
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

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





@add_end_docstrings(lowerCAmelCase_  )
class 							UpperCAmelCase				(  lowerCAmelCase_  ):




	'''simple docstring'''





	def __init__(	self :			str							,**A :			Optional[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 :			int							,A :			Union[str, "Image.Image", List[Dict[str, Any]]]							,A :			Union[str, List[str]] = None							,**A :			Dict							,):
					if "text_queries" in kwargs:
									__A             =			kwargs.pop("text_queries"			)

					if isinstance(__lowerCAmelCase							,(str, Image.Image)			):
									__A             =			{"image": image, "candidate_labels": candidate_labels}
					else:
									__A             =			image
					__A             =			super().__call__(__lowerCAmelCase							,**__lowerCAmelCase			)
					return results






	def     UpperCamelCase_  (	self :			Tuple							,**A :			Union[str, Any]			):
					__A             =			{}
					if "threshold" in kwargs:
									__A             =			kwargs["threshold"]
					if "top_k" in kwargs:
									__A             =			kwargs["top_k"]
					return {}, {}, postprocess_params






	def     UpperCamelCase_  (	self :			str							,A :			int			):
					__A             =			load_image(inputs["image"]			)
					__A             =			inputs["candidate_labels"]
					if isinstance(__lowerCAmelCase							,__lowerCAmelCase			):
									__A             =			candidate_labels.split(","			)

					__A             =			torch.tensor([[image.height, image.width]]							,dtype=torch.intaa			)
					for i, candidate_label in enumerate(__lowerCAmelCase			):
									__A             =			self.tokenizer(__lowerCAmelCase							,return_tensors=self.framework			)
									__A             =			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     UpperCamelCase_  (	self :			Any							,A :			Union[str, Any]			):
					__A             =			model_inputs.pop("target_size"			)
					__A             =			model_inputs.pop("candidate_label"			)
					__A             =			model_inputs.pop("is_last"			)

					__A             =			self.model(**__lowerCAmelCase			)

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






	def     UpperCamelCase_  (	self :			Union[str, Any]							,A :			List[str]							,A :			List[str]=0.1							,A :			Dict=None			):
					__A             =			[]
					for model_output in model_outputs:
									__A             =			model_output["candidate_label"]
									__A             =			BaseModelOutput(__lowerCAmelCase			)
									__A             =			self.image_processor.post_process_object_detection(
									    outputs=__lowerCAmelCase							,threshold=__lowerCAmelCase							,target_sizes=model_output["target_size"]			)[0]

									for index in outputs["scores"].nonzero():
													__A             =			outputs["scores"][index].item()
													__A             =			self._get_bounding_box(outputs["boxes"][index][0]			)

													__A             =			{"score": score, "label": label, "box": box}
													results.append(__lowerCAmelCase			)

					__A             =			sorted(__lowerCAmelCase							,key=lambda A			: x["score"]							,reverse=__lowerCAmelCase			)
					if top_k:
									__A             =			results[:top_k]

					return results







	def     UpperCamelCase_  (	self :			List[str]							,A :			"torch.Tensor"			):
					if self.framework != "pt":
									raise ValueError("The ZeroShotObjectDetectionPipeline is only available in PyTorch."			)
					__A      ,							__A      ,							__A      ,							__A             =			box.int().tolist()
					__A             =			{
					    "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=__SCREAMING_SNAKE_CASE  )
class 							UpperCAmelCase				(  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	snake_case_    =					field(default="automatic-speech-recognition"      ,      metadata={"include_in_asdict_even_if_is_default": True}  )
	snake_case_    =					Features({"audio": Audio()}  )
	snake_case_    =					Features({"transcription": Value("string"  )}  )
	snake_case_    =					"audio"
	snake_case_    =					"transcription"






	def     UpperCamelCase_  (	self :			Optional[int]							,A :			Union[str, Any]			):
					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]							,A			):
									raise ValueError(f'''Column {self.audio_column} is not an Audio type.'''			)
					__A             =			copy.deepcopy(self			)
					__A             =			self.input_schema.copy()
					__A             =			features[self.audio_column]
					__A             =			input_schema
					return task_template







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