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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE				):
						__A:						torch.FloatTensor
						__A:						torch.FloatTensor







def 		lowerCAmelCase_     (     _SCREAMING_SNAKE_CASE      ,	_SCREAMING_SNAKE_CASE=0.999      ,	_SCREAMING_SNAKE_CASE="cosine"      ,	)   ->      str:

							"""simple docstring"""






							if alpha_transform_type == "cosine":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE  ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(_SCREAMING_SNAKE_CASE  ):
																					return math.exp(t * -12.0  )

							else:
														raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""  )

							_UpperCAmelCase  :			Tuple	       =		[]
							for i in range(_SCREAMING_SNAKE_CASE  ):
														_UpperCAmelCase  :			Dict	       =		i / num_diffusion_timesteps
														_UpperCAmelCase  :			List[Any]	       =		(i + 1) / num_diffusion_timesteps
														betas.append(min(1 - alpha_bar_fn(_SCREAMING_SNAKE_CASE  ) / alpha_bar_fn(_SCREAMING_SNAKE_CASE  )      ,	_SCREAMING_SNAKE_CASE  )  )
							return torch.tensor(_SCREAMING_SNAKE_CASE      ,	dtype=torch.floataa  )


class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						SCREAMING_SNAKE_CASE				):
						__A:						Optional[int]				   =      1

						@register_to_config
						def __init__(					self		:			str	,						_lowerCamelCase		:			int = 10_00	,						_lowerCamelCase		:			float = 0.00_01	,						_lowerCamelCase		:			float = 0.02	,						_lowerCamelCase		:			str = "linear"	,						_lowerCamelCase		:			float = 0.0	,						_lowerCamelCase		:			Optional[np.ndarray] = None	,						_lowerCamelCase		:			bool = True	,						):
													if trained_betas is not None:
																				_UpperCAmelCase  :			Tuple	       =		torch.from_numpy(_lowerCamelCase			)
													elif beta_schedule == "linear":
																				_UpperCAmelCase  :			int	       =		torch.linspace(_lowerCamelCase	,						_lowerCamelCase	,						_lowerCamelCase	,						dtype=torch.floataa			)
													elif beta_schedule == "scaled_linear":
																				# this schedule is very specific to the latent diffusion model.
																				_UpperCAmelCase  :			str	       =		(
																				    torch.linspace(beta_start**0.5	,						beta_end**0.5	,						_lowerCamelCase	,						dtype=torch.floataa			) ** 2
																				)
													elif beta_schedule == "squaredcos_cap_v2":
																				# Glide cosine schedule
																				_UpperCAmelCase  :			List[str]	       =		betas_for_alpha_bar(_lowerCamelCase			)
													elif beta_schedule == "sigmoid":
																				# GeoDiff sigmoid schedule
																				_UpperCAmelCase  :			str	       =		torch.linspace(-6	,						6	,						_lowerCamelCase			)
																				_UpperCAmelCase  :			List[str]	       =		torch.sigmoid(_lowerCamelCase			) * (beta_end - beta_start) + beta_start
													else:
																				raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""			)

													_UpperCAmelCase  :			Dict	       =		1.0 - self.betas
													_UpperCAmelCase  :			Any	       =		torch.cumprod(self.alphas	,						dim=0			)
													_UpperCAmelCase  :			Optional[int]	       =		torch.tensor(1.0			)

													_UpperCAmelCase  :			Any	       =		torch.tensor(1.0			)

													# standard deviation of the initial noise distribution
													_UpperCAmelCase  :			str	       =		1.0

													# setable values
													_UpperCAmelCase  :			str	       =		None
													_UpperCAmelCase  :			Any	       =		torch.from_numpy(np.arange(0	,						_lowerCamelCase			)[::-1].copy()			)

													_UpperCAmelCase  :			List[Any]	       =		eta

						def 						a__ (					self		:			Tuple	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			Optional[int] = None			):
													return sample

						def 						a__ (					self		:			str	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			int = 10	,						_lowerCamelCase		:			int = 10	,						_lowerCamelCase		:			Union[str, torch.device] = None	,						):
													_UpperCAmelCase  :			List[Any]	       =		min(self.config.num_train_timesteps	,						_lowerCamelCase			)
													_UpperCAmelCase  :			List[str]	       =		num_inference_steps

													_UpperCAmelCase  :			Any	       =		[]

													_UpperCAmelCase  :			List[Any]	       =		{}
													for j in range(0	,						num_inference_steps - jump_length	,						_lowerCamelCase			):
																				_UpperCAmelCase  :			Optional[int]	       =		jump_n_sample - 1

