






"""simple docstring"""





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import ViltImageProcessor







class 						_a  (		unittest.TestCase       ):





   '''simple docstring'''






   def __init__(       self					, UpperCAmelCase_					, UpperCAmelCase_=7					, UpperCAmelCase_=3					, UpperCAmelCase_=18					, UpperCAmelCase_=30					, UpperCAmelCase_=400					, UpperCAmelCase_=True					, UpperCAmelCase_=None					, UpperCAmelCase_=2					, UpperCAmelCase_=True					, UpperCAmelCase_=[0.5, 0.5, 0.5]					, UpperCAmelCase_=[0.5, 0.5, 0.5]					, )			->   Dict:




          '''simple docstring'''




          lowercase__:							Tuple         =     size if size is not None else {"shortest_edge": 30}
          lowercase__:							Tuple         =     parent
          lowercase__:							Optional[Any]         =     batch_size
          lowercase__:							Optional[Any]         =     num_channels
          lowercase__:							List[str]         =     image_size
          lowercase__:							Union[str, Any]         =     min_resolution
          lowercase__:							Optional[int]         =     max_resolution
          lowercase__:							Optional[Any]         =     do_resize
          lowercase__:							Any         =     size
          lowercase__:							List[Any]         =     size_divisor
          lowercase__:							List[Any]         =     do_normalize
          lowercase__:							Dict         =     image_mean
          lowercase__:							Any         =     image_std





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "size_divisor": self.size_divisor,
          }





   def __lowercase							(       self					, UpperCAmelCase_					, UpperCAmelCase_=False)			->   Optional[int]:




          '''simple docstring'''




          if not batched:
                 lowercase__:							int         =     self.size["shortest_edge"]
                 lowercase__:							int         =     image_inputs[0]
                 if isinstance(UpperCAmelCase_					, Image.Image):
                        lowercase__    ,      lowercase__:							Any         =     image.size
                 else:
                        lowercase__    ,      lowercase__:							List[str]         =     image.shape[1], image.shape[2]
                 lowercase__:							Any         =     size / min(UpperCAmelCase_					, UpperCAmelCase_)
                 if h < w:
                        lowercase__    ,      lowercase__:							List[str]         =     size, scale * w
                 else:
                        lowercase__    ,      lowercase__:							Any         =     scale * h, size

                 lowercase__:							List[Any]         =     int((1_333 / 800) * size)
                 if max(UpperCAmelCase_					, UpperCAmelCase_) > max_size:
                        lowercase__:							str         =     max_size / max(UpperCAmelCase_					, UpperCAmelCase_)
                        lowercase__:							Union[str, Any]         =     newh * scale
                        lowercase__:							Optional[Any]         =     neww * scale

                 lowercase__    ,      lowercase__:							Dict         =     int(newh + 0.5), int(neww + 0.5)
                 lowercase__    ,      lowercase__:							int         =     (
                     newh // self.size_divisor * self.size_divisor,
                     neww // self.size_divisor * self.size_divisor,
                 )

          else:
                 lowercase__:							str         =     []
                 for image in image_inputs:
                        lowercase__    ,      lowercase__:							Tuple         =     self.get_expected_values([image])
                        expected_values.append((expected_height, expected_width))
                 lowercase__:							List[str]         =     max(UpperCAmelCase_					, key=lambda UpperCAmelCase_: item[0])[0]
                 lowercase__:							str         =     max(UpperCAmelCase_					, key=lambda UpperCAmelCase_: item[1])[1]

          return expected_height, expected_width







@require_torch
@require_vision
class 						_a  (		lowercase_							,	unittest.TestCase       ):





   '''simple docstring'''






   UpperCamelCase__     	=							ViltImageProcessor if is_vision_available() else None





   def __lowercase							(       self)			->   Tuple:




          '''simple docstring'''




          lowercase__:							List[str]         =     ViltImageProcessingTester(self)





   @property
   def __lowercase							(       self)			->   Optional[int]:




          '''simple docstring'''




          return self.image_processor_tester.prepare_image_processor_dict()





   def __lowercase							(       self)			->   List[Any]:




          '''simple docstring'''




          lowercase__:							Optional[Any]         =     self.image_processing_class(**self.image_processor_dict)
          self.assertTrue(hasattr(UpperCAmelCase_					, "image_mean"))
          self.assertTrue(hasattr(UpperCAmelCase_					, "image_std"))
          self.assertTrue(hasattr(UpperCAmelCase_					, "do_normalize"))
          self.assertTrue(hasattr(UpperCAmelCase_					, "do_resize"))
          self.assertTrue(hasattr(UpperCAmelCase_					, "size"))
          self.assertTrue(hasattr(UpperCAmelCase_					, "size_divisor"))





   def __lowercase							(       self)			->   List[str]:




