






'''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      __SCREAMING_SNAKE_CASE					:


  """simple docstring"""


  _a     :   CommonSchedulerState
  _a     :   jnp.ndarray

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







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






         """simple docstring"""


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





@dataclass
class      __SCREAMING_SNAKE_CASE					(__A					):


  """simple docstring"""


  _a     :   DDIMSchedulerState





class      __SCREAMING_SNAKE_CASE					(__A       ,					__A					):


  """simple docstring"""


  _a     :   Any			 =    [e.name for e in FlaxKarrasDiffusionSchedulers]

  _a     :   jnp.dtype







  @property
  def       _a					( self  ):






         """simple docstring"""


         return True







  @register_to_config
  def __init__( self      ,  UpperCamelCase__ = 1_000      ,  UpperCamelCase__ = 0.0_001      ,  UpperCamelCase__ = 0.02      ,  UpperCamelCase__ = "linear"      ,  UpperCamelCase__ = None      ,  UpperCamelCase__ = True      ,  UpperCamelCase__ = 0      ,  UpperCamelCase__ = "epsilon"      ,  UpperCamelCase__ = jnp.floataa      ,  ):






         """simple docstring"""


         a_	    = dtype







  def       _a					( self      ,  UpperCamelCase__ = None  ):






         """simple docstring"""


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

         a_	    = 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      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = None  ):






         """simple docstring"""


         return sample







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






         """simple docstring"""


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

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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


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

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

         return variance







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__ = 0.0      ,  UpperCamelCase__ = 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_	    = timestep - self.config.num_train_timesteps // state.num_inference_steps

         a_	    = state.common.alphas_cumprod
         a_	    = state.final_alpha_cumprod

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

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

         # 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
         a_	    = (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_	    = 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      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  ):






         """simple docstring"""


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







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  UpperCamelCase__      ,  ):






         """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 random
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():
  import PIL

  from transformers import FlavaImageProcessor
  from transformers.image_utils import PILImageResampling
  from transformers.models.flava.image_processing_flava import (
      FLAVA_CODEBOOK_MEAN,
      FLAVA_CODEBOOK_STD,
      FLAVA_IMAGE_MEAN,
      FLAVA_IMAGE_STD,
  )
else:
  __lowerCAmelCase					   =       __lowerCAmelCase					   =       __lowerCAmelCase					   =       __lowerCAmelCase					   =       None




class      __SCREAMING_SNAKE_CASE					(unittest.TestCase					):


  """simple docstring"""


  def __init__( self      ,  UpperCamelCase__      ,  UpperCamelCase__=7      ,  UpperCamelCase__=3      ,  UpperCamelCase__=30      ,  UpperCamelCase__=400      ,  UpperCamelCase__=True      ,  UpperCamelCase__=None      ,  UpperCamelCase__=True      ,  UpperCamelCase__=None      ,  UpperCamelCase__=None      ,  UpperCamelCase__=True      ,  UpperCamelCase__=1 / 255      ,  UpperCamelCase__=True      ,  UpperCamelCase__=FLAVA_IMAGE_MEAN      ,  UpperCamelCase__=FLAVA_IMAGE_STD      ,  UpperCamelCase__=14      ,  UpperCamelCase__=75      ,  UpperCamelCase__=None      ,  UpperCamelCase__=16      ,  UpperCamelCase__=0.3      ,  UpperCamelCase__=None      ,  UpperCamelCase__=True      ,  UpperCamelCase__=None      ,  UpperCamelCase__=None      ,  UpperCamelCase__=True      ,  UpperCamelCase__=None      ,  UpperCamelCase__=True      ,  UpperCamelCase__=True      ,  UpperCamelCase__=FLAVA_CODEBOOK_MEAN      ,  UpperCamelCase__=FLAVA_CODEBOOK_STD      ,  ):






