


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 MobileNetVaImageProcessor




class    A_   ( unittest.TestCase     ):







 """simple docstring"""





 def __init__(			self					: Dict       ,    _a					: List[str]       ,    _a					: List[Any]=7       ,    _a					: Union[str, Any]=3       ,    _a					: List[Any]=1_8       ,    _a					: Tuple=3_0       ,    _a					: str=4_0_0       ,    _a					: List[Any]=True       ,    _a					: Any=None       ,    _a					: int=True       ,    _a					: Any=None       ,    )    ->			Tuple:
      UpperCAmelCase						:				int		  =			size if size is not None else {"shortest_edge": 2_0}
      UpperCAmelCase						:				Tuple		  =			crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}
      UpperCAmelCase						:				str		  =			parent
      UpperCAmelCase						:				Any		  =			batch_size
      UpperCAmelCase						:				Dict		  =			num_channels
      UpperCAmelCase						:				int		  =			image_size
      UpperCAmelCase						:				Union[str, Any]		  =			min_resolution
      UpperCAmelCase						:				Tuple		  =			max_resolution
      UpperCAmelCase						:				Any		  =			do_resize
      UpperCAmelCase						:				Optional[int]		  =			size
      UpperCAmelCase						:				Tuple		  =			do_center_crop
      UpperCAmelCase						:				List[str]		  =			crop_size

 def   _UpperCamelCase    (			self					: Any				)    ->			str:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }




@require_torch
@require_vision
class    A_   ( a_      ,						unittest.TestCase     ):







 """simple docstring"""





 _UpperCamelCase				: List[Any]      =					MobileNetVaImageProcessor if is_vision_available() else None
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Any:
      UpperCAmelCase						:				Dict		  =			MobileNetVaImageProcessingTester(self				)
 @property
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			Dict:
      return self.image_processor_tester.prepare_image_processor_dict()
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			Tuple:
      UpperCAmelCase						:				List[Any]		  =			self.image_processing_class(**self.image_processor_dict				)
      self.assertTrue(hasattr(__lowerCamelCase       ,    "do_resize"				)				)
      self.assertTrue(hasattr(__lowerCamelCase       ,    "size"				)				)
      self.assertTrue(hasattr(__lowerCamelCase       ,    "do_center_crop"				)				)
      self.assertTrue(hasattr(__lowerCamelCase       ,    "center_crop"				)				)
 def   _UpperCamelCase    (			self					: Optional[int]				)    ->			Union[str, Any]:
      UpperCAmelCase						:				int		  =			self.image_processing_class.from_dict(self.image_processor_dict				)
      self.assertEqual(image_processor.size       ,    {"shortest_edge": 2_0}				)
      self.assertEqual(image_processor.crop_size       ,    {"height": 1_8, "width": 1_8}				)

      UpperCAmelCase						:				Any		  =			self.image_processing_class.from_dict(self.image_processor_dict       ,    size=4_2       ,    crop_size=8_4				)
      self.assertEqual(image_processor.size       ,    {"shortest_edge": 4_2}				)
      self.assertEqual(image_processor.crop_size       ,    {"height": 8_4, "width": 8_4}				)
 def   _UpperCamelCase    (			self					: Any				)    ->			Union[str, Any]:
      pass
 def   _UpperCamelCase    (			self					: List[str]				)    ->			List[Any]:
      # Initialize image_processing
      UpperCAmelCase						:				Optional[int]		  =			self.image_processing_class(**self.image_processor_dict				)
      # create random PIL images
      UpperCAmelCase						:				List[Any]		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCamelCase				)
      for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase       ,    Image.Image				)

      # Test not batched input
      UpperCAmelCase						:				str		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				).pixel_values
      self.assertEqual(
          encoded_images.shape       ,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,    )

      # Test batched
      UpperCAmelCase						:				Dict		  =			image_processing(__lowerCamelCase       ,    return_tensors="pt"				).pixel_values
      self.assertEqual(
          encoded_images.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,    )
 def   _UpperCamelCase    (			self					: List[Any]				)    ->			str:
      # Initialize image_processing
      UpperCAmelCase						:				List[Any]		  =			self.image_processing_class(**self.image_processor_dict				)
      # create random numpy tensors
      UpperCAmelCase						:				int		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCamelCase       ,    numpify=__lowerCamelCase				)
      for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase       ,    np.ndarray				)

      # Test not batched input
      UpperCAmelCase						:				Tuple		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				).pixel_values
      self.assertEqual(
          encoded_images.shape       ,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,    )

      # Test batched
      UpperCAmelCase						:				str		  =			image_processing(__lowerCamelCase       ,    return_tensors="pt"				).pixel_values
      self.assertEqual(
          encoded_images.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,    )

 def   _UpperCamelCase    (			self					: Dict				)    ->			Tuple:
      # Initialize image_processing
      UpperCAmelCase						:				str		  =			self.image_processing_class(**self.image_processor_dict				)
      # create random PyTorch tensors
      UpperCAmelCase						:				List[str]		  =			prepare_image_inputs(self.image_processor_tester       ,    equal_resolution=__lowerCamelCase       ,    torchify=__lowerCamelCase				)
      for image in image_inputs:
           self.assertIsInstance(__lowerCamelCase       ,    torch.Tensor				)

