





"""simple docstring"""
import io
import os
import unicodedata
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


__UpperCamelCase     :						Tuple						       =  logging.get_logger(__name__)

__UpperCamelCase     :						Optional[int]						       =  '''▁'''

__UpperCamelCase     :						str						       =  {'''vocab_file''': '''vocab.txt''', '''sentencepiece_model_ckpt''': '''sentencepiece.bpe.model'''}

__UpperCamelCase     :						Tuple						       =  {
    '''sentencepiece_model_file''': '''sentencepiece.bpe.model''',
    '''vocab_file''': '''vocab.txt''',
}

__UpperCamelCase     :						Tuple						       =  {
    '''vocab_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/vocab.txt''',
    },
    '''sentencepiece_model_file''': {
        '''ernie-m-base''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
        '''ernie-m-large''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/sentencepiece.bpe.model''',
    },
}

__UpperCamelCase     :						Dict						       =  {
    '''ernie-m-base''': 514,
    '''ernie-m-large''': 514,
}

__UpperCamelCase     :						Dict						       =  {
    '''ernie-m-base''': {'''do_lower_case''': False},
    '''ernie-m-large''': {'''do_lower_case''': False},
}







class       a							(   a__  ):
 snake_case__             =       ["input_ids"]

 snake_case__             =       VOCAB_FILES_NAMES
 snake_case__             =       PRETRAINED_INIT_CONFIGURATION
 snake_case__             =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 snake_case__             =       PRETRAINED_VOCAB_FILES_MAP
 snake_case__             =       RESOURCE_FILES_NAMES



 def __init__(     self				,       _snake_case				,       _snake_case=None				,       _snake_case=False				,       _snake_case="utf8"				,       _snake_case="[UNK]"				,       _snake_case="[SEP]"				,       _snake_case="[PAD]"				,       _snake_case="[CLS]"				,       _snake_case="[MASK]"				,       _snake_case = None				,       **_snake_case				,       ):







  """simple docstring"""






  lowerCAmelCase    				= {} if sp_model_kwargs is None else sp_model_kwargs
  super().__init__(
      do_lower_case=_snake_case				,       unk_token=_snake_case				,       sep_token=_snake_case				,       pad_token=_snake_case				,       cls_token=_snake_case				,       mask_token=_snake_case				,       vocab_file=_snake_case				,       encoding=_snake_case				,       sp_model_kwargs=self.sp_model_kwargs				,       **_snake_case				,       )
  lowerCAmelCase    				= do_lower_case
  lowerCAmelCase    				= sentencepiece_model_ckpt
  lowerCAmelCase    				= spm.SentencePieceProcessor(**self.sp_model_kwargs     )
  self.sp_model.Load(_snake_case     )

  # to mimic paddlenlp.transformers.ernie_m.tokenizer.ErnieMTokenizer functioning
  if vocab_file is not None:
   lowerCAmelCase    				= self.load_vocab(filepath=_snake_case     )
  else:
   lowerCAmelCase    				= {self.sp_model.id_to_piece(_snake_case     ): id for id in range(self.sp_model.get_piece_size()     )}
  lowerCAmelCase    				= {v: k for k, v in self.vocab.items()}



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if text is None:
   return None

  lowerCAmelCase    				= self.tokenize(_snake_case     )
  lowerCAmelCase				,lowerCAmelCase    				= '', []

  for i, ch in enumerate(_snake_case     ):
   if ch in self.SP_CHAR_MAPPING:
    lowerCAmelCase    				= self.SP_CHAR_MAPPING.get(_snake_case     )
   else:
    lowerCAmelCase    				= unicodedata.normalize('NFKC'				,       _snake_case     )
   if self.is_whitespace(_snake_case     ):
    continue
   normalized_text += ch
   char_mapping.extend([i] * len(_snake_case     )     )

  lowerCAmelCase				,lowerCAmelCase				,lowerCAmelCase    				= normalized_text, [], 0

  if self.do_lower_case:
   lowerCAmelCase    				= text.lower()

  for token in split_tokens:
   if token[:1] == "▁":
    lowerCAmelCase    				= token[1:]
   lowerCAmelCase    				= text[offset:].index(_snake_case     ) + offset
   lowerCAmelCase    				= start + len(_snake_case     )

   token_mapping.append((char_mapping[start], char_mapping[end - 1] + 1)     )
   lowerCAmelCase    				= end
  return token_mapping



