'''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    lowerCamelCase__  :
   # setable values
   __UpperCAmelCase           =						None
   __UpperCAmelCase           =						None
   __UpperCAmelCase           =						None



   @classmethod
   def  _UpperCamelCase		(    cls     )				->     List[str]:
        """simple docstring"""





        return cls()




@dataclass
class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						42
   __UpperCAmelCase           =						42
   __UpperCAmelCase           =						None




class    lowerCamelCase__  (   __snake_case       ,				__snake_case     ):



   @property
   def  _UpperCamelCase		(    self     )				->     List[Any]:
        """simple docstring"""





        return True



   @register_to_config
   def __init__(    self					,      lowerCAmelCase__ = 2_000					,      lowerCAmelCase__ = 0.15					,      lowerCAmelCase__ = 0.01					,      lowerCAmelCase__ = 1348.0					,      lowerCAmelCase__ = 1e-5					,      lowerCAmelCase__ = 1					,      )				->     int:
        """simple docstring"""





        pass



   def  _UpperCamelCase		(    self     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Dict       =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  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = ()					,      lowerCAmelCase__ = None     )				->     ScoreSdeVeSchedulerState:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =sampling_eps if sampling_eps is not None else self.config.sampling_eps

        _UpperCamelCase  :List[Any]       =jnp.linspace(1					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        return state.replace(timesteps=lowerCAmelCase__     )



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      lowerCAmelCase__ = None					,      )				->     ScoreSdeVeSchedulerState:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =sigma_min if sigma_min is not None else self.config.sigma_min
        _UpperCamelCase  :List[Any]       =sigma_max if sigma_max is not None else self.config.sigma_max
        _UpperCamelCase  :List[Any]       =sampling_eps if sampling_eps is not None else self.config.sampling_eps
        if state.timesteps is None:
             _UpperCamelCase  :List[str]       =self.set_timesteps(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )

        _UpperCamelCase  :List[str]       =jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase__     )					,      jnp.log(lowerCAmelCase__     )					,      lowerCAmelCase__     )     )
        _UpperCamelCase  :List[Any]       =jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps]     )

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



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )				->     Tuple:
        """simple docstring"""





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



   def  _UpperCamelCase		(    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"""     )

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

        _UpperCamelCase  :List[str]       =state.discrete_sigmas[timesteps]
        _UpperCamelCase  :List[Any]       =self.get_adjacent_sigma(lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__     )
        _UpperCamelCase  :Optional[Any]       =jnp.zeros_like(lowerCAmelCase__     )
        _UpperCamelCase  :Tuple       =(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
        _UpperCamelCase  :Optional[Any]       =diffusion.flatten()
        _UpperCamelCase  :List[str]       =broadcast_to_shape_from_left(lowerCAmelCase__					,      sample.shape     )
        _UpperCamelCase  :Tuple       =drift - diffusion**2 * model_output

        #  equation 6: sample noise for the diffusion term of
        _UpperCamelCase  :str       =random.split(lowerCAmelCase__					,      num=1     )
        _UpperCamelCase  :Optional[Any]       =random.normal(key=lowerCAmelCase__					,      shape=sample.shape     )
        _UpperCamelCase  :List[str]       =sample - drift  # subtract because `dt` is a small negative timestep
        # TODO is the variable diffusion the correct scaling term for the noise?
        _UpperCamelCase  :Optional[Any]       =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  _UpperCamelCase		(    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
        _UpperCamelCase  :Dict       =random.split(lowerCAmelCase__					,      num=1     )
        _UpperCamelCase  :Union[str, Any]       =random.normal(key=lowerCAmelCase__					,      shape=sample.shape     )

        # compute step size from the model_output, the noise, and the snr
        _UpperCamelCase  :int       =jnp.linalg.norm(lowerCAmelCase__     )
        _UpperCamelCase  :List[str]       =jnp.linalg.norm(lowerCAmelCase__     )
        _UpperCamelCase  :Tuple       =(self.config.snr * noise_norm / grad_norm) ** 2 * 2
        _UpperCamelCase  :int       =step_size * jnp.ones(sample.shape[0]     )

        # compute corrected sample: model_output term and noise term
        _UpperCamelCase  :List[Any]       =step_size.flatten()
        _UpperCamelCase  :Optional[int]       =broadcast_to_shape_from_left(lowerCAmelCase__					,      sample.shape     )
        _UpperCamelCase  :str       =sample + step_size * model_output
        _UpperCamelCase  :List[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 datasets import load_dataset

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 SegformerImageProcessor







class __snake_case     (     unittest.TestCase						):







   def __init__( self						:		List[Any]   ,		_snake_case						:		List[str]   ,		_snake_case						:		Any=7   ,		_snake_case						:		Any=3   ,		_snake_case						:		str=30   ,		_snake_case						:		str=400   ,		_snake_case						:		Any=True   ,		_snake_case						:		Dict=None   ,		_snake_case						:		List[str]=True   ,		_snake_case						:		Union[str, Any]=[0.5, 0.5, 0.5]   ,		_snake_case						:		Tuple=[0.5, 0.5, 0.5]   ,		_snake_case						:		str=False   ,		):

        """simple docstring"""


        UpperCAmelCase_		=				size if size is not None else {'''height''': 30, '''width''': 30}
        UpperCAmelCase_		=				parent
        UpperCAmelCase_		=				batch_size
        UpperCAmelCase_		=				num_channels
        UpperCAmelCase_		=				min_resolution
        UpperCAmelCase_		=				max_resolution
        UpperCAmelCase_		=				do_resize
        UpperCAmelCase_		=				size
        UpperCAmelCase_		=				do_normalize
        UpperCAmelCase_		=				image_mean
        UpperCAmelCase_		=				image_std
        UpperCAmelCase_		=				do_reduce_labels







   def lowerCamelCase		( self						:		Tuple):

        """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_reduce_labels": self.do_reduce_labels,
        }







def   A		()  -> List[str]:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)

     return image, map


def   A		()  -> str:







     """simple docstring"""


     UpperCAmelCase_		=				load_dataset('''hf-internal-testing/fixtures_ade20k'''  ,							split='''test'''			)

     UpperCAmelCase_		=				Image.open(dataset[0]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[1]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[2]['''file''']			)
     UpperCAmelCase_		=				Image.open(dataset[3]['''file''']			)

     return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class __snake_case     (     a					,    unittest.TestCase						):
   UpperCAmelCase__    :       int		=    SegformerImageProcessor if is_vision_available() else None







   def lowerCamelCase		( self						:		Tuple):

        """simple docstring"""


        UpperCAmelCase_		=				SegformerImageProcessingTester(self)







   @property
   def lowerCamelCase		( self						:		int):

        """simple docstring"""


        return self.image_processor_tester.prepare_image_processor_dict()







   def lowerCamelCase		( self						:		List[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        self.assertTrue(hasattr(_snake_case   ,		'''do_resize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''size'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_normalize'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_mean'''))
        self.assertTrue(hasattr(_snake_case   ,		'''image_std'''))
        self.assertTrue(hasattr(_snake_case   ,		'''do_reduce_labels'''))







   def lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict)
        self.assertEqual(image_processor.size   ,		{'''height''': 30, '''width''': 30})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)

        UpperCAmelCase_		=				self.image_processing_class.from_dict(self.image_processor_dict   ,		size=42   ,		reduce_labels=_snake_case)
        self.assertEqual(image_processor.size   ,		{'''height''': 42, '''width''': 42})
        self.assertEqual(image_processor.do_reduce_labels   ,		_snake_case)







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


        pass







   def lowerCamelCase		( self						:		Dict):

        """simple docstring"""


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

        # Test not batched input
        UpperCAmelCase_		=				image_processing(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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		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 lowerCamelCase		( self						:		Optional[int]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random numpy tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		numpify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		np.ndarray)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		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 lowerCamelCase		( self						:		Union[str, Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)

        # Test not batched input
        UpperCAmelCase_		=				image_processing(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
        UpperCAmelCase_		=				image_processing(_snake_case   ,		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 lowerCamelCase		( self						:		str):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)
        # create random PyTorch tensors
        UpperCAmelCase_		=				prepare_image_inputs(self.image_processor_tester   ,		equal_resolution=_snake_case   ,		torchify=_snake_case)
        UpperCAmelCase_		=				[]
        for image in image_inputs:
             self.assertIsInstance(_snake_case   ,		torch.Tensor)
             maps.append(torch.zeros(image.shape[-2:]).long())

        # Test not batched input
        UpperCAmelCase_		=				image_processing(image_inputs[0]   ,		maps[0]   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].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'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                self.image_processor_tester.batch_size,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test not batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                1,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                1,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)

        # Test batched input (PIL images)
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_batch_inputs()

        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertEqual(
            encoding['''pixel_values'''].shape   ,		(
                2,
                self.image_processor_tester.num_channels,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(
            encoding['''labels'''].shape   ,		(
                2,
                self.image_processor_tester.size['''height'''],
                self.image_processor_tester.size['''width'''],
            )   ,		)
        self.assertEqual(encoding['''labels'''].dtype   ,		torch.long)
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)







   def lowerCamelCase		( self						:		Optional[Any]):

        """simple docstring"""


        UpperCAmelCase_		=				self.image_processing_class(**self.image_processor_dict)

        # ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
        UpperCAmelCase_	,			UpperCAmelCase_		=				prepare_semantic_single_inputs()
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 150)

        UpperCAmelCase_		=				True
        UpperCAmelCase_		=				image_processing(_snake_case   ,		_snake_case   ,		return_tensors='''pt''')
        self.assertTrue(encoding['''labels'''].min().item() >= 0)
        self.assertTrue(encoding['''labels'''].max().item() <= 255)






