"""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, ChunkPipeline


if is_vision_available():
       from PIL import Image

       from ..image_utils import load_image

if is_torch_available():
       import torch

       from transformers.modeling_outputs import BaseModelOutput

       from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

_UpperCamelCase   :						Dict      =    logging.get_logger(__name__)


@add_end_docstrings(_a)
class      UpperCAmelCase_	(    _a):


      def __init__(   self		,			**a							)       ->					Dict:
             super().__init__(**a							)

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

             requires_backends(self		,			'vision'							)
             self.check_model_type(a							)


      def __call__(   self		,			a		,			a = None		,			**a		,			)       ->					List[str]:
             if "text_queries" in kwargs:
                    lowercase__			:		Optional[Any]       =	kwargs.pop('text_queries'							)

             if isinstance(a		,			(str, Image.Image)							):
                    lowercase__			:		Optional[Any]       =	{'image': image, 'candidate_labels': candidate_labels}
             else:
                    lowercase__			:		List[str]       =	image
             lowercase__			:		Optional[Any]       =	super().__call__(a		,			**a							)
             return results


      def 		_UpperCAmelCase       (   self		,			**a							)       ->					Dict:
             lowercase__			:		Optional[Any]       =	{}
             if "threshold" in kwargs:
                    lowercase__			:		Tuple       =	kwargs['threshold']
             if "top_k" in kwargs:
                    lowercase__			:		List[Any]       =	kwargs['top_k']
             return {}, {}, postprocess_params


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict:
             lowercase__			:		Any       =	load_image(inputs['image']							)
             lowercase__			:		Optional[int]       =	inputs['candidate_labels']
             if isinstance(a		,			a							):
                    lowercase__			:		Optional[int]       =	candidate_labels.split(','							)

             lowercase__			:		Optional[int]       =	torch.tensor([[image.height, image.width]]		,			dtype=torch.intaa							)
             for i, candidate_label in enumerate(a							):
                    lowercase__			:		List[str]       =	self.tokenizer(a		,			return_tensors=self.framework							)
                    lowercase__			:		List[Any]       =	self.image_processor(a		,			return_tensors=self.framework							)
                    yield {
                        "is_last": i == len(a							) - 1,
                        "target_size": target_size,
                        "candidate_label": candidate_label,
                        **text_inputs,
                        **image_features,
                    }


      def 		_UpperCAmelCase       (   self		,			a							)       ->					List[Any]:
             lowercase__			:		List[Any]       =	model_inputs.pop('target_size'							)
             lowercase__			:		Dict       =	model_inputs.pop('candidate_label'							)
             lowercase__			:		Dict       =	model_inputs.pop('is_last'							)

             lowercase__			:		Optional[int]       =	self.model(**a							)

             lowercase__			:		Any       =	{'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
             return model_outputs


      def 		_UpperCAmelCase       (   self		,			a		,			a=0.1		,			a=None							)       ->					Union[str, Any]:
             lowercase__			:		Dict       =	[]
             for model_output in model_outputs:
                    lowercase__			:		List[Any]       =	model_output['candidate_label']
                    lowercase__			:		Optional[int]       =	BaseModelOutput(a							)
                    lowercase__			:		Any       =	self.image_processor.post_process_object_detection(
                        outputs=a		,			threshold=a		,			target_sizes=model_output['target_size']							)[0]

                    for index in outputs["scores"].nonzero():
                           lowercase__			:		Union[str, Any]       =	outputs['scores'][index].item()
                           lowercase__			:		Tuple       =	self._get_bounding_box(outputs['boxes'][index][0]							)

                           lowercase__			:		Tuple       =	{'score': score, 'label': label, 'box': box}
                           results.append(a							)

             lowercase__			:		Dict       =	sorted(a		,			key=lambda a							: x["score"]		,			reverse=a							)
             if top_k:
                    lowercase__			:		Dict       =	results[:top_k]

             return results


      def 		_UpperCAmelCase       (   self		,			a							)       ->					Dict[str, int]:
             if self.framework != "pt":
                    raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'							)
             lowercase__    ,      lowercase__    ,      lowercase__    ,      lowercase__			:		Tuple       =	box.int().tolist()
             lowercase__			:		Any       =	{
                 'xmin': xmin,
                 'ymin': ymin,
                 'xmax': xmax,
                 'ymax': ymax,
             }
             return bbox


import warnings

from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum

from ...processing_utils import ProcessorMixin


if is_torch_available():
      import torch


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  '''char'''
  lowerCamelCase__   =  '''bpe'''
  lowerCamelCase__   =  '''wp'''



A   :     Tuple       =				(DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)


class      lowerCamelCase    (SCREAMING_SNAKE_CASE__    ):
  """simple docstring"""







  lowerCamelCase__   =  ['''image_processor''', '''char_tokenizer''']
  lowerCamelCase__   =  '''ViTImageProcessor'''
  lowerCamelCase__   =  '''MgpstrTokenizer'''







  def __init__(							self       :				Optional[Any]     ,   __magic_name__       :				Tuple=None     ,   __magic_name__       :				int=None     ,   **__magic_name__       :				Optional[Any]      )     ->		str:
        SCREAMING_SNAKE_CASE_      =     None
        if "feature_extractor" in kwargs:
              warnings.warn(
                  "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                  " instead."     ,   __magic_name__     ,   )
              SCREAMING_SNAKE_CASE_      =     kwargs.pop("feature_extractor"      )

