




"""simple docstring"""







import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a								=							logging.get_logger(__name__)  # pylint: disable=invalid-name






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  SCREAMING_SNAKE_CASE__   (   _a   ):
						_a        =  42
						_a        =  None





def 							lowercase   (snake_case__				:	Tuple ,    snake_case__				:	Union[str, Any]=0.999 ,    snake_case__				:	int="cosine" ,    )       ->					Tuple:


				'''simple docstring'''






				if alpha_transform_type == "cosine":

								def alpha_bar_fn(snake_case__				:	int	):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2	) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(snake_case__				:	Any	):
												return math.exp(t * -12.0	)

				else:
								raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''	)

				lowerCAmelCase		=			[]
				for i in range(snake_case__	):
								lowerCAmelCase		=			i / num_diffusion_timesteps
								lowerCAmelCase		=			(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(snake_case__	) / alpha_bar_fn(snake_case__	) ,    snake_case__	)	)
				return torch.tensor(snake_case__ ,    dtype=torch.floataa	)






class  SCREAMING_SNAKE_CASE__   (   _a  ,			_a   ):
						_a        =  [e.name for e in KarrasDiffusionSchedulers]
						_a        =  1


						@register_to_config
						def __init__(							self			:     Tuple  ,  lowerCAmelCase			:     int = 1000  ,  lowerCAmelCase			:     float = 0.0001  ,  lowerCAmelCase			:     float = 0.02  ,  lowerCAmelCase			:     str = "linear"  ,  lowerCAmelCase			:     Optional[Union[np.ndarray, List[float]]] = None  ,  lowerCAmelCase			:     str = "epsilon"  ,  lowerCAmelCase			:     str = "linspace"  ,  lowerCAmelCase			:     int = 0  ,  ):
										if trained_betas is not None:
														lowerCAmelCase		=			torch.tensor(lowerCAmelCase  ,  dtype=torch.floataa   )
										elif beta_schedule == "linear":
														lowerCAmelCase		=			torch.linspace(lowerCAmelCase  ,  lowerCAmelCase  ,  lowerCAmelCase  ,  dtype=torch.floataa   )
										elif beta_schedule == "scaled_linear":
														# this schedule is very specific to the latent diffusion model.
														lowerCAmelCase		=			(
														    torch.linspace(beta_start**0.5  ,  beta_end**0.5  ,  lowerCAmelCase  ,  dtype=torch.floataa   ) ** 2
														)
										elif beta_schedule == "squaredcos_cap_v2":
														# Glide cosine schedule
														lowerCAmelCase		=			betas_for_alpha_bar(lowerCAmelCase   )
										else:
														raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''   )

										lowerCAmelCase		=			1.0 - self.betas
										lowerCAmelCase		=			torch.cumprod(self.alphas  ,  dim=0   )

										lowerCAmelCase		=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
										lowerCAmelCase		=			np.concatenate([sigmas[::-1], [0.0]]   ).astype(np.floataa   )
										lowerCAmelCase		=			torch.from_numpy(lowerCAmelCase   )

										# setable values
										lowerCAmelCase		=			None
										lowerCAmelCase		=			np.linspace(0  ,  num_train_timesteps - 1  ,  lowerCAmelCase  ,  dtype=lowerCAmelCase   )[::-1].copy()
										lowerCAmelCase		=			torch.from_numpy(lowerCAmelCase   )
										lowerCAmelCase		=			False


						@property
						def 					__lowercase	(							self			:     Any   ):
										# standard deviation of the initial noise distribution
										if self.config.timestep_spacing in ["linspace", "trailing"]:
														return self.sigmas.max()

										return (self.sigmas.max() ** 2 + 1) ** 0.5


						def 					__lowercase	(							self			:     Optional[Any]  ,  lowerCAmelCase			:     torch.FloatTensor  ,  lowerCAmelCase			:     Union[float, torch.FloatTensor]   ):
										if isinstance(lowerCAmelCase  ,  torch.Tensor   ):
														lowerCAmelCase		=			timestep.to(self.timesteps.device   )
										lowerCAmelCase		=			(self.timesteps == timestep).nonzero().item()
										lowerCAmelCase		=			self.sigmas[step_index]
										lowerCAmelCase		=			sample / ((sigma**2 + 1) ** 0.5)
										lowerCAmelCase		=			True
										return sample


						def 					__lowercase	(							self			:     List[Any]  ,  lowerCAmelCase			:     int  ,  lowerCAmelCase			:     Union[str, torch.device] = None   ):
										lowerCAmelCase		=			num_inference_steps

										# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
										if self.config.timestep_spacing == "linspace":
														lowerCAmelCase		=			np.linspace(0  ,  self.config.num_train_timesteps - 1  ,  lowerCAmelCase  ,  dtype=lowerCAmelCase   )[
														    ::-1
														].copy()
										elif self.config.timestep_spacing == "leading":
														lowerCAmelCase		=			self.config.num_train_timesteps // self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														lowerCAmelCase		=			(np.arange(0  ,  lowerCAmelCase   ) * step_ratio).round()[::-1].copy().astype(lowerCAmelCase   )
														timesteps += self.config.steps_offset
										elif self.config.timestep_spacing == "trailing":
														lowerCAmelCase		=			self.config.num_train_timesteps / self.num_inference_steps
														# creates integer timesteps by multiplying by ratio
														# casting to int to avoid issues when num_inference_step is power of 3
														lowerCAmelCase		=			(np.arange(self.config.num_train_timesteps  ,  0  ,  -step_ratio   )).round().copy().astype(lowerCAmelCase   )
														timesteps -= 1
										else:
														raise ValueError(
														    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''   )

