'''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		:		Optional[int]  ,				*a		:		int  ,				**a		:		List[Any]    ):





         """simple docstring"""



         super().__init__(*a  ,				**a    )

         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    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				**a		:		str    ):





         """simple docstring"""



         __lowerCamelCase		=  {}
         if "threshold" in kwargs:
             __lowerCamelCase		=  kwargs['''threshold''']
         return {}, {}, postprocess_kwargs






     def __call__( self		:		str  ,				*a		:		str  ,				**a		:		Union[str, Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Union[str, Any]  ,				a		:		int    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		str  ,				a		:		Optional[Any]    ):





         """simple docstring"""



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






     def    SCREAMING_SNAKE_CASE__   ( self		:		Optional[Any]  ,				a		:		int  ,				a		:		List[str]=0.9    ):





         """simple docstring"""



         __lowerCamelCase		=  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.
             __lowerCamelCase	,  __lowerCamelCase		=  target_size[0].tolist()

             def unnormalize(a		:		Any    ):
                 return self._get_bounding_box(
                     torch.Tensor(
                         [
                             (width * bbox[0] / 10_00),
                             (height * bbox[1] / 10_00),
                             (width * bbox[2] / 10_00),
                             (height * bbox[3] / 10_00),
                         ]    )    )

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

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

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

         return annotation






     def    SCREAMING_SNAKE_CASE__   ( self		:		List[Any]  ,				a		:		"torch.Tensor"    ):





         """simple docstring"""



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

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCamelCase					:	Union[str, Any]       =  {
    '''configuration_chinese_clip''': [
        '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''ChineseCLIPConfig''',
        '''ChineseCLIPOnnxConfig''',
        '''ChineseCLIPTextConfig''',
        '''ChineseCLIPVisionConfig''',
    ],
    '''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}

try:
		if not is_vision_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	Any       =  ['''ChineseCLIPFeatureExtractor''']
		__lowerCamelCase					:	Optional[int]       =  ['''ChineseCLIPImageProcessor''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__lowerCamelCase					:	int       =  [
		    '''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ChineseCLIPModel''',
		    '''ChineseCLIPPreTrainedModel''',
		    '''ChineseCLIPTextModel''',
		    '''ChineseCLIPVisionModel''',
		]

if TYPE_CHECKING:
		from .configuration_chinese_clip import (
		    CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    ChineseCLIPConfig,
		    ChineseCLIPOnnxConfig,
		    ChineseCLIPTextConfig,
		    ChineseCLIPVisionConfig,
		)
		from .processing_chinese_clip import ChineseCLIPProcessor

		try:
				if not is_vision_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_chinese_clip import (
				    CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ChineseCLIPModel,
				    ChineseCLIPPreTrainedModel,
				    ChineseCLIPTextModel,
				    ChineseCLIPVisionModel,
				)

else:
		import sys

		__lowerCamelCase					:	Any       =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
