


'''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  _lowercase   (  unittest.TestCase       ):
   def __init__(	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Dict=7					,      SCREAMING_SNAKE_CASE_					:			int=3					,      SCREAMING_SNAKE_CASE_					:			Optional[int]=18					,      SCREAMING_SNAKE_CASE_					:			Any=30					,      SCREAMING_SNAKE_CASE_					:			Dict=400					,      SCREAMING_SNAKE_CASE_					:			str=True					,      SCREAMING_SNAKE_CASE_					:			Tuple=None					,      SCREAMING_SNAKE_CASE_					:			Union[str, Any]=2					,      SCREAMING_SNAKE_CASE_					:			int=True					,      SCREAMING_SNAKE_CASE_					:			Dict=[0.5, 0.5, 0.5]					,      SCREAMING_SNAKE_CASE_					:			List[Any]=[0.5, 0.5, 0.5]					,      )   ->       Any:
        __snake_case    			=	size if size is not None else {'''shortest_edge''': 30}
        __snake_case    			=	parent
        __snake_case    			=	batch_size
        __snake_case    			=	num_channels
        __snake_case    			=	image_size
        __snake_case    			=	min_resolution
        __snake_case    			=	max_resolution
        __snake_case    			=	do_resize
        __snake_case    			=	size
        __snake_case    			=	size_divisor
        __snake_case    			=	do_normalize
        __snake_case    			=	image_mean
        __snake_case    			=	image_std
   def a     (	self					:			Optional[int]			)   ->       Optional[Any]:
        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 a     (	self					:			List[str]					,      SCREAMING_SNAKE_CASE_					:			Optional[int]					,      SCREAMING_SNAKE_CASE_					:			Tuple=False			)   ->       List[str]:
        if not batched:
             __snake_case    			=	self.size['''shortest_edge''']
             __snake_case    			=	image_inputs[0]
             if isinstance(SCREAMING_SNAKE_CASE_					,      Image.Image			):
                  __snake_case    			=	image.size
             else:
                  __snake_case    			=	image.shape[1], image.shape[2]
             __snake_case    			=	size / min(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
             if h < w:
                  __snake_case    			=	size, scale * w
             else:
                  __snake_case    			=	scale * h, size

             __snake_case    			=	int((1333 / 800) * size			)
             if max(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			) > max_size:
                  __snake_case    			=	max_size / max(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)
                  __snake_case    			=	newh * scale
                  __snake_case    			=	neww * scale

             __snake_case    			=	int(newh + 0.5			), int(neww + 0.5			)
             __snake_case    			=	(
                 newh // self.size_divisor * self.size_divisor,
                 neww // self.size_divisor * self.size_divisor,
             )

        else:
             __snake_case    			=	[]
             for image in image_inputs:
                  __snake_case    			=	self.get_expected_values([image]			)
                  expected_values.append((expected_height, expected_width)			)
             __snake_case    			=	max(SCREAMING_SNAKE_CASE_					,      key=lambda SCREAMING_SNAKE_CASE_			: item[0]			)[0]
             __snake_case    			=	max(SCREAMING_SNAKE_CASE_					,      key=lambda SCREAMING_SNAKE_CASE_			: item[1]			)[1]

        return expected_height, expected_width




@require_torch
@require_vision
class  _lowercase   (  __lowercase	,				unittest.TestCase       ):
   _SCREAMING_SNAKE_CASE     :     Any									=  ViltImageProcessor if is_vision_available() else None
   def a     (	self					:			int			)   ->       Dict:
        __snake_case    			=	ViltImageProcessingTester(self			)
   @property
   def a     (	self					:			Dict			)   ->       List[str]:
        return self.image_processor_tester.prepare_image_processor_dict()
   def a     (	self					:			Optional[Any]			)   ->       str:
        __snake_case    			=	self.image_processing_class(**self.image_processor_dict			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'image_mean'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'image_std'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'do_normalize'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'do_resize'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'size'			)			)
        self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_					,      'size_divisor'			)			)
   def a     (	self					:			Dict			)   ->       int:
        __snake_case    			=	self.image_processing_class.from_dict(self.image_processor_dict			)
        self.assertEqual(image_processor.size					,      {'shortest_edge': 30}			)

        __snake_case    			=	self.image_processing_class.from_dict(self.image_processor_dict					,      size=42			)
        self.assertEqual(image_processor.size					,      {'shortest_edge': 42}			)
   def a     (	self					:			Optional[Any]			)   ->       int:
        pass
   def a     (	self					:			Tuple			)   ->       Dict:
        # Initialize image_processing
        __snake_case    			=	self.image_processing_class(**self.image_processor_dict			)
        # create random PIL images
        __snake_case    			=	prepare_image_inputs(self.image_processor_tester					,      equal_resolution=SCREAMING_SNAKE_CASE_			)
        for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_					,      Image.Image			)

        # Test not batched input
        __snake_case    			=	image_processing(image_inputs[0]					,      return_tensors='pt'			).pixel_values

