


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     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  "char"
    UpperCAmelCase__						:		List[str]      =  "bpe"
    UpperCAmelCase__						:		Tuple      =  "wp"
_A		       =						(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)






class     UpperCAmelCase__       (					A_		):



    """simple docstring"""






    UpperCAmelCase__						:		Optional[int]      =  ["image_processor", "char_tokenizer"]
    UpperCAmelCase__						:		str      =  "ViTImageProcessor"
    UpperCAmelCase__						:		Optional[Any]      =  "MgpstrTokenizer"






    def __init__(			self					,   A_=None					,   A_=None					,   **A_    )  ->       Union[str, Any]:
         __UpperCamelCase    							=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_					,   )
              __UpperCamelCase    							=kwargs.pop('feature_extractor'    )

         __UpperCamelCase    							=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`.'    )

         __UpperCamelCase    							=tokenizer
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('gpt2'    )
         __UpperCamelCase    							=AutoTokenizer.from_pretrained('bert-base-uncased'    )

         super().__init__(A_					,   A_    )






    def __call__(			self					,   A_=None					,   A_=None					,   A_=None					,   **A_    )  ->       Any:
         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:
              __UpperCamelCase    							=self.image_processor(A_					,   return_tensors=A_					,   **A_    )
         if text is not None:
              __UpperCamelCase    							=self.char_tokenizer(A_					,   return_tensors=A_					,   **A_    )

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






    def _a					(			self					,   A_    )  ->       int:
         __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=sequences
         __UpperCamelCase    							=char_preds.size(0    )

         __UpperCamelCase							,      __UpperCamelCase    							=self._decode_helper(A_					,   'char'    )
         __UpperCamelCase							,      __UpperCamelCase    							=self._decode_helper(A_					,   'bpe'    )
         __UpperCamelCase							,      __UpperCamelCase    							=self._decode_helper(A_					,   'wp'    )

         __UpperCamelCase    							=[]
         __UpperCamelCase    							=[]
         for i in range(A_    ):
              __UpperCamelCase    							=[char_scores[i], bpe_scores[i], wp_scores[i]]
              __UpperCamelCase    							=[char_strs[i], bpe_strs[i], wp_strs[i]]
              __UpperCamelCase    							=scores.index(max(A_    )    )
              final_strs.append(strs[max_score_index]    )
              final_scores.append(scores[max_score_index]    )

         __UpperCamelCase    							={}
         __UpperCamelCase    							=final_strs
         __UpperCamelCase    							=final_scores
         __UpperCamelCase    							=char_strs
         __UpperCamelCase    							=bpe_strs
         __UpperCamelCase    							=wp_strs
         return out






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

         __UpperCamelCase							,      __UpperCamelCase    							=[], []
         __UpperCamelCase    							=pred_logits.size(0    )
         __UpperCamelCase    							=pred_logits.size(1    )
         __UpperCamelCase							,      __UpperCamelCase    							=pred_logits.topk(1					,   dim=-1					,   largest=A_					,   sorted=A_    )
         __UpperCamelCase    							=preds_index.view(-1					,   A_    )[:, 1:]
         __UpperCamelCase    							=decoder(A_    )
         __UpperCamelCase							,      __UpperCamelCase    							=torch.nn.functional.softmax(A_					,   dim=2    ).max(dim=2    )
         __UpperCamelCase    							=preds_max_prob[:, 1:]

         for index in range(A_    ):
              __UpperCamelCase    							=preds_str[index].find(A_    )
              __UpperCamelCase    							=preds_str[index][:pred_eos]
              __UpperCamelCase    							=preds_index[index].cpu().tolist()
              __UpperCamelCase    							=pred_index.index(A_    ) if eos_token in pred_index else -1
              __UpperCamelCase    							=preds_max_prob[index][: pred_eos_index + 1]
              __UpperCamelCase    							=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 _a					(			self					,   A_    )  ->       int:
         __UpperCamelCase    							=[seq.replace(' '					,   ''    ) for seq in self.char_tokenizer.batch_decode(A_    )]
         return decode_strs






    def _a					(			self					,   A_    )  ->       str:
         return self.bpe_tokenizer.batch_decode(A_    )






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









from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A		       =						{
    'configuration_jukebox': [
        'JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'JukeboxConfig',
        'JukeboxPriorConfig',
        'JukeboxVQVAEConfig',
    ],
    'tokenization_jukebox': ['JukeboxTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _A		       =						[
       'JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST',
       'JukeboxModel',
       'JukeboxPreTrainedModel',
       'JukeboxVQVAE',
       'JukeboxPrior',
   ]

if TYPE_CHECKING:
   from .configuration_jukebox import (
       JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
       JukeboxConfig,
       JukeboxPriorConfig,
       JukeboxVQVAEConfig,
   )
   from .tokenization_jukebox import JukeboxTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_jukebox import (
          JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
          JukeboxModel,
          JukeboxPreTrainedModel,
          JukeboxPrior,
          JukeboxVQVAE,
      )

else:
   import sys

   _A		       =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





