



"""simple docstring"""







import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

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

def    __SCREAMING_SNAKE_CASE		( lowerCamelCase_:       Dict    ,		lowerCamelCase_:       Optional[int]=0.9_9_9    ,		lowerCamelCase_:       List[str]="cosine"    ,		):
						"""simple docstring"""






						if alpha_transform_type == "cosine":

												def alpha_bar_fn(lowerCamelCase_:       Union[str, Any]   ):
																		return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2   ) ** 2

						elif alpha_transform_type == "exp":

												def alpha_bar_fn(lowerCamelCase_:       Optional[int]   ):
																		return math.exp(t * -1_2.0   )

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

						snake_case      : Dict							=   []
						for i in range(lowerCamelCase_   ):
												snake_case      : Optional[Any]							=   i / num_diffusion_timesteps
												snake_case      : str							=   (i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar_fn(lowerCamelCase_   ) / alpha_bar_fn(lowerCamelCase_   )    ,		lowerCamelCase_   )   )
						return torch.tensor(lowerCamelCase_    ,		dtype=torch.floataa   )

class 			_a     (   SCREAMING_SNAKE_CASE__    ,  SCREAMING_SNAKE_CASE__):
			__magic_name__    				=    [e.name for e in KarrasDiffusionSchedulers]
			__magic_name__    				=    2



			@register_to_config
			def __init__( self       :							str      ,  _lowercase       :							int = 1000      ,  _lowercase       :							float = 0.00085      ,  _lowercase       :							float = 0.012      ,  _lowercase       :							str = "linear"      ,  _lowercase       :							Optional[Union[np.ndarray, List[float]]] = None      ,  _lowercase       :							str = "epsilon"      ,  _lowercase       :							str = "linspace"      ,  _lowercase       :							int = 0      ,  )       ->				Optional[Any]:
									if trained_betas is not None:
															snake_case      : Dict							=   torch.tensor(_lowercase      ,  dtype=torch.floataa		)
									elif beta_schedule == "linear":
															snake_case      : Tuple							=   torch.linspace(_lowercase      ,  _lowercase      ,  _lowercase      ,  dtype=torch.floataa		)
									elif beta_schedule == "scaled_linear":
															# this schedule is very specific to the latent diffusion model.
															snake_case      : str							=   (
															    torch.linspace(beta_start**0.5      ,  beta_end**0.5      ,  _lowercase      ,  dtype=torch.floataa		) ** 2
															)
									elif beta_schedule == "squaredcos_cap_v2":
															# Glide cosine schedule
															snake_case      : Optional[int]							=   betas_for_alpha_bar(_lowercase		)
									else:
															raise NotImplementedError(F'''{beta_schedule} does is not implemented for {self.__class__}'''		)

									snake_case      : Optional[Any]							=   1.0 - self.betas
									snake_case      : Optional[Any]							=   torch.cumprod(self.alphas      ,  dim=0		)

									#  set all values
									self.set_timesteps(_lowercase      ,  _lowercase      ,  _lowercase		)



			def 							__lowercase				( self       :							int      ,  _lowercase       :							Optional[int]      ,  _lowercase       :							Dict=None		)       ->				int:
									if schedule_timesteps is None:
															snake_case      : Tuple							=   self.timesteps

									snake_case      : Optional[int]							=   (schedule_timesteps == timestep).nonzero()

									# The sigma index that is taken for the **very** first `step`
									# is always the second index (or the last index if there is only 1)
									# This way we can ensure we don't accidentally skip a sigma in
									# case we start in the middle of the denoising schedule (e.g. for image-to-image)
									if len(self._index_counter		) == 0:
															snake_case      : str							=   1 if len(_lowercase		) > 1 else 0
									else:
															snake_case      : Union[str, Any]							=   timestep.cpu().item() if torch.is_tensor(_lowercase		) else timestep
															snake_case      : Dict							=   self._index_counter[timestep_int]

									return indices[pos].item()



