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 TvltImageProcessor
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=10			,					lowerCamelCase__=10			,					lowerCamelCase__=False			,					lowerCamelCase__=False       )					->Union[str, Any]:

  '''simple docstring'''




  a__  		=  []
  for i in range(image_processor_tester.num_frames       ):
    video.append(np.random.randint(2_55			,					size=(image_processor_tester.num_channels, width, height)			,					dtype=np.uinta       )       )

  if not numpify and not torchify:
    # PIL expects the channel dimension as last dimension
    a__  		=  [Image.fromarray(np.moveaxis(lowerCamelCase__			,					0			,					-1       )       ) for frame in video]

  if torchify:
    a__  		=  [torch.from_numpy(lowerCamelCase__       ) for frame in video]

  return video
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__=False			,					lowerCamelCase__=False			,					lowerCamelCase__=False       )					->Dict:

  '''simple docstring'''




  assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

  a__  		=  []
  for i in range(image_processor_tester.batch_size       ):
    if equal_resolution:
      a__  		=  a__  		=  image_processor_tester.max_resolution
    else:
      a__		,a__  		=  np.random.choice(
          np.arange(image_processor_tester.min_resolution			,					image_processor_tester.max_resolution       )			,					2       )
      a__  		=  prepare_video(
          image_processor_tester=lowerCamelCase__			,					width=lowerCamelCase__			,					height=lowerCamelCase__			,					numpify=lowerCamelCase__			,					torchify=lowerCamelCase__			,					)
    video_inputs.append(lowerCamelCase__       )

  return video_inputs







class        UpperCAmelCase_					(				unittest.TestCase     ):




    def __init__(       self	: Optional[Any]   ,   lowerCAmelCase	: List[str]   ,   lowerCAmelCase	: Tuple=7   ,   lowerCAmelCase	: Optional[int]=3   ,   lowerCAmelCase	: Tuple=4   ,   lowerCAmelCase	: List[Any]=18   ,   lowerCAmelCase	: Dict=30   ,   lowerCAmelCase	: Dict=400   ,   lowerCAmelCase	: int=True   ,   lowerCAmelCase	: Optional[Any]=None   ,   lowerCAmelCase	: List[Any]=True   ,   lowerCAmelCase	: Dict=[0.5, 0.5, 0.5]   ,   lowerCAmelCase	: Tuple=[0.5, 0.5, 0.5]   ,   lowerCAmelCase	: Dict=True   ,   lowerCAmelCase	: List[str]=None   ,   )						->			List[Any]:
      a__  		=  size if size is not None else {"shortest_edge": 18}
      a__  		=  crop_size if crop_size is not None else {"height": 18, "width": 18}

      a__  		=  parent
      a__  		=  batch_size
      a__  		=  num_channels
      a__  		=  num_frames
      a__  		=  image_size
      a__  		=  min_resolution
      a__  		=  max_resolution
      a__  		=  do_resize
      a__  		=  size
      a__  		=  do_normalize
      a__  		=  image_mean
      a__  		=  image_std
      a__  		=  do_center_crop
      a__  		=  crop_size




    def     __lowerCamelCase			(       self	: List[str] )						->			str:
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }







@require_torch
@require_vision
class        UpperCAmelCase_					(				lowerCAmelCase__  ,  unittest.TestCase     ):
    UpperCAmelCase_      :							List[Any]    			=				TvltImageProcessor if is_vision_available() else None




    def     __lowerCamelCase			(       self	: str )						->			int:
      a__  		=  TvltImageProcessorTester(self )




    @property
    def     __lowerCamelCase			(       self	: List[str] )						->			List[str]:
      return self.image_processor_tester.prepare_image_processor_dict()




    def     __lowerCamelCase			(       self	: int )						->			str:
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(_UpperCAmelCase   ,   "image_mean" ) )
      self.assertTrue(hasattr(_UpperCAmelCase   ,   "image_std" ) )
      self.assertTrue(hasattr(_UpperCAmelCase   ,   "do_normalize" ) )
      self.assertTrue(hasattr(_UpperCAmelCase   ,   "do_resize" ) )
      self.assertTrue(hasattr(_UpperCAmelCase   ,   "do_center_crop" ) )
      self.assertTrue(hasattr(_UpperCAmelCase   ,   "size" ) )




    def     __lowerCamelCase			(       self	: int )						->			Any:
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      # create random PIL videos
      a__  		=  prepare_video_inputs(self.image_processor_tester   ,   equal_resolution=_UpperCAmelCase )
      for video in video_inputs:
        self.assertIsInstance(_UpperCAmelCase   ,   _UpperCAmelCase )
        self.assertIsInstance(video[0]   ,   Image.Image )

      # Test not batched input
      a__  		=  image_processor(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__  		=  image_processor(_UpperCAmelCase   ,   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     __lowerCamelCase			(       self	: List[str] )						->			List[str]:
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      a__  		=  prepare_video_inputs(self.image_processor_tester   ,   equal_resolution=_UpperCAmelCase   ,   numpify=_UpperCAmelCase )
      for video in video_inputs:
        self.assertIsInstance(_UpperCAmelCase   ,   _UpperCAmelCase )
        self.assertIsInstance(video[0]   ,   np.ndarray )

