






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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)
@flax.struct.dataclass
class   UpperCamelCase			:







		"""simple docstring"""



		snake_case			 = 42

		# setable values
		snake_case			 = 42
		snake_case			 = 42
		snake_case			 = 42
		snake_case			 = None

		# running values
		snake_case			 = None
		@classmethod
		def    A(	cls		:							Union[str, Any]    ,_SCREAMING_SNAKE_CASE		:							CommonSchedulerState    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray		)     -> Dict:


						'''simple docstring'''
						return cls(common=_SCREAMING_SNAKE_CASE    ,init_noise_sigma=_SCREAMING_SNAKE_CASE    ,timesteps=_SCREAMING_SNAKE_CASE    ,sigmas=_SCREAMING_SNAKE_CASE		)


@dataclass
class   UpperCamelCase			(					snake_case__			):







		"""simple docstring"""



		snake_case			 = 42


class   UpperCamelCase			(					snake_case__      ,							snake_case__			):







		"""simple docstring"""



		snake_case			 = [e.name for e in FlaxKarrasDiffusionSchedulers]

		snake_case			 = 42
		@property
		def    A(	self		:							List[str]		)     -> int:


						'''simple docstring'''
						return True
		@register_to_config
		def __init__(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							int = 1_0_0_0    ,_SCREAMING_SNAKE_CASE		:							float = 0.0001    ,_SCREAMING_SNAKE_CASE		:							float = 0.02    ,_SCREAMING_SNAKE_CASE		:							str = "linear"    ,_SCREAMING_SNAKE_CASE		:							Optional[jnp.ndarray] = None    ,_SCREAMING_SNAKE_CASE		:							str = "epsilon"    ,_SCREAMING_SNAKE_CASE		:							jnp.dtype = jnp.floataa    ,)     -> str:


						'''simple docstring'''
						A									=   dtype
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Optional[CommonSchedulerState] = None		)     -> LMSDiscreteSchedulerState:


						'''simple docstring'''
						if common is None:
										A									=   CommonSchedulerState.create(self		)

						A									=   jnp.arange(0    ,self.config.num_train_timesteps		).round()[::-1]
						A									=   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

						# standard deviation of the initial noise distribution
						A									=   sigmas.max()

						return LMSDiscreteSchedulerState.create(
						    common=_SCREAMING_SNAKE_CASE    ,init_noise_sigma=_SCREAMING_SNAKE_CASE    ,timesteps=_SCREAMING_SNAKE_CASE    ,sigmas=_SCREAMING_SNAKE_CASE    ,)
		def    A(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							LMSDiscreteSchedulerState    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							int		)     -> jnp.ndarray:


						'''simple docstring'''
						((A)			,		)									=   jnp.where(state.timesteps == timestep    ,size=1		)
						A									=   step_index[0]

						A									=   state.sigmas[step_index]
						A									=   sample / ((sigma**2 + 1) ** 0.5)
						return sample
		def    A(	self		:							Any    ,_SCREAMING_SNAKE_CASE		:							LMSDiscreteSchedulerState    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							List[str]		)     -> Optional[int]:


						'''simple docstring'''

						def lms_derivative(_SCREAMING_SNAKE_CASE		:							Dict		):
										A									=   1.0
										for k in range(_SCREAMING_SNAKE_CASE		):
														if current_order == k:
																		continue
														prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
										return prod

						A									=   integrate.quad(_SCREAMING_SNAKE_CASE    ,state.sigmas[t]    ,state.sigmas[t + 1]    ,epsrel=1E-4		)[0]

						return integrated_coeff
		def    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							LMSDiscreteSchedulerState    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							Tuple = ()		)     -> LMSDiscreteSchedulerState:


						'''simple docstring'''
						A									=   jnp.linspace(self.config.num_train_timesteps - 1    ,0    ,_SCREAMING_SNAKE_CASE    ,dtype=self.dtype		)

						A									=   jnp.floor(_SCREAMING_SNAKE_CASE		).astype(jnp.intaa		)
						A									=   jnp.ceil(_SCREAMING_SNAKE_CASE		).astype(jnp.intaa		)

						A									=   jnp.mod(_SCREAMING_SNAKE_CASE    ,1.0		)

