





'''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 ConvNextImageProcessor


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=0.875			,		A=True			,		A=[0.5, 0.5, 0.5]			,		A=[0.5, 0.5, 0.5]			,		)		->       Tuple:
        _SCREAMING_SNAKE_CASE     =      size if size is not None else {"""shortest_edge""": 20}
        _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     =      crop_pct
        _SCREAMING_SNAKE_CASE     =      do_normalize
        _SCREAMING_SNAKE_CASE     =      image_mean
        _SCREAMING_SNAKE_CASE     =      image_std





    def       snake_case_(		self	)		->       List[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,
            "crop_pct": self.crop_pct,
        }




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

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


    def       snake_case_(		self	)		->       Optional[int]:
        _SCREAMING_SNAKE_CASE     =      ConvNextImageProcessingTester(self	)


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


    def       snake_case_(		self	)		->       List[str]:
        _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			,		"""crop_pct"""	)	)
        self.assertTrue(hasattr(A			,		"""do_normalize"""	)	)
        self.assertTrue(hasattr(A			,		"""image_mean"""	)	)
        self.assertTrue(hasattr(A			,		"""image_std"""	)	)


    def       snake_case_(		self	)		->       List[str]:
        _SCREAMING_SNAKE_CASE     =      self.image_processing_class.from_dict(self.image_processor_dict	)
        self.assertEqual(image_processor.size			,		{"""shortest_edge""": 20}	)

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


    def       snake_case_(		self	)		->       Any:
        pass


    def       snake_case_(		self	)		->       int:
        # 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.size["""shortest_edge"""],
                self.image_processor_tester.size["""shortest_edge"""],
            )			,		)

        # 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.size["""shortest_edge"""],
                self.image_processor_tester.size["""shortest_edge"""],
            )			,		)


    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.size["""shortest_edge"""],
                self.image_processor_tester.size["""shortest_edge"""],
            )			,		)

        # 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.size["""shortest_edge"""],
                self.image_processor_tester.size["""shortest_edge"""],
            )			,		)





    def       snake_case_(		self	)		->       Dict:
        # 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.size["""shortest_edge"""],
                self.image_processor_tester.size["""shortest_edge"""],
            )			,		)

        # 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.size["""shortest_edge"""],
                self.image_processor_tester.size["""shortest_edge"""],
            )			,		)










'''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