         """simple docstring"""


         a_	    = size if size is not None else {'height': 224, 'width': 224}
         a_	    = crop_size if crop_size is not None else {'height': 224, 'width': 224}
         a_	    = codebook_size if codebook_size is not None else {'height': 112, 'width': 112}
         a_	    = codebook_crop_size if codebook_crop_size is not None else {'height': 112, 'width': 112}

         a_	    = parent
         a_	    = batch_size
         a_	    = num_channels
         a_	    = do_resize
         a_	    = do_rescale
         a_	    = rescale_factor
         a_	    = min_resolution
         a_	    = max_resolution
         a_	    = size
         a_	    = resample if resample is not None else PILImageResampling.BICUBIC
         a_	    = do_normalize
         a_	    = image_mean
         a_	    = image_std
         a_	    = do_center_crop
         a_	    = crop_size

         a_	    = input_size_patches
         a_	    = total_mask_patches
         a_	    = mask_group_max_patches
         a_	    = mask_group_min_patches
         a_	    = mask_group_min_aspect_ratio
         a_	    = mask_group_max_aspect_ratio

         a_	    = codebook_do_resize
         a_	    = codebook_size
         a_	    = codebook_resample if codebook_resample is not None else PILImageResampling.LANCZOS
         a_	    = codebook_do_center_crop
         a_	    = codebook_crop_size
         a_	    = codebook_do_map_pixels
         a_	    = codebook_do_normalize
         a_	    = codebook_image_mean
         a_	    = codebook_image_std







  def       _a					( 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,
             "resample": self.resample,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_center_crop": self.do_center_crop,
             "crop_size": self.crop_size,
             "input_size_patches": self.input_size_patches,
             "total_mask_patches": self.total_mask_patches,
             "mask_group_max_patches": self.mask_group_max_patches,
             "mask_group_min_patches": self.mask_group_min_patches,
             "mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
             "mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
             "codebook_do_resize": self.codebook_do_resize,
             "codebook_size": self.codebook_size,
             "codebook_resample": self.codebook_resample,
             "codebook_do_center_crop": self.codebook_do_center_crop,
             "codebook_crop_size": self.codebook_crop_size,
             "codebook_do_map_pixels": self.codebook_do_map_pixels,
             "codebook_do_normalize": self.codebook_do_normalize,
             "codebook_image_mean": self.codebook_image_mean,
             "codebook_image_std": self.codebook_image_std,
         }







  def       _a					( self  ):






         """simple docstring"""


         return (self.size["height"], self.size["width"])







  def       _a					( self  ):






         """simple docstring"""


         return (
             (self.input_size_patches, self.input_size_patches)
             if not isinstance(self.input_size_patches      ,  UpperCamelCase__  )
             else self.input_size_patches
         )







  def       _a					( self  ):






         """simple docstring"""


         return (self.codebook_size["height"], self.codebook_size["width"])





@require_torch
@require_vision
class      __SCREAMING_SNAKE_CASE					(__A       ,					unittest.TestCase					):


  """simple docstring"""


  _a     :   Union[str, Any]			 =    FlavaImageProcessor if is_vision_available() else None
  _a     :   Dict			 =    None







  def       _a					( self  ):






         """simple docstring"""


         a_	    = FlavaImageProcessingTester(self  )







  @property
  def       _a					( self  ):






         """simple docstring"""


         return self.image_processor_tester.prepare_image_processor_dict()







  def       _a					( self  ):






         """simple docstring"""


         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__      ,  'resample'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'crop_size'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'do_center_crop'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'do_rescale'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'rescale_factor'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'masking_generator'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_do_resize'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_size'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_resample'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_do_center_crop'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_crop_size'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_do_map_pixels'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_do_normalize'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_image_mean'  )  )
         self.assertTrue(hasattr(UpperCamelCase__      ,  'codebook_image_std'  )  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.image_processing_class.from_dict(self.image_processor_dict  )
         self.assertEqual(image_processor.size      ,  {'height': 224, 'width': 224}  )
         self.assertEqual(image_processor.crop_size      ,  {'height': 224, 'width': 224}  )
         self.assertEqual(image_processor.codebook_size      ,  {'height': 112, 'width': 112}  )
         self.assertEqual(image_processor.codebook_crop_size      ,  {'height': 112, 'width': 112}  )

         a_	    = self.image_processing_class.from_dict(
             self.image_processor_dict      ,  size=42      ,  crop_size=84      ,  codebook_size=33      ,  codebook_crop_size=66  )
         self.assertEqual(image_processor.size      ,  {'height': 42, 'width': 42}  )
         self.assertEqual(image_processor.crop_size      ,  {'height': 84, 'width': 84}  )
         self.assertEqual(image_processor.codebook_size      ,  {'height': 33, 'width': 33}  )
         self.assertEqual(image_processor.codebook_crop_size      ,  {'height': 66, 'width': 66}  )







  def       _a					( self  ):