          '''simple docstring'''




          lowercase__:							str         =     self.image_processing_class.from_dict(self.image_processor_dict)
          self.assertEqual(image_processor.size					, {"shortest_edge": 30})

          lowercase__:							Dict         =     self.image_processing_class.from_dict(self.image_processor_dict					, size=42)
          self.assertEqual(image_processor.size					, {"shortest_edge": 42})





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          pass





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




          '''simple docstring'''




          lowercase__:							str         =     self.image_processing_class(**self.image_processor_dict)
          # create random PIL images
          lowercase__:							Tuple         =     prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_)
          for image in image_inputs:
                 self.assertIsInstance(UpperCAmelCase_					, Image.Image)

          # Test not batched input
          lowercase__:							List[str]         =     image_processing(image_inputs[0]					, return_tensors="pt").pixel_values

          lowercase__    ,      lowercase__:							List[Any]         =     self.image_processor_tester.get_expected_values(UpperCAmelCase_)
          self.assertEqual(
              encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

          # Test batched
          lowercase__:							Optional[int]         =     image_processing(UpperCAmelCase_					, return_tensors="pt").pixel_values

          lowercase__    ,      lowercase__:							Optional[int]         =     self.image_processor_tester.get_expected_values(UpperCAmelCase_					, batched=UpperCAmelCase_)
          self.assertEqual(
              encoded_images.shape					, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )					, )





   def __lowercase							(       self)			->   str:




          '''simple docstring'''




          lowercase__:							List[Any]         =     self.image_processing_class(**self.image_processor_dict)
          # create random numpy tensors
          lowercase__:							List[str]         =     prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, numpify=UpperCAmelCase_)
          for image in image_inputs:
                 self.assertIsInstance(UpperCAmelCase_					, np.ndarray)

          # Test not batched input
          lowercase__:							Tuple         =     image_processing(image_inputs[0]					, return_tensors="pt").pixel_values

          lowercase__    ,      lowercase__:							List[str]         =     self.image_processor_tester.get_expected_values(UpperCAmelCase_)
          self.assertEqual(
              encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

          # Test batched
          lowercase__:							List[Any]         =     image_processing(UpperCAmelCase_					, return_tensors="pt").pixel_values

          lowercase__    ,      lowercase__:							int         =     self.image_processor_tester.get_expected_values(UpperCAmelCase_					, batched=UpperCAmelCase_)
          self.assertEqual(
              encoded_images.shape					, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )					, )





   def __lowercase							(       self)			->   Any:




          '''simple docstring'''




          lowercase__:							Dict         =     self.image_processing_class(**self.image_processor_dict)
          # create random PyTorch tensors
          lowercase__:							int         =     prepare_image_inputs(self.image_processor_tester					, equal_resolution=UpperCAmelCase_					, torchify=UpperCAmelCase_)
          for image in image_inputs:
                 self.assertIsInstance(UpperCAmelCase_					, torch.Tensor)

          # Test not batched input
          lowercase__:							int         =     image_processing(image_inputs[0]					, return_tensors="pt").pixel_values

          lowercase__    ,      lowercase__:							int         =     self.image_processor_tester.get_expected_values(UpperCAmelCase_)
          self.assertEqual(
              encoded_images.shape					, (1, self.image_processor_tester.num_channels, expected_height, expected_width)					, )

          # Test batched
          lowercase__:							Any         =     image_processing(UpperCAmelCase_					, return_tensors="pt").pixel_values

          lowercase__    ,      lowercase__:							Optional[int]         =     self.image_processor_tester.get_expected_values(UpperCAmelCase_					, batched=UpperCAmelCase_)
          self.assertEqual(
              encoded_images.shape					, (
                  self.image_processor_tester.batch_size,
                  self.image_processor_tester.num_channels,
                  expected_height,
                  expected_width,
              )					, )












import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()




class        snake_case__				(unittest.TestCase  ):
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			:				Optional[int]									=   """hf-internal-testing/tiny-stable-diffusion-pipe"""
				SCREAMING_SNAKE_CASE_			:				str									=   1
				SCREAMING_SNAKE_CASE_			:				Dict									=   77
				SCREAMING_SNAKE_CASE_			:				List[str]									=   32
				SCREAMING_SNAKE_CASE_			:				List[str]									=   4
				SCREAMING_SNAKE_CASE_			:				Union[str, Any]									=   64
				SCREAMING_SNAKE_CASE_			:				int									=   torch.manual_seed(0  )





