import torch

from diffusers import DDIMParallelScheduler

from .test_schedulers import SchedulerCommonTest



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		(DDIMParallelScheduler,)
							_lowerCamelCase						=		(('eta', 0.0), ('num_inference_steps', 50))



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													_snake_case						:					str    =   {
													    "num_train_timesteps": 1_000,
													    "beta_start": 0.0_001,
													    "beta_end": 0.02,
													    "beta_schedule": "linear",
													    "clip_sample": True,
													}

													config.update(**lowercase_    )
													return config



							def 	UpperCamelCase	(						self				,    **lowercase_    ):
													_snake_case						:					Any    =   self.scheduler_classes[0]
													_snake_case						:					Dict    =   self.get_scheduler_config(**lowercase_    )
													_snake_case						:					Dict    =   scheduler_class(**lowercase_    )

													_snake_case    ,_snake_case						:					int    =   10, 0.0

													_snake_case						:					Union[str, Any]    =   self.dummy_model()
													_snake_case						:					int    =   self.dummy_sample_deter

													scheduler.set_timesteps(lowercase_    )

													for t in scheduler.timesteps:
																			_snake_case						:					Any    =   model(lowercase_				,    lowercase_    )
																			_snake_case						:					Optional[int]    =   scheduler.step(lowercase_				,    lowercase_				,    lowercase_				,    lowercase_    ).prev_sample

													return sample



							def 	UpperCamelCase	(						self    ):
													for timesteps in [100, 500, 1_000]:
																			self.check_over_configs(num_train_timesteps=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for steps_offset in [0, 1]:
																			self.check_over_configs(steps_offset=lowercase_    )

													_snake_case						:					str    =   self.scheduler_classes[0]
													_snake_case						:					Optional[int]    =   self.get_scheduler_config(steps_offset=1    )
													_snake_case						:					Any    =   scheduler_class(**lowercase_    )
													scheduler.set_timesteps(5    )
													assert torch.equal(scheduler.timesteps				,    torch.LongTensor([801, 601, 401, 201, 1]    )    )



							def 	UpperCamelCase	(						self    ):
													for beta_start, beta_end in zip([0.0_001, 0.001, 0.01, 0.1]				,    [0.002, 0.02, 0.2, 2]    ):
																			self.check_over_configs(beta_start=lowercase_				,    beta_end=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for schedule in ["linear", "squaredcos_cap_v2"]:
																			self.check_over_configs(beta_schedule=lowercase_    )



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



							def 	UpperCamelCase	(						self    ):
													for clip_sample in [True, False]:
																			self.check_over_configs(clip_sample=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for timestep_spacing in ["trailing", "leading"]:
																			self.check_over_configs(timestep_spacing=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for rescale_betas_zero_snr in [True, False]:
																			self.check_over_configs(rescale_betas_zero_snr=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													self.check_over_configs(thresholding=lowercase_    )
													for threshold in [0.5, 1.0, 2.0]:
																			for prediction_type in ["epsilon", "v_prediction"]:
																									self.check_over_configs(
																									    thresholding=lowercase_				,    prediction_type=lowercase_				,    sample_max_value=lowercase_				,    )



							def 	UpperCamelCase	(						self    ):
													for t in [1, 10, 49]:
																			self.check_over_forward(time_step=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for t, num_inference_steps in zip([1, 10, 50]				,    [10, 50, 500]    ):
																			self.check_over_forward(time_step=lowercase_				,    num_inference_steps=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													for t, eta in zip([1, 10, 49]				,    [0.0, 0.5, 1.0]    ):
																			self.check_over_forward(time_step=lowercase_				,    eta=lowercase_    )



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Tuple    =   self.scheduler_classes[0]
													_snake_case						:					int    =   self.get_scheduler_config()
													_snake_case						:					str    =   scheduler_class(**lowercase_    )

													assert torch.sum(torch.abs(scheduler._get_variance(0				,    0    ) - 0.0    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(420				,    400    ) - 0.14_771    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(980				,    960    ) - 0.32_460    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(0				,    0    ) - 0.0    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(487				,    486    ) - 0.00_979    )    ) < 1e-5
													assert torch.sum(torch.abs(scheduler._get_variance(999				,    998    ) - 0.02    )    ) < 1e-5



