



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(A_      )
class 				__A					( A_      ):





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

						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        _snake_case    (self							,    **__magic_name__				):
						lowerCamelCase__ :      Optional[int]								=		{}
						if "threshold" in kwargs:
									lowerCamelCase__ :      Optional[int]								=		kwargs["""threshold"""]
						return {}, {}, postprocess_kwargs





			def __call__(self							,    *__magic_name__							,    **__magic_name__				):
						return super().__call__(*__magic_name__							,    **__magic_name__				)





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      List[Any]								=		load_image(__magic_name__				)
						lowerCamelCase__ :      Dict								=		torch.IntTensor([[image.height, image.width]]				)
						lowerCamelCase__ :      Dict								=		self.image_processor(images=[image]							,    return_tensors="""pt"""				)
						if self.tokenizer is not None:
									lowerCamelCase__ :      Optional[Any]								=		self.tokenizer(text=inputs["""words"""]							,    boxes=inputs["""boxes"""]							,    return_tensors="""pt"""				)
						lowerCamelCase__ :      Tuple								=		target_size
						return inputs





			def        _snake_case    (self							,    __magic_name__				):
						lowerCamelCase__ :      Tuple								=		model_inputs.pop("""target_size"""				)
						lowerCamelCase__ :      Tuple								=		self.model(**__magic_name__				)
						lowerCamelCase__ :      Optional[Any]								=		outputs.__class__({"""target_size""": target_size, **outputs}				)
						if self.tokenizer is not None:
									lowerCamelCase__ :      List[str]								=		model_inputs["""bbox"""]
						return model_outputs





			def        _snake_case    (self							,    __magic_name__							,    __magic_name__=0.9				):
						lowerCamelCase__ :      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.
									lowerCamelCase__							,lowerCamelCase__ :      List[str]								=		target_size[0].tolist()

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

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

									lowerCamelCase__ :      List[Any]								=		scores.tolist()
									lowerCamelCase__ :      Union[str, Any]								=		[self.model.config.idalabel[label.item()] for label in labels]
									lowerCamelCase__ :      Optional[int]								=		[self._get_bounding_box(__magic_name__				) for box in boxes]

									# {"scores": [...], ...} --> [{"score":x, ...}, ...]
									lowerCamelCase__ :      Tuple								=		["""score""", """label""", """box"""]
									lowerCamelCase__ :      Optional[int]								=		[
									    dict(zip(__magic_name__							,    __magic_name__				)				)
									    for vals in zip(raw_annotation["""scores"""]							,    raw_annotation["""labels"""]							,    raw_annotation["""boxes"""]				)
									]

						return annotation





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








import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=A_      )
class 				__A					( A_      ):
			UpperCamelCase					:str     =			field(default='''automatic-speech-recognition'''					, metadata={'''include_in_asdict_even_if_is_default''': True}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''audio''': Audio()}      )
			UpperCamelCase					:ClassVar[Features]     =			Features({'''transcription''': Value('''string'''      )}      )
			UpperCamelCase					:str     =			"audio"
			UpperCamelCase					:str     =			"transcription"





			def        _snake_case    (self							,    __magic_name__				):
						if self.audio_column not in features:
									raise ValueError(f"Column {self.audio_column} is not present in features."				)
						if not isinstance(features[self.audio_column]							,    __magic_name__				):
									raise ValueError(f"Column {self.audio_column} is not an Audio type."				)
						lowerCamelCase__ :      int								=		copy.deepcopy(self				)
						lowerCamelCase__ :      Tuple								=		self.input_schema.copy()
						lowerCamelCase__ :      Tuple								=		features[self.audio_column]
						lowerCamelCase__ :      List[str]								=		input_schema
						return task_template





			@property
			def        _snake_case    (self				):
						return {self.audio_column: "audio", self.transcription_column: "transcription"}



