





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

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


__A							:			Dict         =			Dict[str, Any]
__A							:			Optional[int]         =			List[Prediction]







@add_end_docstrings(__snake_case	)
class      lowerCamelCase(						__snake_case	):
     '''simple docstring'''


     def __init__(    self				,     *snake_case_				,     **snake_case_							):
         super().__init__(*snake_case_				,     **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        lowerCAmelCase__		(    self				,     **snake_case_							):
         _A     =							{}
         if "threshold" in kwargs:
             _A     =							kwargs['threshold']
         return {}, {}, postprocess_kwargs




     def __call__(    self				,     *snake_case_				,     **snake_case_							):
         return super().__call__(*snake_case_				,     **snake_case_							)




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							load_image(snake_case_							)
         _A     =							torch.IntTensor([[image.height, image.width]]							)
         _A     =							self.image_processor(images=[image]				,     return_tensors='pt'							)
         if self.tokenizer is not None:
             _A     =							self.tokenizer(text=inputs['words']				,     boxes=inputs['boxes']				,     return_tensors='pt'							)
         _A     =							target_size
         return inputs




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         _A     =							model_inputs.pop('target_size'							)
         _A     =							self.model(**snake_case_							)
         _A     =							outputs.__class__({'target_size': target_size, **outputs}							)
         if self.tokenizer is not None:
             _A     =							model_inputs['bbox']
         return model_outputs




     def        lowerCAmelCase__		(    self				,     snake_case_				,     snake_case_=0.9							):
         _A     =							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     =							target_size[0].tolist()

             def unnormalize(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),
                         ]							)							)

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

             _A     =							scores.tolist()
             _A     =							[self.model.config.idalabel[label.item()] for label in labels]
             _A     =							[self._get_bounding_box(snake_case_							) for box in boxes]

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

         return annotation




     def        lowerCAmelCase__		(    self				,     snake_case_							):
         if self.framework != "pt":
             raise ValueError('The ObjectDetectionPipeline is only available in PyTorch.'							)
         _A,			_A,			_A,			_A     =							box.int().tolist()
         _A     =							{
             'xmin': xmin,
             'ymin': ymin,
             'xmax': xmax,
             'ymax': ymax,
         }
         return bbox






"""simple docstring"""


from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
    import datasets


class 							a					(		lowerCAmelCase_ ):


    @slow
    @require_torch
    def   lowerCAmelCase_	(       self						:	str       ):
      _UpperCAmelCase    			=   EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" ,					"""prajjwal1/bert-tiny"""       )
      _UpperCAmelCase    			=   BertTokenizer.from_pretrained("""bert-base-uncased"""       )

      _UpperCAmelCase    			=   bertabert.config.encoder.vocab_size
      _UpperCAmelCase    			=   tokenizer.sep_token_id
      _UpperCAmelCase    			=   tokenizer.cls_token_id
      _UpperCAmelCase    			=   128

      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""train[:1%]"""       )
      _UpperCAmelCase    			=   datasets.load_dataset("""cnn_dailymail""" ,					"""3.0.0""" ,					split="""validation[:1%]"""       )

      _UpperCAmelCase    			=   train_dataset.select(range(32       )       )
      _UpperCAmelCase    			=   val_dataset.select(range(16       )       )

      _UpperCAmelCase    			=   4

      def _map_to_encoder_decoder_inputs(__lowerCAmelCase						:	Dict       ):
        # Tokenizer will automatically set [BOS] <text> [EOS]
        _UpperCAmelCase    			=   tokenizer(batch["""article"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=512       )
        _UpperCAmelCase    			=   tokenizer(batch["""highlights"""] ,					padding="""max_length""" ,					truncation=__lowerCAmelCase ,					max_length=128       )
        _UpperCAmelCase    			=   inputs.input_ids
        _UpperCAmelCase    			=   inputs.attention_mask

        _UpperCAmelCase    			=   outputs.input_ids
        _UpperCAmelCase    			=   outputs.input_ids.copy()
        _UpperCAmelCase    			=   [
            [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
        ]
        _UpperCAmelCase    			=   outputs.attention_mask

        assert all(len(__lowerCAmelCase       ) == 512 for x in inputs.input_ids       )
        assert all(len(__lowerCAmelCase       ) == 128 for x in outputs.input_ids       )

        return batch

      def _compute_metrics(__lowerCAmelCase						:	int       ):
        _UpperCAmelCase    			=   pred.label_ids
        _UpperCAmelCase    			=   pred.predictions

        # all unnecessary tokens are removed
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )
        _UpperCAmelCase    			=   tokenizer.batch_decode(__lowerCAmelCase ,					skip_special_tokens=__lowerCAmelCase       )

        _UpperCAmelCase    			=   sum([int(pred_str[i] == label_str[i]       ) for i in range(len(__lowerCAmelCase       )       )]       ) / len(__lowerCAmelCase       )

        return {"accuracy": accuracy}

      # map train dataset
      _UpperCAmelCase    			=   train_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      train_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      # same for validation dataset
      _UpperCAmelCase    			=   val_dataset.map(
          _map_to_encoder_decoder_inputs ,					batched=__lowerCAmelCase ,					batch_size=__lowerCAmelCase ,					remove_columns=["""article""", """highlights"""] ,					)
      val_dataset.set_format(
          type="""torch""" ,					columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] ,					)

      _UpperCAmelCase    			=   self.get_auto_remove_tmp_dir()

      _UpperCAmelCase    			=   SeqaSeqTrainingArguments(
          output_dir=__lowerCAmelCase ,					per_device_train_batch_size=__lowerCAmelCase ,					per_device_eval_batch_size=__lowerCAmelCase ,					predict_with_generate=__lowerCAmelCase ,					evaluation_strategy="""steps""" ,					do_train=__lowerCAmelCase ,					do_eval=__lowerCAmelCase ,					warmup_steps=0 ,					eval_steps=2 ,					logging_steps=2 ,					)

      # instantiate trainer
      _UpperCAmelCase    			=   SeqaSeqTrainer(
          model=__lowerCAmelCase ,					args=__lowerCAmelCase ,					compute_metrics=_compute_metrics ,					train_dataset=__lowerCAmelCase ,					eval_dataset=__lowerCAmelCase ,					tokenizer=__lowerCAmelCase ,					)

      # start training
      trainer.train()
