





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 ChineseCLIPImageProcessor






class 					lowercase__ (			unittest.TestCase       ):

   '''simple docstring'''


   def __init__(					self,	__magic_name__,	__magic_name__=7,	__magic_name__=3,	__magic_name__=18,	__magic_name__=30,	__magic_name__=400,	__magic_name__=True,	__magic_name__=None,	__magic_name__=True,	__magic_name__=None,	__magic_name__=True,	__magic_name__=[0.4814_5466, 0.457_8275, 0.4082_1073],	__magic_name__=[0.2686_2954, 0.2613_0258, 0.2757_7711],	__magic_name__=True,	)				->   List[Any]:






       """simple docstring"""
       UpperCamelCase__							:       Optional[Any]      =   size if size is not None else {'''height''': 224, '''width''': 224}
       UpperCamelCase__							:       Union[str, Any]      =   crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
       UpperCamelCase__							:       Tuple      =   parent
       UpperCamelCase__							:       Dict      =   batch_size
       UpperCamelCase__							:       List[str]      =   num_channels
       UpperCamelCase__							:       int      =   image_size
       UpperCamelCase__							:       List[str]      =   min_resolution
       UpperCamelCase__							:       List[Any]      =   max_resolution
       UpperCamelCase__							:       Dict      =   do_resize
       UpperCamelCase__							:       int      =   size
       UpperCamelCase__							:       Optional[int]      =   do_center_crop
       UpperCamelCase__							:       str      =   crop_size
       UpperCamelCase__							:       Union[str, Any]      =   do_normalize
       UpperCamelCase__							:       Dict      =   image_mean
       UpperCamelCase__							:       Union[str, Any]      =   image_std
       UpperCamelCase__							:       Optional[Any]      =   do_convert_rgb




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






       """simple docstring"""
       return {
           "do_resize": self.do_resize,
           "size": self.size,
           "do_center_crop": self.do_center_crop,
           "crop_size": self.crop_size,
           "do_normalize": self.do_normalize,
           "image_mean": self.image_mean,
           "image_std": self.image_std,
           "do_convert_rgb": self.do_convert_rgb,
       }




   def    UpperCamelCase__						(					self,	__magic_name__=False,	__magic_name__=False,	__magic_name__=False							)				->   int:






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

       if equal_resolution:
           UpperCamelCase__							:       int      =   []
           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:
           UpperCamelCase__							:       Dict      =   []
           for i in range(self.batch_size							):
               UpperCamelCase__							:       Dict      =   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
           UpperCamelCase__							:       Dict      =   [Image.fromarray(np.moveaxis(__magic_name__,	0,	-1							)							) for x in image_inputs]

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

       return image_inputs






@require_torch
@require_vision
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							Dict     	=	ChineseCLIPImageProcessor if is_vision_available() else None




   def    UpperCamelCase__						(					self							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       int      =   ChineseCLIPImageProcessingTester(self,	do_center_crop=__magic_name__							)




   @property
   def    UpperCamelCase__						(					self							)				->   Dict:






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




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(__magic_name__,	'''do_resize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''size'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_normalize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_mean'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_std'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_convert_rgb'''							)							)




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






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   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''': 18, '''width''': 18}							)

       UpperCamelCase__							:       List[Any]      =   self.image_processing_class.from_dict(self.image_processor_dict,	size=42,	crop_size=84							)
       self.assertEqual(image_processor.size,	{'''shortest_edge''': 42}							)
       self.assertEqual(image_processor.crop_size,	{'''height''': 84, '''width''': 84}							)




   def    UpperCamelCase__						(					self							)				->   Optional[Any]:






       """simple docstring"""
       pass




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCamelCase__							:       str      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	Image.Image							)

       # Test not batched input
       UpperCamelCase__							:       Union[str, Any]      =   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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       Any      =   image_processing(__magic_name__,	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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)




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






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   self.image_processing_class(**self.image_processor_dict							)
       # create random numpy tensors
       UpperCamelCase__							:       List[Any]      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__,	numpify=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	np.ndarray							)

       # Test not batched input
       UpperCamelCase__							:       Optional[int]      =   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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       Tuple      =   image_processing(__magic_name__,	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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)




   def    UpperCamelCase__						(					self							)				->   Any:






