



'''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, prepare_image_inputs


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import GLPNImageProcessor

class 	__magic_name__						(    unittest.TestCase   ):







							def __init__(							self					:       Dict	,_UpperCAmelCase					:       Union[str, Any]	,_UpperCAmelCase					:       str=7	,_UpperCAmelCase					:       int=3	,_UpperCAmelCase					:       str=18	,_UpperCAmelCase					:       List[Any]=30	,_UpperCAmelCase					:       str=400	,_UpperCAmelCase					:       Any=True	,_UpperCAmelCase					:       List[str]=32	,_UpperCAmelCase					:       Dict=True	,):
									_a     :      str        =							parent
									_a     :      Union[str, Any]        =							batch_size
									_a     :      Tuple        =							num_channels
									_a     :      Optional[Any]        =							image_size
									_a     :      List[Any]        =							min_resolution
									_a     :      Dict        =							max_resolution
									_a     :      Optional[Any]        =							do_resize
									_a     :      Dict        =							size_divisor
									_a     :      Union[str, Any]        =							do_rescale







							def 							__lowercase			(							self					:       Any		):
									return {
									    "do_resize": self.do_resize,
									    "size_divisor": self.size_divisor,
									    "do_rescale": self.do_rescale,
									}

@require_torch
@require_vision
class 	__magic_name__						(    _UpperCamelCase			,						unittest.TestCase   ):
							lowerCAmelCase       :    str   				=					GLPNImageProcessor if is_vision_available() else None







							def 							__lowercase			(							self					:       List[str]		):
									_a     :      List[Any]        =							GLPNImageProcessingTester(self		)







							@property
							def 							__lowercase			(							self					:       List[Any]		):
									return self.image_processor_tester.prepare_image_processor_dict()







							def 							__lowercase			(							self					:       int		):
									_a     :      Dict        =							self.image_processing_class(**self.image_processor_dict		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'do_resize'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'size_divisor'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'resample'		)		)
									self.assertTrue(hasattr(_UpperCAmelCase	,'do_rescale'		)		)







							def 							__lowercase			(							self					:       Any		):
									pass







							def 							__lowercase			(							self					:       Union[str, Any]		):
									# Initialize image_processing
									_a     :      Tuple        =							self.image_processing_class(**self.image_processor_dict		)
									# create random PIL images
									_a     :      Union[str, Any]        =							prepare_image_inputs(self.image_processor_tester	,equal_resolution=_UpperCAmelCase		)
									for image in image_inputs:
											self.assertIsInstance(_UpperCAmelCase	,Image.Image		)

									# Test not batched input (GLPNImageProcessor doesn't support batching)
									_a     :      List[Any]        =							image_processing(image_inputs[0]	,return_tensors='pt'		).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)







							def 							__lowercase			(							self					:       Optional[Any]		):
									# Initialize image_processing
									_a     :      Optional[Any]        =							self.image_processing_class(**self.image_processor_dict		)
									# create random numpy tensors
									_a     :      Any        =							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 (GLPNImageProcessor doesn't support batching)
									_a     :      str        =							image_processing(image_inputs[0]	,return_tensors='pt'		).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)







							def 							__lowercase			(							self					:       Dict		):
									# Initialize image_processing
									_a     :      Optional[Any]        =							self.image_processing_class(**self.image_processor_dict		)
									# create random PyTorch tensors
									_a     :      Any        =							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 (GLPNImageProcessor doesn't support batching)
									_a     :      str        =							image_processing(image_inputs[0]	,return_tensors='pt'		).pixel_values
									self.assertTrue(encoded_images.shape[-1] % self.image_processor_tester.size_divisor == 0		)
									self.assertTrue(encoded_images.shape[-2] % self.image_processor_tester.size_divisor == 0		)












"""simple docstring"""
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 ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin







@flax.struct.dataclass
class 			UpperCamelCase_       :
	# setable values
	__magic_name__	      =				None
	__magic_name__	      =				None
	__magic_name__	      =				None  # sigma(t_i)





	@classmethod
	def      _SCREAMING_SNAKE_CASE      (      cls					: List[str]						)						->			Optional[Any]:
				return cls()







@dataclass
class 			UpperCamelCase_       (__A     ):
	__magic_name__	      =				42
	__magic_name__	      =				42
	__magic_name__	      =				42







class 			UpperCamelCase_       (__A				,						__A     ):





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





	@register_to_config
	def __init__(      self					: List[str]	,	lowerCAmelCase_					: float = 0.0_2	,	lowerCAmelCase_					: float = 100	,	lowerCAmelCase_					: float = 1.0_0_7	,	lowerCAmelCase_					: float = 80	,	lowerCAmelCase_					: float = 0.0_5	,	lowerCAmelCase_					: float = 50	,	)						->			Union[str, Any]:
				pass





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]						)						->			Tuple:
				return KarrasVeSchedulerState.create()





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: int	,	lowerCAmelCase_					: Tuple = ()						)						->			KarrasVeSchedulerState:
				UpperCAmelCase_     : Dict			     =					jnp.arange(0	,	lowerCAmelCase_						)[::-1].copy()
				UpperCAmelCase_     : Dict			     =					[
				    (
				        self.config.sigma_max**2
				        * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
				    )
				    for i in timesteps
				]

				return state.replace(
				    num_inference_steps=lowerCAmelCase_	,	schedule=jnp.array(lowerCAmelCase_	,	dtype=jnp.floataa						)	,	timesteps=lowerCAmelCase_	,	)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: random.KeyArray	,	)						->			Tuple[jnp.ndarray, float]:
				if self.config.s_min <= sigma <= self.config.s_max:
							UpperCAmelCase_     : Any			     =					min(self.config.s_churn / state.num_inference_steps	,	2**0.5 - 1						)
				else:
							UpperCAmelCase_     : Optional[int]			     =					0

				# sample eps ~ N(0, S_noise^2 * I)
				UpperCAmelCase_     : List[Any]			     =					random.split(lowerCAmelCase_	,	num=1						)
				UpperCAmelCase_     : List[str]			     =					self.config.s_noise * random.normal(key=lowerCAmelCase_	,	shape=sample.shape						)
				UpperCAmelCase_     : Optional[Any]			     =					sigma + gamma * sigma
				UpperCAmelCase_     : Any			     =					sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

				return sample_hat, sigma_hat





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + sigma_hat * model_output
				UpperCAmelCase_     : List[Any]			     =					(sample_hat - pred_original_sample) / sigma_hat
				UpperCAmelCase_     : Union[str, Any]			     =					sample_hat + (sigma_prev - sigma_hat) * derivative

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: List[str]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: float	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: jnp.ndarray	,	lowerCAmelCase_					: bool = True	,	)						->			Union[FlaxKarrasVeOutput, Tuple]:
				UpperCAmelCase_     : str			     =					sample_prev + sigma_prev * model_output
				UpperCAmelCase_     : Any			     =					(sample_prev - pred_original_sample) / sigma_prev
				UpperCAmelCase_     : int			     =					sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

				if not return_dict:
							return (sample_prev, derivative, state)

				return FlaxKarrasVeOutput(prev_sample=lowerCAmelCase_	,	derivative=lowerCAmelCase_	,	state=lowerCAmelCase_						)





	def      _SCREAMING_SNAKE_CASE      (      self					: Union[str, Any]	,	lowerCAmelCase_					: KarrasVeSchedulerState	,	lowerCAmelCase_					: List[Any]	,	lowerCAmelCase_					: List[str]	,	lowerCAmelCase_					: Optional[Any]						)						->			Dict:
				raise NotImplementedError()



