'''simple docstring'''
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
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 lowerCamelCase							:




  '''simple docstring'''



  lowerCAmelCase__										=	42

  # setable values
  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	42
  lowerCAmelCase__										=	None






  @classmethod
  def  lowerCAmelCase__ (      cls			:       str  ,							UpperCAmelCase__			:       CommonSchedulerState  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       jnp.ndarray  )				->List[str]:
        return cls(common=UpperCAmelCase__  ,							init_noise_sigma=UpperCAmelCase__  ,							timesteps=UpperCAmelCase__  )







@dataclass
class lowerCamelCase							(	lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	42







class lowerCamelCase							(	lowerCamelCase     ,		lowerCamelCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	[e.name for e in FlaxKarrasDiffusionSchedulers]

  lowerCAmelCase__										=	42






  @property
  def  lowerCAmelCase__ (      self			:       str  )				->str:
        return True






  @register_to_config
  def __init__(      self			:       List[Any]  ,							UpperCAmelCase__			:       int = 1000  ,							UpperCAmelCase__			:       float = 0.0001  ,							UpperCAmelCase__			:       float = 0.02  ,							UpperCAmelCase__			:       str = "linear"  ,							UpperCAmelCase__			:       Optional[jnp.ndarray] = None  ,							UpperCAmelCase__			:       str = "fixed_small"  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       str = "epsilon"  ,							UpperCAmelCase__			:       jnp.dtype = jnp.floataa  ,							)				->Tuple:
        UpperCAmelCase_            =  dtype






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       Optional[CommonSchedulerState] = None  )				->DDPMSchedulerState:




        if common is None:
              UpperCAmelCase_            =  CommonSchedulerState.create(self  )

        # standard deviation of the initial noise distribution
        UpperCAmelCase_            =  jnp.array(1.0  ,							dtype=self.dtype  )

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

        return DDPMSchedulerState.create(
            common=UpperCAmelCase__  ,							init_noise_sigma=UpperCAmelCase__  ,							timesteps=UpperCAmelCase__  ,							)






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       DDPMSchedulerState  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       Optional[int] = None  )				->jnp.ndarray:
        return sample






  def  lowerCAmelCase__ (      self			:       str  ,							UpperCAmelCase__			:       DDPMSchedulerState  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       Tuple = ()  )				->DDPMSchedulerState:
        UpperCAmelCase_            =  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
        UpperCAmelCase_            =  (jnp.arange(0  ,							UpperCAmelCase__  ) * step_ratio).round()[::-1]

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






  def  lowerCAmelCase__ (      self			:       Any  ,							UpperCAmelCase__			:       DDPMSchedulerState  ,							UpperCAmelCase__			:       str  ,							UpperCAmelCase__			:       Any=None  ,							UpperCAmelCase__			:       Any=None  )				->Optional[int]:
        UpperCAmelCase_            =  state.common.alphas_cumprod[t]
        UpperCAmelCase_            =  jnp.where(t > 0  ,							state.common.alphas_cumprod[t - 1]  ,							jnp.array(1.0  ,							dtype=self.dtype  )  )

        # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
        # and sample from it to get previous sample
        # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
        UpperCAmelCase_            =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
              UpperCAmelCase_            =  self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
              UpperCAmelCase_            =  jnp.clip(UpperCAmelCase__  ,							a_min=1e-20  )
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
              UpperCAmelCase_            =  jnp.log(jnp.clip(UpperCAmelCase__  ,							a_min=1e-20  )  )
        elif variance_type == "fixed_large":
              UpperCAmelCase_            =  state.common.betas[t]
        elif variance_type == "fixed_large_log":
              # Glide max_log
              UpperCAmelCase_            =  jnp.log(state.common.betas[t]  )
        elif variance_type == "learned":
              return predicted_variance
        elif variance_type == "learned_range":
              UpperCAmelCase_            =  variance
              UpperCAmelCase_            =  state.common.betas[t]
              UpperCAmelCase_            =  (predicted_variance + 1) / 2
              UpperCAmelCase_            =  frac * max_log + (1 - frac) * min_log

        return variance






  def  lowerCAmelCase__ (      self			:       List[str]  ,							UpperCAmelCase__			:       DDPMSchedulerState  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       int  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       Optional[jax.random.KeyArray] = None  ,							UpperCAmelCase__			:       bool = True  ,							)				->Union[FlaxDDPMSchedulerOutput, Tuple]:
        UpperCAmelCase_            =  timestep

        if key is None:
              UpperCAmelCase_            =  jax.random.PRNGKey(0  )

