"""simple docstring"""
from typing import List

import numpy as np







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->int:
  a__:					Dict      							=     {key: len(_SCREAMING_SNAKE_CASE       ) for key, value in gen_kwargs.items() if isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )}
  if len(set(lists_lengths.values()       )       ) > 1:
    raise RuntimeError(
        (
            'Sharding is ambiguous for this dataset: '
            + 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
            + '\n'.join(F'\t- key {key} has length {length}' for key, length in lists_lengths.items()       )
            + '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
            + 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
        )       )
  a__:					Optional[Any]      							=     max(lists_lengths.values()		,					default=0       )
  return max(1		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[range]:
  a__:					Optional[int]      							=     []
  for group_idx in range(_SCREAMING_SNAKE_CASE       ):
    a__:					Union[str, Any]      							=     num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
    if num_shards_to_add == 0:
      break
    a__:					Optional[int]      							=     shards_indices_per_group[-1].stop if shards_indices_per_group else 0
    a__:					List[Any]      							=     range(_SCREAMING_SNAKE_CASE		,					start + num_shards_to_add       )
    shards_indices_per_group.append(_SCREAMING_SNAKE_CASE       )
  return shards_indices_per_group







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->List[dict]:
  a__:					Any      							=     _number_of_shards_in_gen_kwargs(_SCREAMING_SNAKE_CASE       )
  if num_shards == 1:
    return [dict(_SCREAMING_SNAKE_CASE       )]
  else:
    a__:					Dict      							=     _distribute_shards(num_shards=_SCREAMING_SNAKE_CASE		,					max_num_jobs=_SCREAMING_SNAKE_CASE       )
    return [
        {
            key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
            if isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
            else value
            for key, value in gen_kwargs.items()
        }
        for group_idx in range(len(_SCREAMING_SNAKE_CASE       )       )
    ]







def 						__a       (     _SCREAMING_SNAKE_CASE       )     ->dict:
  return {
      key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
      if isinstance(gen_kwargs_list[0][key]		,					_SCREAMING_SNAKE_CASE       )
      else gen_kwargs_list[0][key]
      for key in gen_kwargs_list[0]
  }







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->dict:
  a__:					int      							=     {len(_SCREAMING_SNAKE_CASE       ) for value in gen_kwargs.values() if isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )}
  a__:					Dict      							=     {}
  for size in list_sizes:
    a__:					Optional[int]      							=     list(range(_SCREAMING_SNAKE_CASE       )       )
    rng.shuffle(indices_per_size[size]       )
  # Now let's copy the gen_kwargs and shuffle the lists based on their sizes
  a__:					List[str]      							=     dict(_SCREAMING_SNAKE_CASE       )
  for key, value in shuffled_kwargs.items():
    if isinstance(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       ):
      a__:					Optional[int]      							=     [value[i] for i in indices_per_size[len(_SCREAMING_SNAKE_CASE       )]]
  return shuffled_kwargs



"""simple docstring"""
from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
 from ..image_utils import load_image


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowercase__ 	=     logging.get_logger(__name__)


lowercase__ 	=     Dict[str, Any]
lowercase__ 	=     List[Prediction]




@add_end_docstrings(__lowerCAmelCase    )
class        __snake_case	(					__lowerCAmelCase    ):



      def __init__(						self   ,      *lowercase   ,      **lowercase)     ->      Dict:





        '''simple docstring'''





        super().__init__(*lowercase   ,      **lowercase)

        if self.framework == "tf":
          raise ValueError(f'The {self.__class__} is only available in PyTorch.')

        requires_backends(self   ,      'vision')
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()))



      def   lowerCamelCase_       (						self   ,      **lowercase)     ->      int:





        '''simple docstring'''





        a__:					Optional[Any]      							=     {}
        if "threshold" in kwargs:
          a__:					Dict      							=     kwargs['threshold']
        return {}, {}, postprocess_kwargs



      def __call__(						self   ,      *lowercase   ,      **lowercase)     ->      Union[Predictions, List[Prediction]]:





