



"""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

_UpperCAmelCase  =      logging.get_logger(__name__)


_UpperCAmelCase  =      Dict[str, Any]
_UpperCAmelCase  =      List[Prediction]
@add_end_docstrings(UpperCamelCase_							)
class        a						(	UpperCamelCase_							):



  def __init__(    self			:       Tuple							,		*lowerCAmelCase			:       List[str]							,		**lowerCAmelCase			:       int		)  ->       List[str]:


     '''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			:       List[str]							,		**lowerCAmelCase			:       Union[str, Any]		)  ->       Optional[Any]:


     '''simple docstring'''




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



  def __call__(    self			:       Any							,		*lowerCAmelCase			:       Optional[Any]							,		**lowerCAmelCase			:       Dict		)  ->       Union[Predictions, List[Prediction]]:


     '''simple docstring'''




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



  def 						lowerCamelCase__							(    self			:       Any							,		lowerCAmelCase			:       Union[str, Any]		)  ->       List[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Union[str, Any]        =load_image(lowercase_		)
     SCREAMING_SNAKE_CASE_:			Tuple        =torch.IntTensor([[image.height, image.width]]		)
     SCREAMING_SNAKE_CASE_:			Optional[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_:			Any        =target_size
     return inputs



  def 						lowerCamelCase__							(    self			:       List[str]							,		lowerCAmelCase			:       Any		)  ->       Any:


     '''simple docstring'''




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



  def 						lowerCamelCase__							(    self			:       Any							,		lowerCAmelCase			:       List[Any]							,		lowerCAmelCase			:       Dict=0.9		)  ->       Optional[Any]:


     '''simple docstring'''




     SCREAMING_SNAKE_CASE_:			Dict        =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_:			Union[str, Any]        =target_size[0].tolist()

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

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

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

     return annotation





  def 						lowerCamelCase__							(    self			:       int							,		lowerCAmelCase			:       "torch.Tensor"		)  ->       Dict[str, int]:


     '''simple docstring'''




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







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    (  UpperCamelCase_	):






      @slow
      @require_torch
      def 			lowerCamelCase			(   self		:	Any					)   ->		Any:
             """simple docstring"""




             _lowerCamelCase			:						int            =EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'     ,  'prajjwal1/bert-tiny'					)
             _lowerCamelCase			:						Dict            =BertTokenizer.from_pretrained('bert-base-uncased'					)

             _lowerCamelCase			:						Union[str, Any]            =bertabert.config.encoder.vocab_size
             _lowerCamelCase			:						Dict            =tokenizer.sep_token_id
             _lowerCamelCase			:						List[Any]            =tokenizer.cls_token_id
             _lowerCamelCase			:						Optional[int]            =128

             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='train[:1%]'					)
             _lowerCamelCase			:						int            =datasets.load_dataset('cnn_dailymail'     ,  '3.0.0'     ,  split='validation[:1%]'					)

             _lowerCamelCase			:						Optional[int]            =train_dataset.select(range(32					)					)
             _lowerCamelCase			:						Optional[int]            =val_dataset.select(range(16					)					)

             _lowerCamelCase			:						Optional[int]            =4

             def _map_to_encoder_decoder_inputs(lowercase_		:	Tuple					):
                    # Tokenizer will automatically set [BOS] <text> [EOS]
                    _lowerCamelCase			:						Optional[int]            =tokenizer(batch['article']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=512					)
                    _lowerCamelCase			:						List[str]            =tokenizer(batch['highlights']     ,  padding='max_length'     ,  truncation=lowercase_     ,  max_length=128					)
                    _lowerCamelCase			:						List[str]            =inputs.input_ids
                    _lowerCamelCase			:						Any            =inputs.attention_mask

                    _lowerCamelCase			:						List[str]            =outputs.input_ids
                    _lowerCamelCase			:						int            =outputs.input_ids.copy()
                    _lowerCamelCase			:						List[str]            =[
                        [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
                    ]
                    _lowerCamelCase			:						Dict            =outputs.attention_mask

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

                    return batch

             def _compute_metrics(lowercase_		:	str					):
                    _lowerCamelCase			:						List[Any]            =pred.label_ids
                    _lowerCamelCase			:						List[Any]            =pred.predictions

                    # all unnecessary tokens are removed
                    _lowerCamelCase			:						str            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)
                    _lowerCamelCase			:						Tuple            =tokenizer.batch_decode(lowercase_     ,  skip_special_tokens=lowercase_					)

                    _lowerCamelCase			:						List[str]            =sum([int(pred_str[i] == label_str[i]					) for i in range(len(lowercase_					)					)]					) / len(lowercase_					)

                    return {"accuracy": accuracy}

             # map train dataset
             _lowerCamelCase			:						Tuple            =train_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  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
             _lowerCamelCase			:						Tuple            =val_dataset.map(
                 _map_to_encoder_decoder_inputs     ,  batched=lowercase_     ,  batch_size=lowercase_     ,  remove_columns=['article', 'highlights']     ,  )
             val_dataset.set_format(
                 type='torch'     ,  columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']     ,  )

             _lowerCamelCase			:						Dict            =self.get_auto_remove_tmp_dir()

             _lowerCamelCase			:						Optional[int]            =SeqaSeqTrainingArguments(
                 output_dir=lowercase_     ,  per_device_train_batch_size=lowercase_     ,  per_device_eval_batch_size=lowercase_     ,  predict_with_generate=lowercase_     ,  evaluation_strategy='steps'     ,  do_train=lowercase_     ,  do_eval=lowercase_     ,  warmup_steps=0     ,  eval_steps=2     ,  logging_steps=2     ,  )

             # instantiate trainer
             _lowerCamelCase			:						Optional[Any]            =SeqaSeqTrainer(
                 model=lowercase_     ,  args=lowercase_     ,  compute_metrics=_compute_metrics     ,  train_dataset=lowercase_     ,  eval_dataset=lowercase_     ,  tokenizer=lowercase_     ,  )

             # start training
             trainer.train()