										lowerCAmelCase		=			np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5   )
										lowerCAmelCase		=			np.interp(lowerCAmelCase  ,  np.arange(0  ,  len(lowerCAmelCase   )   )  ,  lowerCAmelCase   )
										lowerCAmelCase		=			np.concatenate([sigmas, [0.0]]   ).astype(np.floataa   )
										lowerCAmelCase		=			torch.from_numpy(lowerCAmelCase   ).to(device=lowerCAmelCase   )
										if str(lowerCAmelCase   ).startswith("""mps"""   ):
														# mps does not support float64
														lowerCAmelCase		=			torch.from_numpy(lowerCAmelCase   ).to(lowerCAmelCase  ,  dtype=torch.floataa   )
										else:
														lowerCAmelCase		=			torch.from_numpy(lowerCAmelCase   ).to(device=lowerCAmelCase   )


						def 					__lowercase	(							self			:     int  ,  lowerCAmelCase			:     torch.FloatTensor  ,  lowerCAmelCase			:     Union[float, torch.FloatTensor]  ,  lowerCAmelCase			:     torch.FloatTensor  ,  lowerCAmelCase			:     Optional[torch.Generator] = None  ,  lowerCAmelCase			:     bool = True  ,  ):

										if (
										    isinstance(lowerCAmelCase  ,  lowerCAmelCase   )
										    or isinstance(lowerCAmelCase  ,  torch.IntTensor   )
										    or isinstance(lowerCAmelCase  ,  torch.LongTensor   )
										):
														raise ValueError(
														    (
														        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
														        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
														        """ one of the `scheduler.timesteps` as a timestep."""
														    )  ,  )

										if not self.is_scale_input_called:
														logger.warning(
														    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
														    """See `StableDiffusionPipeline` for a usage example."""   )

										if isinstance(lowerCAmelCase  ,  torch.Tensor   ):
														lowerCAmelCase		=			timestep.to(self.timesteps.device   )

										lowerCAmelCase		=			(self.timesteps == timestep).nonzero().item()
										lowerCAmelCase		=			self.sigmas[step_index]

										# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
										if self.config.prediction_type == "epsilon":
														lowerCAmelCase		=			sample - sigma * model_output
										elif self.config.prediction_type == "v_prediction":
														# * c_out + input * c_skip
														lowerCAmelCase		=			model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
										elif self.config.prediction_type == "sample":
														raise NotImplementedError("""prediction_type not implemented yet: sample"""   )
										else:
														raise ValueError(
														    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''   )

										lowerCAmelCase		=			self.sigmas[step_index]
										lowerCAmelCase		=			self.sigmas[step_index + 1]
										lowerCAmelCase		=			(sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
										lowerCAmelCase		=			(sigma_to**2 - sigma_up**2) ** 0.5

										# 2. Convert to an ODE derivative
										lowerCAmelCase		=			(sample - pred_original_sample) / sigma

										lowerCAmelCase		=			sigma_down - sigma

										lowerCAmelCase		=			sample + derivative * dt

										lowerCAmelCase		=			model_output.device
										lowerCAmelCase		=			randn_tensor(model_output.shape  ,  dtype=model_output.dtype  ,  device=lowerCAmelCase  ,  generator=lowerCAmelCase   )

										lowerCAmelCase		=			prev_sample + noise * sigma_up

										if not return_dict:
														return (prev_sample,)

										return EulerAncestralDiscreteSchedulerOutput(
										    prev_sample=lowerCAmelCase  ,  pred_original_sample=lowerCAmelCase   )


						def 					__lowercase	(							self			:     str  ,  lowerCAmelCase			:     torch.FloatTensor  ,  lowerCAmelCase			:     torch.FloatTensor  ,  lowerCAmelCase			:     torch.FloatTensor  ,  ):
										# Make sure sigmas and timesteps have the same device and dtype as original_samples
										lowerCAmelCase		=			self.sigmas.to(device=original_samples.device  ,  dtype=original_samples.dtype   )
										if original_samples.device.type == "mps" and torch.is_floating_point(lowerCAmelCase   ):
														# mps does not support float64
														lowerCAmelCase		=			self.timesteps.to(original_samples.device  ,  dtype=torch.floataa   )
														lowerCAmelCase		=			timesteps.to(original_samples.device  ,  dtype=torch.floataa   )
										else:
														lowerCAmelCase		=			self.timesteps.to(original_samples.device   )
														lowerCAmelCase		=			timesteps.to(original_samples.device   )

										lowerCAmelCase		=			[(schedule_timesteps == t).nonzero().item() for t in timesteps]

										lowerCAmelCase		=			sigmas[step_indices].flatten()
										while len(sigma.shape   ) < len(original_samples.shape   ):
														lowerCAmelCase		=			sigma.unsqueeze(-1   )

										lowerCAmelCase		=			original_samples + noise * sigma
										return noisy_samples




						def __len__(							self			:     str   ):
										return self.config.num_train_timesteps











"""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 OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_resize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """size"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_normalize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_mean"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_std"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_convert_rgb"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

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


						def 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

										# Test not batched input
										lowerCAmelCase		=			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
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

										# Test not batched input
										lowerCAmelCase		=			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
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

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