        SCREAMING_SNAKE_CASE_      =     image_processor if image_processor is not None else feature_extractor
        if image_processor is None:
              raise ValueError("You need to specify an `image_processor`."      )
        if tokenizer is None:
              raise ValueError("You need to specify a `tokenizer`."      )

        SCREAMING_SNAKE_CASE_      =     tokenizer
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("gpt2"      )
        SCREAMING_SNAKE_CASE_      =     AutoTokenizer.from_pretrained("bert-base-uncased"      )

        super().__init__(__magic_name__     ,   __magic_name__      )







  def __call__(							self       :				Dict     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Union[str, Any]=None     ,   __magic_name__       :				Dict=None     ,   **__magic_name__       :				Tuple      )     ->		int:
        if images is None and text is None:
              raise ValueError("You need to specify either an `images` or `text` input to process."      )

        if images is not None:
              SCREAMING_SNAKE_CASE_      =     self.image_processor(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )
        if text is not None:
              SCREAMING_SNAKE_CASE_      =     self.char_tokenizer(__magic_name__     ,   return_tensors=__magic_name__     ,   **__magic_name__      )

        if text is None:
              return inputs
        elif images is None:
              return encodings
        else:
              SCREAMING_SNAKE_CASE_      =     encodings["input_ids"]
              return inputs







  def       __A   (							self       :				Tuple     ,   __magic_name__       :				int      )     ->		Any:
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     sequences
        SCREAMING_SNAKE_CASE_      =     char_preds.size(0      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "char"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "bpe"      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     self._decode_helper(__magic_name__     ,   "wp"      )

        SCREAMING_SNAKE_CASE_      =     []
        SCREAMING_SNAKE_CASE_      =     []
        for i in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     [char_scores[i], bpe_scores[i], wp_scores[i]]
              SCREAMING_SNAKE_CASE_      =     [char_strs[i], bpe_strs[i], wp_strs[i]]
              SCREAMING_SNAKE_CASE_      =     scores.index(max(__magic_name__      )      )
              final_strs.append(strs[max_score_index]      )
              final_scores.append(scores[max_score_index]      )

        SCREAMING_SNAKE_CASE_      =     {}
        SCREAMING_SNAKE_CASE_      =     final_strs
        SCREAMING_SNAKE_CASE_      =     final_scores
        SCREAMING_SNAKE_CASE_      =     char_strs
        SCREAMING_SNAKE_CASE_      =     bpe_strs
        SCREAMING_SNAKE_CASE_      =     wp_strs
        return out







  def       __A   (							self       :				int     ,   __magic_name__       :				List[Any]     ,   __magic_name__       :				str      )     ->		Any:
        if format == DecodeType.CHARACTER:
              SCREAMING_SNAKE_CASE_      =     self.char_decode
              SCREAMING_SNAKE_CASE_      =     1
              SCREAMING_SNAKE_CASE_      =     "[s]"
        elif format == DecodeType.BPE:
              SCREAMING_SNAKE_CASE_      =     self.bpe_decode
              SCREAMING_SNAKE_CASE_      =     2
              SCREAMING_SNAKE_CASE_      =     "#"
        elif format == DecodeType.WORDPIECE:
              SCREAMING_SNAKE_CASE_      =     self.wp_decode
              SCREAMING_SNAKE_CASE_      =     102
              SCREAMING_SNAKE_CASE_      =     "[SEP]"
        else:
              raise ValueError(F'''Format {format} is not supported.'''      )

        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     [], []
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(0      )
        SCREAMING_SNAKE_CASE_      =     pred_logits.size(1      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     pred_logits.topk(1     ,   dim=-1     ,   largest=__magic_name__     ,   sorted=__magic_name__      )
        SCREAMING_SNAKE_CASE_      =     preds_index.view(-1     ,   __magic_name__      )[:, 1:]
        SCREAMING_SNAKE_CASE_      =     decoder(__magic_name__      )
        SCREAMING_SNAKE_CASE_   ,       SCREAMING_SNAKE_CASE_      =     torch.nn.functional.softmax(__magic_name__     ,   dim=2      ).max(dim=2      )
        SCREAMING_SNAKE_CASE_      =     preds_max_prob[:, 1:]

        for index in range(__magic_name__      ):
              SCREAMING_SNAKE_CASE_      =     preds_str[index].find(__magic_name__      )
              SCREAMING_SNAKE_CASE_      =     preds_str[index][:pred_eos]
              SCREAMING_SNAKE_CASE_      =     preds_index[index].cpu().tolist()
              SCREAMING_SNAKE_CASE_      =     pred_index.index(__magic_name__      ) if eos_token in pred_index else -1
              SCREAMING_SNAKE_CASE_      =     preds_max_prob[index][: pred_eos_index + 1]
              SCREAMING_SNAKE_CASE_      =     pred_max_prob.cumprod(dim=0      )[-1] if pred_max_prob.nelement() != 0 else 0.0
              dec_strs.append(__magic_name__      )
              conf_scores.append(__magic_name__      )

        return dec_strs, conf_scores







  def       __A   (							self       :				Any     ,   __magic_name__       :				Dict      )     ->		List[str]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.char_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs







  def       __A   (							self       :				Any     ,   __magic_name__       :				Union[str, Any]      )     ->		Tuple:
        return self.bpe_tokenizer.batch_decode(__magic_name__      )







  def       __A   (							self       :				str     ,   __magic_name__       :				Dict      )     ->		List[Any]:
        SCREAMING_SNAKE_CASE_      =     [seq.replace(" "     ,   ""      ) for seq in self.wp_tokenizer.batch_decode(__magic_name__      )]
        return decode_strs


