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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left






@flax.struct.dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					Tuple				       =					None
   __UpperCamelCase	:					List[Any]				       =					None
   __UpperCamelCase	:					Union[str, Any]				       =					None


   @classmethod
   def 				__magic_name__       (      cls  :							int  ):

      """simple docstring"""
      return cls()






@dataclass
class UpperCAmelCase     (		A__     ):





   '''simple docstring'''
   __UpperCamelCase	:					int				       =					42
   __UpperCamelCase	:					Any				       =					42
   __UpperCamelCase	:					Union[str, Any]				       =					None






class UpperCAmelCase     (		A__							,							A__     ):





   '''simple docstring'''


   @property
   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      return True


   @register_to_config
   def __init__(      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							int = 2_0_0_0    ,    lowerCAmelCase_  :							float = 0.15    ,    lowerCAmelCase_  :							float = 0.01    ,    lowerCAmelCase_  :							float = 1_3_4_8.0    ,    lowerCAmelCase_  :							float = 1e-5    ,    lowerCAmelCase_  :							int = 1    ,    ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      _A:						Optional[Any]	    = ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          _lowerCAmelCase    ,    self.config.num_train_timesteps    ,    self.config.sigma_min    ,    self.config.sigma_max    ,    self.config.sampling_eps    ,    )


   def 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							ScoreSdeVeSchedulerState    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							Tuple = ()    ,    lowerCAmelCase_  :							float = None  ):

      """simple docstring"""
      _A:						Tuple	    = sampling_eps if sampling_eps is not None else self.config.sampling_eps

      _A:						str	    = jnp.linspace(1    ,    _lowerCAmelCase    ,    _lowerCAmelCase  )
      return state.replace(timesteps=_lowerCAmelCase  )


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							ScoreSdeVeSchedulerState    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							float = None    ,    lowerCAmelCase_  :							float = None    ,    lowerCAmelCase_  :							float = None    ,    ):

      """simple docstring"""
      _A:						List[Any]	    = sigma_min if sigma_min is not None else self.config.sigma_min
      _A:						List[str]	    = sigma_max if sigma_max is not None else self.config.sigma_max
      _A:						str	    = sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
         _A:						List[Any]	    = self.set_timesteps(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase  )

      _A:						Optional[Any]	    = jnp.exp(jnp.linspace(jnp.log(_lowerCAmelCase  )    ,    jnp.log(_lowerCAmelCase  )    ,    _lowerCAmelCase  )  )
      _A:						Union[str, Any]	    = jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]  )

      return state.replace(discrete_sigmas=_lowerCAmelCase    ,    sigmas=_lowerCAmelCase  )


   def 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							Dict    ,    lowerCAmelCase_  :							Union[str, Any]  ):

      """simple docstring"""
      return jnp.where(timesteps == 0    ,    jnp.zeros_like(_lowerCAmelCase  )    ,    state.discrete_sigmas[timesteps - 1]  )


   def 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							ScoreSdeVeSchedulerState    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							int    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							random.KeyArray    ,    lowerCAmelCase_  :							bool = True    ,    ):

      """simple docstring"""
      if state.timesteps is None:
         raise ValueError(
             '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''  )

      _A:						Optional[int]	    = timestep * jnp.ones(
          sample.shape[0]    ,    )
      _A:						Any	    = (timestep * (len(state.timesteps  ) - 1)).long()

      _A:						str	    = state.discrete_sigmas[timesteps]
      _A:						str	    = self.get_adjacent_sigma(_lowerCAmelCase    ,    _lowerCAmelCase    ,    _lowerCAmelCase  )
      _A:						List[str]	    = jnp.zeros_like(_lowerCAmelCase  )
      _A:						List[Any]	    = (sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      _A:						Union[str, Any]	    = diffusion.flatten()
      _A:						Tuple	    = broadcast_to_shape_from_left(_lowerCAmelCase    ,    sample.shape  )
      _A:						List[Any]	    = drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      _A:						Any	    = random.split(_lowerCAmelCase    ,    num=1  )
      _A:						Tuple	    = random.normal(key=_lowerCAmelCase    ,    shape=sample.shape  )
      _A:						Any	    = sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      _A:						Optional[int]	    = prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
         return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase    ,    prev_sample_mean=_lowerCAmelCase    ,    state=_lowerCAmelCase  )


   def 				__magic_name__       (      self  :							Any    ,    lowerCAmelCase_  :							ScoreSdeVeSchedulerState    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							jnp.ndarray    ,    lowerCAmelCase_  :							random.KeyArray    ,    lowerCAmelCase_  :							bool = True    ,    ):

