




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

__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)

@add_end_docstrings(__magic_name__ )
class 		__lowerCAmelCase	(	__magic_name__ ):
							def __init__(						self						:str		,      **__magic_name__						:Optional[int]				):







								'''simple docstring'''







								super().__init__(**__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(__magic_name__				)
							def __call__(						self						:Any		,      __magic_name__						:Union[str, "Image.Image", List[Dict[str, Any]]]		,      __magic_name__						:Union[str, List[str]] = None		,      **__magic_name__						:List[Any]		,      ):







								'''simple docstring'''







								if "text_queries" in kwargs:
									a										=							kwargs.pop("""text_queries"""				)

								if isinstance(__magic_name__		,      (str, Image.Image)				):
									a										=							{"""image""": image, """candidate_labels""": candidate_labels}
								else:
									a										=							image
								a										=							super().__call__(__magic_name__		,      **__magic_name__				)
								return results
							def       lowerCamelCase__							(						self						:Any		,      **__magic_name__						:Tuple				):







								'''simple docstring'''







								a										=							{}
								if "threshold" in kwargs:
									a										=							kwargs["""threshold"""]
								if "top_k" in kwargs:
									a										=							kwargs["""top_k"""]
								return {}, {}, postprocess_params
							def       lowerCamelCase__							(						self						:List[Any]		,      __magic_name__						:Any				):







								'''simple docstring'''







								a										=							load_image(inputs["""image"""]				)
								a										=							inputs["""candidate_labels"""]
								if isinstance(__magic_name__		,      __magic_name__				):
									a										=							candidate_labels.split(""","""				)

								a										=							torch.tensor([[image.height, image.width]]		,      dtype=torch.intaa				)
								for i, candidate_label in enumerate(__magic_name__				):
									a										=							self.tokenizer(__magic_name__		,      return_tensors=self.framework				)
									a										=							self.image_processor(__magic_name__		,      return_tensors=self.framework				)
									yield {
									    "is_last": i == len(__magic_name__				) - 1,
									    "target_size": target_size,
									    "candidate_label": candidate_label,
									    **text_inputs,
									    **image_features,
									}
							def       lowerCamelCase__							(						self						:Dict		,      __magic_name__						:Optional[int]				):







								'''simple docstring'''







								a										=							model_inputs.pop("""target_size"""				)
								a										=							model_inputs.pop("""candidate_label"""				)
								a										=							model_inputs.pop("""is_last"""				)

								a										=							self.model(**__magic_name__				)

								a										=							{"""target_size""": target_size, """candidate_label""": candidate_label, """is_last""": is_last, **outputs}
								return model_outputs
							def       lowerCamelCase__							(						self						:int		,      __magic_name__						:List[str]		,      __magic_name__						:Any=0.1		,      __magic_name__						:int=None				):







								'''simple docstring'''







								a										=							[]
								for model_output in model_outputs:
									a										=							model_output["""candidate_label"""]
									a										=							BaseModelOutput(__magic_name__				)
									a										=							self.image_processor.post_process_object_detection(
									    outputs=__magic_name__		,      threshold=__magic_name__		,      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(__magic_name__				)

								a										=							sorted(__magic_name__		,      key=lambda __magic_name__				: x["score"]		,      reverse=__magic_name__				)
								if top_k:
									a										=							results[:top_k]

								return results





							def       lowerCamelCase__							(						self						:str		,      __magic_name__						:"torch.Tensor"				):







								'''simple docstring'''







								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







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__UpperCamelCase					:       Optional[Any]    					=      {
    "configuration_jukebox": [
        "JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "JukeboxConfig",
        "JukeboxPriorConfig",
        "JukeboxVQVAEConfig",
    ],
    "tokenization_jukebox": ["JukeboxTokenizer"],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCamelCase					:       Tuple    					=      [
		    "JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST",
		    "JukeboxModel",
		    "JukeboxPreTrainedModel",
		    "JukeboxVQVAE",
		    "JukeboxPrior",
		]

if TYPE_CHECKING:
		from .configuration_jukebox import (
		    JUKEBOX_PRETRAINED_CONFIG_ARCHIVE_MAP,
		    JukeboxConfig,
		    JukeboxPriorConfig,
		    JukeboxVQVAEConfig,
		)
		from .tokenization_jukebox import JukeboxTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_jukebox import (
				    JUKEBOX_PRETRAINED_MODEL_ARCHIVE_LIST,
				    JukeboxModel,
				    JukeboxPreTrainedModel,
				    JukeboxPrior,
				    JukeboxVQVAE,
				)

else:
		import sys

		__UpperCamelCase					:       List[Any]    					=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

