
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

__a  			=					logging.get_logger(__name__)
@add_end_docstrings(UpperCAmelCase							)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	def __init__(       self  :			List[Any]       ,							**SCREAMING_SNAKE_CASE_  :			List[Any]       )       ->     Tuple:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

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

					requires_backends(self       ,							'''vision'''       )
					self.check_model_type(SCREAMING_SNAKE_CASE_       )
	def __call__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Union[str, "Image.Image", List[Dict[str, Any]]]       ,							SCREAMING_SNAKE_CASE_  :			Union[str, List[str]] = None       ,							**SCREAMING_SNAKE_CASE_  :			Any       ,							)       ->     Dict:
					if "text_queries" in kwargs:
									lowercase_       =					kwargs.pop('''text_queries'''       )

					if isinstance(SCREAMING_SNAKE_CASE_       ,							(str, Image.Image)       ):
									lowercase_       =					{'''image''': image, '''candidate_labels''': candidate_labels}
					else:
									lowercase_       =					image
					lowercase_       =					super().__call__(SCREAMING_SNAKE_CASE_       ,							**SCREAMING_SNAKE_CASE_       )
					return results
	def 						_lowercase  (       self  :			Optional[int]       ,							**SCREAMING_SNAKE_CASE_  :			Tuple       )       ->     Optional[Any]:
					lowercase_       =					{}
					if "threshold" in kwargs:
									lowercase_       =					kwargs['''threshold''']
					if "top_k" in kwargs:
									lowercase_       =					kwargs['''top_k''']
					return {}, {}, postprocess_params
	def 						_lowercase  (       self  :			int       ,							SCREAMING_SNAKE_CASE_  :			int       )       ->     List[str]:
					lowercase_       =					load_image(inputs['''image''']       )
					lowercase_       =					inputs['''candidate_labels''']
					if isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					candidate_labels.split(''','''       )

					lowercase_       =					torch.tensor([[image.height, image.width]]       ,							dtype=torch.intaa       )
					for i, candidate_label in enumerate(SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					self.tokenizer(SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )
									lowercase_       =					self.image_processor(SCREAMING_SNAKE_CASE_       ,							return_tensors=self.framework       )
									yield {
									    "is_last": i == len(SCREAMING_SNAKE_CASE_       ) - 1,
									    "target_size": target_size,
									    "candidate_label": candidate_label,
									    **text_inputs,
									    **image_features,
									}
	def 						_lowercase  (       self  :			str       ,							SCREAMING_SNAKE_CASE_  :			str       )       ->     Optional[int]:
					lowercase_       =					model_inputs.pop('''target_size'''       )
					lowercase_       =					model_inputs.pop('''candidate_label'''       )
					lowercase_       =					model_inputs.pop('''is_last'''       )

					lowercase_       =					self.model(**SCREAMING_SNAKE_CASE_       )

					lowercase_       =					{'''target_size''': target_size, '''candidate_label''': candidate_label, '''is_last''': is_last, **outputs}
					return model_outputs
	def 						_lowercase  (       self  :			Tuple       ,							SCREAMING_SNAKE_CASE_  :			Any       ,							SCREAMING_SNAKE_CASE_  :			Any=0.1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=None       )       ->     Dict:
					lowercase_       =					[]
					for model_output in model_outputs:
									lowercase_       =					model_output['''candidate_label''']
									lowercase_       =					BaseModelOutput(SCREAMING_SNAKE_CASE_       )
									lowercase_       =					self.image_processor.post_process_object_detection(
									    outputs=SCREAMING_SNAKE_CASE_       ,							threshold=SCREAMING_SNAKE_CASE_       ,							target_sizes=model_output['''target_size''']       )[0]

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

													lowercase_       =					{'''score''': score, '''label''': label, '''box''': box}
													results.append(SCREAMING_SNAKE_CASE_       )

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

					return results




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






from ...utils import (
    OptionalDependencyNotAvailable,
    is_torch_available,
    is_transformers_available,
    is_transformers_version,
)


try:
						if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
						from .pipeline_unclip import UnCLIPPipeline
						from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
						from .text_proj import UnCLIPTextProjModel




