


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase_							 =       logging.get_logger(__name__)







class       __a	(   UpperCamelCase__       ):
				lowerCamelCase    :					Union[str, Any] 					=['pixel_values']







				def __init__(   self			,  UpperCAmelCase = True			,  UpperCAmelCase = None			,  UpperCAmelCase = PILImageResampling.BILINEAR			,  UpperCAmelCase = True			,  UpperCAmelCase = 1 / 255			,  UpperCAmelCase = True			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  **UpperCAmelCase			,  ):

					'''simple docstring'''






					super().__init__(**snake_case_      )
					lowerCAmelCase_			       = size if size is not None else {'''height''': 224, '''width''': 224}
					lowerCAmelCase_			       = get_size_dict(snake_case_      )
					lowerCAmelCase_			       = do_resize
					lowerCAmelCase_			       = do_rescale
					lowerCAmelCase_			       = do_normalize
					lowerCAmelCase_			       = size
					lowerCAmelCase_			       = resample
					lowerCAmelCase_			       = rescale_factor
					lowerCAmelCase_			       = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					lowerCAmelCase_			       = image_std if image_std is not None else IMAGENET_STANDARD_STD







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = PILImageResampling.BILINEAR			,  UpperCAmelCase = None			,  **UpperCAmelCase			,  ):

					'''simple docstring'''






					lowerCAmelCase_			       = get_size_dict(snake_case_      )
					if "height" not in size or "width" not in size:
						raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"""      )
					return resize(
					    snake_case_			,  size=(size['''height'''], size['''width'''])			,  resample=snake_case_			,  data_format=snake_case_			,  **snake_case_      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = None			,  **UpperCAmelCase      ):

					'''simple docstring'''






					return rescale(snake_case_			,  scale=snake_case_			,  data_format=snake_case_			,  **snake_case_      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase = None			,  **UpperCAmelCase			,  ):

					'''simple docstring'''






					return normalize(snake_case_			,  mean=snake_case_			,  std=snake_case_			,  data_format=snake_case_			,  **snake_case_      )







				def     lowerCamelCase_    (   self			,  UpperCAmelCase			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = None			,  UpperCAmelCase = ChannelDimension.FIRST			,  **UpperCAmelCase			,  ):

					'''simple docstring'''






					lowerCAmelCase_			       = do_resize if do_resize is not None else self.do_resize
					lowerCAmelCase_			       = do_rescale if do_rescale is not None else self.do_rescale
					lowerCAmelCase_			       = do_normalize if do_normalize is not None else self.do_normalize
					lowerCAmelCase_			       = resample if resample is not None else self.resample
					lowerCAmelCase_			       = rescale_factor if rescale_factor is not None else self.rescale_factor
					lowerCAmelCase_			       = image_mean if image_mean is not None else self.image_mean
					lowerCAmelCase_			       = image_std if image_std is not None else self.image_std

					lowerCAmelCase_			       = size if size is not None else self.size
					lowerCAmelCase_			       = get_size_dict(snake_case_      )

					lowerCAmelCase_			       = make_list_of_images(snake_case_      )

					if not valid_images(snake_case_      ):
						raise ValueError(
						    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
						    '''torch.Tensor, tf.Tensor or jax.ndarray.'''      )

					if do_resize and size is None:
						raise ValueError('''Size must be specified if do_resize is True.'''      )

					if do_rescale and rescale_factor is None:
						raise ValueError('''Rescale factor must be specified if do_rescale is True.'''      )

					# All transformations expect numpy arrays.
					lowerCAmelCase_			       = [to_numpy_array(snake_case_      ) for image in images]

					if do_resize:
						lowerCAmelCase_			       = [self.resize(image=snake_case_			,  size=snake_case_			,  resample=snake_case_      ) for image in images]

					if do_rescale:
						lowerCAmelCase_			       = [self.rescale(image=snake_case_			,  scale=snake_case_      ) for image in images]

					if do_normalize:
						lowerCAmelCase_			       = [self.normalize(image=snake_case_			,  mean=snake_case_			,  std=snake_case_      ) for image in images]

					lowerCAmelCase_			       = [to_channel_dimension_format(snake_case_			,  snake_case_      ) for image in images]

					lowerCAmelCase_			       = {'''pixel_values''': images}
					return BatchFeature(data=snake_case_			,  tensor_type=snake_case_      )



"""simple docstring"""



import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest







class       __a	(   __snake_case       ):
				lowerCamelCase    :					Union[str, Any] 					=(LMSDiscreteScheduler,)
				lowerCamelCase    :					int 					=10







				def     lowerCamelCase_    (   self			,  **UpperCAmelCase      ):

					'''simple docstring'''






					lowerCAmelCase_			       = {
					    '''num_train_timesteps''': 1100,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					}

					config.update(**UpperCAmelCase      )
					return config







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for timesteps in [10, 50, 100, 1000]:
						self.check_over_configs(num_train_timesteps=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					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=UpperCAmelCase			,  beta_end=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for schedule in ["linear", "scaled_linear"]:
						self.check_over_configs(beta_schedule=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for prediction_type in ["epsilon", "v_prediction"]:
						self.check_over_configs(prediction_type=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					for t in [0, 500, 800]:
						self.check_over_forward(time_step=UpperCAmelCase      )







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

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

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config(prediction_type='''v_prediction'''      )
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

					scheduler.set_timesteps(self.num_inference_steps      )

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

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 0.0_0_1_7      ) < 1E-2
					assert abs(result_mean.item() - 2.2_6_7_6E-0_6      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase      )

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

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

					for i, t in enumerate(scheduler.timesteps      ):
						lowerCAmelCase_			       = scheduler.scale_model_input(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 1_0_0_6.3_8_8      ) < 1E-2
					assert abs(result_mean.item() - 1.3_1      ) < 1E-3







				def     lowerCamelCase_    (   self      ):

					'''simple docstring'''






					lowerCAmelCase_			       = self.scheduler_classes[0]
					lowerCAmelCase_			       = self.get_scheduler_config()
					lowerCAmelCase_			       = scheduler_class(**UpperCAmelCase			,  use_karras_sigmas=UpperCAmelCase      )

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

					lowerCAmelCase_			       = self.dummy_model()
					lowerCAmelCase_			       = self.dummy_sample_deter.to(UpperCAmelCase      ) * scheduler.init_noise_sigma
					lowerCAmelCase_			       = sample.to(UpperCAmelCase      )

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

						lowerCAmelCase_			       = model(UpperCAmelCase			,  UpperCAmelCase      )

						lowerCAmelCase_			       = scheduler.step(UpperCAmelCase			,  UpperCAmelCase			,  UpperCAmelCase      )
						lowerCAmelCase_			       = output.prev_sample

					lowerCAmelCase_			       = torch.sum(torch.abs(UpperCAmelCase      )      )
					lowerCAmelCase_			       = torch.mean(torch.abs(UpperCAmelCase      )      )

					assert abs(result_sum.item() - 3_8_1_2.9_9_2_7      ) < 2E-2
					assert abs(result_mean.item() - 4.9_6_4_8      ) < 1E-3