
from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)


class 							lowercase   (						__UpperCAmelCase  ):



		def __init__(					self							, A_							, A_							, A_							, A_							, A_							, )    ->   Optional[int]:
				"""simple docstring"""
				super().__init__()

				self.register_modules(vae=__snake_case							, text_encoder=__snake_case							, tokenizer=__snake_case							, unet=__snake_case							, scheduler=__snake_case			)



		def __UpperCamelCase					(					self							, A_			)    ->   int:
				"""simple docstring"""





				with torch.no_grad():
						UpperCamelCase            =	self.vae.encode(tfms.ToTensor()(__snake_case			).unsqueeze(0			).to(self.device			) * 2 - 1			)
						UpperCamelCase            =	0.1_8215 * latent.latent_dist.sample()
				return latent



		def __UpperCamelCase					(					self							, A_			)    ->   Optional[Any]:
				"""simple docstring"""
				UpperCamelCase            =	(1 / 0.1_8215) * latent
				with torch.no_grad():
						UpperCamelCase            =	self.vae.decode(__snake_case			).sample
				UpperCamelCase            =	(img / 2 + 0.5).clamp(0							, 1			)
				UpperCamelCase            =	img.detach().cpu().permute(0							, 2							, 3							, 1			).numpy()
				UpperCamelCase            =	(img * 255).round().astype('uint8'			)
				return Image.fromarray(img[0]			)



		def __UpperCamelCase					(					self							, A_			)    ->   Tuple:
				"""simple docstring"""
				UpperCamelCase            =	self.tokenizer(
				    __snake_case							, padding='max_length'							, max_length=self.tokenizer.model_max_length							, truncation=__snake_case							, return_tensors='pt'							, )

				UpperCamelCase            =	self.text_encoder(text_input.input_ids.to(self.device			)			)[0]

				UpperCamelCase            =	self.tokenizer(
				    ''							, padding='max_length'							, max_length=self.tokenizer.model_max_length							, truncation=__snake_case							, return_tensors='pt'							, )

				UpperCamelCase            =	self.text_encoder(uncond_input.input_ids.to(self.device			)			)[0]

				return torch.cat([uncond_embedding, text_embedding]			)



		def __call__(					self							, A_							, A_							, A_ = 0.3							, A_ = 0.6							, A_ = 0.5							, A_ = 42							, A_ = 50							, A_ = 7.5							, )    ->   Any:
				"""simple docstring"""
				UpperCamelCase            =	steps - int(kmin * steps			)
				UpperCamelCase            =	steps - int(kmax * steps			)

				UpperCamelCase            =	self.prep_text(__snake_case			)

				self.scheduler.set_timesteps(__snake_case			)

				UpperCamelCase            =	img.size
				UpperCamelCase            =	self.encode(__snake_case			)

				torch.manual_seed(__snake_case			)
				UpperCamelCase            =	torch.randn(
				    (1, self.unet.config.in_channels, height // 8, width // 8)							, ).to(self.device			)

				UpperCamelCase            =	self.scheduler.add_noise(
				    __snake_case							, __snake_case							, timesteps=self.scheduler.timesteps[tmax]							, )

				UpperCamelCase            =	torch.cat([latents] * 2			)

				UpperCamelCase            =	self.scheduler.scale_model_input(__snake_case							, self.scheduler.timesteps[tmax]			)

				with torch.no_grad():
						UpperCamelCase            =	self.unet(
						    __snake_case							, self.scheduler.timesteps[tmax]							, encoder_hidden_states=__snake_case							, ).sample

				UpperCamelCase            =	pred.chunk(2			)
				UpperCamelCase            =	pred_uncond + guidance_scale * (pred_text - pred_uncond)

				UpperCamelCase            =	self.scheduler.step(__snake_case							, self.scheduler.timesteps[tmax]							, __snake_case			).prev_sample

				for i, t in enumerate(tqdm(self.scheduler.timesteps			)			):
						if i > tmax:
								if i < tmin:  # layout generation phase
										UpperCamelCase            =	self.scheduler.add_noise(
										    __snake_case							, __snake_case							, timesteps=__snake_case							, )

										UpperCamelCase            =	(mix_factor * latents) + (
										    1 - mix_factor
										) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
										UpperCamelCase            =	torch.cat([input] * 2			)

								else:  # content generation phase
										UpperCamelCase            =	torch.cat([latents] * 2			)

								UpperCamelCase            =	self.scheduler.scale_model_input(__snake_case							, __snake_case			)

								with torch.no_grad():
										UpperCamelCase            =	self.unet(
										    __snake_case							, __snake_case							, encoder_hidden_states=__snake_case							, ).sample

								UpperCamelCase            =	pred.chunk(2			)
								UpperCamelCase            =	pred_uncond + guidance_scale * (pred_text - pred_uncond)

								UpperCamelCase            =	self.scheduler.step(__snake_case							, __snake_case							, __snake_case			).prev_sample

				return self.decode(__snake_case			)







