

'''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


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": 514,
    "ernie-m-large": 514,
}

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




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[str]  		=  ["input_ids"]

     UpperCAmelCase_						:     List[str]  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:     Any  		=  PRETRAINED_INIT_CONFIGURATION
     UpperCAmelCase_						:     int  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:     List[str]  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:     Union[str, Any]  		=  RESOURCE_FILES_NAMES


     def __init__(		self			:					Union[str, Any]					,      lowercase_			:					Any					,      lowercase_			:					Dict=None					,      lowercase_			:					Optional[Any]=False					,      lowercase_			:					List[str]="utf8"					,      lowercase_			:					List[Any]="[UNK]"					,      lowercase_			:					List[str]="[SEP]"					,      lowercase_			:					Any="[PAD]"					,      lowercase_			:					List[Any]="[CLS]"					,      lowercase_			:					Optional[int]="[MASK]"					,      lowercase_			:					Optional[Dict[str, Any]] = None					,      **lowercase_			:					Tuple					,      )						->     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	:     List[Any]						 =			{} if sp_model_kwargs is None else sp_model_kwargs
      super().__init__(
          do_lower_case=lowercase_					,      unk_token=lowercase_					,      sep_token=lowercase_					,      pad_token=lowercase_					,      cls_token=lowercase_					,      mask_token=lowercase_					,      vocab_file=lowercase_					,      encoding=lowercase_					,      sp_model_kwargs=self.sp_model_kwargs					,      **lowercase_					,      )
      UpperCAmelCase	:     Any						 =			do_lower_case
      UpperCAmelCase	:     int						 =			sentencepiece_model_ckpt
      UpperCAmelCase	:     Dict						 =			spm.SentencePieceProcessor(**self.sp_model_kwargs	)
      self.sp_model.Load(lowercase_	)

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


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					str	)						->     int:
      if text is None:
       return None

      UpperCAmelCase	:     Dict						 =			self.tokenize(lowercase_	)
      UpperCAmelCase ,			UpperCAmelCase	:     List[str]						 =			'', []

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

      UpperCAmelCase ,			UpperCAmelCase ,			UpperCAmelCase	:     Union[str, Any]						 =			normalized_text, [], 0

      if self.do_lower_case:
       UpperCAmelCase	:     Union[str, Any]						 =			text.lower()

      for token in split_tokens:
       if token[:1] == "▁":
        UpperCAmelCase	:     Optional[int]						 =			token[1:]
       UpperCAmelCase	:     Optional[Any]						 =			text[offset:].index(lowercase_	) + offset
       UpperCAmelCase	:     List[Any]						 =			start + len(lowercase_	)

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


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Optional[int]:
      return len(self.vocab	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Any:
      return dict(self.vocab					,      **self.added_tokens_encoder	)


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


     def __setstate__(		self			:					Optional[int]					,      lowercase_			:					Optional[Any]	)						->     Any:
      UpperCAmelCase	:     Optional[Any]						 =			d

      # for backward compatibility
      if not hasattr(self					,      'sp_model_kwargs'	):
       UpperCAmelCase	:     Union[str, Any]						 =			{}

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


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[Any]	)						->     str:
      return "".join((self.SP_CHAR_MAPPING.get(lowercase_					,      lowercase_	) for c in text)	)


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					List[Any]					,      lowercase_			:					int=False					,      lowercase_			:					List[str]=64					,      lowercase_			:					Optional[int]=0.1	)						->     Optional[int]:

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

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


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Union[str, Any]	)						->     Tuple:
      UpperCAmelCase	:     int						 =			''.join(lowercase_	).replace(lowercase_					,      ' '	).strip()
      return out_string


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Optional[int]	)						->     Optional[Any]:
      UpperCAmelCase	:     Any						 =			self.convert_ids_to_tokens(lowercase_	)
      UpperCAmelCase	:     List[str]						 =			''.join(lowercase_	).replace(lowercase_					,      ' '	).strip()
      return out_string


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					List[str]	)						->     int:
      return self.vocab.get(lowercase_					,      self.vocab.get(self.unk_token	)	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					Union[str, Any]	)						->     List[str]:
      return self.reverse_vocab.get(lowercase_					,      self.unk_token	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					List[Any]					,      lowercase_			:					str=None	)						->     Union[str, Any]:
      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      UpperCAmelCase	:     Any						 =			[self.cls_token_id]
      UpperCAmelCase	:     Optional[int]						 =			[self.sep_token_id]
      return _cls + token_ids_a + _sep + _sep + token_ids_a + _sep