						A									=   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
						A									=   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
						A									=   jnp.concatenate([sigmas, jnp.array([0.0]    ,dtype=self.dtype		)]		)

						A									=   timesteps.astype(jnp.intaa		)

						# initial running values
						A									=   jnp.zeros((0,) + shape    ,dtype=self.dtype		)

						return state.replace(
						    timesteps=_SCREAMING_SNAKE_CASE    ,sigmas=_SCREAMING_SNAKE_CASE    ,num_inference_steps=_SCREAMING_SNAKE_CASE    ,derivatives=_SCREAMING_SNAKE_CASE    ,)
		def    A(	self		:							Tuple    ,_SCREAMING_SNAKE_CASE		:							LMSDiscreteSchedulerState    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							int    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							int = 4    ,_SCREAMING_SNAKE_CASE		:							bool = True    ,)     -> Union[FlaxLMSSchedulerOutput, Tuple]:


						'''simple docstring'''
						if state.num_inference_steps is None:
										raise ValueError(
										    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'		)

						A									=   state.sigmas[timestep]

						# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
						if self.config.prediction_type == "epsilon":
										A									=   sample - sigma * model_output
						elif self.config.prediction_type == "v_prediction":
										# * c_out + input * c_skip
										A									=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
						else:
										raise ValueError(
										    f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'		)

						# 2. Convert to an ODE derivative
						A									=   (sample - pred_original_sample) / sigma
						A									=   state.replace(derivatives=jnp.append(state.derivatives    ,_SCREAMING_SNAKE_CASE		)		)
						if len(state.derivatives		) > order:
										A									=   state.replace(derivatives=jnp.delete(state.derivatives    ,0		)		)

						# 3. Compute linear multistep coefficients
						A									=   min(timestep + 1    ,_SCREAMING_SNAKE_CASE		)
						A									=   [self.get_lms_coefficient(_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE    ,_SCREAMING_SNAKE_CASE		) for curr_order in range(_SCREAMING_SNAKE_CASE		)]

						# 4. Compute previous sample based on the derivatives path
						A									=   sample + sum(
						    coeff * derivative for coeff, derivative in zip(_SCREAMING_SNAKE_CASE    ,reversed(state.derivatives		)		)		)

						if not return_dict:
										return (prev_sample, state)

						return FlaxLMSSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE    ,state=_SCREAMING_SNAKE_CASE		)
		def    A(	self		:							str    ,_SCREAMING_SNAKE_CASE		:							LMSDiscreteSchedulerState    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,_SCREAMING_SNAKE_CASE		:							jnp.ndarray    ,)     -> jnp.ndarray:


						'''simple docstring'''
						A									=   state.sigmas[timesteps].flatten()
						A									=   broadcast_to_shape_from_left(_SCREAMING_SNAKE_CASE    ,noise.shape		)

						A									=   original_samples + noise * sigma

						return noisy_samples







		def __len__(	self		:							Optional[int]		)     -> Optional[Any]:


						'''simple docstring'''
						return self.config.num_train_timesteps













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 CLIPImageProcessor
class   UpperCamelCase			(					unittest.TestCase			):







		"""simple docstring"""



		def __init__(	self		:							int    ,_SCREAMING_SNAKE_CASE		:							Any    ,_SCREAMING_SNAKE_CASE		:							Optional[int]=7    ,_SCREAMING_SNAKE_CASE		:							List[Any]=3    ,_SCREAMING_SNAKE_CASE		:							int=1_8    ,_SCREAMING_SNAKE_CASE		:							Tuple=3_0    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=4_0_0    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							int=None    ,_SCREAMING_SNAKE_CASE		:							Tuple=True    ,_SCREAMING_SNAKE_CASE		:							Any=None    ,_SCREAMING_SNAKE_CASE		:							Dict=True    ,_SCREAMING_SNAKE_CASE		:							List[str]=[0.48145466, 0.4578275, 0.40821073]    ,_SCREAMING_SNAKE_CASE		:							int=[0.26862954, 0.26130258, 0.27577711]    ,_SCREAMING_SNAKE_CASE		:							Union[str, Any]=True    ,)     -> List[str]:


