



'''simple docstring'''





import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
   import torch






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   ="""char"""
  a_				   ="""bpe"""
  a_				   ="""wp"""




_UpperCamelCase											=		(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =["""image_processor""", """char_tokenizer"""]
  a_				   ="""ViTImageProcessor"""
  a_				   ="""MgpstrTokenizer"""




  def __init__(					self :      List[str]     ,      _a :      List[str]=None     ,      _a :      str=None     ,      **_a :      str  )   ->   Optional[Any]:
    __lowerCamelCase			:			int       			=					None
    if "feature_extractor" in kwargs:
      warnings.warn(
          'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
          ' instead.'     ,      _a     ,      )
      __lowerCamelCase			:			List[str]       			=					kwargs.pop('feature_extractor'  )

    __lowerCamelCase			:			Union[str, Any]       			=					image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('You need to specify an `image_processor`.'  )
    if tokenizer is None:
      raise ValueError('You need to specify a `tokenizer`.'  )

    __lowerCamelCase			:			Optional[int]       			=					tokenizer
    __lowerCamelCase			:			List[str]       			=					AutoTokenizer.from_pretrained('gpt2'  )
    __lowerCamelCase			:			Tuple       			=					AutoTokenizer.from_pretrained('bert-base-uncased'  )

    super().__init__(_a     ,      _a  )




  def __call__(					self :      Dict     ,      _a :      str=None     ,      _a :      Optional[Any]=None     ,      _a :      Any=None     ,      **_a :      List[str]  )   ->   Optional[int]:
    if images is None and text is None:
      raise ValueError('You need to specify either an `images` or `text` input to process.'  )

    if images is not None:
      __lowerCamelCase			:			int       			=					self.image_processor(_a     ,      return_tensors=_a     ,      **_a  )
    if text is not None:
      __lowerCamelCase			:			Union[str, Any]       			=					self.char_tokenizer(_a     ,      return_tensors=_a     ,      **_a  )

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      __lowerCamelCase			:			List[str]       			=					encodings['input_ids']
      return inputs




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      List[str]  )   ->   Dict:
    __lowerCamelCase			,__lowerCamelCase			,__lowerCamelCase			:			Union[str, Any]       			=					sequences
    __lowerCamelCase			:			List[Any]       			=					char_preds.size(0  )

    __lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					self._decode_helper(_a     ,      'char'  )
    __lowerCamelCase			,__lowerCamelCase			:			str       			=					self._decode_helper(_a     ,      'bpe'  )
    __lowerCamelCase			,__lowerCamelCase			:			Dict       			=					self._decode_helper(_a     ,      'wp'  )

    __lowerCamelCase			:			Dict       			=					[]
    __lowerCamelCase			:			str       			=					[]
    for i in range(_a  ):
      __lowerCamelCase			:			str       			=					[char_scores[i], bpe_scores[i], wp_scores[i]]
      __lowerCamelCase			:			Tuple       			=					[char_strs[i], bpe_strs[i], wp_strs[i]]
      __lowerCamelCase			:			Dict       			=					scores.index(max(_a  )  )
      final_strs.append(strs[max_score_index]  )
      final_scores.append(scores[max_score_index]  )

    __lowerCamelCase			:			str       			=					{}
    __lowerCamelCase			:			Tuple       			=					final_strs
    __lowerCamelCase			:			Dict       			=					final_scores
    __lowerCamelCase			:			Optional[Any]       			=					char_strs
    __lowerCamelCase			:			Any       			=					bpe_strs
    __lowerCamelCase			:			Union[str, Any]       			=					wp_strs
    return out




  def 		_lowercase	(					self :      Any     ,      _a :      List[Any]     ,      _a :      Optional[int]  )   ->   Optional[Any]:
    if format == DecodeType.CHARACTER:
      __lowerCamelCase			:			Union[str, Any]       			=					self.char_decode
      __lowerCamelCase			:			Any       			=					1
      __lowerCamelCase			:			Tuple       			=					'[s]'
    elif format == DecodeType.BPE:
      __lowerCamelCase			:			Optional[int]       			=					self.bpe_decode
      __lowerCamelCase			:			Optional[int]       			=					2
      __lowerCamelCase			:			Tuple       			=					'#'
    elif format == DecodeType.WORDPIECE:
      __lowerCamelCase			:			Optional[int]       			=					self.wp_decode
      __lowerCamelCase			:			Union[str, Any]       			=					102
      __lowerCamelCase			:			List[Any]       			=					'[SEP]'
    else:
      raise ValueError(f'Format {format} is not supported.'  )