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					int    =   self.scheduler_classes[0]
													_snake_case						:					Optional[Any]    =   self.get_scheduler_config()
													_snake_case						:					Any    =   scheduler_class(**lowercase_    )

													_snake_case    ,_snake_case						:					Optional[Any]    =   10, 0.0
													scheduler.set_timesteps(lowercase_    )

													_snake_case						:					Optional[int]    =   self.dummy_model()
													_snake_case						:					Tuple    =   self.dummy_sample_deter
													_snake_case						:					str    =   self.dummy_sample_deter + 0.1
													_snake_case						:					int    =   self.dummy_sample_deter - 0.1

													_snake_case						:					List[Any]    =   samplea.shape[0]
													_snake_case						:					Dict    =   torch.stack([samplea, samplea, samplea]				,    dim=0    )
													_snake_case						:					List[Any]    =   torch.arange(lowercase_    )[0:3, None].repeat(1				,    lowercase_    )

													_snake_case						:					Dict    =   model(samples.flatten(0				,    1    )				,    timesteps.flatten(0				,    1    )    )
													_snake_case						:					int    =   scheduler.batch_step_no_noise(lowercase_				,    timesteps.flatten(0				,    1    )				,    samples.flatten(0				,    1    )				,    lowercase_    )

													_snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					Optional[int]    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 1_147.7_904    ) < 1e-2
													assert abs(result_mean.item() - 0.4_982    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   self.full_loop()

													_snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					Optional[Any]    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 172.0_067    ) < 1e-2
													assert abs(result_mean.item() - 0.223_967    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													_snake_case						:					Optional[Any]    =   self.full_loop(prediction_type="v_prediction"    )

													_snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					int    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 52.5_302    ) < 1e-2
													assert abs(result_mean.item() - 0.0_684    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													# We specify different beta, so that the first alpha is 0.99
													_snake_case						:					str    =   self.full_loop(set_alpha_to_one=lowercase_				,    beta_start=0.01    )
													_snake_case						:					List[str]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					Tuple    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 149.8_295    ) < 1e-2
													assert abs(result_mean.item() - 0.1_951    ) < 1e-3



							def 	UpperCamelCase	(						self    ):
													# We specify different beta, so that the first alpha is 0.99
													_snake_case						:					Any    =   self.full_loop(set_alpha_to_one=lowercase_				,    beta_start=0.01    )
													_snake_case						:					Optional[int]    =   torch.sum(torch.abs(lowercase_    )    )
													_snake_case						:					Optional[int]    =   torch.mean(torch.abs(lowercase_    )    )

													assert abs(result_sum.item() - 149.0_784    ) < 1e-2
													assert abs(result_mean.item() - 0.1_941    ) < 1e-3
from typing import Dict, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    flip_channel_order,
    get_resize_output_image_size,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, is_vision_available, logging


if is_vision_available():
				import PIL

if is_torch_available():
				import torch


__SCREAMING_SNAKE_CASE						:   Optional[int]         =			logging.get_logger(__name__)



class        lowercase_   (					__snake_case		):
							_lowerCamelCase						=		['pixel_values']



							def __init__(						self				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = PILImageResampling.BILINEAR				,    lowercase_ = True				,    lowercase_ = 1 / 255				,    lowercase_ = True				,    lowercase_ = None				,    lowercase_ = True				,    **lowercase_				,    ):
													super().__init__(**lowercase_    )
													_snake_case						:					Dict    =   size if size is not None else {"shortest_edge": 224}
													_snake_case						:					Optional[int]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													_snake_case						:					Any    =   crop_size if crop_size is not None else {"height": 256, "width": 256}
													_snake_case						:					str    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

													_snake_case						:					List[Any]    =   do_resize
													_snake_case						:					Tuple    =   size
													_snake_case						:					Union[str, Any]    =   resample
													_snake_case						:					str    =   do_rescale
													_snake_case						:					Dict    =   rescale_factor
													_snake_case						:					int    =   do_center_crop
													_snake_case						:					int    =   crop_size
													_snake_case						:					List[Any]    =   do_flip_channel_order