      """simple docstring"""
      if state.timesteps is None:
         raise ValueError(
             '''`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler'''  )

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      _A:						Any	    = random.split(_lowerCAmelCase    ,    num=1  )
      _A:						List[str]	    = random.normal(key=_lowerCAmelCase    ,    shape=sample.shape  )

      # compute step size from the model_output, the noise, and the snr
      _A:						Union[str, Any]	    = jnp.linalg.norm(_lowerCAmelCase  )
      _A:						Optional[int]	    = jnp.linalg.norm(_lowerCAmelCase  )
      _A:						int	    = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
      _A:						int	    = step_size * jnp.ones(sample.shape[0]  )

      # compute corrected sample: model_output term and noise term
      _A:						Any	    = step_size.flatten()
      _A:						str	    = broadcast_to_shape_from_left(_lowerCAmelCase    ,    sample.shape  )
      _A:						Tuple	    = sample + step_size * model_output
      _A:						Optional[Any]	    = prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
         return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=_lowerCAmelCase    ,    state=_lowerCAmelCase  )


   def __len__(      self  :							List[Any]  ):

      """simple docstring"""
      return self.config.num_train_timesteps





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 SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size






class UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   def __init__(      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							Optional[int]=7    ,    lowerCAmelCase_  :							Dict=3    ,    lowerCAmelCase_  :							Optional[Any]=1_8    ,    lowerCAmelCase_  :							Union[str, Any]=3_0    ,    lowerCAmelCase_  :							int=4_0_0    ,    lowerCAmelCase_  :							int=True    ,    lowerCAmelCase_  :							Dict=1 / 2_5_5    ,    lowerCAmelCase_  :							Union[str, Any]=True    ,    lowerCAmelCase_  :							str=8    ,    ):

      """simple docstring"""
      _A:						Optional[Any]	    = parent
      _A:						int	    = batch_size
      _A:						List[str]	    = num_channels
      _A:						Union[str, Any]	    = image_size
      _A:						Optional[int]	    = min_resolution
      _A:						int	    = max_resolution
      _A:						int	    = do_rescale
      _A:						Optional[Any]	    = rescale_factor
      _A:						List[Any]	    = do_pad
      _A:						Optional[int]	    = pad_size


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      return {
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
          "pad_size": self.pad_size,
      }


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[Any]=False    ,    lowerCAmelCase_  :							Tuple=False    ,    lowerCAmelCase_  :							Dict=False  ):

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

      if equal_resolution:
         _A:						Dict	    = []
         for i in range(self.batch_size  ):
            image_inputs.append(
                np.random.randint(
                    2_5_5    ,    size=(self.num_channels, self.max_resolution, self.max_resolution)    ,    dtype=np.uinta  )  )
      else:
         _A:						List[str]	    = []
         for i in range(self.batch_size  ):
            _A    ,						_A:						Optional[Any]	    = np.random.choice(np.arange(self.min_resolution    ,    self.max_resolution  )    ,    2  )
            image_inputs.append(np.random.randint(2_5_5    ,    size=(self.num_channels, width, height)    ,    dtype=np.uinta  )  )

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

      if torchify:
         _A:						List[Any]	    = [torch.from_numpy(lowerCAmelCase_  ) for x in image_inputs]

      return image_inputs






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





   '''simple docstring'''
   __UpperCamelCase	:					int				       =					SwinaSRImageProcessor if is_vision_available() else None


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						int	    = SwinaSRImageProcessingTester(self  )


   @property
   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Any	    = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_rescale'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''rescale_factor'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_pad'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''pad_size'''  )  )


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Dict  ):

      """simple docstring"""
      _A    ,						_A:						Any	    = get_image_size(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = self.image_processor_tester.pad_size

      _A:						Optional[Any]	    = (old_height // size + 1) * size - old_height
      _A:						int	    = (old_width // size + 1) * size - old_width
      return old_height + pad_height, old_width + pad_width


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[int]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _A:						str	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    Image.Image  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						str	    = self.calculate_expected_size(np.array(image_inputs[0]  )  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Union[str, Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _A:						List[str]	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    numpify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    np.ndarray  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Union[str, Any]	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _A:						Tuple	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    torchify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    torch.Tensor  )

      # Test not batched input
      _A:						List[str]	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Dict	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )



