




'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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        SCREAMING_SNAKE_CASE:
       # setable values
       snake_case_ :     Optional[jnp.ndarray]    =					None
       snake_case_ :     Optional[jnp.ndarray]    =					None
       snake_case_ :     Optional[jnp.ndarray]    =					None

       @classmethod
       def     snake_case__			(    cls     )    ->					str:

           """simple docstring"""



           return cls()






@dataclass
class        SCREAMING_SNAKE_CASE(				__A     ):
       snake_case_ :     ScoreSdeVeSchedulerState
       snake_case_ :     jnp.ndarray
       snake_case_ :     Optional[jnp.ndarray]    =					None






class        SCREAMING_SNAKE_CASE(				__A   ,      __A     ):

       @property
       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           return True

       @register_to_config
       def __init__(    self					,		lowerCamelCase__ = 2000					,		lowerCamelCase__ = 0.15					,		lowerCamelCase__ = 0.01					,		lowerCamelCase__ = 13_48.0					,		lowerCamelCase__ = 1E-5					,		lowerCamelCase__ = 1					,		)    ->					Dict:

           """simple docstring"""



           pass

       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						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     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = ()					,		lowerCamelCase__ = None     )    ->					ScoreSdeVeSchedulerState:

           """simple docstring"""



           __lowercase							=						sampling_eps if sampling_eps is not None else self.config.sampling_eps

           __lowercase							=						jnp.linspace(1					,		lowerCamelCase__					,		lowerCamelCase__     )
           return state.replace(timesteps=lowerCamelCase__     )

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		lowerCamelCase__ = None					,		)    ->					ScoreSdeVeSchedulerState:

           """simple docstring"""



           __lowercase							=						sigma_min if sigma_min is not None else self.config.sigma_min
           __lowercase							=						sigma_max if sigma_max is not None else self.config.sigma_max
           __lowercase							=						sampling_eps if sampling_eps is not None else self.config.sampling_eps
           if state.timesteps is None:
               __lowercase							=						self.set_timesteps(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )

           __lowercase							=						jnp.exp(jnp.linspace(jnp.log(lowerCamelCase__     )					,		jnp.log(lowerCamelCase__     )					,		lowerCamelCase__     )     )
           __lowercase							=						jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]     )

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

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )    ->					List[str]:

           """simple docstring"""



           return jnp.where(timesteps == 0					,		jnp.zeros_like(lowerCamelCase__     )					,		state.discrete_sigmas[timesteps - 1]     )

       def     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = True					,		)    ->					Union[FlaxSdeVeOutput, Tuple]:

           """simple docstring"""



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

           __lowercase							=						timestep * jnp.ones(
               sample.shape[0]					,		)
           __lowercase							=						(timestep * (len(state.timesteps     ) - 1)).long()

           __lowercase							=						state.discrete_sigmas[timesteps]
           __lowercase							=						self.get_adjacent_sigma(lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__     )
           __lowercase							=						jnp.zeros_like(lowerCamelCase__     )
           __lowercase							=						(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
           __lowercase							=						diffusion.flatten()
           __lowercase							=						broadcast_to_shape_from_left(lowerCamelCase__					,		sample.shape     )
           __lowercase							=						drift - diffusion**2 * model_output

           #  equation 6: sample noise for the diffusion term of
           __lowercase							=						random.split(lowerCamelCase__					,		num=1     )
           __lowercase							=						random.normal(key=lowerCamelCase__					,		shape=sample.shape     )
           __lowercase							=						sample - drift  # subtract because `dt` is a small negative timestep
           # TODO is the variable diffusion the correct scaling term for the noise?
           __lowercase							=						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     snake_case__			(    self					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__					,		lowerCamelCase__ = True					,		)    ->					Union[FlaxSdeVeOutput, Tuple]:

           """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
           __lowercase							=						random.split(lowerCamelCase__					,		num=1     )
           __lowercase							=						random.normal(key=lowerCamelCase__					,		shape=sample.shape     )

           # compute step size from the model_output, the noise, and the snr
           __lowercase							=						jnp.linalg.norm(lowerCamelCase__     )
           __lowercase							=						jnp.linalg.norm(lowerCamelCase__     )
           __lowercase							=						(self.config.snr * noise_norm / grad_norm) ** 2 * 2
           __lowercase							=						step_size * jnp.ones(sample.shape[0]     )

           # compute corrected sample: model_output term and noise term
           __lowercase							=						step_size.flatten()
           __lowercase							=						broadcast_to_shape_from_left(lowerCamelCase__					,		sample.shape     )
           __lowercase							=						sample + step_size * model_output
           __lowercase							=						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     )    ->					Tuple:

           """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


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import BlipImageProcessor






class        SCREAMING_SNAKE_CASE(				unittest.TestCase     ):

       def __init__(    self					,		lowerCamelCase__					,		lowerCamelCase__=7					,		lowerCamelCase__=3					,		lowerCamelCase__=18					,		lowerCamelCase__=30					,		lowerCamelCase__=400					,		lowerCamelCase__=True					,		lowerCamelCase__=None					,		lowerCamelCase__=True					,		lowerCamelCase__=False					,		lowerCamelCase__=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]					,		lowerCamelCase__=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]					,		lowerCamelCase__=True					,		)    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						size if size is not None else {"""height""": 20, """width""": 20}
           __lowercase							=						parent
           __lowercase							=						batch_size
           __lowercase							=						num_channels
           __lowercase							=						image_size
           __lowercase							=						min_resolution
           __lowercase							=						max_resolution
           __lowercase							=						do_resize
           __lowercase							=						size
           __lowercase							=						do_normalize
           __lowercase							=						image_mean
           __lowercase							=						image_std
           __lowercase							=						do_pad
           __lowercase							=						do_convert_rgb

       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



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




       def     snake_case__			(    self					,		lowerCamelCase__=False					,		lowerCamelCase__=False					,		lowerCamelCase__=False     )    ->					Tuple:

           """simple docstring"""



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

           if equal_resolution:
               __lowercase							=						[]
               for i in range(self.batch_size     ):
                   image_inputs.append(
                       np.random.randint(
                           255					,		size=(self.num_channels, self.max_resolution, self.max_resolution)					,		dtype=np.uinta     )     )
           else:
               __lowercase							=						[]
               for i in range(self.batch_size     ):
                   __lowercase				,__lowercase							=						np.random.choice(np.arange(self.min_resolution					,		self.max_resolution     )					,		2     )
                   image_inputs.append(np.random.randint(255					,		size=(self.num_channels, width, height)					,		dtype=np.uinta     )     )

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

           if torchify:
               __lowercase							=						[torch.from_numpy(lowerCamelCase__     ) for x in image_inputs]

           return image_inputs






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Optional[int]    =					BlipImageProcessor if is_vision_available() else None

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						BlipImageProcessingTester(self     )

       @property
       def     snake_case__			(    self     )    ->					Optional[int]:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def     snake_case__			(    self     )    ->					List[Any]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_resize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""size"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_mean"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_std"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_convert_rgb"""     )     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		Image.Image     )

           # Test not batched input
           __lowercase							=						image_processor(image_inputs[0]					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

       def     snake_case__			(    self     )    ->					int:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random numpy tensors
           __lowercase							=						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
           __lowercase							=						image_processor(image_inputs[0]					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)




       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PyTorch tensors
           __lowercase							=						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
           __lowercase							=						image_processor(image_inputs[0]					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   1,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   self.image_processor_tester.batch_size,
                   self.image_processor_tester.num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)






@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE(				__A   ,      unittest.TestCase     ):
       snake_case_ :     Union[str, Any]    =					BlipImageProcessor if is_vision_available() else None

       def     snake_case__			(    self     )    ->					List[str]:

           """simple docstring"""



           __lowercase							=						BlipImageProcessingTester(self					,		num_channels=4     )
           __lowercase							=						3

       @property
       def     snake_case__			(    self     )    ->					str:

           """simple docstring"""



           return self.image_processor_tester.prepare_image_processor_dict()

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_resize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""size"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_normalize"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_mean"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""image_std"""     )     )
           self.assertTrue(hasattr(lowerCamelCase__					,		"""do_convert_rgb"""     )     )

       def     snake_case__			(    self     )    ->					Any:

           """simple docstring"""



           pass




       def     snake_case__			(    self     )    ->					Union[str, Any]:

           """simple docstring"""



           __lowercase							=						self.image_processing_class(**self.image_processor_dict     )
           # create random PIL images
           __lowercase							=						self.image_processor_tester.prepare_inputs(equal_resolution=lowerCamelCase__     )
           for image in image_inputs:
               self.assertIsInstance(lowerCamelCase__					,		Image.Image     )

           # Test not batched input
           __lowercase							=						image_processor(image_inputs[0]					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   1,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)

           # Test batched
           __lowercase							=						image_processor(lowerCamelCase__					,		return_tensors="""pt"""     ).pixel_values
           self.assertEqual(
               encoded_images.shape					,		(
                   self.image_processor_tester.batch_size,
                   self.expected_encoded_image_num_channels,
                   self.image_processor_tester.size["""height"""],
                   self.image_processor_tester.size["""width"""],
               )					,		)