       """simple docstring"""
       UpperCamelCase__							:       List[Any]      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PyTorch tensors
       UpperCamelCase__							:       List[str]      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__,	torchify=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	torch.Tensor							)

       # Test not batched input
       UpperCamelCase__							:       int      =   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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       List[Any]      =   image_processing(__magic_name__,	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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)






@require_torch
@require_vision
class 					lowercase__ (			__lowerCamelCase    ,       unittest.TestCase       ):

   '''simple docstring'''


   a		:							List[str]     	=	ChineseCLIPImageProcessor if is_vision_available() else None




   def    UpperCamelCase__						(					self							)				->   str:






       """simple docstring"""
       UpperCamelCase__							:       List[str]      =   ChineseCLIPImageProcessingTester(self,	num_channels=4,	do_center_crop=__magic_name__							)
       UpperCamelCase__							:       str      =   3




   @property
   def    UpperCamelCase__						(					self							)				->   str:






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




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






       """simple docstring"""
       UpperCamelCase__							:       Dict      =   self.image_processing_class(**self.image_processor_dict							)
       self.assertTrue(hasattr(__magic_name__,	'''do_resize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''size'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''center_crop'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_normalize'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_mean'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''image_std'''							)							)
       self.assertTrue(hasattr(__magic_name__,	'''do_convert_rgb'''							)							)




   def    UpperCamelCase__						(					self							)				->   int:






       """simple docstring"""
       pass




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






       """simple docstring"""
       UpperCamelCase__							:       str      =   self.image_processing_class(**self.image_processor_dict							)
       # create random PIL images
       UpperCamelCase__							:       int      =   self.image_processor_tester.prepare_inputs(equal_resolution=__magic_name__							)
       for image in image_inputs:
           self.assertIsInstance(__magic_name__,	Image.Image							)

       # Test not batched input
       UpperCamelCase__							:       List[Any]      =   image_processing(image_inputs[0],	return_tensors='''pt'''							).pixel_values
       self.assertEqual(
           encoded_images.shape,	(
               1,
               self.expected_encoded_image_num_channels,
               self.image_processor_tester.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)

       # Test batched
       UpperCamelCase__							:       List[Any]      =   image_processing(__magic_name__,	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.crop_size['''height'''],
               self.image_processor_tester.crop_size['''width'''],
           ),	)








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

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

import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput






@dataclass
class 					lowercase__ (			__lowerCamelCase       ):

   '''simple docstring'''


   a		:							torch.FloatTensor
   a		:							torch.FloatTensor






class 					lowercase__ (			__lowerCamelCase    ,       __lowerCamelCase       ):

   '''simple docstring'''


   a		:							List[Any]     	=	1




   @register_to_config
   def __init__(					self,	__magic_name__ = 2000,	__magic_name__ = 0.15,	__magic_name__ = 0.01,	__magic_name__ = 1348.0,	__magic_name__ = 1E-5,	__magic_name__ = 1,	)				->   int:






       """simple docstring"""
       # standard deviation of the initial noise distribution
       UpperCamelCase__							:       int      =   sigma_max

       # setable values
       UpperCamelCase__							:       Optional[int]      =   None

       self.set_sigmas(__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None							)				->   torch.FloatTensor:






       """simple docstring"""
       return sample




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None							)				->   int:






       """simple docstring"""
       UpperCamelCase__							:       str      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps

       UpperCamelCase__							:       List[Any]      =   torch.linspace(1,	__magic_name__,	__magic_name__,	device=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = None,	__magic_name__ = None,	__magic_name__ = None							)				->   Dict:






       """simple docstring"""
       UpperCamelCase__							:       Tuple      =   sigma_min if sigma_min is not None else self.config.sigma_min
       UpperCamelCase__							:       str      =   sigma_max if sigma_max is not None else self.config.sigma_max
       UpperCamelCase__							:       Optional[int]      =   sampling_eps if sampling_eps is not None else self.config.sampling_eps
       if self.timesteps is None:
           self.set_timesteps(__magic_name__,	__magic_name__							)

       UpperCamelCase__							:       str      =   sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
       UpperCamelCase__							:       Optional[Any]      =   torch.exp(torch.linspace(math.log(__magic_name__							),	math.log(__magic_name__							),	__magic_name__							)							)
       UpperCamelCase__							:       Any      =   torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps]							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__							)				->   str:






       """simple docstring"""
       return torch.where(
           timesteps == 0,	torch.zeros_like(t.to(timesteps.device							)							),	self.discrete_sigmas[timesteps - 1].to(timesteps.device							),	)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SdeVeOutput, Tuple]:






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

       UpperCamelCase__							:       Any      =   timestep * torch.ones(
           sample.shape[0],	device=sample.device							)  # torch.repeat_interleave(timestep, sample.shape[0])
       UpperCamelCase__							:       Any      =   (timestep * (len(self.timesteps							) - 1)).long()

       # mps requires indices to be in the same device, so we use cpu as is the default with cuda
       UpperCamelCase__							:       List[Any]      =   timesteps.to(self.discrete_sigmas.device							)

       UpperCamelCase__							:       str      =   self.discrete_sigmas[timesteps].to(sample.device							)
       UpperCamelCase__							:       List[Any]      =   self.get_adjacent_sigma(__magic_name__,	__magic_name__							).to(sample.device							)
       UpperCamelCase__							:       Optional[Any]      =   torch.zeros_like(__magic_name__							)
       UpperCamelCase__							:       str      =   (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__							:       Union[str, Any]      =   diffusion.flatten()
       while len(diffusion.shape							) < len(sample.shape							):
           UpperCamelCase__							:       Any      =   diffusion.unsqueeze(-1							)
       UpperCamelCase__							:       str      =   drift - diffusion**2 * model_output

       #  equation 6: sample noise for the diffusion term of
       UpperCamelCase__							:       Union[str, Any]      =   randn_tensor(
           sample.shape,	layout=sample.layout,	generator=__magic_name__,	device=sample.device,	dtype=sample.dtype							)
       UpperCamelCase__							:       Optional[Any]      =   sample - drift  # subtract because `dt` is a small negative timestep
       # TODO is the variable diffusion the correct scaling term for the noise?
       UpperCamelCase__							:       str      =   prev_sample_mean + diffusion * noise  # add impact of diffusion field g

       if not return_dict:
           return (prev_sample, prev_sample_mean)

       return SdeVeOutput(prev_sample=__magic_name__,	prev_sample_mean=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__ = None,	__magic_name__ = True,	)				->   Union[SchedulerOutput, Tuple]:






       """simple docstring"""
       if self.timesteps is None:
           raise ValueError(
               '''`self.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__							:       List[str]      =   randn_tensor(sample.shape,	layout=sample.layout,	generator=__magic_name__							).to(sample.device							)

       # compute step size from the model_output, the noise, and the snr
       UpperCamelCase__							:       str      =   torch.norm(model_output.reshape(model_output.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Tuple      =   torch.norm(noise.reshape(noise.shape[0],	-1							),	dim=-1							).mean()
       UpperCamelCase__							:       Union[str, Any]      =   (self.config.snr * noise_norm / grad_norm) ** 2 * 2
       UpperCamelCase__							:       Optional[Any]      =   step_size * torch.ones(sample.shape[0]							).to(sample.device							)
       # self.repeat_scalar(step_size, sample.shape[0])

       # compute corrected sample: model_output term and noise term
       UpperCamelCase__							:       Tuple      =   step_size.flatten()
       while len(step_size.shape							) < len(sample.shape							):
           UpperCamelCase__							:       int      =   step_size.unsqueeze(-1							)
       UpperCamelCase__							:       int      =   sample + step_size * model_output
       UpperCamelCase__							:       List[Any]      =   prev_sample_mean + ((step_size * 2) ** 0.5) * noise

       if not return_dict:
           return (prev_sample,)

       return SchedulerOutput(prev_sample=__magic_name__							)




   def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	)				->   torch.FloatTensor:






       """simple docstring"""
       # Make sure sigmas and timesteps have the same device and dtype as original_samples
       UpperCamelCase__							:       Any      =   timesteps.to(original_samples.device							)
       UpperCamelCase__							:       List[str]      =   self.discrete_sigmas.to(original_samples.device							)[timesteps]
       UpperCamelCase__							:       Any      =   (
           noise * sigmas[:, None, None, None]
           if noise is not None
           else torch.randn_like(__magic_name__							) * sigmas[:, None, None, None]
       )
       UpperCamelCase__							:       int      =   noise + original_samples
       return noisy_samples




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






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