        '''simple docstring'''





        return super().__call__(*lowercase   ,      **lowercase)



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      List[Any]:





        '''simple docstring'''





        a__:					Optional[Any]      							=     load_image(lowercase)
        a__:					List[Any]      							=     torch.IntTensor([[image.height, image.width]])
        a__:					Any      							=     self.image_processor(images=[image]   ,      return_tensors='pt')
        if self.tokenizer is not None:
          a__:					Any      							=     self.tokenizer(text=inputs['words']   ,      boxes=inputs['boxes']   ,      return_tensors='pt')
        a__:					List[str]      							=     target_size
        return inputs



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      int:





        '''simple docstring'''





        a__:					Any      							=     model_inputs.pop('target_size')
        a__:					Union[str, Any]      							=     self.model(**lowercase)
        a__:					List[str]      							=     outputs.__class__({'target_size': target_size, **outputs})
        if self.tokenizer is not None:
          a__:					Union[str, Any]      							=     model_inputs['bbox']
        return model_outputs



      def   lowerCamelCase_       (						self   ,      lowercase   ,      lowercase=0.9)     ->      Optional[Any]:





        '''simple docstring'''





        a__:					int      							=     model_outputs['target_size']
        if self.tokenizer is not None:
          # This is a LayoutLMForTokenClassification variant.
          # The OCR got the boxes and the model classified the words.
          a__							,   a__:					str      							=     target_size[0].tolist()

          def unnormalize(lowercase):
            return self._get_bounding_box(
                torch.Tensor(
                    [
                        (width * bbox[0] / 10_00),
                        (height * bbox[1] / 10_00),
                        (width * bbox[2] / 10_00),
                        (height * bbox[3] / 10_00),
                    ]))

          a__							,   a__:					Optional[Any]      							=     model_outputs['logits'].squeeze(0).softmax(dim=-1).max(dim=-1)
          a__:					str      							=     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
          a__:					Union[str, Any]      							=     [unnormalize(lowercase) for bbox in model_outputs['bbox'].squeeze(0)]
          a__:					Dict      							=     ['score', 'label', 'box']
          a__:					Any      							=     [dict(zip(lowercase   ,      lowercase)) for vals in zip(scores.tolist()   ,      lowercase   ,      lowercase) if vals[0] > threshold]
        else:
          # This is a regular ForObjectDetectionModel
          a__:					List[str]      							=     self.image_processor.post_process_object_detection(lowercase   ,      lowercase   ,      lowercase)
          a__:					Tuple      							=     raw_annotations[0]
          a__:					List[str]      							=     raw_annotation['scores']
          a__:					int      							=     raw_annotation['labels']
          a__:					int      							=     raw_annotation['boxes']

          a__:					List[Any]      							=     scores.tolist()
          a__:					Any      							=     [self.model.config.idalabel[label.item()] for label in labels]
          a__:					Dict      							=     [self._get_bounding_box(lowercase) for box in boxes]

          # {"scores": [...], ...} --> [{"score":x, ...}, ...]
          a__:					Optional[Any]      							=     ['score', 'label', 'box']
          a__:					List[Any]      							=     [
              dict(zip(lowercase   ,      lowercase))
              for vals in zip(raw_annotation['scores']   ,      raw_annotation['labels']   ,      raw_annotation['boxes'])
          ]

        return annotation



      def   lowerCamelCase_       (						self   ,      lowercase)     ->      Dict[str, int]:





        '''simple docstring'''





        if self.framework != "pt":
          raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.')
        a__							,   a__							,   a__							,   a__:					List[Any]      							=     box.int().tolist()
        a__:					Optional[int]      							=     {
            'xmin': xmin,
            'ymin': ymin,
            'xmax': xmax,
            'ymax': ymax,
        }
        return bbox


