import os
import re
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase							= logging.get_logger(__name__)
UpperCAmelCase							= {'''vocab_file''': '''spiece.model'''}

UpperCAmelCase							= {
    '''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''',
    },
}

UpperCAmelCase							= {
    '''google/fnet-base''': 5_12,
    '''google/fnet-large''': 5_12,
}

UpperCAmelCase							= '''▁'''




class       __lowerCAmelCase     (				a_  ):



 '''simple docstring'''
 _lowerCamelCase  :		Tuple 			=       VOCAB_FILES_NAMES
 _lowerCamelCase  :		int 			=       PRETRAINED_VOCAB_FILES_MAP
 _lowerCamelCase  :		Optional[Any] 			=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _lowerCamelCase  :		Optional[int] 			=       ['input_ids', 'token_type_ids']

 def __init__(	self				:						str				,	lowerCamelCase				:						Optional[int]				,	lowerCamelCase				:						Dict=False				,	lowerCamelCase				:						int=True				,	lowerCamelCase				:						int=True				,	lowerCamelCase				:						Tuple="<unk>"				,	lowerCamelCase				:						List[Any]="[SEP]"				,	lowerCamelCase				:						Tuple="<pad>"				,	lowerCamelCase				:						Optional[Any]="[CLS]"				,	lowerCamelCase				:						Tuple="[MASK]"				,	lowerCamelCase				:						Optional[Dict[str, Any]] = None				,	**lowerCamelCase				:						Optional[Any]				,	)       ->							None:
        # 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.
        _UpperCamelCase   :				Dict       =     (
            AddedToken(lowerCamelCase				,	lstrip=lowerCamelCase				,	rstrip=lowerCamelCase				,	normalized=lowerCamelCase     )
            if isinstance(lowerCamelCase				,	lowerCamelCase     )
            else mask_token
        )

        _UpperCamelCase   :				int       =     {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            do_lower_case=lowerCamelCase				,	remove_space=lowerCamelCase				,	keep_accents=lowerCamelCase				,	unk_token=lowerCamelCase				,	sep_token=lowerCamelCase				,	pad_token=lowerCamelCase				,	cls_token=lowerCamelCase				,	mask_token=lowerCamelCase				,	sp_model_kwargs=self.sp_model_kwargs				,	**lowerCamelCase				,	)

        _UpperCamelCase   :				Any       =     do_lower_case
        _UpperCamelCase   :				Optional[int]       =     remove_space
        _UpperCamelCase   :				Optional[int]       =     keep_accents
        _UpperCamelCase   :				Tuple       =     vocab_file

        _UpperCamelCase   :				Any       =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(lowerCamelCase     )

 @property
 def     __snake_case    (	self				:						List[str]     )       ->							str:
        return len(self.sp_model     )

 def     __snake_case    (	self				:						List[str]     )       ->							Dict:
        _UpperCamelCase   :				List[str]       =     {self.convert_ids_to_tokens(lowerCamelCase     ): i for i in range(self.vocab_size     )}
        vocab.update(self.added_tokens_encoder     )
        return vocab

 def __getstate__(	self				:						Any     )       ->							str:
        _UpperCamelCase   :				Tuple       =     self.__dict__.copy()
        _UpperCamelCase   :				List[Any]       =     None
        return state

 def __setstate__(	self				:						str				,	lowerCamelCase				:						str     )       ->							Union[str, Any]:
        _UpperCamelCase   :				int       =     d

        # for backward compatibility
        if not hasattr(self				,	'sp_model_kwargs'     ):
               _UpperCamelCase   :				Any       =     {}

        _UpperCamelCase   :				Optional[int]       =     spm.SentencePieceProcessor(**self.sp_model_kwargs     )
        self.sp_model.Load(self.vocab_file     )

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						Dict     )       ->							Dict:
        if self.remove_space:
               _UpperCamelCase   :				int       =     ' '.join(inputs.strip().split()     )
        else:
               _UpperCamelCase   :				Dict       =     inputs
        _UpperCamelCase   :				Union[str, Any]       =     outputs.replace('``'				,	'"'     ).replace('\'\''				,	'"'     )

        if not self.keep_accents:
               _UpperCamelCase   :				str       =     unicodedata.normalize('NFKD'				,	lowerCamelCase     )
               _UpperCamelCase   :				List[Any]       =     ''.join([c for c in outputs if not unicodedata.combining(lowerCamelCase     )]     )
        if self.do_lower_case:
               _UpperCamelCase   :				Union[str, Any]       =     outputs.lower()