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					str					,      lowercase_			:					Optional[int]=None	)						->     Any:
      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			:					Tuple					,      lowercase_			:					Union[str, Any]					,      lowercase_			:					Optional[Any]=None					,      lowercase_			:					List[str]=False	)						->     str:

      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(lowercase_	)) + [1, 1] + ([0] * len(lowercase_	)) + [1]
      return [1] + ([0] * len(lowercase_	)) + [1]


     def        UpperCAmelCase_	(		self			:					Dict					,      lowercase_			:					List[int]					,      lowercase_			:					Optional[List[int]] = None	)						->     List[int]:
      # called when `add_special_tokens` is True, so align with `build_inputs_with_special_tokens` method
      if token_ids_a is None:
       # [CLS] X [SEP]
       return (len(lowercase_	) + 2) * [0]

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


     def        UpperCAmelCase_	(		self			:					List[str]					,      lowercase_			:					List[str]	)						->     Optional[Any]:
      if "\u4e00" <= char <= "\u9fff":
       return True
      return False


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					Any	)						->     Any:
      if ("a" <= char <= "z") or ("A" <= char <= "Z"):
       return True
      return False


     def        UpperCAmelCase_	(		self			:					Union[str, Any]					,      lowercase_			:					Union[str, Any]	)						->     Optional[Any]:
      if char in ",;:.?!~，；：。？！《》【】":
       return True
      return False


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					int	)						->     Dict:
      if char == " " or char == "\t" or char == "\n" or char == "\r":
       return True
      if len(lowercase_	) == 1:
       UpperCAmelCase	:     str						 =			unicodedata.category(lowercase_	)
       if cat == "Zs":
        return True
      return False


     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Any	)						->     Tuple:
      UpperCAmelCase	:     List[Any]						 =			{}
      with io.open(lowercase_					,      'r'					,      encoding='utf-8'	) as f:
       for index, line in enumerate(lowercase_	):
        UpperCAmelCase	:     Any						 =			line.rstrip('\n'	)
        UpperCAmelCase	:     int						 =			int(lowercase_	)

      return token_to_idx




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					str					,      lowercase_			:					Optional[str] = None	)						->     Tuple[str]:
      UpperCAmelCase	:     Union[str, Any]						 =			0
      if os.path.isdir(lowercase_	):
       UpperCAmelCase	:     List[Any]						 =			os.path.join(
           lowercase_					,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)
      else:
       UpperCAmelCase	:     int						 =			(filename_prefix + '-' if filename_prefix else '') + save_directory
      with open(lowercase_					,      'w'					,      encoding='utf-8'	) as writer:
       for token, token_index in sorted(self.vocab.items()					,      key=lambda lowercase_	: 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!'	)
         UpperCAmelCase	:     str						 =			token_index
        writer.write(token + '\n'	)
        index += 1

      UpperCAmelCase	:     Any						 =			os.path.join(lowercase_					,      'sentencepiece.bpe.model'	)
      with open(lowercase_					,      'wb'	) as fi:
       UpperCAmelCase	:     Union[str, Any]						 =			self.sp_model.serialized_model_proto()
       fi.write(lowercase_	)

      return (vocab_file,)







'''simple docstring'''

from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowercase__         =      logging.get_logger(__name__)

lowercase__         =      {
    "deepmind/language-perceiver": "https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json",
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     Optional[int]  		=  """perceiver"""


     def __init__(		self			:					str					,      lowercase_			:					List[str]=256					,      lowercase_			:					List[str]=1_280					,      lowercase_			:					str=768					,      lowercase_			:					Tuple=1					,      lowercase_			:					str=26					,      lowercase_			:					List[Any]=8					,      lowercase_			:					int=8					,      lowercase_			:					List[str]=None					,      lowercase_			:					Dict=None					,      lowercase_			:					int="kv"					,      lowercase_			:					Union[str, Any]=1					,      lowercase_			:					List[str]=1					,      lowercase_			:					Any="gelu"					,      lowercase_			:					Optional[Any]=0.1					,      lowercase_			:					str=0.02					,      lowercase_			:					Optional[Any]=1E-12					,      lowercase_			:					Any=True					,      lowercase_			:					Union[str, Any]=262					,      lowercase_			:					Union[str, Any]=2_048					,      lowercase_			:					Optional[int]=56					,      lowercase_			:					int=[368, 496]					,      lowercase_			:					str=16					,      lowercase_			:					Optional[int]=1_920					,      lowercase_			:					Tuple=16					,      lowercase_			:					int=[1, 16, 224, 224]					,      **lowercase_			:					Union[str, Any]					,      )						->     List[str]:
      super().__init__(**lowercase_	)