			@property
			def 							__lowercase				( self       :							int		)       ->				int:
									# 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       :							List[str]      ,  _lowercase       :							torch.FloatTensor      ,  _lowercase       :							Union[float, torch.FloatTensor]      ,  )       ->				torch.FloatTensor:
									snake_case      : Optional[int]							=   self.index_for_timestep(_lowercase		)

									if self.state_in_first_order:
															snake_case      : Optional[Any]							=   self.sigmas[step_index]
									else:
															snake_case      : Tuple							=   self.sigmas_interpol[step_index - 1]

									snake_case      : Tuple							=   sample / ((sigma**2 + 1) ** 0.5)
									return sample



			def 							__lowercase				( self       :							Dict      ,  _lowercase       :							int      ,  _lowercase       :							Union[str, torch.device] = None      ,  _lowercase       :							Optional[int] = None      ,  )       ->				Any:
									snake_case      : Any							=   num_inference_steps

									snake_case      : Optional[int]							=   num_train_timesteps or self.config.num_train_timesteps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
															snake_case      : str							=   np.linspace(0      ,  num_train_timesteps - 1      ,  _lowercase      ,  dtype=_lowercase		)[::-1].copy()
									elif self.config.timestep_spacing == "leading":
															snake_case      : Optional[Any]							=   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
															snake_case      : Optional[int]							=   (np.arange(0      ,  _lowercase		) * step_ratio).round()[::-1].copy().astype(_lowercase		)
															timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
															snake_case      : int							=   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
															snake_case      : Union[str, Any]							=   (np.arange(_lowercase      ,  0      ,  -step_ratio		)).round().copy().astype(_lowercase		)
															timesteps -= 1
									else:
															raise ValueError(
															    F'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''		)

									snake_case      : List[str]							=   np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5		)
									snake_case      : Any							=   torch.from_numpy(np.log(_lowercase		)		).to(_lowercase		)

									snake_case      : Tuple							=   np.interp(_lowercase      ,  np.arange(0      ,  len(_lowercase		)		)      ,  _lowercase		)
									snake_case      : Optional[int]							=   np.concatenate([sigmas, [0.0]]		).astype(np.floataa		)
									snake_case      : Dict							=   torch.from_numpy(_lowercase		).to(device=_lowercase		)

									# compute up and down sigmas
									snake_case      : int							=   sigmas.roll(-1		)
									snake_case      : Optional[int]							=   0.0
									snake_case      : Dict							=   (sigmas_next**2 * (sigmas**2 - sigmas_next**2) / sigmas**2) ** 0.5
									snake_case      : Union[str, Any]							=   (sigmas_next**2 - sigmas_up**2) ** 0.5
									snake_case      : Dict							=   0.0

									# compute interpolated sigmas
									snake_case      : List[str]							=   sigmas.log().lerp(sigmas_down.log()      ,  0.5		).exp()
									snake_case      : Union[str, Any]							=   0.0

									# set sigmas
									snake_case      : Tuple							=   torch.cat([sigmas[:1], sigmas[1:].repeat_interleave(2		), sigmas[-1:]]		)
									snake_case      : List[str]							=   torch.cat(
									    [sigmas_interpol[:1], sigmas_interpol[1:].repeat_interleave(2		), sigmas_interpol[-1:]]		)
									snake_case      : List[str]							=   torch.cat([sigmas_up[:1], sigmas_up[1:].repeat_interleave(2		), sigmas_up[-1:]]		)
									snake_case      : List[str]							=   torch.cat([sigmas_down[:1], sigmas_down[1:].repeat_interleave(2		), sigmas_down[-1:]]		)

									if str(_lowercase		).startswith("mps"		):
															# mps does not support float64
															snake_case      : Any							=   torch.from_numpy(_lowercase		).to(_lowercase      ,  dtype=torch.floataa		)
									else:
															snake_case      : int							=   torch.from_numpy(_lowercase		).to(_lowercase		)

									snake_case      : Any							=   self.sigma_to_t(_lowercase		).to(_lowercase      ,  dtype=timesteps.dtype		)
									snake_case      : str							=   torch.stack((timesteps_interpol[:-2, None], timesteps[1:, None])      ,  dim=-1		).flatten()

									snake_case      : Any							=   torch.cat([timesteps[:1], interleaved_timesteps]		)

									snake_case      : Union[str, Any]							=   None

									# for exp beta schedules, such as the one for `pipeline_shap_e.py`
									# we need an index counter
									snake_case      : Tuple							=   defaultdict(_lowercase		)



			def 							__lowercase				( self       :							Optional[Any]      ,  _lowercase       :							Union[str, Any]		)       ->				Tuple:
									# get log sigma
									snake_case      : Dict							=   sigma.log()

									# get distribution
									snake_case      : int							=   log_sigma - self.log_sigmas[:, None]

									# get sigmas range
									snake_case      : str							=   dists.ge(0		).cumsum(dim=0		).argmax(dim=0		).clamp(max=self.log_sigmas.shape[0] - 2		)
									snake_case      : Optional[int]							=   low_idx + 1

									snake_case      : List[Any]							=   self.log_sigmas[low_idx]
									snake_case      : Optional[int]							=   self.log_sigmas[high_idx]

