
'''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

lowercase__						       =      logging.get_logger(__name__)


lowercase__						       =      Dict[str, Any]
lowercase__						       =      List[Prediction]






@add_end_docstrings(__SCREAMING_SNAKE_CASE					)
class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		def __init__(      self				:			str  ,	*UpperCamelCase__				:			List[str]  ,	**UpperCamelCase__				:			List[Any]      )						->			Union[str, Any]:






				"""simple docstring"""







				super().__init__(*UpperCamelCase__  ,	**UpperCamelCase__      )

				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				:			Dict  ,	**UpperCamelCase__				:			List[Any]      )						->			Optional[int]:






				"""simple docstring"""







				snake_case  :       Any     			=						{}
				if "threshold" in kwargs:
						snake_case  :       Tuple     			=						kwargs['''threshold''']
				return {}, {}, postprocess_kwargs



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






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			Tuple  ,	UpperCamelCase__				:			Optional[int]      )						->			List[Any]:






				"""simple docstring"""







				snake_case  :       List[str]     			=						load_image(UpperCamelCase__      )
				snake_case  :       List[str]     			=						torch.IntTensor([[image.height, image.width]]      )
				snake_case  :       int     			=						self.image_processor(images=[image]  ,	return_tensors='''pt'''      )
				if self.tokenizer is not None:
						snake_case  :       str     			=						self.tokenizer(text=inputs['''words''']  ,	boxes=inputs['''boxes''']  ,	return_tensors='''pt'''      )
				snake_case  :       List[Any]     			=						target_size
				return inputs



		def       lowerCAmelCase				(      self				:			Optional[Any]  ,	UpperCamelCase__				:			int      )						->			Optional[Any]:






				"""simple docstring"""







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



		def       lowerCAmelCase				(      self				:			int  ,	UpperCamelCase__				:			str  ,	UpperCamelCase__				:			Optional[int]=0.9      )						->			int:






				"""simple docstring"""







				snake_case  :       Optional[int]     			=						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.
						snake_case			,snake_case  :       str     			=						target_size[0].tolist()

						def unnormalize(UpperCamelCase__				:			Union[str, Any]      ):
								return self._get_bounding_box(
								    torch.Tensor(
								        [
								            (width * bbox[0] / 1000),
								            (height * bbox[1] / 1000),
								            (width * bbox[2] / 1000),
								            (height * bbox[3] / 1000),
								        ]      )      )

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

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

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

				return annotation



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






				"""simple docstring"""







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




'''simple docstring'''
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__						       =      logging.get_logger(__name__)

lowercase__						       =      {
    "microsoft/swin-tiny-patch4-window7-224": (
        "https://huggingface.co/microsoft/swin-tiny-patch4-window7-224/resolve/main/config.json"
    ),
    # See all Swin models at https://huggingface.co/models?filter=swin
}






class 	snake_case__       (      __SCREAMING_SNAKE_CASE    ,					__SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						"""swin"""

		lowerCamelCase     							=						{
		    """num_attention_heads""": """num_heads""",
		    """num_hidden_layers""": """num_layers""",
		}



		def __init__(      self				:			Tuple  ,	UpperCamelCase__				:			int=224  ,	UpperCamelCase__				:			Optional[int]=4  ,	UpperCamelCase__				:			str=3  ,	UpperCamelCase__				:			str=96  ,	UpperCamelCase__				:			List[Any]=[2, 2, 6, 2]  ,	UpperCamelCase__				:			Optional[Any]=[3, 6, 12, 24]  ,	UpperCamelCase__				:			Optional[Any]=7  ,	UpperCamelCase__				:			Tuple=4.0  ,	UpperCamelCase__				:			Tuple=True  ,	UpperCamelCase__				:			str=0.0  ,	UpperCamelCase__				:			Optional[Any]=0.0  ,	UpperCamelCase__				:			List[str]=0.1  ,	UpperCamelCase__				:			Optional[Any]="gelu"  ,	UpperCamelCase__				:			Optional[Any]=False  ,	UpperCamelCase__				:			Tuple=0.02  ,	UpperCamelCase__				:			List[Any]=1e-5  ,	UpperCamelCase__				:			Any=32  ,	UpperCamelCase__				:			List[str]=None  ,	UpperCamelCase__				:			int=None  ,	**UpperCamelCase__				:			Optional[Any]  ,	)						->			Optional[int]:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__      )

				snake_case  :       Any     			=						image_size
				snake_case  :       Optional[int]     			=						patch_size
				snake_case  :       List[Any]     			=						num_channels
				snake_case  :       Union[str, Any]     			=						embed_dim
				snake_case  :       str     			=						depths
				snake_case  :       str     			=						len(UpperCamelCase__      )
				snake_case  :       List[Any]     			=						num_heads
				snake_case  :       List[Any]     			=						window_size
				snake_case  :       Optional[int]     			=						mlp_ratio
				snake_case  :       Union[str, Any]     			=						qkv_bias
				snake_case  :       Optional[int]     			=						hidden_dropout_prob
				snake_case  :       Optional[int]     			=						attention_probs_dropout_prob
				snake_case  :       Optional[int]     			=						drop_path_rate
				snake_case  :       List[Any]     			=						hidden_act
				snake_case  :       int     			=						use_absolute_embeddings
				snake_case  :       str     			=						layer_norm_eps
				snake_case  :       Optional[Any]     			=						initializer_range
				snake_case  :       Any     			=						encoder_stride
				# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
				# this indicates the channel dimension after the last stage of the model
				snake_case  :       str     			=						int(embed_dim * 2 ** (len(UpperCamelCase__      ) - 1)      )
				snake_case  :       int     			=						['''stem'''] + [f'stage{idx}' for idx in range(1  ,	len(UpperCamelCase__      ) + 1      )]
				snake_case			,snake_case  :       List[Any]     			=						get_aligned_output_features_output_indices(
				    out_features=UpperCamelCase__  ,	out_indices=UpperCamelCase__  ,	stage_names=self.stage_names      )







class 	snake_case__       (      __SCREAMING_SNAKE_CASE					):







		"""simple docstring"""

		lowerCamelCase     							=						version.parse("""1.11"""					)



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			Mapping[str, Mapping[int, str]]:






				"""simple docstring"""







				return OrderedDict(
				    [
				        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
				    ]      )



		@property
		def       lowerCAmelCase				(      self				:			List[Any]      )						->			float:






				"""simple docstring"""







				return 1e-4