        __snake_case    			=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        __snake_case    			=	image_processing(SCREAMING_SNAKE_CASE_					,      return_tensors='pt'			).pixel_values

        __snake_case    			=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_					,      batched=SCREAMING_SNAKE_CASE_			)
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )
   def a     (	self					:			List[str]			)   ->       int:
        # Initialize image_processing
        __snake_case    			=	self.image_processing_class(**self.image_processor_dict			)
        # create random numpy tensors
        __snake_case    			=	prepare_image_inputs(self.image_processor_tester					,      equal_resolution=SCREAMING_SNAKE_CASE_					,      numpify=SCREAMING_SNAKE_CASE_			)
        for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_					,      np.ndarray			)

        # Test not batched input
        __snake_case    			=	image_processing(image_inputs[0]					,      return_tensors='pt'			).pixel_values

        __snake_case    			=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        __snake_case    			=	image_processing(SCREAMING_SNAKE_CASE_					,      return_tensors='pt'			).pixel_values

        __snake_case    			=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_					,      batched=SCREAMING_SNAKE_CASE_			)
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )





   def a     (	self					:			Dict			)   ->       Dict:
        # Initialize image_processing
        __snake_case    			=	self.image_processing_class(**self.image_processor_dict			)
        # create random PyTorch tensors
        __snake_case    			=	prepare_image_inputs(self.image_processor_tester					,      equal_resolution=SCREAMING_SNAKE_CASE_					,      torchify=SCREAMING_SNAKE_CASE_			)
        for image in image_inputs:
             self.assertIsInstance(SCREAMING_SNAKE_CASE_					,      torch.Tensor			)

        # Test not batched input
        __snake_case    			=	image_processing(image_inputs[0]					,      return_tensors='pt'			).pixel_values

        __snake_case    			=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_			)
        self.assertEqual(
            encoded_images.shape					,      (1, self.image_processor_tester.num_channels, expected_height, expected_width)					,      )

        # Test batched
        __snake_case    			=	image_processing(SCREAMING_SNAKE_CASE_					,      return_tensors='pt'			).pixel_values

        __snake_case    			=	self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_					,      batched=SCREAMING_SNAKE_CASE_			)
        self.assertEqual(
            encoded_images.shape					,      (
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )					,      )








'''simple docstring'''




import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class  _lowercase   (  __lowercase       ):
   _SCREAMING_SNAKE_CASE     :     List[str]									=  (EulerAncestralDiscreteScheduler,)
   _SCREAMING_SNAKE_CASE     :     Dict									=  1_0
   def a     (	self					:			List[str]					,      **SCREAMING_SNAKE_CASE_					:			Union[str, Any]			)   ->       List[str]:
        __snake_case    			=	{
            'num_train_timesteps': 1100,
            'beta_start': 0.0_0_0_1,
            'beta_end': 0.0_2,
            'beta_schedule': 'linear',
        }

        config.update(**SCREAMING_SNAKE_CASE_			)
        return config
   def a     (	self					:			Optional[int]			)   ->       int:
        for timesteps in [10, 50, 100, 1000]:
             self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Any			)   ->       List[Any]:
        for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]					,      [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
             self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE_					,      beta_end=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[str]			)   ->       int:
        for schedule in ["linear", "scaled_linear"]:
             self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			Dict			)   ->       Optional[Any]:
        for prediction_type in ["epsilon", "v_prediction"]:
             self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE_			)
   def a     (	self					:			List[Any]			)   ->       str:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3
   def a     (	self					:			List[Any]			)   ->       Optional[Any]:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config(prediction_type='v_prediction'			)
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps			)

        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for i, t in enumerate(scheduler.timesteps			):
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_0_8.4_4_3_9			) < 1e-2
        assert abs(result_mean.item() - 0.1_4_1_2			) < 1e-3





   def a     (	self					:			Optional[int]			)   ->       Dict:
        __snake_case    			=	self.scheduler_classes[0]
        __snake_case    			=	self.get_scheduler_config()
        __snake_case    			=	scheduler_class(**SCREAMING_SNAKE_CASE_			)

        scheduler.set_timesteps(self.num_inference_steps					,      device=SCREAMING_SNAKE_CASE_			)
        __snake_case    			=	torch.manual_seed(0			)

        __snake_case    			=	self.dummy_model()
        __snake_case    			=	self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        __snake_case    			=	sample.to(SCREAMING_SNAKE_CASE_			)

        for t in scheduler.timesteps:
             __snake_case    			=	scheduler.scale_model_input(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	model(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_			)

             __snake_case    			=	scheduler.step(SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      SCREAMING_SNAKE_CASE_					,      generator=SCREAMING_SNAKE_CASE_			)
             __snake_case    			=	output.prev_sample

        __snake_case    			=	torch.sum(torch.abs(SCREAMING_SNAKE_CASE_			)			)
        __snake_case    			=	torch.mean(torch.abs(SCREAMING_SNAKE_CASE_			)			)

        assert abs(result_sum.item() - 1_5_2.3_1_9_2			) < 1e-2
        assert abs(result_mean.item() - 0.1_9_8_3			) < 1e-3




