

'''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  :		str  =		logging.get_logger(__name__)

@add_end_docstrings(__SCREAMING_SNAKE_CASE )
class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , **snake_case_   ):
   super().__init__(**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(snake_case_   )







 def __call__(       self  , snake_case_  , snake_case_ = None  , **snake_case_  , ):
   if "text_queries" in kwargs:
     lowercase									=kwargs.pop('''text_queries'''   )

   if isinstance(snake_case_  , (str, Image.Image)   ):
     lowercase									={'''image''': image, '''candidate_labels''': candidate_labels}
   else:
     lowercase									=image
   lowercase									=super().__call__(snake_case_  , **snake_case_   )
   return results







 def 						_A(       self  , **snake_case_   ):
   lowercase									={}
   if "threshold" in kwargs:
     lowercase									=kwargs['''threshold''']
   if "top_k" in kwargs:
     lowercase									=kwargs['''top_k''']
   return {}, {}, postprocess_params







 def 						_A(       self  , snake_case_   ):
   lowercase									=load_image(inputs['''image''']   )
   lowercase									=inputs['''candidate_labels''']
   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=candidate_labels.split(''','''   )

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







 def 						_A(       self  , snake_case_   ):
   lowercase									=model_inputs.pop('''target_size'''   )
   lowercase									=model_inputs.pop('''candidate_label'''   )
   lowercase									=model_inputs.pop('''is_last'''   )

   lowercase									=self.model(**snake_case_   )

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







 def 						_A(       self  , snake_case_  , snake_case_=0.1  , snake_case_=None   ):
   lowercase									=[]
   for model_output in model_outputs:
     lowercase									=model_output['''candidate_label''']
     lowercase									=BaseModelOutput(snake_case_   )
     lowercase									=self.image_processor.post_process_object_detection(
         outputs=snake_case_  , threshold=snake_case_  , target_sizes=model_output['''target_size''']   )[0]

     for index in outputs["scores"].nonzero():
       lowercase									=outputs['''scores'''][index].item()
       lowercase									=self._get_bounding_box(outputs['''boxes'''][index][0]   )

       lowercase									={'''score''': score, '''label''': label, '''box''': box}
       results.append(snake_case_   )

   lowercase									=sorted(snake_case_  , key=lambda snake_case_   : x["score"]  , reverse=snake_case_   )
   if top_k:
     lowercase									=results[:top_k]

   return results







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


import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding



class    a_ (			_a    ):
			a	:			Optional[Any]					   =							['''image_processor''', '''tokenizer''']
			a	:			Optional[Any]					   =							'''ChineseCLIPImageProcessor'''
			a	:			List[str]					   =							('''BertTokenizer''', '''BertTokenizerFast''')






			def __init__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):
									_lowercase					       =					None
									if "feature_extractor" in kwargs:
															warnings.warn(
															    """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
															    """ instead."""				,			__UpperCamelCase				,			)
															_lowercase					       =					kwargs.pop("""feature_extractor"""  )

									_lowercase					       =					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`."""  )

									super().__init__(__UpperCamelCase				,			__UpperCamelCase  )
									_lowercase					       =					self.image_processor






			def __call__(      self				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			__UpperCamelCase=None				,			**__UpperCamelCase  ):

									if text is None and images is None:
															raise ValueError("""You have to specify either text or images. Both cannot be none."""  )

									if text is not None:
															_lowercase					       =					self.tokenizer(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if images is not None:
															_lowercase					       =					self.image_processor(__UpperCamelCase				,			return_tensors=__UpperCamelCase				,			**__UpperCamelCase  )

									if text is not None and images is not None:
															_lowercase					       =					image_features.pixel_values
															return encoding
									elif text is not None:
															return encoding
									else:
															return BatchEncoding(data=dict(**__UpperCamelCase  )				,			tensor_type=__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.batch_decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			def    UpperCamelCase_				(      self				,			*__UpperCamelCase				,			**__UpperCamelCase  ):
									return self.tokenizer.decode(*__UpperCamelCase				,			**__UpperCamelCase  )






			@property
			def    UpperCamelCase_				(      self  ):
									_lowercase					       =					self.tokenizer.model_input_names
									_lowercase					       =					self.image_processor.model_input_names
									return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






			@property
			def    UpperCamelCase_				(      self  ):
									warnings.warn(
									    """`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."""				,			__UpperCamelCase				,			)
									return self.image_processor_class