





"""simple docstring"""


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 (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


a__   :       Optional[Any]   						=			logging.get_logger(__name__)


if is_vision_available():
   import PIL



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Tuple										=       ["""pixel_values"""]

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






        '''simple docstring'''

        super().__init__(**__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			size if size is not None else {"height": 2560, "width": 1920}
        if isinstance(__SCREAMING_SNAKE_CASE				,   (tuple, list)       ):
         # The previous feature extractor size parameter was in (width, height) format
         snake_case__      							=			size[::-1]
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			do_resize
        snake_case__      							=			size
        snake_case__      							=			resample
        snake_case__      							=			do_thumbnail
        snake_case__      							=			do_align_long_axis
        snake_case__      							=			do_pad
        snake_case__      							=			do_rescale
        snake_case__      							=			rescale_factor
        snake_case__      							=			do_normalize
        snake_case__      							=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
        snake_case__      							=			image_std if image_std is not None else IMAGENET_STANDARD_STD

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = None       ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			get_image_size(__SCREAMING_SNAKE_CASE       )
        snake_case__							,				snake_case__      							=			size["height"], size["width"]

        if (output_width < output_height and input_width > input_height) or (
            output_width > output_height and input_width < input_height
        ):
         snake_case__      							=			np.rotaa(__SCREAMING_SNAKE_CASE				,   3       )

        if data_format is not None:
         snake_case__      							=			to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

        return image

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = False				,   __SCREAMING_SNAKE_CASE = None				,   ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			size["height"], size["width"]
        snake_case__							,				snake_case__      							=			get_image_size(__SCREAMING_SNAKE_CASE       )

        snake_case__      							=			output_width - input_width
        snake_case__      							=			output_height - input_height

        if random_padding:
         snake_case__      							=			np.random.randint(low=0				,   high=delta_height + 1       )
         snake_case__      							=			np.random.randint(low=0				,   high=delta_width + 1       )
        else:
         snake_case__      							=			delta_height // 2
         snake_case__      							=			delta_width // 2

        snake_case__      							=			delta_height - pad_top
        snake_case__      							=			delta_width - pad_left

        snake_case__      							=			((pad_top, pad_bottom), (pad_left, pad_right))
        return pad(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   data_format=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   *__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        logger.info("pad is deprecated and will be removed in version 4.27. Please use pad_image instead."       )
        return self.pad_image(*__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,   __SCREAMING_SNAKE_CASE = None				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__							,				snake_case__      							=			get_image_size(__SCREAMING_SNAKE_CASE       )
        snake_case__							,				snake_case__      							=			size["height"], size["width"]

        # We always resize to the smallest of either the input or output size.
        snake_case__      							=			min(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )
        snake_case__      							=			min(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       )

        if height == input_height and width == input_width:
         return image

        if input_height > input_width:
         snake_case__      							=			int(input_width * height / input_height       )
        elif input_width > input_height:
         snake_case__      							=			int(input_height * width / input_width       )

        return resize(
            __SCREAMING_SNAKE_CASE				,   size=(height, width)				,   resample=__SCREAMING_SNAKE_CASE				,   reducing_gap=2.0				,   data_format=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC				,   __SCREAMING_SNAKE_CASE = None				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			min(size["height"]				,   size["width"]       )
        snake_case__      							=			get_resize_output_image_size(__SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE				,   default_to_square=__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			resize(__SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE				,   resample=__SCREAMING_SNAKE_CASE				,   data_format=__SCREAMING_SNAKE_CASE				,   **__SCREAMING_SNAKE_CASE       )
        return resized_image

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = None				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = None				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self				,   __SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = False				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = None				,   __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST				,   **__SCREAMING_SNAKE_CASE				,   ):






        '''simple docstring'''

        snake_case__      							=			do_resize if do_resize is not None else self.do_resize
        snake_case__      							=			size if size is not None else self.size
        if isinstance(__SCREAMING_SNAKE_CASE				,   (tuple, list)       ):
         # Previous feature extractor had size in (width, height) format
         snake_case__      							=			size[::-1]
        snake_case__      							=			get_size_dict(__SCREAMING_SNAKE_CASE       )
        snake_case__      							=			resample if resample is not None else self.resample
        snake_case__      							=			do_thumbnail if do_thumbnail is not None else self.do_thumbnail
        snake_case__      							=			do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
        snake_case__      							=			do_pad if do_pad is not None else self.do_pad
        snake_case__      							=			do_rescale if do_rescale is not None else self.do_rescale
        snake_case__      							=			rescale_factor if rescale_factor is not None else self.rescale_factor
        snake_case__      							=			do_normalize if do_normalize is not None else self.do_normalize
        snake_case__      							=			image_mean if image_mean is not None else self.image_mean
        snake_case__      							=			image_std if image_std is not None else self.image_std

        snake_case__      							=			make_list_of_images(__SCREAMING_SNAKE_CASE       )

        if not valid_images(__SCREAMING_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."       )

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

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."       )

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

        if do_align_long_axis:
         snake_case__      							=			[self.align_long_axis(__SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE       ) for image in images]

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

        if do_thumbnail:
         snake_case__      							=			[self.thumbnail(image=__SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE       ) for image in images]

        if do_pad:
         snake_case__      							=			[self.pad_image(image=__SCREAMING_SNAKE_CASE				,   size=__SCREAMING_SNAKE_CASE				,   random_padding=__SCREAMING_SNAKE_CASE       ) for image in images]

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

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

        snake_case__      							=			[to_channel_dimension_format(__SCREAMING_SNAKE_CASE				,   __SCREAMING_SNAKE_CASE       ) for image in images]

        snake_case__      							=			{"pixel_values": images}
        return BatchFeature(data=__SCREAMING_SNAKE_CASE				,   tensor_type=__SCREAMING_SNAKE_CASE       )








"""simple docstring"""


import torch

from diffusers import LMSDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class        lowercase_			(       __lowerCAmelCase							):
       snake_case__			:							Optional[int]										=       (LMSDiscreteScheduler,)
       snake_case__			:							int										=       10

       def 						_SCREAMING_SNAKE_CASE(     self				,   **__SCREAMING_SNAKE_CASE       ):






        '''simple docstring'''

        snake_case__      							=			{
            "num_train_timesteps": 1100,
            "beta_start": 0.0001,
            "beta_end": 0.02,
            "beta_schedule": "linear",
        }

        config.update(**__SCREAMING_SNAKE_CASE       )
        return config

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        for beta_start, beta_end in zip([0.00001, 0.0001, 0.001]				,   [0.0002, 0.002, 0.02]       ):
         self.check_over_configs(beta_start=__SCREAMING_SNAKE_CASE				,   beta_end=__SCREAMING_SNAKE_CASE       )

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        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__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter * scheduler.init_noise_sigma

        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       )
         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() - 0.0017       ) < 1e-2
        assert abs(result_mean.item() - 2.2676e-06       ) < 1e-3

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

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

       def 						_SCREAMING_SNAKE_CASE(     self       ):






        '''simple docstring'''

        snake_case__      							=			self.scheduler_classes[0]
        snake_case__      							=			self.get_scheduler_config()
        snake_case__      							=			scheduler_class(**__SCREAMING_SNAKE_CASE				,   use_karras_sigmas=__SCREAMING_SNAKE_CASE       )

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

        snake_case__      							=			self.dummy_model()
        snake_case__      							=			self.dummy_sample_deter.to(__SCREAMING_SNAKE_CASE       ) * scheduler.init_noise_sigma
        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       )
         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() - 3812.9927       ) < 2e-2
        assert abs(result_mean.item() - 4.9648       ) < 1e-3