        return outputs

 def     __snake_case    (	self				:						Union[str, Any]				,	lowerCamelCase				:						str     )       ->							List[str]:
        _UpperCamelCase   :				str       =     self.preprocess_text(lowerCamelCase     )
        _UpperCamelCase   :				Tuple       =     self.sp_model.encode(lowerCamelCase				,	out_type=lowerCamelCase     )
        _UpperCamelCase   :				int       =     []
        for piece in pieces:
               if len(lowerCamelCase     ) > 1 and piece[-1] == str(','     ) and piece[-2].isdigit():
                      _UpperCamelCase   :				Any       =     self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase				,	''     )     )
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                             if len(cur_pieces[0]     ) == 1:
                                    _UpperCamelCase   :				Dict       =     cur_pieces[1:]
                             else:
                                    _UpperCamelCase   :				int       =     cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]     )
                      new_pieces.extend(lowerCamelCase     )
               else:
                      new_pieces.append(lowerCamelCase     )

        return new_pieces

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						List[Any]     )       ->							Union[str, Any]:
        return self.sp_model.PieceToId(lowerCamelCase     )

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						List[str]     )       ->							Union[str, Any]:
        return self.sp_model.IdToPiece(lowerCamelCase     )

 def     __snake_case    (	self				:						Optional[Any]				,	lowerCamelCase				:						Union[str, Any]     )       ->							Optional[int]:
        _UpperCamelCase   :				Union[str, Any]       =     []
        _UpperCamelCase   :				Any       =     ''
        _UpperCamelCase   :				Optional[Any]       =     False
        for token in tokens:
               # make sure that special tokens are not decoded using sentencepiece model
               if token in self.all_special_tokens:
                      if not prev_is_special:
                             out_string += " "
                      out_string += self.sp_model.decode(lowerCamelCase     ) + token
                      _UpperCamelCase   :				List[Any]       =     True
                      _UpperCamelCase   :				Any       =     []
               else:
                      current_sub_tokens.append(lowerCamelCase     )
                      _UpperCamelCase   :				int       =     False
        out_string += self.sp_model.decode(lowerCamelCase     )
        return out_string.strip()

 def     __snake_case    (	self				:						str				,	lowerCamelCase				:						List[int]				,	lowerCamelCase				:						bool = False				,	lowerCamelCase				:						bool = None				,	lowerCamelCase				:						bool = True				,	**lowerCamelCase				:						str				,	)       ->							str:
        _UpperCamelCase   :				Any       =     kwargs.pop('use_source_tokenizer'				,	lowerCamelCase     )

        _UpperCamelCase   :				List[str]       =     self.convert_ids_to_tokens(lowerCamelCase				,	skip_special_tokens=lowerCamelCase     )

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separately for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        _UpperCamelCase   :				Optional[int]       =     []
        _UpperCamelCase   :				Dict       =     []
        for token in filtered_tokens:
               if skip_special_tokens and token in self.all_special_ids:
                      continue
               if token in self.added_tokens_encoder:
                      if current_sub_text:
                             sub_texts.append(self.convert_tokens_to_string(lowerCamelCase     )     )
                             _UpperCamelCase   :				Optional[int]       =     []
                      sub_texts.append(lowerCamelCase     )
               else:
                      current_sub_text.append(lowerCamelCase     )
        if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(lowerCamelCase     )     )

        # Mimic the behavior of the Rust tokenizer:
        # No space after <unk>
        if spaces_between_special_tokens:
               _UpperCamelCase   :				Dict       =     re.sub(r'(<unk>) '				,	r'\1'				,	' '.join(lowerCamelCase     )     )
        else:
               _UpperCamelCase   :				Any       =     ''.join(lowerCamelCase     )

        _UpperCamelCase   :				Optional[Any]       =     (
            clean_up_tokenization_spaces
            if clean_up_tokenization_spaces is not None
            else self.clean_up_tokenization_spaces
        )
        if clean_up_tokenization_spaces:
               _UpperCamelCase   :				str       =     self.clean_up_tokenization(lowerCamelCase     )
               return clean_text
        else:
               return text

 def     __snake_case    (	self				:						List[Any]				,	lowerCamelCase				:						List[int]				,	lowerCamelCase				:						Optional[List[int]] = None     )       ->							List[int]:
        _UpperCamelCase   :				Optional[int]       =     [self.sep_token_id]
        _UpperCamelCase   :				Dict       =     [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     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						List[int]				,	lowerCamelCase				:						Optional[List[int]] = None				,	lowerCamelCase				:						bool = False     )       ->							List[int]:

        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 not None:
               return [1] + ([0] * len(lowerCamelCase     )) + [1] + ([0] * len(lowerCamelCase     )) + [1]
        return [1] + ([0] * len(lowerCamelCase     )) + [1]

 def     __snake_case    (	self				:						Dict				,	lowerCamelCase				:						List[int]				,	lowerCamelCase				:						Optional[List[int]] = None     )       ->							List[int]:
        _UpperCamelCase   :				Optional[Any]       =     [self.sep_token_id]
        _UpperCamelCase   :				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     ) * [0] + len(token_ids_a + sep     ) * [1]

 def     __snake_case    (	self				:						Tuple				,	lowerCamelCase				:						str				,	lowerCamelCase				:						Optional[str] = None     )       ->							Tuple[str]:
        if not os.path.isdir(lowerCamelCase     ):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"     )
               return
        _UpperCamelCase   :				Tuple       =     os.path.join(
            lowerCamelCase				,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCamelCase     ) and os.path.isfile(self.vocab_file     ):
               copyfile(self.vocab_file				,	lowerCamelCase     )
        elif not os.path.isfile(self.vocab_file     ):
               with open(lowerCamelCase				,	'wb'     ) as fi:
                      _UpperCamelCase   :				int       =     self.sp_model.serialized_model_proto()
                      fi.write(lowerCamelCase     )

        return (out_vocab_file,)




import os

from packaging import version

from .. import __version__
from .accelerate_utils import apply_forward_hook
from .constants import (
    CONFIG_NAME,
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    DIFFUSERS_DYNAMIC_MODULE_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    ONNX_EXTERNAL_WEIGHTS_NAME,
    ONNX_WEIGHTS_NAME,
    SAFETENSORS_WEIGHTS_NAME,
    TEXT_ENCODER_ATTN_MODULE,
    WEIGHTS_NAME,
)
from .deprecation_utils import deprecate
from .doc_utils import replace_example_docstring
from .dynamic_modules_utils import get_class_from_dynamic_module
from .hub_utils import (
    HF_HUB_OFFLINE,
    _add_variant,
    _get_model_file,
    extract_commit_hash,
    http_user_agent,
)
from .import_utils import (
    BACKENDS_MAPPING,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    DummyObject,
    OptionalDependencyNotAvailable,
    is_accelerate_available,
    is_accelerate_version,
    is_bsa_available,
    is_flax_available,
    is_ftfy_available,
    is_inflect_available,
    is_invisible_watermark_available,
    is_k_diffusion_available,
    is_k_diffusion_version,
    is_librosa_available,
    is_note_seq_available,
    is_omegaconf_available,
    is_onnx_available,
    is_safetensors_available,
    is_scipy_available,
    is_tensorboard_available,
    is_tf_available,
    is_torch_available,
    is_torch_version,
    is_torchsde_available,
    is_transformers_available,
    is_transformers_version,
    is_unidecode_available,
    is_wandb_available,
    is_xformers_available,
    requires_backends,
)
from .logging import get_logger
from .outputs import BaseOutput
from .pil_utils import PIL_INTERPOLATION, numpy_to_pil, pt_to_pil
from .torch_utils import is_compiled_module, randn_tensor


if is_torch_available():
 from .testing_utils import (
     floats_tensor,
     load_hf_numpy,
     load_image,
     load_numpy,
     load_pt,
     nightly,
     parse_flag_from_env,
     print_tensor_test,
     require_torch_a,
     require_torch_gpu,
     skip_mps,
     slow,
     torch_all_close,
     torch_device,
 )
 from .torch_utils import maybe_allow_in_graph

from .testing_utils import export_to_gif, export_to_video


UpperCAmelCase							= get_logger(__name__)


def SCREAMING_SNAKE_CASE (   lowercase_:		Any ):
       if version.parse(lowercase_ ) < version.parse(lowercase_ ):
              if "dev" in min_version:
                     _UpperCamelCase   :				Tuple       =     (
                         'This example requires a source install from HuggingFace diffusers (see '
                         '`https://huggingface.co/docs/diffusers/installation#install-from-source`),'
                     )
              else:
                     _UpperCamelCase   :				str       =     F"This example requires a minimum version of {min_version},"
              error_message += F" but the version found is {__version__}.\n"
              raise ImportError(lowercase_ )



