





"""simple docstring"""



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 	__snake_case      :





	"""simple docstring"""


	_lowerCamelCase				 =  42
	_lowerCamelCase				 =  42

	# setable values
	_lowerCamelCase				 =  42
	_lowerCamelCase				 =  42
	_lowerCamelCase				 =  None

	@classmethod
	def       UpperCamelCase__(					cls      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



					'''simple docstring'''




					return cls(
					    common=UpperCamelCase__      ,		final_alpha_cumprod=UpperCamelCase__      ,		init_noise_sigma=UpperCamelCase__      ,		timesteps=UpperCamelCase__      ,		)






@dataclass
class 	__snake_case      (	_SCREAMING_SNAKE_CASE  ):





	"""simple docstring"""


	_lowerCamelCase				 =  42






class 	__snake_case      (	_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      ,		__lowerCamelCase = 1000      ,		__lowerCamelCase = 0.0_0_0_1      ,		__lowerCamelCase = 0.0_2      ,		__lowerCamelCase = "linear"      ,		__lowerCamelCase = None      ,		__lowerCamelCase = True      ,		__lowerCamelCase = 0      ,		__lowerCamelCase = "epsilon"      ,		__lowerCamelCase = jnp.floataa      ,		):



					'''simple docstring'''




					__A					:		str						=  dtype

	def       UpperCamelCase__(					self      ,		__lowerCamelCase = None						):



					'''simple docstring'''




					if common is None:
									__A					:		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.
					__A					:		Any						=  (
					    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
					__A					:		str						=  jnp.array(1.0      ,		dtype=self.dtype						)

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

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

	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = None						):



					'''simple docstring'''




					return sample

	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = ()						):



					'''simple docstring'''




					__A					:		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
					__A					:		Tuple						=  (jnp.arange(0      ,		UpperCamelCase__						) * step_ratio).round()[::-1] + self.config.steps_offset

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

	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase						):



					'''simple docstring'''




					__A					:		Any						=  state.common.alphas_cumprod[timestep]
					__A					:		Union[str, Any]						=  jnp.where(
					    prev_timestep >= 0      ,		state.common.alphas_cumprod[prev_timestep]      ,		state.final_alpha_cumprod						)
					__A					:		Optional[Any]						=  1 - alpha_prod_t
					__A					:		Union[str, Any]						=  1 - alpha_prod_t_prev

					__A					:		Optional[int]						=  (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

					return variance

	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase = 0.0      ,		__lowerCamelCase = 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)
					__A					:		int						=  timestep - self.config.num_train_timesteps // state.num_inference_steps

					__A					:		str						=  state.common.alphas_cumprod
					__A					:		Optional[int]						=  state.final_alpha_cumprod

					# 2. compute alphas, betas
					__A					:		Dict						=  alphas_cumprod[timestep]
					__A					:		Dict						=  jnp.where(prev_timestep >= 0      ,		alphas_cumprod[prev_timestep]      ,		UpperCamelCase__						)

					__A					:		str						=  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":
									__A					:		Any						=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									__A					:		Optional[int]						=  model_output
					elif self.config.prediction_type == "sample":
									__A					:		Any						=  model_output
									__A					:		Optional[Any]						=  (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
					elif self.config.prediction_type == "v_prediction":
									__A					:		Union[str, Any]						=  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									__A					:		Optional[Any]						=  (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)
					__A					:		Optional[Any]						=  self._get_variance(UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__						)
					__A					:		Dict						=  eta * variance ** 0.5

					# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
					__A					:		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
					__A					:		Tuple						=  alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

					if not return_dict:
									return (prev_sample, state)

					return FlaxDDIMSchedulerOutput(prev_sample=UpperCamelCase__      ,		state=UpperCamelCase__						)

	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



					'''simple docstring'''




					return add_noise_common(state.common      ,		UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__						)

	def       UpperCamelCase__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		__lowerCamelCase      ,		):



					'''simple docstring'''




					return get_velocity_common(state.common      ,		UpperCamelCase__      ,		UpperCamelCase__      ,		UpperCamelCase__						)







	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, prepare_video_inputs


if is_torch_available():
       import torch

if is_vision_available():
       from PIL import Image

       from transformers import VideoMAEImageProcessor






class 	__snake_case      (	unittest.TestCase  ):