      # Test not batched input
      a__  		=  image_processor(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__  		=  image_processor(_UpperCAmelCase   ,   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     __lowerCamelCase			(       self	: Tuple )						->			Tuple:
      a__  		=  self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      a__  		=  prepare_video_inputs(self.image_processor_tester   ,   equal_resolution=_UpperCAmelCase   ,   torchify=_UpperCAmelCase )
      for video in video_inputs:
        self.assertIsInstance(_UpperCAmelCase   ,   _UpperCAmelCase )
        self.assertIsInstance(video[0]   ,   torch.Tensor )

      # Test not batched input
      a__  		=  image_processor(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__  		=  image_processor(_UpperCAmelCase   ,   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"],
          )   ,   )



# 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       A__  :
							lowerCAmelCase__						:		CommonSchedulerState
							lowerCAmelCase__						:		jnp.ndarray

							# setable values
							lowerCAmelCase__						:		jnp.ndarray
							lowerCAmelCase__						:		jnp.ndarray
							lowerCAmelCase__						:		Optional[int]            =				None





							@classmethod
							def      a__					(					cls    :  Optional[Any]	,					_UpperCAmelCase    :  CommonSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					)		->						Any:



											"""simple docstring"""


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






@dataclass
class       A__  (		lowerCAmelCase__							):
							lowerCAmelCase__						:		DDIMSchedulerState






class       A__  (		lowerCAmelCase__       ,		lowerCAmelCase__							):
							lowerCAmelCase__						:		Union[str, Any]            =				[e.name for e in FlaxKarrasDiffusionSchedulers]

							lowerCAmelCase__						:		jnp.dtype





							@property
							def      a__					(					self    :  List[str]							)		->						Union[str, Any]:



											"""simple docstring"""


											return True





							@register_to_config
							def __init__(					self    :  str	,					_UpperCAmelCase    :  int = 10_00	,					_UpperCAmelCase    :  float = 0.0_001	,					_UpperCAmelCase    :  float = 0.02	,					_UpperCAmelCase    :  str = "linear"	,					_UpperCAmelCase    :  Optional[jnp.ndarray] = None	,					_UpperCAmelCase    :  bool = True	,					_UpperCAmelCase    :  int = 0	,					_UpperCAmelCase    :  str = "epsilon"	,					_UpperCAmelCase    :  jnp.dtype = jnp.floataa	,					)		->						Any:



											"""simple docstring"""


											__lowercase								=   dtype





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  Optional[CommonSchedulerState] = None							)		->						DDIMSchedulerState:



											"""simple docstring"""


											if common is None:
															__lowercase								=   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								=   (
											    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								=   jnp.array(1.0	,					dtype=self.dtype							)

											__lowercase								=   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      a__					(					self    :  Optional[Any]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  Optional[int] = None							)		->						jnp.ndarray:



											"""simple docstring"""


											return sample





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  Tuple = ()							)		->						DDIMSchedulerState:



											"""simple docstring"""


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

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





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  Union[str, Any]	,					_UpperCAmelCase    :  int							)		->						int:



											"""simple docstring"""


											__lowercase								=   state.common.alphas_cumprod[timestep]
											__lowercase								=   jnp.where(
											    prev_timestep >= 0	,					state.common.alphas_cumprod[prev_timestep]	,					state.final_alpha_cumprod							)
											__lowercase								=   1 - alpha_prod_t
											__lowercase								=   1 - alpha_prod_t_prev

											__lowercase								=   (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

											return variance





							def      a__					(					self    :  List[Any]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  int	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  float = 0.0	,					_UpperCAmelCase    :  bool = True	,					)		->						Union[FlaxDDIMSchedulerOutput, Tuple]:



											"""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								=   timestep - self.config.num_train_timesteps // state.num_inference_steps

											__lowercase								=   state.common.alphas_cumprod
											__lowercase								=   state.final_alpha_cumprod

											# 2. compute alphas, betas
											__lowercase								=   alphas_cumprod[timestep]
											__lowercase								=   jnp.where(prev_timestep >= 0	,					alphas_cumprod[prev_timestep]	,					_UpperCAmelCase							)

											__lowercase								=   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								=   (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
															__lowercase								=   model_output
											elif self.config.prediction_type == "sample":
															__lowercase								=   model_output
															__lowercase								=   (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
											elif self.config.prediction_type == "v_prediction":
															__lowercase								=   (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
															__lowercase								=   (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								=   self._get_variance(_UpperCAmelCase	,					_UpperCAmelCase	,					_UpperCAmelCase							)
											__lowercase								=   eta * variance ** 0.5

											# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
											__lowercase								=   (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								=   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      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					)		->						jnp.ndarray:



											"""simple docstring"""


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





							def      a__					(					self    :  Optional[int]	,					_UpperCAmelCase    :  DDIMSchedulerState	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					_UpperCAmelCase    :  jnp.ndarray	,					)		->						jnp.ndarray:



											"""simple docstring"""


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





							def __len__(					self    :  List[Any]							)		->						List[str]:



											"""simple docstring"""


											return self.config.num_train_timesteps