      # Test not batched input
      UpperCAmelCase						:				str		  =			image_processing(image_inputs[0]       ,    return_tensors="pt"				).pixel_values
      self.assertEqual(
          encoded_images.shape       ,    (
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,    )

      # Test batched
      UpperCAmelCase						:				Any		  =			image_processing(__lowerCamelCase       ,    return_tensors="pt"				).pixel_values
      self.assertEqual(
          encoded_images.shape       ,    (
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )       ,    )









import torch

from diffusers import EulerAncestralDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: Any      =					(EulerAncestralDiscreteScheduler,)
 _UpperCamelCase				: Union[str, Any]      =					10
 def   _UpperCamelCase    (			self					: int       ,    **_a					: str				)    ->			Dict:
      UpperCAmelCase						:				List[Any]		  =			{
          "num_train_timesteps": 1_1_0_0,
          "beta_start": 0.0_0_0_1,
          "beta_end": 0.0_2,
          "beta_schedule": "linear",
      }

      config.update(**_a				)
      return config
 def   _UpperCamelCase    (			self					: str				)    ->			Union[str, Any]:
      for timesteps in [1_0, 5_0, 1_0_0, 1_0_0_0]:
           self.check_over_configs(num_train_timesteps=_a				)
 def   _UpperCamelCase    (			self					: Tuple				)    ->			int:
      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=_a       ,    beta_end=_a				)
 def   _UpperCamelCase    (			self					: Optional[Any]				)    ->			List[str]:
      for schedule in ["linear", "scaled_linear"]:
           self.check_over_configs(beta_schedule=_a				)
 def   _UpperCamelCase    (			self					: Dict				)    ->			str:
      for prediction_type in ["epsilon", "v_prediction"]:
           self.check_over_configs(prediction_type=_a				)
 def   _UpperCamelCase    (			self					: Any				)    ->			Optional[Any]:
      UpperCAmelCase						:				int		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				Optional[int]		  =			self.get_scheduler_config()
      UpperCAmelCase						:				List[Any]		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps				)

      UpperCAmelCase						:				Dict		  =			torch.manual_seed(0				)

      UpperCAmelCase						:				Union[str, Any]		  =			self.dummy_model()
      UpperCAmelCase						:				Dict		  =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase						:				Optional[Any]		  =			sample.to(_a				)

      for i, t in enumerate(scheduler.timesteps				):
           UpperCAmelCase						:				List[Any]		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				str		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				int		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				Optional[int]		  =			output.prev_sample

      UpperCAmelCase						:				Tuple		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				str		  =			torch.mean(torch.abs(_a				)				)

      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   _UpperCamelCase    (			self					: Optional[int]				)    ->			Tuple:
      UpperCAmelCase						:				Optional[int]		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				Tuple		  =			self.get_scheduler_config(prediction_type="v_prediction"				)
      UpperCAmelCase						:				str		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps				)

      UpperCAmelCase						:				Dict		  =			torch.manual_seed(0				)

      UpperCAmelCase						:				List[Any]		  =			self.dummy_model()
      UpperCAmelCase						:				Optional[int]		  =			self.dummy_sample_deter * scheduler.init_noise_sigma
      UpperCAmelCase						:				Any		  =			sample.to(_a				)

      for i, t in enumerate(scheduler.timesteps				):
           UpperCAmelCase						:				Union[str, Any]		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				Union[str, Any]		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				Union[str, Any]		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				Optional[int]		  =			output.prev_sample

      UpperCAmelCase						:				Dict		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				Any		  =			torch.mean(torch.abs(_a				)				)

      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   _UpperCamelCase    (			self					: List[str]				)    ->			Optional[Any]:
      UpperCAmelCase						:				str		  =			self.scheduler_classes[0]
      UpperCAmelCase						:				str		  =			self.get_scheduler_config()
      UpperCAmelCase						:				Union[str, Any]		  =			scheduler_class(**_a				)

      scheduler.set_timesteps(self.num_inference_steps       ,    device=_a				)
      UpperCAmelCase						:				str		  =			torch.manual_seed(0				)

      UpperCAmelCase						:				Optional[int]		  =			self.dummy_model()
      UpperCAmelCase						:				Any		  =			self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
      UpperCAmelCase						:				Optional[Any]		  =			sample.to(_a				)

      for t in scheduler.timesteps:
           UpperCAmelCase						:				Tuple		  =			scheduler.scale_model_input(_a       ,    _a				)

           UpperCAmelCase						:				int		  =			model(_a       ,    _a				)

           UpperCAmelCase						:				Tuple		  =			scheduler.step(_a       ,    _a       ,    _a       ,    generator=_a				)
           UpperCAmelCase						:				str		  =			output.prev_sample

      UpperCAmelCase						:				Tuple		  =			torch.sum(torch.abs(_a				)				)
      UpperCAmelCase						:				Optional[Any]		  =			torch.mean(torch.abs(_a				)				)

      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