									# interpolate sigmas
									snake_case      : Any							=   (low - log_sigma) / (low - high)
									snake_case      : List[Any]							=   w.clamp(0      ,  1		)

									# transform interpolation to time range
									snake_case      : Optional[Any]							=   (1 - w) * low_idx + w * high_idx
									snake_case      : List[Any]							=   t.view(sigma.shape		)
									return t



			@property
			def 							__lowercase				( self       :							int		)       ->				Optional[Any]:
									return self.sample is None



			def 							__lowercase				( self       :							List[str]      ,  _lowercase       :							Union[torch.FloatTensor, np.ndarray]      ,  _lowercase       :							Union[float, torch.FloatTensor]      ,  _lowercase       :							Union[torch.FloatTensor, np.ndarray]      ,  _lowercase       :							Optional[torch.Generator] = None      ,  _lowercase       :							bool = True      ,  )       ->				Union[SchedulerOutput, Tuple]:
									snake_case      : Tuple							=   self.index_for_timestep(_lowercase		)

									# advance index counter by 1
									snake_case      : Optional[Any]							=   timestep.cpu().item() if torch.is_tensor(_lowercase		) else timestep
									self._index_counter[timestep_int] += 1

									if self.state_in_first_order:
															snake_case      : List[str]							=   self.sigmas[step_index]
															snake_case      : Tuple							=   self.sigmas_interpol[step_index]
															snake_case      : Optional[int]							=   self.sigmas_up[step_index]
															snake_case      : List[Any]							=   self.sigmas_down[step_index - 1]
									else:
															# 2nd order / KPDM2's method
															snake_case      : Tuple							=   self.sigmas[step_index - 1]
															snake_case      : Optional[int]							=   self.sigmas_interpol[step_index - 1]
															snake_case      : Optional[Any]							=   self.sigmas_up[step_index - 1]
															snake_case      : List[str]							=   self.sigmas_down[step_index - 1]

									# currently only gamma=0 is supported. This usually works best anyways.
									# We can support gamma in the future but then need to scale the timestep before
									# passing it to the model which requires a change in API
									snake_case      : Tuple							=   0
									snake_case      : Optional[int]							=   sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

									snake_case      : Dict							=   model_output.device
									snake_case      : Dict							=   randn_tensor(model_output.shape      ,  dtype=model_output.dtype      ,  device=_lowercase      ,  generator=_lowercase		)

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									if self.config.prediction_type == "epsilon":
															snake_case      : Optional[Any]							=   sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case      : str							=   sample - sigma_input * model_output
									elif self.config.prediction_type == "v_prediction":
															snake_case      : int							=   sigma_hat if self.state_in_first_order else sigma_interpol
															snake_case      : Optional[int]							=   model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
															    sample / (sigma_input**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`'''		)

									if self.state_in_first_order:
															# 2. Convert to an ODE derivative for 1st order
															snake_case      : List[str]							=   (sample - pred_original_sample) / sigma_hat
															# 3. delta timestep
															snake_case      : List[Any]							=   sigma_interpol - sigma_hat

															# store for 2nd order step
															snake_case      : Any							=   sample
															snake_case      : Tuple							=   dt
															snake_case      : int							=   sample + derivative * dt
									else:
															# DPM-Solver-2
															# 2. Convert to an ODE derivative for 2nd order
															snake_case      : Dict							=   (sample - pred_original_sample) / sigma_interpol
															# 3. delta timestep
															snake_case      : Optional[Any]							=   sigma_down - sigma_hat

															snake_case      : str							=   self.sample
															snake_case      : str							=   None

															snake_case      : Union[str, Any]							=   sample + derivative * dt
															snake_case      : str							=   prev_sample + noise * sigma_up

									if not return_dict:
															return (prev_sample,)

									return SchedulerOutput(prev_sample=_lowercase		)



			def 							__lowercase				( self       :							Union[str, Any]      ,  _lowercase       :							torch.FloatTensor      ,  _lowercase       :							torch.FloatTensor      ,  _lowercase       :							torch.FloatTensor      ,  )       ->				torch.FloatTensor:
									# Make sure sigmas and timesteps have the same device and dtype as original_samples
									snake_case      : Optional[Any]							=   self.sigmas.to(device=original_samples.device      ,  dtype=original_samples.dtype		)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase		):
															# mps does not support float64
															snake_case      : Optional[int]							=   self.timesteps.to(original_samples.device      ,  dtype=torch.floataa		)
															snake_case      : int							=   timesteps.to(original_samples.device      ,  dtype=torch.floataa		)
									else:
															snake_case      : List[Any]							=   self.timesteps.to(original_samples.device		)
															snake_case      : str							=   timesteps.to(original_samples.device		)