'''simple docstring'''






import unittest

from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    PreTrainedTokenizer,
    is_vision_available,
)
from transformers.pipelines import ImageClassificationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_torch_or_tf,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
		from PIL import Image
else:







		class       lowerCamelCase       :


							@staticmethod
							def  SCREAMING_SNAKE_CASE_		(       *__snake_case						:	Optional[Any]							,  **__snake_case						:	str							):





													'''simple docstring'''





													pass







@is_pipeline_test
@require_torch_or_tf
@require_vision
class       lowerCamelCase       (    unittest.TestCase	):
					_SCREAMING_SNAKE_CASE				 =     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
					_SCREAMING_SNAKE_CASE				 =     TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING


					def  SCREAMING_SNAKE_CASE_		(       self						:	Union[str, Any]							,  __snake_case						:	Any							,  __snake_case						:	Tuple							,  __snake_case						:	Union[str, Any]							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		ImageClassificationPipeline(model=__snake_case							,  image_processor=__snake_case							,  top_k=2							)
											_snake_case: Tuple	       =		[
											    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											    'http://images.cocodataset.org/val2017/000000039769.jpg',
											]
											return image_classifier, examples


					def  SCREAMING_SNAKE_CASE_		(       self						:	Dict							,  __snake_case						:	Any							,  __snake_case						:	str							):





											'''simple docstring'''





											_snake_case: Dict	       =		image_classifier('./tests/fixtures/tests_samples/COCO/000000039769.png'							)

											self.assertEqual(
											    __snake_case							,  [
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											    ]							,  )

											import datasets

											_snake_case: str	       =		datasets.load_dataset('hf-internal-testing/fixtures_image_utils'							,  'image'							,  split='test'							)

											# Accepts URL + PIL.Image + lists
											_snake_case: List[str]	       =		image_classifier(
											    [
											        Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							),
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        # RGBA
											        dataset[0]['file'],
											        # LA
											        dataset[1]['file'],
											        # L
											        dataset[2]['file'],
											    ]							)
											self.assertEqual(
											    __snake_case							,  [
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											        [
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											            {'score': ANY(__snake_case							), 'label': ANY(__snake_case							)},
											        ],
											    ]							,  )


					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	Optional[int]							):





											'''simple docstring'''





											_snake_case: Any	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							)

											_snake_case: List[str]	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: Dict	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					@require_tf
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: List[str]	       =		'hf-internal-testing/tiny-random-vit'
											_snake_case: List[str]	       =		pipeline('image-classification'							,  model=__snake_case							,  framework='tf'							)

											_snake_case: Any	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}]							,  )

											_snake_case: str	       =		image_classifier(
											    [
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											        'http://images.cocodataset.org/val2017/000000039769.jpg',
											    ]							,  top_k=2							,  )
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											        [{'label': 'LABEL_1', 'score': 0.574}, {'label': 'LABEL_0', 'score': 0.426}],
											    ]							,  )


					def  SCREAMING_SNAKE_CASE_		(       self						:	Tuple							):





											'''simple docstring'''





											_snake_case: Union[str, Any]	       =		PreTrainedTokenizer()

											# Assert that the pipeline can be initialized with a feature extractor that is not in any mapping
											_snake_case: Any	       =		pipeline(
											    'image-classification'							,  model='hf-internal-testing/tiny-random-vit'							,  tokenizer=__snake_case							)

											self.assertIs(image_classifier.tokenizer							,  __snake_case							)




					@slow
					@require_torch
					def  SCREAMING_SNAKE_CASE_		(       self						:	List[str]							):





											'''simple docstring'''





											_snake_case: Any	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-conv'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.4_385, 'label': 'tabby, tabby cat'},
											        {'score': 0.321, 'label': 'tiger cat'},
											        {'score': 0.0_502, 'label': 'Egyptian cat'},
											        {'score': 0.0_137, 'label': 'crib, cot'},
											        {'score': 0.007, 'label': 'radiator'},
											    ]							,  )

											_snake_case: Union[str, Any]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-fourier'							)
											_snake_case: int	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.5_658, 'label': 'tabby, tabby cat'},
											        {'score': 0.1_309, 'label': 'tiger cat'},
											        {'score': 0.0_722, 'label': 'Egyptian cat'},
											        {'score': 0.0_707, 'label': 'remote control, remote'},
											        {'score': 0.0_082, 'label': 'computer keyboard, keypad'},
											    ]							,  )

											_snake_case: Optional[int]	       =		pipeline('image-classification'							,  model='deepmind/vision-perceiver-learned'							)
											_snake_case: str	       =		image_classifier('http://images.cocodataset.org/val2017/000000039769.jpg'							)
											self.assertEqual(
											    nested_simplify(__snake_case							,  decimals=4							)							,  [
											        {'score': 0.3_022, 'label': 'tabby, tabby cat'},
											        {'score': 0.2_362, 'label': 'Egyptian cat'},
											        {'score': 0.1_856, 'label': 'tiger cat'},
											        {'score': 0.0_324, 'label': 'remote control, remote'},
											        {'score': 0.0_096, 'label': 'quilt, comforter, comfort, puff'},
											    ]							,  )