 @property
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return len(self.vocab     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






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



 def __getstate__(     self     ):







  """simple docstring"""






  lowerCAmelCase    				= self.__dict__.copy()
  lowerCAmelCase    				= None
  return state



 def __setstate__(     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= d

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

  lowerCAmelCase    				= spm.SentencePieceProcessor(**self.sp_model_kwargs     )
  self.sp_model.Load(self.sentencepiece_model_ckpt     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return "".join((self.SP_CHAR_MAPPING.get(_snake_case				,       _snake_case     ) for c in text)     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=False				,       _snake_case=64				,       _snake_case=0.1     ):







  """simple docstring"""






  if self.sp_model_kwargs.get('enable_sampling'     ) is True:
   lowerCAmelCase    				= True
  if self.sp_model_kwargs.get('alpha'     ) is not None:
   lowerCAmelCase    				= self.sp_model_kwargs.get('alpha'     )
  if self.sp_model_kwargs.get('nbest_size'     ) is not None:
   lowerCAmelCase    				= self.sp_model_kwargs.get('nbest_size'     )

  if not enable_sampling:
   lowerCAmelCase    				= self.sp_model.EncodeAsPieces(_snake_case     )
  else:
   lowerCAmelCase    				= self.sp_model.SampleEncodeAsPieces(_snake_case				,       _snake_case				,       _snake_case     )
  lowerCAmelCase    				= []
  for pi, piece in enumerate(_snake_case     ):
   if piece == SPIECE_UNDERLINE:
    if not pieces[pi + 1].startswith(_snake_case     ) and pi != 0:
     new_pieces.append(_snake_case     )
     continue
    else:
     continue
   lowerCAmelCase    				= 0
   for i, chunk in enumerate(_snake_case     ):
    if chunk == SPIECE_UNDERLINE:
     continue
    if self.is_ch_char(_snake_case     ) or self.is_punct(_snake_case     ):
     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
      new_pieces.append(piece[lst_i:i]     )
     new_pieces.append(_snake_case     )
     lowerCAmelCase    				= i + 1
    elif chunk.isdigit() and i > 0 and not piece[i - 1].isdigit():
     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
      new_pieces.append(piece[lst_i:i]     )
     lowerCAmelCase    				= i
    elif not chunk.isdigit() and i > 0 and piece[i - 1].isdigit():
     if i > lst_i and piece[lst_i:i] != SPIECE_UNDERLINE:
      new_pieces.append(piece[lst_i:i]     )
     lowerCAmelCase    				= i
   if len(_snake_case     ) > lst_i:
    new_pieces.append(piece[lst_i:]     )
  return new_pieces



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= ''.join(_snake_case     ).replace(_snake_case				,       ' '     ).strip()
  return out_string



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= self.convert_ids_to_tokens(_snake_case     )
  lowerCAmelCase    				= ''.join(_snake_case     ).replace(_snake_case				,       ' '     ).strip()
  return out_string



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.vocab.get(_snake_case				,       self.vocab.get(self.unk_token     )     )



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  return self.reverse_vocab.get(_snake_case				,       self.unk_token     )



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  if token_ids_a is None:
   return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
  lowerCAmelCase    				= [self.cls_token_id]
  lowerCAmelCase    				= [self.sep_token_id]
  return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None     ):







  """simple docstring"""






  if offset_mapping_a is None:
   return [(0, 0)] + offset_mapping_a + [(0, 0)]

  return [(0, 0)] + offset_mapping_a + [(0, 0), (0, 0)] + offset_mapping_a + [(0, 0)]



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=None				,       _snake_case=False     ):







  """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(_snake_case     )) + [1, 1] + ([0] * len(_snake_case     )) + [1]
  return [1] + ([0] * len(_snake_case     )) + [1]



