


"""simple docstring"""





from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase  							=					transforms.Compose(
    [
        transforms.Resize((2_56, 2_56)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)




def 	UpperCAmelCase     (		A	:						Optional[Any]       ):

		'''simple docstring'''


		if isinstance(A     ,				torch.Tensor       ):
				return image
		elif isinstance(A     ,				PIL.Image.Image       ):
				_UpperCAmelCase							 =		[image]

		_UpperCAmelCase							 =		[trans(img.convert('RGB'       )       ) for img in image]
		_UpperCAmelCase							 =		torch.stack(A       )
		return image






class 			lowercase__			(      A    ):
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case						)			->							List[Any]:
									super().__init__()

									# make sure scheduler can always be converted to DDIM
									_UpperCAmelCase							 =		DDIMScheduler.from_config(scheduler.config						)

									self.register_modules(unet=snake_case	,       scheduler=snake_case						)






							def        lowerCamelCase_   (  self	,       snake_case						)			->							Optional[Any]:
									if strength < 0 or strength > 1:
											raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}'						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							str:
									# get the original timestep using init_timestep
									_UpperCAmelCase							 =		min(int(num_inference_steps * strength						)	,       snake_case						)

									_UpperCAmelCase							 =		max(num_inference_steps - init_timestep	,       0						)
									_UpperCAmelCase							 =		self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case	,       snake_case=None						)			->							List[str]:
									if not isinstance(snake_case	,       (torch.Tensor, PIL.Image.Image, list)						):
											raise ValueError(
											    f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(snake_case						)}'						)

									_UpperCAmelCase							 =		image.to(device=snake_case	,       dtype=snake_case						)

									if isinstance(snake_case	,       snake_case						) and len(snake_case						) != batch_size:
											raise ValueError(
											    f'You have passed a list of generators of length {len(snake_case						)}, but requested an effective batch'
											    f' size of {batch_size}. Make sure the batch size matches the length of the generators.'						)

									_UpperCAmelCase							 =		init_latents.shape
									_UpperCAmelCase							 =		randn_tensor(snake_case	,       generator=snake_case	,       device=snake_case	,       dtype=snake_case						)

									# get latents
									print('add noise to latents at timestep'	,       snake_case						)
									_UpperCAmelCase							 =		self.scheduler.add_noise(snake_case	,       snake_case	,       snake_case						)
									_UpperCAmelCase							 =		init_latents

									return latents






							@torch.no_grad()
							def __call__(  self	,       snake_case = None	,       snake_case = 0.8	,       snake_case = 1	,       snake_case = None	,       snake_case = 0.0	,       snake_case = 50	,       snake_case = None	,       snake_case = "pil"	,       snake_case = True	,       )			->							Union[ImagePipelineOutput, Tuple]:
									self.check_inputs(snake_case						)

									# 2. Preprocess image
									_UpperCAmelCase							 =		preprocess(snake_case						)

									# 3. set timesteps
									self.scheduler.set_timesteps(snake_case	,       device=self.device						)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.get_timesteps(snake_case	,       snake_case	,       self.device						)
									_UpperCAmelCase							 =		timesteps[:1].repeat(snake_case						)

									# 4. Prepare latent variables
									_UpperCAmelCase							 =		self.prepare_latents(snake_case	,       snake_case	,       snake_case	,       self.unet.dtype	,       self.device	,       snake_case						)
									_UpperCAmelCase							 =		latents

									# 5. Denoising loop
									for t in self.progress_bar(snake_case						):
											# 1. predict noise model_output
											_UpperCAmelCase							 =		self.unet(snake_case	,       snake_case						).sample

											# 2. predict previous mean of image x_t-1 and add variance depending on eta
											# eta corresponds to η in paper and should be between [0, 1]
											# do x_t -> x_t-1
											_UpperCAmelCase							 =		self.scheduler.step(
											    snake_case	,       snake_case	,       snake_case	,       eta=snake_case	,       use_clipped_model_output=snake_case	,       generator=snake_case	,       ).prev_sample

									_UpperCAmelCase							 =		(image / 2 + 0.5).clamp(0	,       1						)
									_UpperCAmelCase							 =		image.cpu().permute(0	,       2	,       3	,       1						).numpy()
									if output_type == "pil":
											_UpperCAmelCase							 =		self.numpy_to_pil(snake_case						)

									if not return_dict:
											return (image, latent_timestep.item())

									return ImagePipelineOutput(images=snake_case						)







