


"""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_fnet import FNetTokenizer
else:
		a__    :							Dict            =					None

a__    :							str            =					logging.get_logger(__name__)
a__    :							Tuple            =					{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a__    :							Optional[int]            =					{
    '''vocab_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/spiece.model''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/spiece.model''',
    },
    '''tokenizer_file''': {
        '''google/fnet-base''': '''https://huggingface.co/google/fnet-base/resolve/main/tokenizer.json''',
        '''google/fnet-large''': '''https://huggingface.co/google/fnet-large/resolve/main/tokenizer.json''',
    },
}

a__    :							List[str]            =					{
    '''google/fnet-base''': 5_1_2,
    '''google/fnet-large''': 5_1_2,
}

a__    :							Optional[Any]            =					'''▁'''




class 			UpperCamelCase_     (			UpperCamelCase):

	"""simple docstring"""





	snake_case__    :  Tuple    =      VOCAB_FILES_NAMES
	snake_case__    :  str    =      PRETRAINED_VOCAB_FILES_MAP
	snake_case__    :  Dict    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	snake_case__    :  List[Any]    =      ["input_ids", "token_type_ids"]
	snake_case__    :  Dict    =      FNetTokenizer




	def __init__(			self      :    Tuple      ,      UpperCAmelCase__      :    int=None      ,      UpperCAmelCase__      :    List[Any]=None      ,      UpperCAmelCase__      :    Tuple=False      ,      UpperCAmelCase__      :    Any=True      ,      UpperCAmelCase__      :    Optional[Any]=True      ,      UpperCAmelCase__      :    str="<unk>"      ,      UpperCAmelCase__      :    Optional[int]="[SEP]"      ,      UpperCAmelCase__      :    Optional[Any]="<pad>"      ,      UpperCAmelCase__      :    Union[str, Any]="[CLS]"      ,      UpperCAmelCase__      :    str="[MASK]"      ,      **UpperCAmelCase__      :    List[str]      ,      )	->     Optional[Any]:
						# Mask token behave like a normal word, i.e. include the space before it and
						# is included in the raw text, there should be a match in a non-normalized sentence.
						__SCREAMING_SNAKE_CASE						       =    (
						    AddedToken(UpperCAmelCase__      ,      lstrip=UpperCAmelCase__      ,      rstrip=UpperCAmelCase__      ,      normalized=UpperCAmelCase__       )
						    if isinstance(UpperCAmelCase__      ,      UpperCAmelCase__       )
						    else mask_token
						)

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

						__SCREAMING_SNAKE_CASE						       =    do_lower_case
						__SCREAMING_SNAKE_CASE						       =    remove_space
						__SCREAMING_SNAKE_CASE						       =    keep_accents
						__SCREAMING_SNAKE_CASE						       =    vocab_file
						__SCREAMING_SNAKE_CASE						       =    False if not self.vocab_file else True




	def   UpperCAmelCase_				(			self      :    str      ,      UpperCAmelCase__      :    List[int]      ,      UpperCAmelCase__      :    Optional[List[int]] = None       )	->     List[int]:
						__SCREAMING_SNAKE_CASE						       =    [self.sep_token_id]
						__SCREAMING_SNAKE_CASE						       =    [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   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    List[int]      ,      UpperCAmelCase__      :    Optional[List[int]] = None       )	->     List[int]:
						__SCREAMING_SNAKE_CASE						       =    [self.sep_token_id]
						__SCREAMING_SNAKE_CASE						       =    [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   UpperCAmelCase_				(			self      :    Any      ,      UpperCAmelCase__      :    str      ,      UpperCAmelCase__      :    Optional[str] = None       )	->     Tuple[str]:
						if not os.path.isdir(UpperCAmelCase__       ):
											logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""       )
											return
						__SCREAMING_SNAKE_CASE						       =    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,)




"""simple docstring"""







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


try:
		if not (is_transformers_available() and is_torch_available()):
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
		from .pipeline_kandinsky import KandinskyPipeline
		from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
		from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
		from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
		from .text_encoder import MultilingualCLIP
