

"""simple docstring"""


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

import numpy as np
import PIL
from PIL import Image

from ...utils import BaseOutput, is_torch_available, is_transformers_available





@dataclass
class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Union[List[PIL.Image.Image], np.ndarray]
				SCREAMING_SNAKE_CASE_   :      Optional[List[bool]]


if is_transformers_available() and is_torch_available():
			from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline





"""simple docstring"""


import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
			from .tokenization_rembert import RemBertTokenizer
else:
			__A						   =			None

__A						   =			logging.get_logger(__name__)
__A						   =			{"vocab_file": "sentencepiece.model", "tokenizer_file": "tokenizer.json"}

__A						   =			{
    "vocab_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/sentencepiece.model",
    },
    "tokenizer_file": {
        "google/rembert": "https://huggingface.co/google/rembert/resolve/main/tokenizer.json",
    },
}

__A						   =			{
    "google/rembert": 256,
}

__A						   =			"▁"





class     snake_case	(				__snake_case  ):
				SCREAMING_SNAKE_CASE_   :      Tuple         =  VOCAB_FILES_NAMES
				SCREAMING_SNAKE_CASE_   :      Any         =  PRETRAINED_VOCAB_FILES_MAP
				SCREAMING_SNAKE_CASE_   :      Tuple         =  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				SCREAMING_SNAKE_CASE_   :      Dict         =  RemBertTokenizer

				def __init__(	self							:				Tuple     ,  UpperCamelCase__							:				Dict=None     ,  UpperCamelCase__							:				List[Any]=None     ,  UpperCamelCase__							:				str=True     ,  UpperCamelCase__							:				Optional[int]=True     ,  UpperCamelCase__							:				Optional[Any]=False     ,  UpperCamelCase__							:				int="[CLS]"     ,  UpperCamelCase__							:				Optional[Any]="[SEP]"     ,  UpperCamelCase__							:				List[str]="<unk>"     ,  UpperCamelCase__							:				Dict="[SEP]"     ,  UpperCamelCase__							:				int="<pad>"     ,  UpperCamelCase__							:				Any="[CLS]"     ,  UpperCamelCase__							:				str="[MASK]"     ,  **UpperCamelCase__							:				Optional[Any]     ,  )->   List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:       int          =			AddedToken(UpperCamelCase__     ,  lstrip=UpperCamelCase__     ,  rstrip=UpperCamelCase__) if isinstance(UpperCamelCase__     ,  UpperCamelCase__) else mask_token

						super().__init__(
						    UpperCamelCase__     ,  tokenizer_file=UpperCamelCase__     ,  do_lower_case=UpperCamelCase__     ,  remove_space=UpperCamelCase__     ,  keep_accents=UpperCamelCase__     ,  bos_token=UpperCamelCase__     ,  eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  sep_token=UpperCamelCase__     ,  pad_token=UpperCamelCase__     ,  cls_token=UpperCamelCase__     ,  mask_token=UpperCamelCase__     ,  **UpperCamelCase__     ,  )

						__lowerCAmelCase:       Optional[int]          =			do_lower_case
						__lowerCAmelCase:       int          =			remove_space
						__lowerCAmelCase:       int          =			keep_accents
						__lowerCAmelCase:       str          =			vocab_file
						__lowerCAmelCase:       Tuple          =			False if not self.vocab_file else True

				def 							lowercase_     (	self							:				Any     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Any          =			[self.cls_token_id]
						if token_ids_a is None:
								return cls + token_ids_a + sep
						return cls + token_ids_a + sep + token_ids_a + sep

				def 							lowercase_     (	self							:				Optional[Any]     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None     ,  UpperCamelCase__							:				bool = False)->   List[int]:



						'''simple docstring'''


						if already_has_special_tokens:
								if token_ids_a is not None:
										raise ValueError(
										    "You should not supply a second sequence if the provided sequence of "
										    "ids is already formatted with special tokens for the model.")
								return [1 if x in [self.sep_token_id, self.cls_token_id] else 0 for x in token_ids_a]

						if token_ids_a is not None:
								return [1] + ([0] * len(UpperCamelCase__)) + [1] + ([0] * len(UpperCamelCase__)) + [1]
						return [1] + ([0] * len(UpperCamelCase__)) + [1]

				def 							lowercase_     (	self							:				Tuple     ,  UpperCamelCase__							:				List[int]     ,  UpperCamelCase__							:				Optional[List[int]] = None)->   List[int]:



						'''simple docstring'''


						__lowerCAmelCase:       Optional[int]          =			[self.sep_token_id]
						__lowerCAmelCase:       Dict          =			[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) * [0] + len(token_ids_a + sep) * [1]




				def 							lowercase_     (	self							:				Union[str, Any]     ,  UpperCamelCase__							:				str     ,  UpperCamelCase__							:				Optional[str] = None)->   Tuple[str]:



						'''simple docstring'''


						if not os.path.isdir(UpperCamelCase__):
								logger.error("Vocabulary path ({}) should be a directory".format(UpperCamelCase__))
								return
						__lowerCAmelCase:       Optional[Any]          =			os.path.join(
						    UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])

						if os.path.abspath(self.vocab_file) != os.path.abspath(UpperCamelCase__):
								copyfile(self.vocab_file     ,  UpperCamelCase__)

						return (out_vocab_file,)


