




import json
import os
from functools import lru_cache
from typing import TYPE_CHECKING, List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
   from transformers.pipelines.conversational import Conversation

lowercase__   :    Any 					=      logging.get_logger(__name__)

lowercase__   :    List[Any] 					=      {
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
}

lowercase__   :    List[Any] 					=      {
    "vocab_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/vocab.json",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/vocab.json",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/vocab.json",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/vocab.json",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/vocab.json",
    },
    "merges_file": {
        "gpt2": "https://huggingface.co/gpt2/resolve/main/merges.txt",
        "gpt2-medium": "https://huggingface.co/gpt2-medium/resolve/main/merges.txt",
        "gpt2-large": "https://huggingface.co/gpt2-large/resolve/main/merges.txt",
        "gpt2-xl": "https://huggingface.co/gpt2-xl/resolve/main/merges.txt",
        "distilgpt2": "https://huggingface.co/distilgpt2/resolve/main/merges.txt",
    },
}

lowercase__   :    Dict 					=      {
    "gpt2": 1_024,
    "gpt2-medium": 1_024,
    "gpt2-large": 1_024,
    "gpt2-xl": 1_024,
    "distilgpt2": 1_024,
}

@lru_cache()
def       SCREAMING_SNAKE_CASE							(     )    ->  Optional[Any]:
     a											=   (
         list(range(ord("!")	,      ord("~") + 1)) + list(range(ord("¡")	,      ord("¬") + 1)) + list(range(ord("®")	,      ord("ÿ") + 1))
     )
     a											=   bs[:]
     a											=   0
     for b in range(2**8):
          if b not in bs:
               bs.append(__UpperCamelCase)
               cs.append(2**8 + n)
               n += 1
     a											=   [chr(__UpperCamelCase) for n in cs]
     return dict(zip(__UpperCamelCase	,      __UpperCamelCase))

def       SCREAMING_SNAKE_CASE							(     __UpperCamelCase)    ->  List[str]:
     a											=   set()
     a											=   word[0]
     for char in word[1:]:
          pairs.add((prev_char, char))
          a											=   char
     return pairs





class     a__ (      UpperCamelCase__   ):
     a	:    Any	      =			VOCAB_FILES_NAMES
     a	:    Dict	      =			PRETRAINED_VOCAB_FILES_MAP
     a	:    List[str]	      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     a	:    Optional[int]	      =			["""input_ids""", """attention_mask"""]







     def __init__(			self						,	A						,	A						,	A="replace"						,	A="<|endoftext|>"						,	A="<|endoftext|>"						,	A="<|endoftext|>"						,	A=None						,	A=False						,	A=False						,	**A						,	)						->					int:



          '''simple docstring'''



          a											=   AddedToken(A						,	lstrip=A						,	rstrip=A				) if isinstance(A						,	A				) else bos_token
          a											=   AddedToken(A						,	lstrip=A						,	rstrip=A				) if isinstance(A						,	A				) else eos_token
          a											=   AddedToken(A						,	lstrip=A						,	rstrip=A				) if isinstance(A						,	A				) else unk_token
          a											=   AddedToken(A						,	lstrip=A						,	rstrip=A				) if isinstance(A						,	A				) else pad_token
          super().__init__(
              errors=A						,	unk_token=A						,	bos_token=A						,	eos_token=A						,	pad_token=A						,	add_prefix_space=A						,	add_bos_token=A						,	**A						,	)
          a											=   add_bos_token

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

          # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
          a											=   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				)						->					int:



          '''simple docstring'''



          return len(self.encoder				)







     def     lowerCAmelCase_       (			self				)						->					Optional[int]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[Any]:



          '''simple docstring'''



          if token in self.cache:
               return self.cache[token]
          a											=   tuple(A				)
          a											=   get_pairs(A				)

          if not pairs:
               return token

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







     def     lowerCAmelCase_       (			self						,	A						,	A=None				)						->					int:



          '''simple docstring'''



          if self.add_bos_token:
               a											=   [self.bos_token_id]
          else:
               a											=   []

          a											=   bos_token_ids + token_ids_a

          if token_ids_a is None:
               return output

          return output + bos_token_ids + token_ids_a







     def     lowerCAmelCase_       (			self						,	A						,	A = None						,	A = False				)						->					List[int]:



          '''simple docstring'''



          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 not self.add_bos_token:
               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				))
          return [1] + ([0] * len(A				)) + [1] + ([0] * len(A				))







     def     lowerCAmelCase_       (			self						,	A				)						->					int:



          '''simple docstring'''



          a											=   []
          for token in re.findall(self.pat						,	A				):
               a											=   "".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     lowerCAmelCase_       (			self						,	A				)						->					List[str]:



          '''simple docstring'''



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







     def     lowerCAmelCase_       (			self						,	A				)						->					Optional[Any]:



          '''simple docstring'''



          return self.decoder.get(A				)







     def     lowerCAmelCase_       (			self						,	A				)						->					List[Any]:



          '''simple docstring'''



          a											=   "".join(A				)
          a											=   bytearray([self.byte_decoder[c] for c in text]				).decode("utf-8"						,	errors=self.errors				)
          return text







     def     lowerCAmelCase_       (			self						,	A						,	A = None				)						->					Tuple[str]:



          '''simple docstring'''



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

          a											=   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!"				)
                         a											=   token_index
                    writer.write(" ".join(A				) + "\n"				)
                    index += 1

          return vocab_file, merge_file







     def     lowerCAmelCase_       (			self						,	A						,	A=False						,	**A				)						->					Any:



          '''simple docstring'''



          a											=   kwargs.pop("add_prefix_space"						,	self.add_prefix_space				)
          if is_split_into_words or add_prefix_space:
               a											=   " " + text
          return (text, kwargs)







     def     lowerCAmelCase_       (			self						,	A				)						->					List[int]:



          '''simple docstring'''



          a											=   []
          for is_user, text in conversation.iter_texts():
               input_ids.extend(self.encode(A						,	add_special_tokens=A				) + [self.eos_token_id]				)
          if len(A				) > self.model_max_length:
               a											=   input_ids[-self.model_max_length :]
          return input_ids







from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import PIL

from ...utils import BaseOutput, is_invisible_watermark_available, is_torch_available, is_transformers_available




@dataclass
class     a__ (      UpperCamelCase__   ):
     a	:    Union[List[PIL.Image.Image], np.ndarray]


if is_transformers_available() and is_torch_available() and is_invisible_watermark_available():
   from .pipeline_stable_diffusion_xl import StableDiffusionXLPipeline
   from .pipeline_stable_diffusion_xl_imgaimg import StableDiffusionXLImgaImgPipeline

