from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin


@dataclass
class 						a__							(       __snake_case		):
			A__    :       torch.FloatTensor
			A__    :       torch.FloatTensor
			A__    :       Optional[torch.FloatTensor]        =      None






class 						a__							(       __snake_case				,						__snake_case		):
			A__    :       Optional[Any]        =      2



			@register_to_config
			def __init__(						self				,						UpperCAmelCase = 0.02				,						UpperCAmelCase = 1_0_0				,						UpperCAmelCase = 1.007				,						UpperCAmelCase = 8_0				,						UpperCAmelCase = 0.05				,						UpperCAmelCase = 5_0				,						)			->   Optional[Any]:
								# standard deviation of the initial noise distribution
								__a		 =       sigma_max

								# setable values
								__a		 =       None
								__a		 =       None
								__a		 =       None  # sigma(t_i)



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   torch.FloatTensor:
								return sample



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   int:
								__a		 =       num_inference_steps
								__a		 =       np.arange(0				,						self.num_inference_steps   )[::-1].copy()
								__a		 =       torch.from_numpy(UpperCAmelCase   ).to(UpperCAmelCase   )
								__a		 =       [
								    (
								        self.config.sigma_max**2
								        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
								    )
								    for i in self.timesteps
								]
								__a		 =       torch.tensor(UpperCAmelCase				,						dtype=torch.floataa				,						device=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = None   )			->   Tuple[torch.FloatTensor, float]:
								if self.config.s_min <= sigma <= self.config.s_max:
													__a		 =       min(self.config.s_churn / self.num_inference_steps				,						2**0.5 - 1   )
								else:
													__a		 =       0

								# sample eps ~ N(0, S_noise^2 * I)
								__a		 =       self.config.s_noise * randn_tensor(sample.shape				,						generator=UpperCAmelCase   ).to(sample.device   )
								__a		 =       sigma + gamma * sigma
								__a		 =       sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

								return sample_hat, sigma_hat



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_hat + sigma_hat * model_output
								__a		 =       (sample_hat - pred_original_sample) / sigma_hat
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * derivative

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase = True				,						)			->   Union[KarrasVeOutput, Tuple]:
								__a		 =       sample_prev + sigma_prev * model_output
								__a		 =       (sample_prev - pred_original_sample) / sigma_prev
								__a		 =       sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

								if not return_dict:
													return (sample_prev, derivative)

								return KarrasVeOutput(
								    prev_sample=UpperCAmelCase				,						derivative=UpperCAmelCase				,						pred_original_sample=UpperCAmelCase   )







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase				,						UpperCAmelCase   )			->   List[Any]:
								raise NotImplementedError()





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_    :		Dict							=	logging.get_logger(__name__)


@add_end_docstrings(__snake_case		)
class 						a__							(       __snake_case		):



			def __init__(						self				,						**UpperCAmelCase   )			->   List[str]:
								super().__init__(**UpperCAmelCase   )

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

								requires_backends(self				,						'vision'   )
								self.check_model_type(UpperCAmelCase   )



			def __call__(						self				,						UpperCAmelCase				,						UpperCAmelCase = None				,						**UpperCAmelCase				,						)			->   Tuple:
								if "text_queries" in kwargs:
													__a		 =       kwargs.pop('text_queries'   )

								if isinstance(UpperCAmelCase				,						(str, Image.Image)   ):
													__a		 =       {'image': image, 'candidate_labels': candidate_labels}
								else:
													__a		 =       image
								__a		 =       super().__call__(UpperCAmelCase				,						**UpperCAmelCase   )
								return results



			def __SCREAMING_SNAKE_CASE							(						self				,						**UpperCAmelCase   )			->   List[str]:
								__a		 =       {}
								if "threshold" in kwargs:
													__a		 =       kwargs['threshold']
								if "top_k" in kwargs:
													__a		 =       kwargs['top_k']
								return {}, {}, postprocess_params



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Union[str, Any]:
								__a		 =       load_image(inputs['image']   )
								__a		 =       inputs['candidate_labels']
								if isinstance(UpperCAmelCase				,						UpperCAmelCase   ):
													__a		 =       candidate_labels.split(','   )

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



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   str:
								__a		 =       model_inputs.pop('target_size'   )
								__a		 =       model_inputs.pop('candidate_label'   )
								__a		 =       model_inputs.pop('is_last'   )

								__a		 =       self.model(**UpperCAmelCase   )

								__a		 =       {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
								return model_outputs



			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase				,						UpperCAmelCase=0.1				,						UpperCAmelCase=None   )			->   Tuple:
								__a		 =       []
								for model_output in model_outputs:
													__a		 =       model_output['candidate_label']
													__a		 =       BaseModelOutput(UpperCAmelCase   )
													__a		 =       self.image_processor.post_process_object_detection(
													    outputs=UpperCAmelCase				,						threshold=UpperCAmelCase				,						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(UpperCAmelCase   )

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

								return results







			def __SCREAMING_SNAKE_CASE							(						self				,						UpperCAmelCase   )			->   Dict[str, int]:
								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