    __lowerCamelCase			,__lowerCamelCase			:			Tuple       			=					[], []
    __lowerCamelCase			:			str       			=					pred_logits.size(0  )
    __lowerCamelCase			:			int       			=					pred_logits.size(1  )
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					pred_logits.topk(1     ,      dim=-1     ,      largest=_a     ,      sorted=_a  )
    __lowerCamelCase			:			int       			=					preds_index.view(-1     ,      _a  )[:, 1:]
    __lowerCamelCase			:			List[Any]       			=					decoder(_a  )
    __lowerCamelCase			,__lowerCamelCase			:			List[Any]       			=					torch.nn.functional.softmax(_a     ,      dim=2  ).max(dim=2  )
    __lowerCamelCase			:			List[str]       			=					preds_max_prob[:, 1:]

    for index in range(_a  ):
      __lowerCamelCase			:			Optional[int]       			=					preds_str[index].find(_a  )
      __lowerCamelCase			:			Union[str, Any]       			=					preds_str[index][:pred_eos]
      __lowerCamelCase			:			Any       			=					preds_index[index].cpu().tolist()
      __lowerCamelCase			:			Optional[Any]       			=					pred_index.index(_a  ) if eos_token in pred_index else -1
      __lowerCamelCase			:			Any       			=					preds_max_prob[index][: pred_eos_index + 1]
      __lowerCamelCase			:			str       			=					pred_max_prob.cumprod(dim=0  )[-1] if pred_max_prob.nelement() != 0 else 0.0
      dec_strs.append(_a  )
      conf_scores.append(_a  )

    return dec_strs, conf_scores




  def 		_lowercase	(					self :      Any     ,      _a :      List[str]  )   ->   Dict:
    __lowerCamelCase			:			Dict       			=					[seq.replace(' '     ,      ''  ) for seq in self.char_tokenizer.batch_decode(_a  )]
    return decode_strs




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Tuple  )   ->   Tuple:
    return self.bpe_tokenizer.batch_decode(_a  )




  def 		_lowercase	(					self :      int     ,      _a :      Optional[int]  )   ->   Optional[int]:
    __lowerCamelCase			:			Tuple       			=					[seq.replace(' '     ,      ''  ) for seq in self.wp_tokenizer.batch_decode(_a  )]
    return decode_strs











'''simple docstring'''





import json
import os
from typing import Optional, Tuple

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


_UpperCamelCase											=		logging.get_logger(__name__)

_UpperCamelCase											=		{'vocab_file': 'vocab.json'}

_UpperCamelCase											=		{
    'vocab_file': {
        'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
    }
}

_UpperCamelCase											=		{'mgp-str': 27}






class    lowerCamelCase_       (							SCREAMING_SNAKE_CASE_	):
  """simple docstring"""




  a_				   =VOCAB_FILES_NAMES
  a_				   =PRETRAINED_VOCAB_FILES_MAP
  a_				   =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES




  def __init__(					self :      Optional[Any]     ,      _a :      Optional[Any]     ,      _a :      Any="[GO]"     ,      _a :      Optional[Any]="[GO]"     ,      _a :      Optional[int]="[s]"     ,      _a :      Optional[int]="[GO]"     ,      **_a :      Union[str, Any]  )   ->   Optional[int]:
    super().__init__(
        unk_token=_a     ,      bos_token=_a     ,      eos_token=_a     ,      pad_token=_a     ,      **_a     ,      )

    with open(_a     ,      encoding='utf-8'  ) as vocab_handle:
      __lowerCamelCase			:			List[str]       			=					json.load(_a  )
    __lowerCamelCase			:			Tuple       			=					{v: k for k, v in self.vocab.items()}




  @property
  def 		_lowercase	(					self :      Union[str, Any]  )   ->   List[str]:
    return len(self.vocab  )




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




  def 		_lowercase	(					self :      Dict     ,      _a :      Tuple  )   ->   int:
    __lowerCamelCase			:			Optional[Any]       			=					[]
    for s in text:
      char_tokens.extend(_a  )
    return char_tokens




  def 		_lowercase	(					self :      Optional[Any]     ,      _a :      Any  )   ->   Dict:
    return self.vocab.get(_a     ,      self.vocab.get(self.unk_token  )  )




  def 		_lowercase	(					self :      List[Any]     ,      _a :      Dict  )   ->   Optional[int]:
    return self.decoder.get(_a  )




  def 		_lowercase	(					self :      Union[str, Any]     ,      _a :      str     ,      _a :      Optional[str] = None  )   ->   Tuple[str]:
    if not os.path.isdir(_a  ):
      logger.error('Vocabulary path ({}) should be a directory'.format(_a  )  )
      return
    __lowerCamelCase			:			List[Any]       			=					os.path.join(
        _a     ,      (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']  )

    with open(_a     ,      'w'     ,      encoding='utf-8'  ) as f:
      f.write(json.dumps(self.vocab     ,      indent=2     ,      sort_keys=_a     ,      ensure_ascii=_a  ) + '\n'  )

    return (vocab_file,)






