

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


lowercase_									=   logging.get_logger(__name__)

lowercase_									=   """▁"""

lowercase_									=   {"""vocab_file""": """vocab.txt""", """sentencepiece_model_ckpt""": """sentencepiece.bpe.model"""}

lowercase_									=   {
    """sentencepiece_model_file""": """sentencepiece.bpe.model""",
    """vocab_file""": """vocab.txt""",
}

lowercase_									=   {
    """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""",
    },
}

lowercase_									=   {
    """ernie-m-base""": 5_1_4,
    """ernie-m-large""": 5_1_4,
}

lowercase_									=   {
    """ernie-m-base""": {"""do_lower_case""": False},
    """ernie-m-large""": {"""do_lower_case""": False},
}





class  __lowerCAmelCase     (					lowercase_							):
  _a									=  ["input_ids"]

  _a									=  VOCAB_FILES_NAMES
  _a									=  PRETRAINED_INIT_CONFIGURATION
  _a									=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a									=  PRETRAINED_VOCAB_FILES_MAP
  _a									=  RESOURCE_FILES_NAMES




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase=None							,   lowerCAmelCase=False							,   lowerCAmelCase="utf8"							,   lowerCAmelCase="[UNK]"							,   lowerCAmelCase="[SEP]"							,   lowerCAmelCase="[PAD]"							,   lowerCAmelCase="[CLS]"							,   lowerCAmelCase="[MASK]"							,   lowerCAmelCase = None							,   **lowerCAmelCase							,   )  -> int:
        '''simple docstring'''






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

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




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






        if text is None:
              return None

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

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

        _lowercase       ,       _lowercase       ,       _lowercase	       =normalized_text, [], 0

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

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

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




  @property
  def   A__     (				self					)  -> Optional[int]:
        '''simple docstring'''






        return len(self.vocab					)




  def   A__     (				self					)  -> Optional[Any]:
        '''simple docstring'''






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




  def __getstate__(				self					)  -> str:
        '''simple docstring'''






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




  def __setstate__(				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






        _lowercase	       =d

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

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




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=False							,   lowerCAmelCase=64							,   lowerCAmelCase=0.1					)  -> Optional[int]:
        '''simple docstring'''






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

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




  def   A__     (				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=None					)  -> List[Any]:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=None					)  -> List[str]:
        '''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   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=None							,   lowerCAmelCase=False					)  -> Union[str, Any]:
        '''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(lowerCAmelCase					)) + [1, 1] + ([0] * len(lowerCAmelCase					)) + [1]
        return [1] + ([0] * len(lowerCAmelCase					)) + [1]




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> Any:
        '''simple docstring'''






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

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




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> int:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Dict:
        '''simple docstring'''






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




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






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

        return token_to_idx




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None					)  -> Tuple:
        '''simple docstring'''






        _lowercase	       =0
        if os.path.isdir(lowerCAmelCase					):
              _lowercase	       =os.path.join(
                  lowerCAmelCase							,   (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']					)
        else:
              _lowercase	       =(filename_prefix + '-' if filename_prefix else '') + save_directory
        with open(lowerCAmelCase							,   'w'							,   encoding='utf-8'					) as writer:
              for token, token_index in sorted(self.vocab.items()							,   key=lambda lowerCAmelCase					: 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!'					)
                          _lowercase	       =token_index
                    writer.write(token + '\n'					)
                    index += 1

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

        return (vocab_file,)





import gc
import unittest

from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
					import jax
					import jax.numpy as jnp
					from flax.jax_utils import replicate
					from flax.training.common_utils import shard







@slow
@require_flax
class  lowercase				(       unittest.TestCase			):




				def     a							(					self       ):
											# clean up the VRAM after each test
											super().tearDown()
											gc.collect()




				def     a							(					self       ):
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
											    '/sd2-inpaint/init_image.png'       )
											snake_case_							=    load_image(
											    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png'       )

											snake_case_							=    'xvjiarui/stable-diffusion-2-inpainting'
											snake_case_				,    snake_case_							=    FlaxStableDiffusionInpaintPipeline.from_pretrained(snake_case      ,   safety_checker=snake_case       )

											snake_case_							=    'Face of a yellow cat, high resolution, sitting on a park bench'

											snake_case_							=    jax.random.PRNGKey(0       )
											snake_case_							=    50

											snake_case_							=    jax.device_count()
											snake_case_							=    num_samples * [prompt]
											snake_case_							=    num_samples * [init_image]
											snake_case_							=    num_samples * [mask_image]
											snake_case_				,    snake_case_				,    snake_case_							=    pipeline.prepare_inputs(snake_case      ,   snake_case      ,   snake_case       )

											# shard inputs and rng
											snake_case_							=    replicate(snake_case       )
											snake_case_							=    jax.random.split(snake_case      ,   jax.device_count()       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )
											snake_case_							=    shard(snake_case       )

											snake_case_							=    pipeline(
											    snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   snake_case      ,   jit=snake_case       )

											snake_case_							=    output.images.reshape(snake_case      ,   512      ,   512      ,   3       )

											snake_case_							=    images[0, 253:256, 253:256, -1]

											snake_case_							=    jnp.asarray(jax.device_get(image_slice.flatten()       )       )
											snake_case_							=    jnp.array(
											    [0.3_61_13_07, 0.37_64_97_36, 0.3_75_74_08, 0.38_21_39_53, 0.39_29_51_67, 0.3_84_16_31, 0.41_55_49_78, 0.4_13_74_75, 0.4_21_70_84]       )
											print(F'''output_slice: {output_slice}'''       )
											assert jnp.abs(output_slice - expected_slice       ).max() < 1e-2




