



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


a__      =		logging.get_logger(__name__)
a__      =		{"""vocab_file""": """spiece.model"""}

a__      =		{
    """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""",
    },
}

a__      =		{
    """google/fnet-base""": 5_12,
    """google/fnet-large""": 5_12,
}

a__      =		"""▁"""







class 						snake_case  (		SCREAMING_SNAKE_CASE_					):



   '''simple docstring'''






   snake_case_   :	Tuple       =						VOCAB_FILES_NAMES
   snake_case_   :	Tuple       =						PRETRAINED_VOCAB_FILES_MAP
   snake_case_   :	Union[str, Any]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   snake_case_   :	Any       =						["""input_ids""", """token_type_ids"""]

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

    """simple docstring"""







    _snake_case      :					List[Any]					=   (
        AddedToken(lowerCAmelCase			,						lstrip=lowerCAmelCase			,						rstrip=lowerCAmelCase			,						normalized=lowerCAmelCase)
        if isinstance(lowerCAmelCase			,						lowerCAmelCase)
        else mask_token
    )

    _snake_case      :					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			,						)

    _snake_case      :					Union[str, Any]					=   do_lower_case
    _snake_case      :					int					=   remove_space
    _snake_case      :					List[Any]					=   keep_accents
    _snake_case      :					Optional[Any]					=   vocab_file

    _snake_case      :					Optional[Any]					=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.Load(lowerCAmelCase)

   @property
   def        UpperCamelCase_					(   self							:     List[Any])			->			Any:

    """simple docstring"""







    return len(self.sp_model)

   def        UpperCamelCase_					(   self							:     List[str])			->			Union[str, Any]:

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   {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							:     Union[str, Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[Any]					=   self.__dict__.copy()
    _snake_case      :					Union[str, Any]					=   None
    return state

   def __setstate__(   self							:     Any			,						lowerCAmelCase							:     Optional[int])			->			Dict:

    """simple docstring"""







    _snake_case      :					int					=   d

    # for backward compatibility
    if not hasattr(self			,						"""sp_model_kwargs"""):
     _snake_case      :					str					=   {}

    _snake_case      :					Any					=   spm.SentencePieceProcessor(**self.sp_model_kwargs)
    self.sp_model.Load(self.vocab_file)

   def        UpperCamelCase_					(   self							:     Tuple			,						lowerCAmelCase							:     Dict)			->			Tuple:

    """simple docstring"""







    if self.remove_space:
     _snake_case      :					Optional[Any]					=   """ """.join(inputs.strip().split())
    else:
     _snake_case      :					Dict					=   inputs
    _snake_case      :					str					=   outputs.replace("""``"""			,						"""\"""").replace("""''"""			,						"""\"""")

    if not self.keep_accents:
     _snake_case      :					Any					=   unicodedata.normalize("""NFKD"""			,						lowerCAmelCase)
     _snake_case      :					Optional[Any]					=   """""".join([c for c in outputs if not unicodedata.combining(lowerCAmelCase)])
    if self.do_lower_case:
     _snake_case      :					int					=   outputs.lower()

    return outputs

   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     str)			->			List[str]:

    """simple docstring"""







    _snake_case      :					List[Any]					=   self.preprocess_text(lowerCAmelCase)
    _snake_case      :					Union[str, Any]					=   self.sp_model.encode(lowerCAmelCase			,						out_type=lowerCAmelCase)
    _snake_case      :					str					=   []
    for piece in pieces:
     if len(lowerCAmelCase) > 1 and piece[-1] == str(""",""") and piece[-2].isdigit():
      _snake_case      :					List[str]					=   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:
        _snake_case      :					Dict					=   cur_pieces[1:]
       else:
        _snake_case      :					List[Any]					=   cur_pieces[0][1:]
      cur_pieces.append(piece[-1])
      new_pieces.extend(lowerCAmelCase)
     else:
      new_pieces.append(lowerCAmelCase)

    return new_pieces

   def        UpperCamelCase_					(   self							:     List[Any]			,						lowerCAmelCase							:     List[str])			->			int:

    """simple docstring"""







    return self.sp_model.PieceToId(lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     Optional[int])			->			int:

    """simple docstring"""







    return self.sp_model.IdToPiece(lowerCAmelCase)

   def        UpperCamelCase_					(   self							:     Optional[Any]			,						lowerCAmelCase							:     str)			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   []
    _snake_case      :					Union[str, Any]					=   """"""
    _snake_case      :					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
      _snake_case      :					Union[str, Any]					=   True
      _snake_case      :					Optional[Any]					=   []
     else:
      current_sub_tokens.append(lowerCAmelCase)
      _snake_case      :					Optional[int]					=   False
    out_string += self.sp_model.decode(lowerCAmelCase)
    return out_string.strip()

   def        UpperCamelCase_					(   self							:     Union[str, Any]			,						lowerCAmelCase							:     List[int]			,						lowerCAmelCase							:     bool = False			,						lowerCAmelCase							:     bool = None			,						lowerCAmelCase							:     bool = True			,						**lowerCAmelCase							:     Optional[Any]			,						)			->			str:

    """simple docstring"""







    _snake_case      :					Optional[int]					=   kwargs.pop("""use_source_tokenizer"""			,						lowerCAmelCase)