         """simple docstring"""


         pass







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         a_	    = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase__  )
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__      ,  PIL.Image.Image  )

         # Test not batched input
         a_	    = image_processing(image_inputs[0]      ,  return_tensors='pt'  )

         # Test no bool masked pos
         self.assertFalse('bool_masked_pos' in encoded_images  )

         a_    ,  a_	    = self.image_processor_tester.get_expected_image_size()

         self.assertEqual(
             encoded_images.pixel_values.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

         # Test batched
         a_	    = image_processing(UpperCamelCase__      ,  return_tensors='pt'  )
         a_    ,  a_	    = self.image_processor_tester.get_expected_image_size()

         # Test no bool masked pos
         self.assertFalse('bool_masked_pos' in encoded_images  )

         self.assertEqual(
             encoded_images.pixel_values.shape      ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,  )







  def       _a					( self      ,  UpperCamelCase__      ,  UpperCamelCase__  ):






         """simple docstring"""


         a_	    = self.image_processing_class(**self.image_processor_dict  )
         # create random tensors
         a_	    = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase__      ,  **UpperCamelCase__  )
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__      ,  UpperCamelCase__  )

         # Test not batched input
         a_	    = image_processing(image_inputs[0]      ,  return_tensors='pt'  )

         a_    ,  a_	    = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

         a_	    = image_processing(UpperCamelCase__      ,  return_image_mask=UpperCamelCase__      ,  return_tensors='pt'  )

         a_    ,  a_	    = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape      ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,  )

         a_    ,  a_	    = self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape      ,  (
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )      ,  )

         # Test batched
         a_	    = image_processing(UpperCamelCase__      ,  return_tensors='pt'  ).pixel_values

         a_    ,  a_	    = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.shape      ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,  )

         # Test masking
         a_	    = image_processing(UpperCamelCase__      ,  return_image_mask=UpperCamelCase__      ,  return_tensors='pt'  )

         a_    ,  a_	    = self.image_processor_tester.get_expected_image_size()
         self.assertEqual(
             encoded_images.pixel_values.shape      ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,  )

         a_    ,  a_	    = self.image_processor_tester.get_expected_mask_size()
         self.assertEqual(
             encoded_images.bool_masked_pos.shape      ,  (
                 self.image_processor_tester.batch_size,
                 expected_height,
                 expected_width,
             )      ,  )







  def       _a					( self  ):






         """simple docstring"""


         self._test_call_framework(np.ndarray      ,  prepare_kwargs={'numpify': True}  )







  def       _a					( self  ):






         """simple docstring"""


         self._test_call_framework(torch.Tensor      ,  prepare_kwargs={'torchify': True}  )







  def       _a					( self  ):






         """simple docstring"""


         random.seed(1_234  )
         a_	    = self.image_processing_class(**self.image_processor_dict  )
         a_	    = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase__      ,  torchify=UpperCamelCase__  )

         # Test not batched input
         a_	    = image_processing(image_inputs[0]      ,  return_image_mask=UpperCamelCase__      ,  return_tensors='pt'  )
         self.assertEqual(encoded_images.bool_masked_pos.sum().item()      ,  75  )







  def       _a					( self  ):






         """simple docstring"""


         a_	    = self.image_processing_class(**self.image_processor_dict  )
         # create random PIL images
         a_	    = prepare_image_inputs(self.image_processor_tester      ,  equal_resolution=UpperCamelCase__  )
         for image in image_inputs:
                self.assertIsInstance(UpperCamelCase__      ,  PIL.Image.Image  )

         # Test not batched input
         a_	    = image_processing(image_inputs[0]      ,  return_codebook_pixels=UpperCamelCase__      ,  return_tensors='pt'  )
         a_    ,  a_	    = self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape      ,  (1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,  )

         # Test batched
         a_	    = image_processing(UpperCamelCase__      ,  return_codebook_pixels=UpperCamelCase__      ,  return_tensors='pt'  )
         a_    ,  a_	    = self.image_processor_tester.get_expected_codebook_image_size()
         self.assertEqual(
             encoded_images.codebook_pixel_values.shape      ,  (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,  )




