

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__								=		logging.get_logger(__name__)







@add_end_docstrings(_lowercase							)
class 	lowercase    (				_lowercase							):


		"""simple docstring"""




		def __init__(				self	,       **__snake_case):
			super().__init__(**__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(__snake_case)


		def __call__(				self	,       __snake_case	,       __snake_case = None	,       **__snake_case	,       ):
			if "text_queries" in kwargs:
				_UpperCamelCase   :		List[Any]			=						kwargs.pop('text_queries')

			if isinstance(__snake_case	,       (str, Image.Image)):
				_UpperCamelCase   :		Union[str, Any]			=						{'image': image, 'candidate_labels': candidate_labels}
			else:
				_UpperCamelCase   :		int			=						image
			_UpperCamelCase   :		Tuple			=						super().__call__(__snake_case	,       **__snake_case)
			return results


		def 		A__					(				self	,       **__snake_case):
			_UpperCamelCase   :		int			=						{}
			if "threshold" in kwargs:
				_UpperCamelCase   :		Tuple			=						kwargs['threshold']
			if "top_k" in kwargs:
				_UpperCamelCase   :		Optional[Any]			=						kwargs['top_k']
			return {}, {}, postprocess_params


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Dict			=						load_image(inputs['image'])
			_UpperCamelCase   :		Dict			=						inputs['candidate_labels']
			if isinstance(__snake_case	,       __snake_case):
				_UpperCamelCase   :		Tuple			=						candidate_labels.split(',')

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


		def 		A__					(				self	,       __snake_case):
			_UpperCamelCase   :		Any			=						model_inputs.pop('target_size')
			_UpperCamelCase   :		str			=						model_inputs.pop('candidate_label')
			_UpperCamelCase   :		List[str]			=						model_inputs.pop('is_last')

			_UpperCamelCase   :		Any			=						self.model(**__snake_case)

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


		def 		A__					(				self	,       __snake_case	,       __snake_case=0.1	,       __snake_case=None):
			_UpperCamelCase   :		Union[str, Any]			=						[]
			for model_output in model_outputs:
				_UpperCamelCase   :		Union[str, Any]			=						model_output['candidate_label']
				_UpperCamelCase   :		Any			=						BaseModelOutput(__snake_case)
				_UpperCamelCase   :		Union[str, Any]			=						self.image_processor.post_process_object_detection(
				    outputs=__snake_case	,       threshold=__snake_case	,       target_sizes=model_output['target_size'])[0]

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

					_UpperCamelCase   :		Optional[int]			=						{'score': score, 'label': label, 'box': box}
					results.append(__snake_case)

			_UpperCamelCase   :		Any			=						sorted(__snake_case	,       key=lambda __snake_case: x["score"]	,       reverse=__snake_case)
			if top_k:
				_UpperCamelCase   :		str			=						results[:top_k]

			return results






		def 		A__					(				self	,       __snake_case):
			if self.framework != "pt":
				raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.')
			_UpperCamelCase   :		List[Any]			=						box.int().tolist()
			_UpperCamelCase   :		str			=						{
			    'xmin': xmin,
			    'ymin': ymin,
			    'xmax': xmax,
			    'ymax': ymax,
			}
			return bbox







import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowerCAmelCase__								=		False







class 	lowercase    (				unittest.TestCase							):


		"""simple docstring"""




		def 		A__					(				self	,       __snake_case=32):
			set_seed(0)
			_UpperCamelCase   :		int			=						UNetaDModel(sample_size=__snake_case	,       in_channels=3	,       out_channels=3)
			_UpperCamelCase   :		str			=						torch.optim.SGD(model.parameters()	,       lr=0.0_0_0_1)
			return model, optimizer






		@slow
		def 		A__					(				self):
			_UpperCamelCase   :		Tuple			=						'cpu'  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
			_UpperCamelCase   :		List[Any]			=						DDPMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )
			_UpperCamelCase   :		List[Any]			=						DDIMScheduler(
			    num_train_timesteps=10_00	,       beta_start=0.0_0_0_1	,       beta_end=0.0_2	,       beta_schedule='linear'	,       clip_sample=__snake_case	,       )

			assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

			# shared batches for DDPM and DDIM
			set_seed(0)
			_UpperCamelCase   :		Optional[Any]			=						[torch.randn((4, 3, 32, 32)).clip(-1	,       1).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		str			=						[torch.randn((4, 3, 32, 32)).to(__snake_case) for _ in range(4)]
			_UpperCamelCase   :		int			=						[torch.randint(0	,       10_00	,       (4,)).long().to(__snake_case) for _ in range(4)]

			# train with a DDPM scheduler
			_UpperCamelCase	,		_UpperCamelCase   :		List[Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		int			=						ddpm_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Any			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		str			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			# recreate the model and optimizer, and retry with DDIM
			_UpperCamelCase	,		_UpperCamelCase   :		Union[str, Any]			=						self.get_model_optimizer(resolution=32)
			model.train().to(__snake_case)
			for i in range(4):
				optimizer.zero_grad()
				_UpperCamelCase   :		Dict			=						ddim_scheduler.add_noise(clean_images[i]	,       noise[i]	,       timesteps[i])
				_UpperCamelCase   :		Dict			=						model(__snake_case	,       timesteps[i]).sample
				_UpperCamelCase   :		Tuple			=						torch.nn.functional.mse_loss(__snake_case	,       noise[i])
				loss.backward()
				optimizer.step()
			del model, optimizer

			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))
			self.assertTrue(torch.allclose(__snake_case	,       __snake_case	,       atol=1e-5))




