





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  __lowerCAmelCase	(      unittest.TestCase					):






			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__=32 ,							__magic_name__=True ,							)					->       Tuple:


							'''simple docstring'''



							snake_case_						:   str           =  parent
							snake_case_						:   Any           =  batch_size
							snake_case_						:   Union[str, Any]           =  num_channels
							snake_case_						:   Any           =  image_size
							snake_case_						:   List[Any]           =  min_resolution
							snake_case_						:   List[Any]           =  max_resolution
							snake_case_						:   Union[str, Any]           =  do_resize
							snake_case_						:   Optional[int]           =  size_divisor
							snake_case_						:   Optional[Any]           =  do_rescale






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							return {
							    "do_resize": self.do_resize,
							    "size_divisor": self.size_divisor,
							    "do_rescale": self.do_rescale,
							}




@require_torch
@require_vision
class  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				GLPNImageProcessor if is_vision_available() else None






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


							'''simple docstring'''



							snake_case_						:   List[Any]           =  GLPNImageProcessingTester(self   )






			@property
			def       lowerCamelCase  (self   )					->       str:


							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()






			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   Any           =  self.image_processing_class(**self.image_processor_dict   )
							self.assertTrue(hasattr(__magic_name__ ,							'''do_resize'''   )   )
							self.assertTrue(hasattr(__magic_name__ ,							'''size_divisor'''   )   )
							self.assertTrue(hasattr(__magic_name__ ,							'''resample'''   )   )
							self.assertTrue(hasattr(__magic_name__ ,							'''do_rescale'''   )   )






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


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  self.image_processing_class(**self.image_processor_dict   )
							# create random PIL images
							snake_case_						:   Union[str, Any]           =  prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__   )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__ ,							Image.Image   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:   Dict           =  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       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.image_processing_class(**self.image_processor_dict   )
							# create random numpy tensors
							snake_case_						:   Optional[int]           =  prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__ ,							numpify=__magic_name__   )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__ ,							np.ndarray   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:   Optional[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       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   List[Any]           =  self.image_processing_class(**self.image_processor_dict   )
							# create random PyTorch tensors
							snake_case_						:   Optional[Any]           =  prepare_image_inputs(self.image_processor_tester ,							equal_resolution=__magic_name__ ,							torchify=__magic_name__   )
							for image in image_inputs:
											self.assertIsInstance(__magic_name__ ,							torch.Tensor   )

							# Test not batched input (GLPNImageProcessor doesn't support batching)
							snake_case_						:   Optional[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   )







# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

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

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)



@flax.struct.dataclass
class  __lowerCAmelCase	:
			lowerCamelCase_					:					CommonSchedulerState

			# setable values
			lowerCamelCase_					:					jnp.ndarray
			lowerCamelCase_					:					jnp.ndarray
			lowerCamelCase_					:					Optional[int]	      =				None






			@classmethod
			def       lowerCamelCase  (cls ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )					->       Dict:


							'''simple docstring'''



							return cls(common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__   )




@dataclass
class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					DDPMSchedulerState




class  __lowerCAmelCase	(      _a,					_a					):
			lowerCamelCase_					:					List[Any]	      =				[e.name for e in FlaxKarrasDiffusionSchedulers]

			lowerCamelCase_					:					jnp.dtype






			@property
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							return True






			@register_to_config
			def __init__(self ,							__magic_name__ = 1000 ,							__magic_name__ = 0.0_001 ,							__magic_name__ = 0.02 ,							__magic_name__ = "linear" ,							__magic_name__ = None ,							__magic_name__ = "fixed_small" ,							__magic_name__ = True ,							__magic_name__ = "epsilon" ,							__magic_name__ = jnp.floataa ,							)					->       List[str]:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  dtype






			def       lowerCamelCase  (self ,							__magic_name__ = None   )					->       DDPMSchedulerState:


							'''simple docstring'''



							if common is None:
											snake_case_						:   Any           =  CommonSchedulerState.create(self   )

							# standard deviation of the initial noise distribution
							snake_case_						:   Any           =  jnp.array(1.0 ,							dtype=self.dtype   )

							snake_case_						:   Optional[int]           =  jnp.arange(0 ,							self.config.num_train_timesteps   ).round()[::-1]

							return DDPMSchedulerState.create(
							    common=__magic_name__ ,							init_noise_sigma=__magic_name__ ,							timesteps=__magic_name__ ,							)






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = None   )					->       jnp.ndarray:


							'''simple docstring'''



							return sample






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ = ()   )					->       DDPMSchedulerState:


							'''simple docstring'''



							snake_case_						:   str           =  self.config.num_train_timesteps // num_inference_steps
							# creates integer timesteps by multiplying by ratio
							# rounding to avoid issues when num_inference_step is power of 3
							snake_case_						:   List[str]           =  (jnp.arange(0 ,							__magic_name__   ) * step_ratio).round()[::-1]

							return state.replace(
							    num_inference_steps=__magic_name__ ,							timesteps=__magic_name__ ,							)






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


							'''simple docstring'''



							snake_case_						:   Any           =  state.common.alphas_cumprod[t]
							snake_case_						:   Dict           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )

							# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
							# and sample from it to get previous sample
							# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
							snake_case_						:   Tuple           =  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

							if variance_type is None:
											snake_case_						:   Any           =  self.config.variance_type

							# hacks - were probably added for training stability
							if variance_type == "fixed_small":
											snake_case_						:   int           =  jnp.clip(__magic_name__ ,							a_min=1e-20   )
							# for rl-diffuser https://arxiv.org/abs/2205.09991
							elif variance_type == "fixed_small_log":
											snake_case_						:   List[str]           =  jnp.log(jnp.clip(__magic_name__ ,							a_min=1e-20   )   )
							elif variance_type == "fixed_large":
											snake_case_						:   str           =  state.common.betas[t]
							elif variance_type == "fixed_large_log":
											# Glide max_log
											snake_case_						:   int           =  jnp.log(state.common.betas[t]   )
							elif variance_type == "learned":
											return predicted_variance
							elif variance_type == "learned_range":
											snake_case_						:   str           =  variance
											snake_case_						:   Optional[Any]           =  state.common.betas[t]
											snake_case_						:   Optional[Any]           =  (predicted_variance + 1) / 2
											snake_case_						:   Optional[Any]           =  frac * max_log + (1 - frac) * min_log

							return variance






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


							'''simple docstring'''



							snake_case_						:   Tuple           =  timestep

							if key is None:
											snake_case_						:   List[Any]           =  jax.random.PRNGKey(0   )

							if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
											snake_case_      ,							snake_case_						:   Any           =  jnp.split(__magic_name__ ,							sample.shape[1] ,							axis=1   )
							else:
											snake_case_						:   Optional[Any]           =  None

							# 1. compute alphas, betas
							snake_case_						:   List[Any]           =  state.common.alphas_cumprod[t]
							snake_case_						:   Union[str, Any]           =  jnp.where(t > 0 ,							state.common.alphas_cumprod[t - 1] ,							jnp.array(1.0 ,							dtype=self.dtype   )   )
							snake_case_						:   List[Any]           =  1 - alpha_prod_t
							snake_case_						:   str           =  1 - alpha_prod_t_prev

							# 2. compute predicted original sample from predicted noise also called
							# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
							if self.config.prediction_type == "epsilon":
											snake_case_						:   int           =  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
							elif self.config.prediction_type == "sample":
											snake_case_						:   List[Any]           =  model_output
							elif self.config.prediction_type == "v_prediction":
											snake_case_						:   Optional[Any]           =  (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
							else:
											raise ValueError(
											    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
											    ''' for the FlaxDDPMScheduler.'''   )

							# 3. Clip "predicted x_0"
							if self.config.clip_sample:
											snake_case_						:   Optional[int]           =  jnp.clip(__magic_name__ ,							-1 ,							1   )

							# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   str           =  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
							snake_case_						:   Dict           =  state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

							# 5. Compute predicted previous sample µ_t
							# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
							snake_case_						:   Optional[Any]           =  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

							# 6. Add noise
							def random_variance():
											snake_case_						:   List[Any]           =  jax.random.split(__magic_name__ ,							num=1   )
											snake_case_						:   Tuple           =  jax.random.normal(__magic_name__ ,							shape=model_output.shape ,							dtype=self.dtype   )
											return (self._get_variance(__magic_name__ ,							__magic_name__ ,							predicted_variance=__magic_name__   ) ** 0.5) * noise

							snake_case_						:   List[str]           =  jnp.where(t > 0 ,							random_variance() ,							jnp.zeros(model_output.shape ,							dtype=self.dtype   )   )

							snake_case_						:   Any           =  pred_prev_sample + variance

							if not return_dict:
											return (pred_prev_sample, state)

							return FlaxDDPMSchedulerOutput(prev_sample=__magic_name__ ,							state=__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return add_noise_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							__magic_name__ ,							)					->       jnp.ndarray:


							'''simple docstring'''



							return get_velocity_common(state.common ,							__magic_name__ ,							__magic_name__ ,							__magic_name__   )






			def __len__(self   )					->       int:


							'''simple docstring'''



							return self.config.num_train_timesteps
