






'''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 a_    (    UpperCamelCase_							):
   _snake_case        =							"""char"""
   _snake_case        =							"""bpe"""
   _snake_case        =							"""wp"""


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




class a_    (    UpperCamelCase_							):
   _snake_case        =							["""image_processor""", """char_tokenizer"""]
   _snake_case        =							"""ViTImageProcessor"""
   _snake_case        =							"""MgpstrTokenizer"""







   def __init__(self					,   __a=None					,   __a=None					,   **__a)			->      Optional[Any]:
      """simple docstring"""






      __snake_case				:      List[str]     							= 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					,   )
         __snake_case				:      List[Any]     							= kwargs.pop('feature_extractor')

      __snake_case				:      str     							= 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`.')

      __snake_case				:      Tuple     							= tokenizer
      __snake_case				:      Any     							= AutoTokenizer.from_pretrained('gpt2')
      __snake_case				:      List[str]     							= AutoTokenizer.from_pretrained('bert-base-uncased')

      super().__init__(__a					,   __a)







   def __call__(self					,   __a=None					,   __a=None					,   __a=None					,   **__a)			->      Optional[Any]:
      """simple docstring"""






      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:
         __snake_case				:      Union[str, Any]     							= self.image_processor(__a					,   return_tensors=__a					,   **__a)
      if text is not None:
         __snake_case				:      Dict     							= self.char_tokenizer(__a					,   return_tensors=__a					,   **__a)

      if text is None:
         return inputs
      elif images is None:
         return encodings
      else:
         __snake_case				:      Union[str, Any]     							= encodings['input_ids']
         return inputs







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Union[str, Any]:
      """simple docstring"""






      __snake_case							,__snake_case							,__snake_case				:      List[str]     							= sequences
      __snake_case				:      int     							= char_preds.size(0)

      __snake_case							,__snake_case				:      str     							= self._decode_helper(__a					,   'char')
      __snake_case							,__snake_case				:      List[str]     							= self._decode_helper(__a					,   'bpe')
      __snake_case							,__snake_case				:      List[str]     							= self._decode_helper(__a					,   'wp')

      __snake_case				:      Any     							= []
      __snake_case				:      List[str]     							= []
      for i in range(__a):
         __snake_case				:      Union[str, Any]     							= [char_scores[i], bpe_scores[i], wp_scores[i]]
         __snake_case				:      Any     							= [char_strs[i], bpe_strs[i], wp_strs[i]]
         __snake_case				:      List[str]     							= scores.index(max(__a))
         final_strs.append(strs[max_score_index])
         final_scores.append(scores[max_score_index])

      __snake_case				:      str     							= {}
      __snake_case				:      Optional[Any]     							= final_strs
      __snake_case				:      str     							= final_scores
      __snake_case				:      Tuple     							= char_strs
      __snake_case				:      str     							= bpe_strs
      __snake_case				:      Any     							= wp_strs
      return out







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a)			->      str:
      """simple docstring"""






      if format == DecodeType.CHARACTER:
         __snake_case				:      str     							= self.char_decode
         __snake_case				:      Union[str, Any]     							= 1
         __snake_case				:      Union[str, Any]     							= '[s]'
      elif format == DecodeType.BPE:
         __snake_case				:      Tuple     							= self.bpe_decode
         __snake_case				:      str     							= 2
         __snake_case				:      Any     							= '#'
      elif format == DecodeType.WORDPIECE:
         __snake_case				:      Any     							= self.wp_decode
         __snake_case				:      Tuple     							= 1_0_2
         __snake_case				:      Dict     							= '[SEP]'
      else:
         raise ValueError(F"""Format {format} is not supported.""")

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

      for index in range(__a):
         __snake_case				:      List[Any]     							= preds_str[index].find(__a)
         __snake_case				:      List[Any]     							= preds_str[index][:pred_eos]
         __snake_case				:      Union[str, Any]     							= preds_index[index].cpu().tolist()
         __snake_case				:      str     							= pred_index.index(__a) if eos_token in pred_index else -1
         __snake_case				:      Any     							= preds_max_prob[index][: pred_eos_index + 1]
         __snake_case				:      Dict     							= 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     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      str:
      """simple docstring"""






      __snake_case				:      List[Any]     							= [seq.replace(' '					,   '') for seq in self.char_tokenizer.batch_decode(__a)]
      return decode_strs







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Optional[int]:
      """simple docstring"""






      return self.bpe_tokenizer.batch_decode(__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      int:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= [seq.replace(' '					,   '') for seq in self.wp_tokenizer.batch_decode(__a)]
      return decode_strs







'''simple docstring'''






# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A    	=   {'''configuration_timm_backbone''': ['''TimmBackboneConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   ['''TimmBackbone''']


if TYPE_CHECKING:
      from .configuration_timm_backbone import TimmBackboneConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_timm_backbone import TimmBackbone

else:
      import sys

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