






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   :     List[Any] 		= logging.get_logger(__name__)



@add_end_docstrings(UpperCamelCase_		)
class 				SCREAMING_SNAKE_CASE__		(							UpperCamelCase_		):





					"""simple docstring"""






					def __init__(   self	:    str				,						**__A	:    Dict			):
						super().__init__(**__A			)

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

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



					def __call__(   self	:    str				,						__A	:    Any				,						__A	:    Dict = None				,						**__A	:    str				,						):






						if "text_queries" in kwargs:
							snake_case__					: List[str]  =    kwargs.pop("text_queries"			)

						if isinstance(__A				,						(str, Image.Image)			):
							snake_case__					: Dict  =    {"image": image, "candidate_labels": candidate_labels}
						else:
							snake_case__					: List[Any]  =    image
						snake_case__					: Dict  =    super().__call__(__A				,						**__A			)
						return results



					def 			_lowercase	(   self	:    List[str]				,						**__A	:    Optional[int]			):
						snake_case__					: Optional[int]  =    {}
						if "threshold" in kwargs:
							snake_case__					: List[Any]  =    kwargs["threshold"]
						if "top_k" in kwargs:
							snake_case__					: Dict  =    kwargs["top_k"]
						return {}, {}, postprocess_params



					def 			_lowercase	(   self	:    Tuple				,						__A	:    int			):
						snake_case__					: Tuple  =    load_image(inputs["image"]			)
						snake_case__					: List[str]  =    inputs["candidate_labels"]
						if isinstance(__A				,						__A			):
							snake_case__					: Any  =    candidate_labels.split(","			)

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



					def 			_lowercase	(   self	:    List[Any]				,						__A	:    Optional[int]			):
						snake_case__					: Union[str, Any]  =    model_inputs.pop("target_size"			)
						snake_case__					: List[str]  =    model_inputs.pop("candidate_label"			)
						snake_case__					: str  =    model_inputs.pop("is_last"			)

						snake_case__					: Any  =    self.model(**__A			)

						snake_case__					: List[str]  =    {"target_size": target_size, "candidate_label": candidate_label, "is_last": is_last, **outputs}
						return model_outputs



					def 			_lowercase	(   self	:    Optional[Any]				,						__A	:    Dict				,						__A	:    List[Any]=0.1				,						__A	:    str=None			):
						snake_case__					: Dict  =    []
						for model_output in model_outputs:
							snake_case__					: Any  =    model_output["candidate_label"]
							snake_case__					: List[str]  =    BaseModelOutput(__A			)
							snake_case__					: Optional[int]  =    self.image_processor.post_process_object_detection(
							    outputs=__A				,						threshold=__A				,						target_sizes=model_output["target_size"]			)[0]

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

								snake_case__					: List[str]  =    {"score": score, "label": label, "box": box}
								results.append(__A			)

						snake_case__					: List[Any]  =    sorted(__A				,						key=lambda __A			: x["score"]				,						reverse=__A			)
						if top_k:
							snake_case__					: List[str]  =    results[:top_k]

						return results






					def 			_lowercase	(   self	:    Optional[int]				,						__A	:    Union[str, Any]			):






						if self.framework != "pt":
							raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."			)
						snake_case__,		snake_case__,		snake_case__,		snake_case__					: Dict  =    box.int().tolist()
						snake_case__					: Union[str, Any]  =    {
						    "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


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





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilderConfig				):
  _a		     =    None
  _a		     =    None





class 					__lowerCAmelCase	(   folder_based_builder.FolderBasedBuilder				):
  _a		     =    datasets.Audio()
  _a		     =    """audio"""
  _a		     =    AudioFolderConfig
  _a		     =    42  # definition at the bottom of the script
  _a		     =    AudioClassification(audio_column="""audio"""     ,     label_column="""label"""				)
lowercase_   							=  [
    '.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',
]
lowercase_   							=  AUDIO_EXTENSIONS


