

import torch

from transformers import AutoTokenizer



class 					__snake_case     (			lowerCAmelCase__				):

  def __init__(    self  ,						_A):
      SCREAMING_SNAKE_CASE_													=  AutoTokenizer.from_pretrained(_A)

  def   lowerCAmelCase__   (    self  ,						_A):

      if isinstance(_A  ,						_A) and all(isinstance(_x  ,						_A) for _x in x):
          SCREAMING_SNAKE_CASE_													=  None
          for l in x:
              SCREAMING_SNAKE_CASE_													=  self.tokenizer(
                  _A  ,						padding='max_length'  ,						max_length=384  ,						truncation=_A  ,						return_tensors='pt'  ,						)
              SCREAMING_SNAKE_CASE_													=  torch.tensor([len(_A)])
              if d is not None:
                  for k in d.keys():
                      SCREAMING_SNAKE_CASE_													=  torch.cat((d[k], t[k])  ,						0)
              else:
                  SCREAMING_SNAKE_CASE_													=  t

          SCREAMING_SNAKE_CASE_													=  torch.tensor(self.tokenizer.convert_tokens_to_ids('[E]'))
          SCREAMING_SNAKE_CASE_													=  torch.tensor(self.tokenizer.convert_tokens_to_ids('[/E]'))

      elif isinstance(_A  ,						_A) and all(isinstance(_x  ,						_A) for _x in x):
          SCREAMING_SNAKE_CASE_													=  self.tokenizer(
              _A  ,						padding='max_length'  ,						max_length=384  ,						truncation=_A  ,						return_tensors='pt'  ,						)

      else:
          raise Exception(
              'Type of parameter x was not recognized! Only `list of strings` for query or `list of lists of'
              ' strings` for supports are supported.')

      return d


  def   lowerCAmelCase__   (    self  ,						_A  ,						_A  ,						_A  ,						_A  ,						_A=0.7_0):
      SCREAMING_SNAKE_CASE_													=  []
      for idx in range(len(W_query['input_ids'])):
          SCREAMING_SNAKE_CASE_													=  SCREAMING_SNAKE_CASE_													=  range(p_start.shape[1])

          SCREAMING_SNAKE_CASE_													=  []
          for start_id in start_indexes:
              for end_id in end_indexes:
                  if start_id < end_id:
                      output.append(
                          (
                              start_id,
                              end_id,
                              p_start[idx][start_id].item(),
                              p_end[idx][end_id].item(),
                          ))

          output.sort(key=lambda _A: (tup[2] * tup[3])  ,						reverse=_A)
          SCREAMING_SNAKE_CASE_													=  []
          for k in range(len(_A)):
              if output[k][2] * output[k][3] >= thresh:
                  SCREAMING_SNAKE_CASE_							,	SCREAMING_SNAKE_CASE_													=  output[k][0], output[k][1]
                  SCREAMING_SNAKE_CASE_													=  self.tokenizer.decode(W_query['input_ids'][idx][c_start_pos:c_end_pos])
                  temp.append((decoded, output[k][2] * output[k][3]))

          final_outputs.append(_A)

      return final_outputs





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase__     :					Dict								=			{
    "configuration_donut_swin": ["DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP", "DonutSwinConfig"],
    "processing_donut": ["DonutProcessor"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					Optional[int]								=			[
           "DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DonutSwinModel",
           "DonutSwinPreTrainedModel",
       ]

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__     :					List[Any]								=			["DonutFeatureExtractor"]
       UpperCamelCase__     :					Dict								=			["DonutImageProcessor"]


if TYPE_CHECKING:
       from .configuration_donut_swin import DONUT_SWIN_PRETRAINED_CONFIG_ARCHIVE_MAP, DonutSwinConfig
       from .processing_donut import DonutProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_donut_swin import (
                  DONUT_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
                  DonutSwinModel,
                  DonutSwinPreTrainedModel,
              )

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_donut import DonutFeatureExtractor
              from .image_processing_donut import DonutImageProcessor

else:
       import sys

       UpperCamelCase__     :					Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


