





'''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 MobileNetVaImageProcessor


class  a_					(    unittest.TestCase	):

      '''simple docstring'''


      def __init__(		self			,		A			,		A=7			,		A=3			,		A=18			,		A=30			,		A=400			,		A=True			,		A=None			,		A=True			,		A=None			,		)		->       Optional[int]:
          _SCREAMING_SNAKE_CASE     =      size if size is not None else {"""shortest_edge""": 20}
          _SCREAMING_SNAKE_CASE     =      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
          _SCREAMING_SNAKE_CASE     =      parent
          _SCREAMING_SNAKE_CASE     =      batch_size
          _SCREAMING_SNAKE_CASE     =      num_channels
          _SCREAMING_SNAKE_CASE     =      image_size
          _SCREAMING_SNAKE_CASE     =      min_resolution
          _SCREAMING_SNAKE_CASE     =      max_resolution
          _SCREAMING_SNAKE_CASE     =      do_resize
          _SCREAMING_SNAKE_CASE     =      size
          _SCREAMING_SNAKE_CASE     =      do_center_crop
          _SCREAMING_SNAKE_CASE     =      crop_size





      def       snake_case_(		self	)		->       Tuple:
          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_					(    snake_case_				,   unittest.TestCase	):

      '''simple docstring'''
      UpperCamelCase			   =			MobileNetVaImageProcessor if is_vision_available() else None


      def       snake_case_(		self	)		->       Tuple:
          _SCREAMING_SNAKE_CASE     =      MobileNetVaImageProcessingTester(self	)


      @property
      def       snake_case_(		self	)		->       Optional[Any]:
          return self.image_processor_tester.prepare_image_processor_dict()


      def       snake_case_(		self	)		->       Optional[int]:
          _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
          self.assertTrue(hasattr(A			,		"""do_resize"""	)	)
          self.assertTrue(hasattr(A			,		"""size"""	)	)
          self.assertTrue(hasattr(A			,		"""do_center_crop"""	)	)
          self.assertTrue(hasattr(A			,		"""center_crop"""	)	)


      def       snake_case_(		self	)		->       str:
          _SCREAMING_SNAKE_CASE     =      self.image_processing_class.from_dict(self.image_processor_dict	)
          self.assertEqual(image_processor.size			,		{"""shortest_edge""": 20}	)
          self.assertEqual(image_processor.crop_size			,		{"""height""": 18, """width""": 18}	)

          _SCREAMING_SNAKE_CASE     =      self.image_processing_class.from_dict(self.image_processor_dict			,		size=42			,		crop_size=84	)
          self.assertEqual(image_processor.size			,		{"""shortest_edge""": 42}	)
          self.assertEqual(image_processor.crop_size			,		{"""height""": 84, """width""": 84}	)


      def       snake_case_(		self	)		->       Dict:
          pass


      def       snake_case_(		self	)		->       Any:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
          # create random PIL images
          _SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A	)
          for image in image_inputs:
              self.assertIsInstance(A			,		Image.Image	)

          # Test not batched input
          _SCREAMING_SNAKE_CASE     =      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
          _SCREAMING_SNAKE_CASE     =      image_processing(A			,		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       snake_case_(		self	)		->       str:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
          # create random numpy tensors
          _SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A			,		numpify=A	)
          for image in image_inputs:
              self.assertIsInstance(A			,		np.ndarray	)

          # Test not batched input
          _SCREAMING_SNAKE_CASE     =      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
          _SCREAMING_SNAKE_CASE     =      image_processing(A			,		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       snake_case_(		self	)		->       Union[str, Any]:
          # Initialize image_processing
          _SCREAMING_SNAKE_CASE     =      self.image_processing_class(**self.image_processor_dict	)
          # create random PyTorch tensors
          _SCREAMING_SNAKE_CASE     =      prepare_image_inputs(self.image_processor_tester			,		equal_resolution=A			,		torchify=A	)
          for image in image_inputs:
              self.assertIsInstance(A			,		torch.Tensor	)

          # Test not batched input
          _SCREAMING_SNAKE_CASE     =      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
          _SCREAMING_SNAKE_CASE     =      image_processing(A			,		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"""],
              )			,		)










'''simple docstring'''

import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class  a_					(    snake_case_	):

    '''simple docstring'''
    UpperCamelCase			   =			(LMSDiscreteScheduler,)
    UpperCamelCase			   =			10


    def       snake_case_(		self			,		**A	)		->       str:
        _SCREAMING_SNAKE_CASE     =      {
            """num_train_timesteps""": 1100,
            """beta_start""": 0.0001,
            """beta_end""": 0.02,
            """beta_schedule""": """linear""",
        }

        config.update(**A	)
        return config


    def       snake_case_(		self	)		->       str:
        for timesteps in [10, 50, 100, 1000]:
            self.check_over_configs(num_train_timesteps=A	)


    def       snake_case_(		self	)		->       Optional[Any]:
        for beta_start, beta_end in zip([0.0_0001, 0.0001, 0.001]			,		[0.0002, 0.002, 0.02]	):
            self.check_over_configs(beta_start=A			,		beta_end=A	)


    def       snake_case_(		self	)		->       Tuple:
        for schedule in ["linear", "scaled_linear"]:
            self.check_over_configs(beta_schedule=A	)


    def       snake_case_(		self	)		->       List[str]:
        for prediction_type in ["epsilon", "v_prediction"]:
            self.check_over_configs(prediction_type=A	)


    def       snake_case_(		self	)		->       Optional[int]:
        for t in [0, 500, 800]:
            self.check_over_forward(time_step=A	)


    def       snake_case_(		self	)		->       str:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config(prediction_type="""v_prediction"""	)
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 0.0017	) < 1e-2
        assert abs(result_mean.item() - 2.26_76e-06	) < 1e-3


    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for i, t in enumerate(scheduler.timesteps	):
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 1006.388	) < 1e-2
        assert abs(result_mean.item() - 1.31	) < 1e-3





    def       snake_case_(		self	)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      self.scheduler_classes[0]
        _SCREAMING_SNAKE_CASE     =      self.get_scheduler_config()
        _SCREAMING_SNAKE_CASE     =      scheduler_class(**A			,		use_karras_sigmas=A	)

        scheduler.set_timesteps(self.num_inference_steps			,		device=A	)

        _SCREAMING_SNAKE_CASE     =      self.dummy_model()
        _SCREAMING_SNAKE_CASE     =      self.dummy_sample_deter.to(A	) * scheduler.init_noise_sigma
        _SCREAMING_SNAKE_CASE     =      sample.to(A	)

        for t in scheduler.timesteps:
            _SCREAMING_SNAKE_CASE     =      scheduler.scale_model_input(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      model(A			,		A	)

            _SCREAMING_SNAKE_CASE     =      scheduler.step(A			,		A			,		A	)
            _SCREAMING_SNAKE_CASE     =      output.prev_sample

        _SCREAMING_SNAKE_CASE     =      torch.sum(torch.abs(A	)	)
        _SCREAMING_SNAKE_CASE     =      torch.mean(torch.abs(A	)	)

        assert abs(result_sum.item() - 3812.9927	) < 2e-2
        assert abs(result_mean.item() - 4.9648	) < 1e-3