 """simple docstring"""


 def __init__(					self      ,		__lowerCamelCase      ,		__lowerCamelCase=7      ,		__lowerCamelCase=3      ,		__lowerCamelCase=10      ,		__lowerCamelCase=18      ,		__lowerCamelCase=30      ,		__lowerCamelCase=400      ,		__lowerCamelCase=True      ,		__lowerCamelCase=None      ,		__lowerCamelCase=True      ,		__lowerCamelCase=[0.5, 0.5, 0.5]      ,		__lowerCamelCase=[0.5, 0.5, 0.5]      ,		__lowerCamelCase=None      ,		):



     '''simple docstring'''




     __A					:		Optional[Any]						=  size if size is not None else {'''shortest_edge''': 18}
     __A					:		Optional[int]						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

     __A					:		Any						=  parent
     __A					:		Union[str, Any]						=  batch_size
     __A					:		Optional[int]						=  num_channels
     __A					:		int						=  num_frames
     __A					:		Optional[int]						=  image_size
     __A					:		Union[str, Any]						=  min_resolution
     __A					:		int						=  max_resolution
     __A					:		Tuple						=  do_resize
     __A					:		str						=  size
     __A					:		Any						=  do_normalize
     __A					:		Union[str, Any]						=  image_mean
     __A					:		List[str]						=  image_std
     __A					:		Union[str, Any]						=  crop_size







 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return {
         "image_mean": self.image_mean,
         "image_std": self.image_std,
         "do_normalize": self.do_normalize,
         "do_resize": self.do_resize,
         "size": self.size,
         "crop_size": self.crop_size,
     }






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





 """simple docstring"""


 _lowerCamelCase				 =  VideoMAEImageProcessor if is_vision_available() else None

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		List[str]						=  VideoMAEImageProcessingTester(self						)

 @property
 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     return self.image_processor_tester.prepare_image_processor_dict()

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Dict						=  self.image_processing_class(**self.image_processor_dict						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''image_mean'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''image_std'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''do_normalize'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''do_resize'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''do_center_crop'''						)						)
     self.assertTrue(hasattr(__lowerCamelCase      ,		'''size'''						)						)

 def       UpperCamelCase__(					self						):



     '''simple docstring'''




     __A					:		Optional[int]						=  self.image_processing_class.from_dict(self.image_processor_dict						)
     self.assertEqual(image_processor.size      ,		{'''shortest_edge''': 18}						)
     self.assertEqual(image_processor.crop_size      ,		{'''height''': 18, '''width''': 18}						)

     __A					:		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'''




     __A					:		List[Any]						=  self.image_processing_class(**self.image_processor_dict						)
     # create random PIL videos
     __A					:		Optional[int]						=  prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCamelCase						)
     for video in video_inputs:
         self.assertIsInstance(__lowerCamelCase      ,		__lowerCamelCase						)
         self.assertIsInstance(video[0]      ,		Image.Image						)

     # Test not batched input
     __A					:		Any						=  image_processing(video_inputs[0]      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

     # Test batched
     __A					:		int						=  image_processing(__lowerCamelCase      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             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'''




     __A					:		List[Any]						=  self.image_processing_class(**self.image_processor_dict						)
     # create random numpy tensors
     __A					:		List[str]						=  prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCamelCase      ,		numpify=__lowerCamelCase						)
     for video in video_inputs:
         self.assertIsInstance(__lowerCamelCase      ,		__lowerCamelCase						)
         self.assertIsInstance(video[0]      ,		np.ndarray						)

     # Test not batched input
     __A					:		List[str]						=  image_processing(video_inputs[0]      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

     # Test batched
     __A					:		int						=  image_processing(__lowerCamelCase      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             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'''




     __A					:		Optional[int]						=  self.image_processing_class(**self.image_processor_dict						)
     # create random PyTorch tensors
     __A					:		int						=  prepare_video_inputs(self.image_processor_tester      ,		equal_resolution=__lowerCamelCase      ,		torchify=__lowerCamelCase						)
     for video in video_inputs:
         self.assertIsInstance(__lowerCamelCase      ,		__lowerCamelCase						)
         self.assertIsInstance(video[0]      ,		torch.Tensor						)

     # Test not batched input
     __A					:		Optional[int]						=  image_processing(video_inputs[0]      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             1,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)

     # Test batched
     __A					:		str						=  image_processing(__lowerCamelCase      ,		return_tensors='''pt'''						).pixel_values
     self.assertEqual(
         encoded_videos.shape      ,		(
             self.image_processor_tester.batch_size,
             self.image_processor_tester.num_frames,
             self.image_processor_tester.num_channels,
             self.image_processor_tester.crop_size['''height'''],
             self.image_processor_tester.crop_size['''width'''],
         )      ,		)