									snake_case      : Any							=   [self.index_for_timestep(_lowercase      ,  _lowercase		) for t in timesteps]

									snake_case      : str							=   sigmas[step_indices].flatten()
									while len(sigma.shape		) < len(original_samples.shape		):
															snake_case      : Optional[Any]							=   sigma.unsqueeze(-1		)

									snake_case      : Any							=   original_samples + noise * sigma
									return noisy_samples



			def __len__( self       :							Optional[Any]		)       ->				Dict:
									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 			_a     (   unittest.TestCase):



			def __init__( self       :							Optional[int]      ,  _lowercase       :							int      ,  _lowercase       :							Union[str, Any]=7      ,  _lowercase       :							Union[str, Any]=3      ,  _lowercase       :							int=18      ,  _lowercase       :							Any=30      ,  _lowercase       :							str=400      ,  _lowercase       :							str=True      ,  _lowercase       :							Dict=None      ,  _lowercase       :							int=True      ,  _lowercase       :							Union[str, Any]=None      ,  _lowercase       :							str=True      ,  _lowercase       :							Union[str, Any]=[0.48145466, 0.4578275, 0.40821073]      ,  _lowercase       :							str=[0.26862954, 0.26130258, 0.27577711]      ,  _lowercase       :							Optional[int]=True      ,  )       ->				Optional[int]:
									snake_case      : str							=   parent
									snake_case      : Any							=   batch_size
									snake_case      : Optional[int]							=   num_channels
									snake_case      : Any							=   image_size
									snake_case      : Optional[Any]							=   min_resolution
									snake_case      : int							=   max_resolution
									snake_case      : List[Any]							=   do_resize
									snake_case      : Dict							=   size if size is not None else {"height": 18, "width": 18}
									snake_case      : str							=   do_center_crop
									snake_case      : List[str]							=   crop_size if crop_size is not None else {"height": 18, "width": 18}
									snake_case      : str							=   do_normalize
									snake_case      : str							=   image_mean
									snake_case      : Optional[Any]							=   image_std
									snake_case      : str							=   do_convert_rgb



			def 							__lowercase				( self       :							Tuple		)       ->				Union[str, Any]:
									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 			_a     (   SCREAMING_SNAKE_CASE__    ,  unittest.TestCase):
			__magic_name__    				=    OwlViTImageProcessor if is_vision_available() else None



			def 							__lowercase				( self       :							Any		)       ->				List[Any]:
									snake_case      : Dict							=   OwlViTImageProcessingTester(self		)



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



			def 							__lowercase				( self       :							List[Any]		)       ->				Union[str, Any]:
									snake_case      : List[Any]							=   self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_lowercase      ,  "do_resize"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "size"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_center_crop"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "center_crop"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_normalize"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "image_mean"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "image_std"		)		)
									self.assertTrue(hasattr(_lowercase      ,  "do_convert_rgb"		)		)



			def 							__lowercase				( self       :							Any		)       ->				Any:
									snake_case      : List[Any]							=   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}		)

									snake_case      : int							=   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       :							Any		)       ->				Optional[int]:
									# Initialize image_processing
									snake_case      : List[Any]							=   self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									snake_case      : Tuple							=   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowercase		)

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

									# Test not batched input
									snake_case      : Dict							=   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
									snake_case      : Optional[Any]							=   image_processing(_lowercase      ,  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[Any]		)       ->				Dict:
									# Initialize image_processing
									snake_case      : Optional[int]							=   self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									snake_case      : int							=   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowercase      ,  numpify=_lowercase		)
									for image in image_inputs:
															self.assertIsInstance(_lowercase      ,  np.ndarray		)

									# Test not batched input
									snake_case      : str							=   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
									snake_case      : str							=   image_processing(_lowercase      ,  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       :							List[Any]		)       ->				List[str]:
									# Initialize image_processing
									snake_case      : Any							=   self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									snake_case      : Dict							=   prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=_lowercase      ,  torchify=_lowercase		)
									for image in image_inputs:
															self.assertIsInstance(_lowercase      ,  torch.Tensor		)

									# Test not batched input
									snake_case      : Union[str, Any]							=   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
									snake_case      : Any							=   image_processing(_lowercase      ,  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"],
									    )      ,  )