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  if token_ids_a is None:
   # [CLS] X [SEP]
   return (len(_snake_case     ) + 2) * [0]

  # [CLS] A [SEP] [SEP] B [SEP]
  return [0] * (len(_snake_case     ) + 1) + [1] * (len(_snake_case     ) + 3)



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if "\u4e00" <= char <= "\u9fff":
   return True
  return False



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if ("a" <= char <= "z") or ("A" <= char <= "Z"):
   return True
  return False



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if char in ",;:.?!~，；：。？！《》【】":
   return True
  return False



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  if char == " " or char == "\t" or char == "\n" or char == "\r":
   return True
  if len(_snake_case     ) == 1:
   lowerCAmelCase    				= unicodedata.category(_snake_case     )
   if cat == "Zs":
    return True
  return False



 def    UpperCamelCase__  (     self				,       _snake_case     ):







  """simple docstring"""






  lowerCAmelCase    				= {}
  with io.open(_snake_case				,       'r'				,       encoding='utf-8'     ) as f:
   for index, line in enumerate(_snake_case     ):
    lowerCAmelCase    				= line.rstrip('\n'     )
    lowerCAmelCase    				= int(_snake_case     )

  return token_to_idx



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case = None     ):







  """simple docstring"""






  lowerCAmelCase    				= 0
  if os.path.isdir(_snake_case     ):
   lowerCAmelCase    				= os.path.join(
       _snake_case				,       (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']     )
  else:
   lowerCAmelCase    				= (filename_prefix + '-' if filename_prefix else '') + save_directory
  with open(_snake_case				,       'w'				,       encoding='utf-8'     ) as writer:
   for token, token_index in sorted(self.vocab.items()				,       key=lambda _snake_case     : kv[1]     ):
    if index != token_index:
     logger.warning(
         F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
         ' Please check that the vocabulary is not corrupted!'     )
     lowerCAmelCase    				= token_index
    writer.write(token + '\n'     )
    index += 1

  lowerCAmelCase    				= os.path.join(_snake_case				,       'sentencepiece.bpe.model'     )
  with open(_snake_case				,       'wb'     ) as fi:
   lowerCAmelCase    				= self.sp_model.serialized_model_proto()
   fi.write(_snake_case     )

  return (vocab_file,)












"""simple docstring"""
import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin







@skip_mps
class       a							(   a__   , a__   , unittest.TestCase  ):
 snake_case__             =       IFInpaintingPipeline
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {'''width''', '''height'''}
 snake_case__             =       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
 snake_case__             =       PipelineTesterMixin.required_optional_params - {'''latents'''}



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  return self._get_dummy_components()



 def    UpperCamelCase__  (     self				,       _snake_case				,       _snake_case=0     ):







  """simple docstring"""






  if str(_snake_case     ).startswith('mps'     ):
   lowerCAmelCase    				= torch.manual_seed(_snake_case     )
  else:
   lowerCAmelCase    				= torch.Generator(device=_snake_case     ).manual_seed(_snake_case     )

  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )
  lowerCAmelCase    				= floats_tensor((1, 3, 32, 32)				,       rng=random.Random(_snake_case     )     ).to(_snake_case     )

  lowerCAmelCase    				= {
      'prompt': 'A painting of a squirrel eating a burger',
      'image': image,
      'mask_image': mask_image,
      'generator': generator,
      'num_inference_steps': 2,
      'output_type': 'numpy',
  }

  return inputs



 @unittest.skipIf(
     torch_device != 'cuda' or not is_xformers_available()				,       reason='XFormers attention is only available with CUDA and `xformers` installed'				,       )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_optional_components()



 @unittest.skipIf(torch_device != 'cuda'				,       reason='float16 requires CUDA'     )
 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  super().test_save_load_floataa(expected_max_diff=1E-1     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_save_load_local()



 def    UpperCamelCase__  (     self     ):







  """simple docstring"""






  self._test_inference_batch_single_identical(
      expected_max_diff=1E-2				,       )





