImagePipeline,
  )
else:
  from .modeling_text_unet import UNetFlatConditionModel
  from .pipeline_versatile_diffusion import VersatileDiffusionPipeline
  from .pipeline_versatile_diffusion_dual_guided import VersatileDiffusionDualGuidedPipeline
  from .pipeline_versatile_diffusion_image_variation import VersatileDiffusionImageVariationPipeline
  from .pipeline_versatile_diffusion_text_to_image import VersatileDiffusionTextToImagePipeline