"""simple docstring"""





import inspect
import unittest
from math import floor

from transformers import CvtConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
					import torch

					from transformers import CvtForImageClassification, CvtModel
					from transformers.models.cvt.modeling_cvt import CVT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor




class 			lowercase__			(      A    ):
							'''simple docstring'''







							def        lowerCamelCase_   (  self						)			->							int:
									_UpperCAmelCase							 =		self.config_class(**self.inputs_dict						)
									self.parent.assertTrue(hasattr(snake_case	,       'embed_dim'						)						)
									self.parent.assertTrue(hasattr(snake_case	,       'num_heads'						)						)







class 			lowercase__			:
							'''simple docstring'''







							def __init__(  self	,       snake_case	,       snake_case=13	,       snake_case=64	,       snake_case=3	,       snake_case=[16, 48, 96]	,       snake_case=[1, 3, 6]	,       snake_case=[1, 2, 10]	,       snake_case=[7, 3, 3]	,       snake_case=[4, 2, 2]	,       snake_case=[2, 1, 1]	,       snake_case=[2, 2, 2]	,       snake_case=[False, False, True]	,       snake_case=[0.0, 0.0, 0.0]	,       snake_case=0.02	,       snake_case=1E-12	,       snake_case=True	,       snake_case=True	,       snake_case=2	,       )			->							Tuple:
									_UpperCAmelCase							 =		parent
									_UpperCAmelCase							 =		batch_size
									_UpperCAmelCase							 =		image_size
									_UpperCAmelCase							 =		patch_sizes
									_UpperCAmelCase							 =		patch_stride
									_UpperCAmelCase							 =		patch_padding
									_UpperCAmelCase							 =		is_training
									_UpperCAmelCase							 =		use_labels
									_UpperCAmelCase							 =		num_labels
									_UpperCAmelCase							 =		num_channels
									_UpperCAmelCase							 =		embed_dim
									_UpperCAmelCase							 =		num_heads
									_UpperCAmelCase							 =		stride_kv
									_UpperCAmelCase							 =		depth
									_UpperCAmelCase							 =		cls_token
									_UpperCAmelCase							 =		attention_drop_rate
									_UpperCAmelCase							 =		initializer_range
									_UpperCAmelCase							 =		layer_norm_eps






							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

									_UpperCAmelCase							 =		None
									if self.use_labels:
											_UpperCAmelCase							 =		ids_tensor([self.batch_size]	,       self.num_labels						)

									_UpperCAmelCase							 =		self.get_config()
									return config, pixel_values, labels






							def        lowerCamelCase_   (  self						)			->							List[str]:
									return CvtConfig(
									    image_size=self.image_size	,       num_labels=self.num_labels	,       num_channels=self.num_channels	,       embed_dim=self.embed_dim	,       num_heads=self.num_heads	,       patch_sizes=self.patch_sizes	,       patch_padding=self.patch_padding	,       patch_stride=self.patch_stride	,       stride_kv=self.stride_kv	,       depth=self.depth	,       cls_token=self.cls_token	,       attention_drop_rate=self.attention_drop_rate	,       initializer_range=self.initializer_range	,       )






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[int]:
									_UpperCAmelCase							 =		CvtModel(config=snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case						)
									_UpperCAmelCase							 =		(self.image_size, self.image_size)
									_UpperCAmelCase ,					_UpperCAmelCase							 =		image_size[0], image_size[1]
									for i in range(len(self.depth						)						):
											_UpperCAmelCase							 =		floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
											_UpperCAmelCase							 =		floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1						)
									self.parent.assertEqual(result.last_hidden_state.shape	,       (self.batch_size, self.embed_dim[-1], height, width)						)






							def        lowerCamelCase_   (  self	,       snake_case	,       snake_case	,       snake_case						)			->							Optional[Any]:
									_UpperCAmelCase							 =		self.num_labels
									_UpperCAmelCase							 =		CvtForImageClassification(snake_case						)
									model.to(snake_case						)
									model.eval()
									_UpperCAmelCase							 =		model(snake_case	,       labels=snake_case						)
									self.parent.assertEqual(result.logits.shape	,       (self.batch_size, self.num_labels)						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		self.prepare_config_and_inputs()
									_UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase							 =		config_and_inputs
									_UpperCAmelCase							 =		{'pixel_values': pixel_values}
									return config, inputs_dict