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = PIL.Image.BILINEAR				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					Optional[Any]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													if "shortest_edge" not in size:
																			raise ValueError(f"""The `size` dictionary must contain the key `shortest_edge`. Got {size.keys()}"""    )
													_snake_case						:					int    =   get_resize_output_image_size(lowercase_				,    size=size["shortest_edge"]				,    default_to_square=lowercase_    )
													return resize(lowercase_				,    size=lowercase_				,    resample=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
													_snake_case						:					List[str]    =   get_size_dict(lowercase_    )
													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 center_crop(lowercase_				,    size=(size["height"], size["width"])				,    data_format=lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_				,    lowercase_ = None				,    **lowercase_				,    ):
													return rescale(lowercase_				,    scale=lowercase_				,    data_format=lowercase_				,    **lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													return flip_channel_order(lowercase_				,    data_format=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = None				,    lowercase_ = ChannelDimension.FIRST				,    **lowercase_				,    ):
													_snake_case						:					Dict    =   do_resize if do_resize is not None else self.do_resize
													_snake_case						:					Union[str, Any]    =   resample if resample is not None else self.resample
													_snake_case						:					Dict    =   do_rescale if do_rescale is not None else self.do_rescale
													_snake_case						:					str    =   rescale_factor if rescale_factor is not None else self.rescale_factor
													_snake_case						:					Optional[int]    =   do_center_crop if do_center_crop is not None else self.do_center_crop
													_snake_case						:					List[str]    =   (
													    do_flip_channel_order if do_flip_channel_order is not None else self.do_flip_channel_order
													)

													_snake_case						:					Optional[int]    =   size if size is not None else self.size
													_snake_case						:					Optional[int]    =   get_size_dict(lowercase_				,    default_to_square=lowercase_    )
													_snake_case						:					Optional[int]    =   crop_size if crop_size is not None else self.crop_size
													_snake_case						:					Union[str, Any]    =   get_size_dict(lowercase_				,    param_name="crop_size"    )

													_snake_case						:					Tuple    =   make_list_of_images(lowercase_    )

													if not valid_images(lowercase_    ):
																			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_center_crop and crop_size is None:
																			raise ValueError("Crop size must be specified if do_center_crop is True."    )

													# All transformations expect numpy arrays.
													_snake_case						:					Tuple    =   [to_numpy_array(lowercase_    ) for image in images]

													if do_resize:
																			_snake_case						:					int    =   [self.resize(image=lowercase_				,    size=lowercase_				,    resample=lowercase_    ) for image in images]

													if do_center_crop:
																			_snake_case						:					List[Any]    =   [self.center_crop(image=lowercase_				,    size=lowercase_    ) for image in images]

													if do_rescale:
																			_snake_case						:					Optional[int]    =   [self.rescale(image=lowercase_				,    scale=lowercase_    ) for image in images]

													# the pretrained checkpoints assume images are BGR, not RGB
													if do_flip_channel_order:
																			_snake_case						:					int    =   [self.flip_channel_order(image=lowercase_    ) for image in images]

													_snake_case						:					str    =   [to_channel_dimension_format(lowercase_				,    lowercase_    ) for image in images]

													_snake_case						:					List[Any]    =   {"pixel_values": images}
													return BatchFeature(data=lowercase_				,    tensor_type=lowercase_    )



							def 	UpperCamelCase	(						self				,    lowercase_				,    lowercase_ = None    ):
													_snake_case						:					int    =   outputs.logits

													# Resize logits and compute semantic segmentation maps
													if target_sizes is not None:
																			if len(lowercase_    ) != len(lowercase_    ):
																									raise ValueError(
																									    "Make sure that you pass in as many target sizes as the batch dimension of the logits"    )

																			if is_torch_tensor(lowercase_    ):
																									_snake_case						:					Union[str, Any]    =   target_sizes.numpy()

																			_snake_case						:					int    =   []

																			for idx in range(len(lowercase_    )    ):
																									_snake_case						:					Optional[Any]    =   torch.nn.functional.interpolate(
																									    logits[idx].unsqueeze(dim=0    )				,    size=target_sizes[idx]				,    mode="bilinear"				,    align_corners=lowercase_    )
																									_snake_case						:					List[str]    =   resized_logits[0].argmax(dim=0    )
																									semantic_segmentation.append(lowercase_    )
													else:
																			_snake_case						:					List[Any]    =   logits.argmax(dim=1    )
																			_snake_case						:					Optional[Any]    =   [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]    )]

													return semantic_segmentation