      UpperCAmelCase	:     Union[str, Any]						 =			num_latents
      UpperCAmelCase	:     List[Any]						 =			d_latents
      UpperCAmelCase	:     Dict						 =			d_model
      UpperCAmelCase	:     Dict						 =			num_blocks
      UpperCAmelCase	:     Optional[int]						 =			num_self_attends_per_block
      UpperCAmelCase	:     Optional[Any]						 =			num_self_attention_heads
      UpperCAmelCase	:     Optional[Any]						 =			num_cross_attention_heads
      UpperCAmelCase	:     Tuple						 =			qk_channels
      UpperCAmelCase	:     Optional[int]						 =			v_channels
      UpperCAmelCase	:     str						 =			cross_attention_shape_for_attention
      UpperCAmelCase	:     Union[str, Any]						 =			self_attention_widening_factor
      UpperCAmelCase	:     List[Any]						 =			cross_attention_widening_factor
      UpperCAmelCase	:     Optional[Any]						 =			hidden_act
      UpperCAmelCase	:     int						 =			attention_probs_dropout_prob
      UpperCAmelCase	:     Optional[int]						 =			initializer_range
      UpperCAmelCase	:     Dict						 =			layer_norm_eps
      UpperCAmelCase	:     Any						 =			use_query_residual
      # masked language modeling attributes
      UpperCAmelCase	:     Any						 =			vocab_size
      UpperCAmelCase	:     List[Any]						 =			max_position_embeddings
      # image classification attributes
      UpperCAmelCase	:     str						 =			image_size
      # flow attributes
      UpperCAmelCase	:     Any						 =			train_size
      # multimodal autoencoding attributes
      UpperCAmelCase	:     Any						 =			num_frames
      UpperCAmelCase	:     List[Any]						 =			audio_samples_per_frame
      UpperCAmelCase	:     Tuple						 =			samples_per_patch
      UpperCAmelCase	:     Union[str, Any]						 =			output_shape




class 	A_			(    _snake_case							):






     '''simple docstring'''



     @property
     def        UpperCAmelCase_	(		self			:					Union[str, Any]	)						->     Mapping[str, Mapping[int, str]]:
      if self.task == "multiple-choice":
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'choice', 2: 'sequence'}
      else:
       UpperCAmelCase	:     Tuple						 =			{0: 'batch', 1: 'sequence'}
      return OrderedDict(
          [
              ('inputs', dynamic_axis),
              ('attention_mask', dynamic_axis),
          ]	)


     @property
     def        UpperCAmelCase_	(		self			:					str	)						->     float:
      return 1E-4




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					int = -1					,      lowercase_			:					bool = False					,      lowercase_			:					Optional[TensorType] = None					,      lowercase_			:					int = 3					,      lowercase_			:					int = 40					,      lowercase_			:					int = 40					,      )						->     Mapping[str, Any]:
      # copied from `transformers.onnx.config.OnnxConfig` and slightly altered/simplified

      if isinstance(lowercase_					,      lowercase_	):
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch					,      num_token_to_add=0	)
       # If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
       UpperCAmelCase	:     int						 =			preprocessor.num_special_tokens_to_add(lowercase_	)
       UpperCAmelCase	:     Union[str, Any]						 =			compute_effective_axis_dimension(
           lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_sequence					,      num_token_to_add=lowercase_	)
       # Generate dummy inputs according to compute batch and sequence
       UpperCAmelCase	:     List[Any]						 =			[' '.join(['a']	) * seq_length] * batch_size
       UpperCAmelCase	:     Union[str, Any]						 =			dict(preprocessor(lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Union[str, Any]						 =			inputs.pop('input_ids'	)
       return inputs
      elif isinstance(lowercase_					,      lowercase_	) and preprocessor.model_input_names[0] == "pixel_values":
       # If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
       UpperCAmelCase	:     Tuple						 =			compute_effective_axis_dimension(lowercase_					,      fixed_dimension=OnnxConfig.default_fixed_batch	)
       UpperCAmelCase	:     Any						 =			self._generate_dummy_images(lowercase_					,      lowercase_					,      lowercase_					,      lowercase_	)
       UpperCAmelCase	:     Tuple						 =			dict(preprocessor(images=lowercase_					,      return_tensors=lowercase_	)	)
       UpperCAmelCase	:     Dict						 =			inputs.pop('pixel_values'	)
       return inputs
      else:
       raise ValueError(
           'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'	)