						'''simple docstring'''
						A									=   size if size is not None else {'shortest_edge': 2_0}
						A									=   crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
						A									=   parent
						A									=   batch_size
						A									=   num_channels
						A									=   image_size
						A									=   min_resolution
						A									=   max_resolution
						A									=   do_resize
						A									=   size
						A									=   do_center_crop
						A									=   crop_size
						A									=   do_normalize
						A									=   image_mean
						A									=   image_std
						A									=   do_convert_rgb
		def    A(	self		:							List[str]		)     -> Optional[int]:


						'''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    A(	self		:							Optional[int]    ,_SCREAMING_SNAKE_CASE		:							str=False    ,_SCREAMING_SNAKE_CASE		:							List[str]=False    ,_SCREAMING_SNAKE_CASE		:							Optional[Any]=False		)     -> str:


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

						if equal_resolution:
										A									=   []
										for i in range(self.batch_size		):
														image_inputs.append(
														    np.random.randint(
														        2_5_5    ,size=(self.num_channels, self.max_resolution, self.max_resolution)    ,dtype=np.uinta		)		)
						else:
										A									=   []
										for i in range(self.batch_size		):
														A			,		A									=   np.random.choice(np.arange(self.min_resolution    ,self.max_resolution		)    ,2		)
														image_inputs.append(np.random.randint(2_5_5    ,size=(self.num_channels, width, height)    ,dtype=np.uinta		)		)

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

						if torchify:
										A									=   [torch.from_numpy(_SCREAMING_SNAKE_CASE		) for x in image_inputs]

						return image_inputs


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Optional[Any]		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self		)
		@property
		def    A(	self		:							Optional[int]		)     -> Any:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Optional[int]		)     -> Tuple:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Optional[Any]		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class.from_dict(self.image_processor_dict		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 2_0}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 1_8, 'width': 1_8}		)

						A									=   self.image_processing_class.from_dict(self.image_processor_dict    ,size=4_2    ,crop_size=8_4		)
						self.assertEqual(image_processor.size    ,{'shortest_edge': 4_2}		)
						self.assertEqual(image_processor.crop_size    ,{'height': 8_4, 'width': 8_4}		)
		def    A(	self		:							Any		)     -> Union[str, Any]:


						'''simple docstring'''
						pass
		def    A(	self		:							Tuple		)     -> Optional[int]:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   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
						A									=   image_processing(_SCREAMING_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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)
		def    A(	self		:							int		)     -> Tuple:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random numpy tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,numpify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,np.ndarray		)

						# Test not batched input
						A									=   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
						A									=   image_processing(_SCREAMING_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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)







		def    A(	self		:							str		)     -> str:


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PyTorch tensors
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE    ,torchify=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,torch.Tensor		)

						# Test not batched input
						A									=   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
						A									=   image_processing(_SCREAMING_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.crop_size['height'],
						        self.image_processor_tester.crop_size['width'],
						    )    ,)


@require_torch
@require_vision
class   UpperCamelCase			(					snake_case__      ,							unittest.TestCase			):







		"""simple docstring"""



		snake_case			 = CLIPImageProcessor if is_vision_available() else None
		def    A(	self		:							Tuple		)     -> Union[str, Any]:


						'''simple docstring'''
						A									=   CLIPImageProcessingTester(self    ,num_channels=4		)
						A									=   3
		@property
		def    A(	self		:							str		)     -> Dict:


						'''simple docstring'''
						return self.image_processor_tester.prepare_image_processor_dict()
		def    A(	self		:							Any		)     -> int:


						'''simple docstring'''
						A									=   self.image_processing_class(**self.image_processor_dict		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_resize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'size'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'center_crop'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_normalize'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_mean'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'image_std'		)		)
						self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,'do_convert_rgb'		)		)
		def    A(	self		:							Any		)     -> str:


						'''simple docstring'''
						pass







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


						'''simple docstring'''
						# Initialize image_processing
						A									=   self.image_processing_class(**self.image_processor_dict		)
						# create random PIL images
						A									=   self.image_processor_tester.prepare_inputs(equal_resolution=_SCREAMING_SNAKE_CASE		)
						for image in image_inputs:
										self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,Image.Image		)

						# Test not batched input
						A									=   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
						A									=   image_processing(_SCREAMING_SNAKE_CASE    ,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'],
						    )    ,)