    _snake_case      :					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
    _snake_case      :					str					=   []
    _snake_case      :					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))
       _snake_case      :					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:
     _snake_case      :					Dict					=   re.sub(r"""(<unk>) """			,						r"""\1"""			,						""" """.join(lowerCAmelCase))
    else:
     _snake_case      :					Union[str, Any]					=   """""".join(lowerCAmelCase)

    _snake_case      :					Any					=   (
        clean_up_tokenization_spaces
        if clean_up_tokenization_spaces is not None
        else self.clean_up_tokenization_spaces
    )
    if clean_up_tokenization_spaces:
     _snake_case      :					Any					=   self.clean_up_tokenization(lowerCAmelCase)
     return clean_text
    else:
     return text

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

    """simple docstring"""







    _snake_case      :					Optional[Any]					=   [self.sep_token_id]
    _snake_case      :					str					=   [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							:     Optional[int]			,						lowerCAmelCase							:     List[int]			,						lowerCAmelCase							:     Optional[List[int]] = None			,						lowerCAmelCase							:     bool = False)			->			List[int]:

    """simple docstring"""







    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        UpperCamelCase_					(   self							:     List[str]			,						lowerCAmelCase							:     List[int]			,						lowerCAmelCase							:     Optional[List[int]] = None)			->			List[int]:

    """simple docstring"""







    _snake_case      :					Union[str, Any]					=   [self.sep_token_id]
    _snake_case      :					List[Any]					=   [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							:     Optional[int]			,						lowerCAmelCase							:     str			,						lowerCAmelCase							:     Optional[str] = None)			->			Tuple[str]:

    """simple docstring"""







    if not os.path.isdir(lowerCAmelCase):
     logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
     return
    _snake_case      :					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:
      _snake_case      :					Optional[int]					=   self.sp_model.serialized_model_proto()
      fi.write(lowerCAmelCase)

    return (out_vocab_file,)





import unittest

import numpy as np
import torch

from diffusers import DDPMPipeline, DDPMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device


enable_full_determinism()







class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   @property
   def        UpperCamelCase_					(   self							:     Union[str, Any])			->			int:

    """simple docstring"""







    torch.manual_seed(0)
    _snake_case      :					Any					=   UNetaDModel(
        block_out_channels=(32, 64)			,						layers_per_block=2			,						sample_size=32			,						in_channels=3			,						out_channels=3			,						down_block_types=("""DownBlock2D""", """AttnDownBlock2D""")			,						up_block_types=("""AttnUpBlock2D""", """UpBlock2D""")			,						)
    return model

   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					List[str]					=   """cpu"""
    _snake_case      :					Union[str, Any]					=   self.dummy_uncond_unet
    _snake_case      :					Union[str, Any]					=   DDPMScheduler()

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Optional[int]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Optional[int]					=   torch.Generator(device=lowerCAmelCase).manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy"""			,						return_dict=lowerCAmelCase)[0]

    _snake_case      :					Any					=   image[0, -3:, -3:, -1]
    _snake_case      :					str					=   image_from_tuple[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Optional[int]					=   np.array(
        [9.956E-01, 5.785E-01, 4.675E-01, 9.930E-01, 0.0, 1.000, 1.199E-03, 2.648E-04, 5.101E-04])

    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
    assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1E-2


   def        UpperCamelCase_					(   self							:     List[Any])			->			Optional[int]:

    """simple docstring"""







    _snake_case      :					int					=   self.dummy_uncond_unet
    _snake_case      :					int					=   DDPMScheduler(prediction_type="""sample""")

    _snake_case      :					Union[str, Any]					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Tuple					=   torch.manual_seed(0)
    _snake_case      :					Union[str, Any]					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""").images

    _snake_case      :					Any					=   torch.manual_seed(0)
    _snake_case      :					Dict					=   ddpm(generator=lowerCAmelCase			,						num_inference_steps=2			,						output_type="""numpy""")[0]

    _snake_case      :					str					=   image[0, -3:, -3:, -1]
    _snake_case      :					List[Any]					=   image_eps[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Any					=   1E-2 if torch_device != """mps""" else 3E-2
    assert np.abs(image_slice.flatten() - image_eps_slice.flatten()).max() < tolerance







@slow
@require_torch_gpu
class 						snake_case  (		unittest.TestCase					):



   '''simple docstring'''






   def        UpperCamelCase_					(   self							:     Dict)			->			Optional[Any]:

    """simple docstring"""







    _snake_case      :					Dict					=   """google/ddpm-cifar10-32"""

    _snake_case      :					List[Any]					=   UNetaDModel.from_pretrained(lowerCAmelCase)
    _snake_case      :					Optional[Any]					=   DDPMScheduler.from_pretrained(lowerCAmelCase)

    _snake_case      :					Dict					=   DDPMPipeline(unet=lowerCAmelCase			,						scheduler=lowerCAmelCase)
    ddpm.to(lowerCAmelCase)
    ddpm.set_progress_bar_config(disable=lowerCAmelCase)

    _snake_case      :					Optional[Any]					=   torch.manual_seed(0)
    _snake_case      :					Any					=   ddpm(generator=lowerCAmelCase			,						output_type="""numpy""").images

    _snake_case      :					Optional[Any]					=   image[0, -3:, -3:, -1]

    assert image.shape == (1, 32, 32, 3)
    _snake_case      :					Union[str, Any]					=   np.array([0.4_200, 0.3_588, 0.1_939, 0.3_847, 0.3_382, 0.2_647, 0.4_155, 0.3_582, 0.3_385])
    assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
