






"""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

lowerCamelCase_							      = logging.get_logger(__name__)







@add_end_docstrings(__A	)
class      UpperCamelCase_			(__A	):





	def __init__( self   :					Dict      ,      **lowerCAmelCase_   :					Any		)		->     Union[str, Any]:
				super().__init__(**lowerCAmelCase_		)

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

				requires_backends(self      ,      "vision"		)
				self.check_model_type(lowerCAmelCase_		)





	def __call__( self   :					List[Any]      ,      lowerCAmelCase_   :					Union[str, "Image.Image", List[Dict[str, Any]]]      ,      lowerCAmelCase_   :					Union[str, List[str]] = None      ,      **lowerCAmelCase_   :					Dict      ,      )		->     str:
				if "text_queries" in kwargs:
							UpperCAmelCase_	:					int     	=   kwargs.pop("text_queries"		)

				if isinstance(lowerCAmelCase_      ,      (str, Image.Image)		):
							UpperCAmelCase_	:					List[Any]     	=   {"image": image, "candidate_labels": candidate_labels}
				else:
							UpperCAmelCase_	:					Optional[int]     	=   image
				UpperCAmelCase_	:					Tuple     	=   super().__call__(lowerCAmelCase_      ,      **lowerCAmelCase_		)
				return results





	def 	_SCREAMING_SNAKE_CASE ( self   :					str      ,      **lowerCAmelCase_   :					str		)		->     Optional[Any]:
				UpperCAmelCase_	:					Tuple     	=   {}
				if "threshold" in kwargs:
							UpperCAmelCase_	:					Optional[Any]     	=   kwargs["threshold"]
				if "top_k" in kwargs:
							UpperCAmelCase_	:					int     	=   kwargs["top_k"]
				return {}, {}, postprocess_params





	def 	_SCREAMING_SNAKE_CASE ( self   :					List[str]      ,      lowerCAmelCase_   :					Dict		)		->     List[Any]:
				UpperCAmelCase_	:					Optional[int]     	=   load_image(inputs["image"]		)
				UpperCAmelCase_	:					Optional[Any]     	=   inputs["candidate_labels"]
				if isinstance(lowerCAmelCase_      ,      lowerCAmelCase_		):
							UpperCAmelCase_	:					Any     	=   candidate_labels.split(","		)

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





	def 	_SCREAMING_SNAKE_CASE ( self   :					Union[str, Any]      ,      lowerCAmelCase_   :					Optional[int]		)		->     Any:
				UpperCAmelCase_	:					str     	=   model_inputs.pop("target_size"		)
				UpperCAmelCase_	:					Tuple     	=   model_inputs.pop("candidate_label"		)
				UpperCAmelCase_	:					List[Any]     	=   model_inputs.pop("is_last"		)

				UpperCAmelCase_	:					str     	=   self.model(**lowerCAmelCase_		)

				UpperCAmelCase_	:					Dict     	=   {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
				return model_outputs





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					Dict      ,      lowerCAmelCase_   :					Union[str, Any]=0.1      ,      lowerCAmelCase_   :					Tuple=None		)		->     Union[str, Any]:
				UpperCAmelCase_	:					Dict     	=   []
				for model_output in model_outputs:
							UpperCAmelCase_	:					Union[str, Any]     	=   model_output["candidate_label"]
							UpperCAmelCase_	:					List[Any]     	=   BaseModelOutput(lowerCAmelCase_		)
							UpperCAmelCase_	:					Optional[int]     	=   self.image_processor.post_process_object_detection(
							    outputs=lowerCAmelCase_      ,      threshold=lowerCAmelCase_      ,      target_sizes=model_output["target_size"]		)[0]

							for index in outputs["scores"].nonzero():
										UpperCAmelCase_	:					Any     	=   outputs["scores"][index].item()
										UpperCAmelCase_	:					List[str]     	=   self._get_bounding_box(outputs["boxes"][index][0]		)

										UpperCAmelCase_	:					Dict     	=   {"score": score, "label": label, "box": box}
										results.append(lowerCAmelCase_		)

				UpperCAmelCase_	:					Optional[int]     	=   sorted(lowerCAmelCase_      ,      key=lambda lowerCAmelCase_		: x["score"]      ,      reverse=lowerCAmelCase_		)
				if top_k:
							UpperCAmelCase_	:					int     	=   results[:top_k]

				return results





	def 	_SCREAMING_SNAKE_CASE ( self   :					int      ,      lowerCAmelCase_   :					"torch.Tensor"		)		->     Dict[str, int]:
				if self.framework != "pt":
							raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."		)
				UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Optional[int]     	=   box.int().tolist()
				UpperCAmelCase_	:					Optional[int]     	=   {
				    "xmin": xmin,
				    "ymin": ymin,
				    "xmax": xmax,
				    "ymax": ymax,
				}
				return bbox





from typing import List

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