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
              UpperCAmelCase_      ,		UpperCAmelCase_            =  jnp.split(UpperCAmelCase__  ,							sample.shape[1]  ,							axis=1  )
        else:
              UpperCAmelCase_            =  None

        # 1. compute alphas, betas
        UpperCAmelCase_            =  state.common.alphas_cumprod[t]
        UpperCAmelCase_            =  jnp.where(t > 0  ,							state.common.alphas_cumprod[t - 1]  ,							jnp.array(1.0  ,							dtype=self.dtype  )  )
        UpperCAmelCase_            =  1 - alpha_prod_t
        UpperCAmelCase_            =  1 - alpha_prod_t_prev

        # 2. compute predicted original sample from predicted noise also called
        # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
        if self.config.prediction_type == "epsilon":
              UpperCAmelCase_            =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
              UpperCAmelCase_            =  model_output
        elif self.config.prediction_type == "v_prediction":
              UpperCAmelCase_            =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
        else:
              raise ValueError(
                  f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
                  ''' for the FlaxDDPMScheduler.'''  )

        # 3. Clip "predicted x_0"
        if self.config.clip_sample:
              UpperCAmelCase_            =  jnp.clip(UpperCAmelCase__  ,							-1  ,							1  )

        # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCAmelCase_            =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        UpperCAmelCase_            =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

        # 5. Compute predicted previous sample µ_t
        # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
        UpperCAmelCase_            =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
              UpperCAmelCase_            =  jax.random.split(UpperCAmelCase__  ,							num=1  )
              UpperCAmelCase_            =  jax.random.normal(UpperCAmelCase__  ,							shape=model_output.shape  ,							dtype=self.dtype  )
              return (self._get_variance(UpperCAmelCase__  ,							UpperCAmelCase__  ,							predicted_variance=UpperCAmelCase__  ) ** 0.5) * noise

        UpperCAmelCase_            =  jnp.where(t > 0  ,							random_variance()  ,							jnp.zeros(model_output.shape  ,							dtype=self.dtype  )  )

        UpperCAmelCase_            =  pred_prev_sample + variance

        if not return_dict:
              return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=UpperCAmelCase__  ,							state=UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       List[Any]  ,							UpperCAmelCase__			:       DDPMSchedulerState  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       jnp.ndarray  ,							)				->jnp.ndarray:
        return add_noise_common(state.common  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )






  def  lowerCAmelCase__ (      self			:       Union[str, Any]  ,							UpperCAmelCase__			:       DDPMSchedulerState  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       jnp.ndarray  ,							UpperCAmelCase__			:       jnp.ndarray  ,							)				->jnp.ndarray:
        return get_velocity_common(state.common  ,							UpperCAmelCase__  ,							UpperCAmelCase__  ,							UpperCAmelCase__  )






  def __len__(      self			:       str  )				->str:
        return self.config.num_train_timesteps


'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor







class lowerCamelCase							(	unittest.TestCase					):




  '''simple docstring'''



  def __init__(      self			:       int  ,							UpperCAmelCase__			:       Dict  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Dict[str, int] = None  ,							UpperCAmelCase__			:       int = 32  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[int, float] = 1 / 255  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.4814_5466, 0.457_8275, 0.4082_1073]  ,							UpperCAmelCase__			:       Optional[Union[float, List[float]]] = [0.2686_2954, 0.2613_0258, 0.2757_7711]  ,							UpperCAmelCase__			:       bool = True  ,							UpperCAmelCase__			:       Union[str, Any]=7  ,							UpperCAmelCase__			:       Optional[int]=30  ,							UpperCAmelCase__			:       Union[str, Any]=400  ,							UpperCAmelCase__			:       List[Any]=3  ,							)				->Dict:
        UpperCAmelCase_            =  parent
        UpperCAmelCase_            =  do_resize
        UpperCAmelCase_            =  size if size is not None else {'''shortest_edge''': 288}
        UpperCAmelCase_            =  size_divisor
        UpperCAmelCase_            =  do_rescale
        UpperCAmelCase_            =  rescale_factor
        UpperCAmelCase_            =  do_normalize
        UpperCAmelCase_            =  do_center_crop
        UpperCAmelCase_            =  image_mean
        UpperCAmelCase_            =  image_std
        UpperCAmelCase_            =  do_pad
        UpperCAmelCase_            =  batch_size
        UpperCAmelCase_            =  num_channels
        UpperCAmelCase_            =  min_resolution
        UpperCAmelCase_            =  max_resolution






  def  lowerCAmelCase__ (      self			:       Tuple  )				->List[Any]:
        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }






  def  lowerCAmelCase__ (      self			:       Optional[int]  ,							UpperCAmelCase__			:       List[str]  ,							UpperCAmelCase__			:       Dict=False  )				->Any:
        if not batched:
              UpperCAmelCase_            =  self.size['''shortest_edge''']
              UpperCAmelCase_            =  image_inputs[0]
              if isinstance(UpperCAmelCase__  ,							Image.Image  ):
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.size
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  image.shape[1], image.shape[2]
              UpperCAmelCase_            =  size / min(UpperCAmelCase__  ,							UpperCAmelCase__  )
              if h < w:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  size, scale * w
              else:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  scale * h, size

              UpperCAmelCase_            =  int((1333 / 800) * size  )
              if max(UpperCAmelCase__  ,							UpperCAmelCase__  ) > max_size:
                    UpperCAmelCase_            =  max_size / max(UpperCAmelCase__  ,							UpperCAmelCase__  )
                    UpperCAmelCase_            =  newh * scale
                    UpperCAmelCase_            =  neww * scale

              UpperCAmelCase_      ,		UpperCAmelCase_            =  int(newh + 0.5  ), int(neww + 0.5  )
              UpperCAmelCase_      ,		UpperCAmelCase_            =  (
                  newh // self.size_divisor * self.size_divisor,
                  neww // self.size_divisor * self.size_divisor,
              )

        else:
              UpperCAmelCase_            =  []
              for image in image_inputs:
                    UpperCAmelCase_      ,		UpperCAmelCase_            =  self.get_expected_values([image]  )
                    expected_values.append((expected_height, expected_width)  )
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[0]  )[0]
              UpperCAmelCase_            =  max(UpperCAmelCase__  ,							key=lambda UpperCAmelCase__  : item[1]  )[1]

        return expected_height, expected_width







@require_torch
@require_vision
class lowerCamelCase							(	lowerCamelCase     ,		unittest.TestCase					):




  '''simple docstring'''



  lowerCAmelCase__										=	BridgeTowerImageProcessor if is_vision_available() else None






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->str:
        UpperCAmelCase_            =  BridgeTowerImageProcessingTester(self  )






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






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->int:
        UpperCAmelCase_            =  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__  ,							'''size'''  )  )
        self.assertTrue(hasattr(UpperCAmelCase__  ,							'''size_divisor'''  )  )






  def  lowerCAmelCase__ (      self			:       Optional[int]  )				->Union[str, Any]:
        pass






  def  lowerCAmelCase__ (      self			:       List[str]  )				->Union[str, Any]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PIL images
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							Image.Image  )

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

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       Any  )				->Optional[int]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random numpy tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							numpify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							np.ndarray  )

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

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)






  def  lowerCAmelCase__ (      self			:       int  )				->List[str]:
        # Initialize image processor
        UpperCAmelCase_            =  self.image_processing_class(**self.image_processor_dict  )
        # create random PyTorch tensors
        UpperCAmelCase_            =  prepare_image_inputs(self.image_processor_tester  ,							equal_resolution=UpperCAmelCase__  ,							torchify=UpperCAmelCase__  )
        for image in image_inputs:
              self.assertIsInstance(UpperCAmelCase__  ,							torch.Tensor  )

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

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)  ,							)

        # Test batched
        UpperCAmelCase_            =  image_processing(UpperCAmelCase__  ,							return_tensors='''pt'''  ).pixel_values

        UpperCAmelCase_      ,		UpperCAmelCase_            =  self.image_processor_tester.get_expected_values(UpperCAmelCase__  ,							batched=UpperCAmelCase__  )
        self.assertEqual(
            encoded_images.shape  ,							(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.num_channels,
                expected_height,
                expected_width,
            )  ,							)





