






"""simple docstring"""







# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)


@flax.struct.dataclass
class     __UpperCAmelCase:




						"""simple docstring"""




						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					42

						# setable values
						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					42
						__lowerCamelCase				       =					None




						@classmethod
						def 		UpperCAmelCase_       (   cls       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												return cls(
												    common=snake_case__       ,  final_alpha_cumprod=snake_case__       ,  init_noise_sigma=snake_case__       ,  timesteps=snake_case__       ,  )


@dataclass
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					42


class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  SCREAMING_SNAKE_CASE__							):




						"""simple docstring"""




						__lowerCamelCase				       =					[e.name for e in FlaxKarrasDiffusionSchedulers]

						__lowerCamelCase				       =					42




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return True




						@register_to_config
						def __init__(   self       ,  snake_case__ = 1000       ,  snake_case__ = 0.00_01       ,  snake_case__ = 0.02       ,  snake_case__ = "linear"       ,  snake_case__ = None       ,  snake_case__ = True       ,  snake_case__ = 0       ,  snake_case__ = "epsilon"       ,  snake_case__ = jnp.floataa       ,  ):
												'''simple docstring'''
												lowercase__				:      Dict=       dtype




						def 		UpperCAmelCase_       (   self       ,  snake_case__ = None			):
												'''simple docstring'''
												if common is None:
																		lowercase__				:      Optional[int]=       CommonSchedulerState.create(self			)

												# At every step in ddim, we are looking into the previous alphas_cumprod
												# For the final step, there is no previous alphas_cumprod because we are already at 0
												# `set_alpha_to_one` decides whether we set this parameter simply to one or
												# whether we use the final alpha of the "non-previous" one.
												lowercase__				:      List[str]=       (
												    jnp.array(1.0       ,  dtype=self.dtype			) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
												)

												# standard deviation of the initial noise distribution
												lowercase__				:      Optional[Any]=       jnp.array(1.0       ,  dtype=self.dtype			)

												lowercase__				:      Optional[int]=       jnp.arange(0       ,  self.config.num_train_timesteps			).round()[::-1]

												return DDIMSchedulerState.create(
												    common=snake_case__       ,  final_alpha_cumprod=snake_case__       ,  init_noise_sigma=snake_case__       ,  timesteps=snake_case__       ,  )




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__ = None			):
												'''simple docstring'''
												return sample




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__ = ()			):
												'''simple docstring'''
												lowercase__				:      Tuple=       self.config.num_train_timesteps // num_inference_steps
												# creates integer timesteps by multiplying by ratio
												# rounding to avoid issues when num_inference_step is power of 3
												lowercase__				:      Dict=       (jnp.arange(0       ,  snake_case__			) * step_ratio).round()[::-1] + self.config.steps_offset

												return state.replace(
												    num_inference_steps=snake_case__       ,  timesteps=snake_case__       ,  )




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       state.common.alphas_cumprod[timestep]
												lowercase__				:      Any=       jnp.where(
												    prev_timestep >= 0       ,  state.common.alphas_cumprod[prev_timestep]       ,  state.final_alpha_cumprod			)
												lowercase__				:      Dict=       1 - alpha_prod_t
												lowercase__				:      Optional[Any]=       1 - alpha_prod_t_prev

												lowercase__				:      Optional[Any]=       (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

												return variance




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__ = 0.0       ,  snake_case__ = True       ,  ):
												'''simple docstring'''
												if state.num_inference_steps is None:
																		raise ValueError(
																		    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"			)

												# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
												# Ideally, read DDIM paper in-detail understanding

												# Notation (<variable name> -> <name in paper>
												# - pred_noise_t -> e_theta(x_t, t)
												# - pred_original_sample -> f_theta(x_t, t) or x_0
												# - std_dev_t -> sigma_t
												# - eta -> η
												# - pred_sample_direction -> "direction pointing to x_t"
												# - pred_prev_sample -> "x_t-1"

												# 1. get previous step value (=t-1)
												lowercase__				:      Optional[int]=       timestep - self.config.num_train_timesteps // state.num_inference_steps

												lowercase__				:      Optional[int]=       state.common.alphas_cumprod
												lowercase__				:      Any=       state.final_alpha_cumprod

												# 2. compute alphas, betas
												lowercase__				:      int=       alphas_cumprod[timestep]
												lowercase__				:      List[Any]=       jnp.where(prev_timestep >= 0       ,  alphas_cumprod[prev_timestep]       ,  snake_case__			)

												lowercase__				:      Tuple=       1 - alpha_prod_t

												# 3. compute predicted original sample from predicted noise also called
												# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												if self.config.prediction_type == "epsilon":
																		lowercase__				:      List[Any]=       (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
																		lowercase__				:      Optional[int]=       model_output
												elif self.config.prediction_type == "sample":
																		lowercase__				:      Tuple=       model_output
																		lowercase__				:      Dict=       (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
												elif self.config.prediction_type == "v_prediction":
																		lowercase__				:      Union[str, Any]=       (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
																		lowercase__				:      Optional[int]=       (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
												else:
																		raise ValueError(
																		    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
																		    " `v_prediction`"			)

												# 4. compute variance: "sigma_t(η)" -> see formula (16)
												# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
												lowercase__				:      str=       self._get_variance(snake_case__       ,  snake_case__       ,  snake_case__			)
												lowercase__				:      Tuple=       eta * variance ** 0.5

												# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												lowercase__				:      Optional[Any]=       (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

												# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
												lowercase__				:      Optional[int]=       alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

												if not return_dict:
																		return (prev_sample, state)

												return FlaxDDIMSchedulerOutput(prev_sample=snake_case__       ,  state=snake_case__			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												return add_noise_common(state.common       ,  snake_case__       ,  snake_case__       ,  snake_case__			)




						def 		UpperCAmelCase_       (   self       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  snake_case__       ,  ):
												'''simple docstring'''
												return get_velocity_common(state.common       ,  snake_case__       ,  snake_case__       ,  snake_case__			)




						def __len__(   self			):
												'''simple docstring'''
												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


if is_torch_available():
			import torch

if is_vision_available():
			from PIL import Image

			from transformers import CLIPImageProcessor


class     __UpperCAmelCase(   unittest.TestCase							):




						"""simple docstring"""




						def __init__(   self       ,  snake_case__       ,  snake_case__=7       ,  snake_case__=3       ,  snake_case__=18       ,  snake_case__=30       ,  snake_case__=400       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=None       ,  snake_case__=True       ,  snake_case__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]       ,  snake_case__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]       ,  snake_case__=True       ,  ):
												'''simple docstring'''
												lowercase__				:      int=       size if size is not None else {"shortest_edge": 20}
												lowercase__				:      int=       crop_size if crop_size is not None else {"height": 18, "width": 18}
												lowercase__				:      Union[str, Any]=       parent
												lowercase__				:      Optional[Any]=       batch_size
												lowercase__				:      List[str]=       num_channels
												lowercase__				:      str=       image_size
												lowercase__				:      Optional[int]=       min_resolution
												lowercase__				:      Union[str, Any]=       max_resolution
												lowercase__				:      List[Any]=       do_resize
												lowercase__				:      Any=       size
												lowercase__				:      Optional[Any]=       do_center_crop
												lowercase__				:      List[str]=       crop_size
												lowercase__				:      List[Any]=       do_normalize
												lowercase__				:      Optional[Any]=       image_mean
												lowercase__				:      Dict=       image_std
												lowercase__				:      Dict=       do_convert_rgb




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												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,
												}




						def 		UpperCAmelCase_       (   self       ,  snake_case__=False       ,  snake_case__=False       ,  snake_case__=False			):
												'''simple docstring'''
												assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

												if equal_resolution:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								image_inputs.append(
																								    np.random.randint(
																								        255       ,  size=(self.num_channels, self.max_resolution, self.max_resolution)       ,  dtype=np.uinta			)			)
												else:
																		lowercase__				:      Optional[Any]=       []
																		for i in range(self.batch_size			):
																								lowercase__,	lowercase__				:      str=       np.random.choice(np.arange(self.min_resolution       ,  self.max_resolution			)       ,  2			)
																								image_inputs.append(np.random.randint(255       ,  size=(self.num_channels, width, height)       ,  dtype=np.uinta			)			)

												if not numpify and not torchify:
																		# PIL expects the channel dimension as last dimension
																		lowercase__				:      Dict=       [Image.fromarray(np.moveaxis(snake_case__       ,  0       ,  -1			)			) for x in image_inputs]

												if torchify:
																		lowercase__				:      Tuple=       [torch.from_numpy(snake_case__			) for x in image_inputs]

												return image_inputs


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[Any]=       CLIPImageProcessingTester(self			)




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Dict=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class.from_dict(self.image_processor_dict			)
												self.assertEqual(image_processor.size       ,  {"shortest_edge": 20}			)
												self.assertEqual(image_processor.crop_size       ,  {"height": 18, "width": 18}			)

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




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      Optional[Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      List[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
												lowercase__				:      Dict=       image_processing(snake_case__       ,  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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Any=       self.image_processing_class(**self.image_processor_dict			)
												# create random numpy tensors
												lowercase__				:      Tuple=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  numpify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  np.ndarray			)

												# Test not batched input
												lowercase__				:      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
												lowercase__				:      int=       image_processing(snake_case__       ,  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 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      Optional[Any]=       self.image_processing_class(**self.image_processor_dict			)
												# create random PyTorch tensors
												lowercase__				:      Union[str, Any]=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__       ,  torchify=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  torch.Tensor			)

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


@require_torch
@require_vision
class     __UpperCAmelCase(   SCREAMING_SNAKE_CASE__  ,  unittest.TestCase							):




						"""simple docstring"""




						__lowerCamelCase				       =					CLIPImageProcessor if is_vision_available() else None




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      Optional[int]=       CLIPImageProcessingTester(self       ,  num_channels=4			)
												lowercase__				:      Optional[Any]=       3




						@property
						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												return self.image_processor_tester.prepare_image_processor_dict()




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												lowercase__				:      List[str]=       self.image_processing_class(**self.image_processor_dict			)
												self.assertTrue(hasattr(snake_case__       ,  "do_resize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "size"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "center_crop"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_normalize"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_mean"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "image_std"			)			)
												self.assertTrue(hasattr(snake_case__       ,  "do_convert_rgb"			)			)




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												pass




						def 		UpperCAmelCase_       (   self			):
												'''simple docstring'''
												# Initialize image_processing
												lowercase__				:      int=       self.image_processing_class(**self.image_processor_dict			)
												# create random PIL images
												lowercase__				:      int=       self.image_processor_tester.prepare_inputs(equal_resolution=snake_case__			)
												for image in image_inputs:
																		self.assertIsInstance(snake_case__       ,  Image.Image			)

												# Test not batched input
												lowercase__				:      Optional[int]=       image_processing(image_inputs[0]       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        1,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )

												# Test batched
												lowercase__				:      Any=       image_processing(snake_case__       ,  return_tensors="pt"			).pixel_values
												self.assertEqual(
												    encoded_images.shape       ,  (
												        self.image_processor_tester.batch_size,
												        self.expected_encoded_image_num_channels,
												        self.image_processor_tester.crop_size["height"],
												        self.image_processor_tester.crop_size["width"],
												    )       ,  )






