

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
    from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class  _A		(  unittest.TestCase):

  @slow
  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small' ,					return_dict=_a					).to(_a					)
        SCREAMING_SNAKE_CASE_				:  str           =     AutoTokenizer.from_pretrained('google/mt5-small'					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     tokenizer('Hello there' ,					return_tensors='pt'					).input_ids
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     tokenizer('Hi I am' ,					return_tensors='pt'					).input_ids

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     model(input_ids.to(_a					) ,					labels=labels.to(_a					)					).loss
        SCREAMING_SNAKE_CASE_				:  str           =     -(labels.shape[-1] * loss.item())

        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     -84.9127
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE					) < 1e-4					)









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

lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)


lowerCAmelCase     :   Optional[int]						     =  Dict[str, Any]
lowerCAmelCase     :   Any						     =  List[Prediction]
@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__):

  def __init__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        super().__init__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

        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  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs['threshold']
        return {}, {}, postprocess_kwargs

  def __call__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0.9					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     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.
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     target_size[0].tolist()

              def unnormalize(_SCREAMING_SNAKE_CASE					):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1000),
                                (height * bbox[1] / 1000),
                                (width * bbox[2] / 1000),
                                (height * bbox[3] / 1000),
                            ]					)					)

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

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scores.tolist()
              SCREAMING_SNAKE_CASE_				:  Any           =     [self.model.config.idalabel[label.item()] for label in labels]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self._get_bounding_box(_SCREAMING_SNAKE_CASE					) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              SCREAMING_SNAKE_CASE_				:  str           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
                  dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
                  for vals in zip(raw_annotation['scores'] ,					raw_annotation['labels'] ,					raw_annotation['boxes']					)
              ]

        return annotation



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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