@require_torch
class 			lowercase__			(      A,					A,					unittest.TestCase    ):
							'''simple docstring'''







							_UpperCAmelCase      				=					(CvtModel, CvtForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase      				=					(
							    {'''feature-extraction''': CvtModel, '''image-classification''': CvtForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False
							_UpperCAmelCase      				=					False






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									_UpperCAmelCase							 =		CvtModelTester(self						)
									_UpperCAmelCase							 =		ConfigTester(self	,       config_class=snake_case	,       has_text_modality=snake_case	,       hidden_size=37						)






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()






							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									return






							@unittest.skip(reason='Cvt does not output attentions'						)
							def        lowerCamelCase_   (  self						)			->							str:
									pass






							@unittest.skip(reason='Cvt does not use inputs_embeds'						)
							def        lowerCamelCase_   (  self						)			->							int:
									pass






							@unittest.skip(reason='Cvt does not support input and output embeddings'						)
							def        lowerCamelCase_   (  self						)			->							Union[str, Any]:
									pass






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_UpperCAmelCase							 =		model_class(snake_case						)
											_UpperCAmelCase							 =		inspect.signature(model.forward						)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											_UpperCAmelCase							 =		[*signature.parameters.keys()]

											_UpperCAmelCase							 =		['pixel_values']
											self.assertListEqual(arg_names[:1]	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*snake_case						)






							def        lowerCamelCase_   (  self						)			->							Optional[int]:
									def check_hidden_states_output(snake_case	,       snake_case	,       snake_case						):
											_UpperCAmelCase							 =		model_class(snake_case						)
											model.to(snake_case						)
											model.eval()

											with torch.no_grad():
													_UpperCAmelCase							 =		model(**self._prepare_for_class(snake_case	,       snake_case						)						)

											_UpperCAmelCase							 =		outputs.hidden_states

											_UpperCAmelCase							 =		len(self.model_tester.depth						)
											self.assertEqual(len(snake_case						)	,       snake_case						)

											# verify the first hidden states (first block)
											self.assertListEqual(
											    list(hidden_states[0].shape[-3:]						)	,       [
											        self.model_tester.embed_dim[0],
											        self.model_tester.image_size // 4,
											        self.model_tester.image_size // 4,
											    ]	,       )

									_UpperCAmelCase ,					_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											_UpperCAmelCase							 =		True
											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)

											# check that output_hidden_states also work using config
											del inputs_dict["output_hidden_states"]
											_UpperCAmelCase							 =		True

											check_hidden_states_output(snake_case	,       snake_case	,       snake_case						)






							def        lowerCamelCase_   (  self						)			->							Any:
									_UpperCAmelCase							 =		self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*snake_case						)






							@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'						)
							def        lowerCamelCase_   (  self						)			->							Dict:
									pass






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									for model_name in CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											_UpperCAmelCase							 =		CvtModel.from_pretrained(snake_case						)
											self.assertIsNotNone(snake_case						)







def 	UpperCAmelCase     (		):

		'''simple docstring'''


		_UpperCAmelCase							 =		Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'       )
		return image






@require_torch
@require_vision
class 			lowercase__			(      unittest.TestCase    ):
							'''simple docstring'''







							@cached_property
							def        lowerCamelCase_   (  self						)			->							List[Any]:
									return AutoImageProcessor.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						)






							@slow
							def        lowerCamelCase_   (  self						)			->							Dict:
									_UpperCAmelCase							 =		CvtForImageClassification.from_pretrained(CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(snake_case						)

									_UpperCAmelCase							 =		self.default_image_processor
									_UpperCAmelCase							 =		prepare_img()
									_UpperCAmelCase							 =		image_processor(images=snake_case	,       return_tensors='pt'						).to(snake_case						)

									# forward pass
									with torch.no_grad():
											_UpperCAmelCase							 =		model(**snake_case						)

									# verify the logits
									_UpperCAmelCase							 =		torch.Size((1, 1000)						)
									self.assertEqual(outputs.logits.shape	,       snake_case						)

									_UpperCAmelCase							 =		torch.tensor([0.9285, 0.9015, -0.3150]						).to(snake_case						)

									self.assertTrue(torch.allclose(outputs.logits[0, :3]	,       snake_case	,       atol=1E-4						)						)