				def 				__UpperCAmelCase				( self							:	List[Any]   , __lowerCamelCase							:	List[Any]=0.9_999				)   ->	Tuple:
							a       =     UNetaDConditionModel.from_pretrained(self.model_id   , subfolder="unet"				)
							a       =     unet.to(__lowerCamelCase				)
							a       =     EMAModel(unet.parameters()   , decay=__lowerCamelCase   , model_cls=__lowerCamelCase   , model_config=unet.config				)
							return unet, ema_unet





				def 				__UpperCAmelCase				( self							:	Tuple				)   ->	Any:
							a       =     torch.randn(
							    self.batch_size   , self.num_in_channels   , self.latent_height   , self.latent_width   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randint(0   , 10_00   , size=(self.batch_size,)   , generator=self.generator				).to(__lowerCamelCase				)
							a       =     torch.randn(
							    self.batch_size   , self.prompt_length   , self.text_encoder_hidden_dim   , generator=self.generator				).to(__lowerCamelCase				)
							return noisy_latents, timesteps, encoder_hidden_states





				def 				__UpperCAmelCase				( self							:	Optional[int]   , __lowerCamelCase							:	Any				)   ->	Optional[int]:
							a       =     {}
							for k, param in unet.state_dict().items():
										a       =     torch.randn_like(__lowerCamelCase				) + (param * torch.randn_like(__lowerCamelCase				))
										updated_state_dict.update({k: updated_param}				)
							unet.load_state_dict(__lowerCamelCase				)
							return unet





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Union[str, Any]:
							a       ,						a       =     self.get_models()
							# Take the first (hypothetical) EMA step.
							ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 1

							# Take two more.
							for _ in range(2				):
										ema_unet.step(unet.parameters()				)
							assert ema_unet.optimization_step == 3





				def 				__UpperCAmelCase				( self							:	Optional[int]				)   ->	Optional[int]:
							a       ,						a       =     self.get_models()
							# Since the `unet` is not being updated (i.e., backprop'd)
							# there won't be any difference between the `params` of `unet`
							# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
							ema_unet.step(unet.parameters()				)
							a       =     list(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# The above holds true even if we call `ema.step()` multiple times since
							# `unet` params are still not being updated.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	Dict:
							a       ,						a       =     self.get_models()
							# Here we simulate the parameter updates for `unet`. Since there might
							# be some parameters which are initialized to zero we take extra care to
							# initialize their values to something non-zero before the multiplication.
							a       =     self.simulate_backprop(__lowerCamelCase				)

							# Take the EMA step.
							ema_unet.step(unet_pseudo_updated_step_one.parameters()				)

							# Now the EMA'd parameters won't be equal to the original model parameters.
							a       =     list(unet_pseudo_updated_step_one.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)

							# Ensure this is the case when we take multiple EMA steps.
							for _ in range(4				):
										ema_unet.step(unet.parameters()				)
							for s_param, param in zip(ema_unet.shadow_params   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Optional[Any]				)   ->	Any:
							# If we call EMA step after a backpropagation consecutively for two times,
							# the shadow params from those two steps should be different.
							a       ,						a       =     self.get_models()

							# First backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							# Second backprop + EMA
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert ~torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				def 				__UpperCAmelCase				( self							:	Any				)   ->	List[Any]:
							# If there's no decay even if there are backprops, EMA steps
							# won't take any effect i.e., the shadow params would remain the
							# same.
							a       ,						a       =     self.get_models(decay=0.0				)
							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_one.parameters()				)
							a       =     ema_unet.shadow_params

							a       =     self.simulate_backprop(__lowerCamelCase				)
							ema_unet.step(unet_step_two.parameters()				)
							a       =     ema_unet.shadow_params

							for step_one, step_two in zip(__lowerCamelCase   , __lowerCamelCase				):
										assert torch.allclose(__lowerCamelCase   , __lowerCamelCase				)





				@skip_mps
				def 				__UpperCAmelCase				( self							:	List[str]				)   ->	Dict:
							a       ,						a       =     self.get_models()
							a       ,						a       ,						a       =     self.get_dummy_inputs()

							with tempfile.TemporaryDirectory() as tmpdir:
										ema_unet.save_pretrained(__lowerCamelCase				)
										a       =     UNetaDConditionModel.from_pretrained(__lowerCamelCase   , model_cls=__lowerCamelCase				)
										a       =     loaded_unet.to(unet.device				)

							# Since no EMA step has been performed the outputs should match.
							a       =     unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample
							a       =     loaded_unet(__lowerCamelCase   , __lowerCamelCase   , __lowerCamelCase				).sample

							assert torch.allclose(__lowerCamelCase   , __lowerCamelCase   , atol=1e-4				)