													_UpperCAmelCase  :			Dict	       =		num_inference_steps
													while t >= 1:
																				_UpperCAmelCase  :			Union[str, Any]	       =		t - 1
																				timesteps.append(_lowerCamelCase			)

																				if jumps.get(_lowerCamelCase	,						0			) > 0:
																											_UpperCAmelCase  :			str	       =		jumps[t] - 1
																											for _ in range(_lowerCamelCase			):
																																		_UpperCAmelCase  :			Any	       =		t + 1
																																		timesteps.append(_lowerCamelCase			)

													_UpperCAmelCase  :			int	       =		np.array(_lowerCamelCase			) * (self.config.num_train_timesteps // self.num_inference_steps)
													_UpperCAmelCase  :			Optional[Any]	       =		torch.from_numpy(_lowerCamelCase			).to(_lowerCamelCase			)

						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			Union[str, Any]			):
													_UpperCAmelCase  :			Dict	       =		t - self.config.num_train_timesteps // self.num_inference_steps

													_UpperCAmelCase  :			int	       =		self.alphas_cumprod[t]
													_UpperCAmelCase  :			Optional[int]	       =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
													_UpperCAmelCase  :			List[str]	       =		1 - alpha_prod_t
													_UpperCAmelCase  :			Optional[Any]	       =		1 - alpha_prod_t_prev

													# 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
													# Is equivalent to formula (16) in https://arxiv.org/pdf/2010.02502.pdf
													# without eta.
													# variance = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * self.betas[t]
													_UpperCAmelCase  :			Optional[int]	       =		(beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

													return variance

						def 						a__ (					self		:			str	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			int	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			Optional[torch.Generator] = None	,						_lowerCamelCase		:			bool = True	,						):
													_UpperCAmelCase  :			str	       =		timestep
													_UpperCAmelCase  :			Dict	       =		timestep - self.config.num_train_timesteps // self.num_inference_steps

													# 1. compute alphas, betas
													_UpperCAmelCase  :			List[str]	       =		self.alphas_cumprod[t]
													_UpperCAmelCase  :			Optional[Any]	       =		self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod
													_UpperCAmelCase  :			str	       =		1 - alpha_prod_t

													# 2. compute predicted original sample from predicted noise also called
													# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
													_UpperCAmelCase  :			int	       =		(sample - beta_prod_t**0.5 * model_output) / alpha_prod_t**0.5

													# 3. Clip "predicted x_0"
													if self.config.clip_sample:
																				_UpperCAmelCase  :			Union[str, Any]	       =		torch.clamp(_lowerCamelCase	,						-1	,						1			)

													# We choose to follow RePaint Algorithm 1 to get x_{t-1}, however we
													# substitute formula (7) in the algorithm coming from DDPM paper
													# (formula (4) Algorithm 2 - Sampling) with formula (12) from DDIM paper.
													# DDIM schedule gives the same results as DDPM with eta = 1.0
													# Noise is being reused in 7. and 8., but no impact on quality has
													# been observed.

													# 5. Add noise
													_UpperCAmelCase  :			Optional[int]	       =		model_output.device
													_UpperCAmelCase  :			Any	       =		randn_tensor(model_output.shape	,						generator=_lowerCamelCase	,						device=_lowerCamelCase	,						dtype=model_output.dtype			)
													_UpperCAmelCase  :			List[str]	       =		self.eta * self._get_variance(_lowerCamelCase			) ** 0.5

													_UpperCAmelCase  :			int	       =		0
													if t > 0 and self.eta > 0:
																				_UpperCAmelCase  :			str	       =		std_dev_t * noise

													# 6. compute "direction pointing to x_t" of formula (12)
													# from https://arxiv.org/pdf/2010.02502.pdf
													_UpperCAmelCase  :			int	       =		(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

													# 7. compute x_{t-1} of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
													_UpperCAmelCase  :			Union[str, Any]	       =		alpha_prod_t_prev**0.5 * pred_original_sample + pred_sample_direction + variance

													# 8. Algorithm 1 Line 5 https://arxiv.org/pdf/2201.09865.pdf
													_UpperCAmelCase  :			Optional[Any]	       =		(alpha_prod_t_prev**0.5) * original_image + ((1 - alpha_prod_t_prev) ** 0.5) * noise

													# 9. Algorithm 1 Line 8 https://arxiv.org/pdf/2201.09865.pdf
													_UpperCAmelCase  :			List[Any]	       =		mask * prev_known_part + (1.0 - mask) * prev_unknown_part

													if not return_dict:
																				return (
																				    pred_prev_sample,
																				    pred_original_sample,
																				)

													return RePaintSchedulerOutput(prev_sample=_lowerCamelCase	,						pred_original_sample=_lowerCamelCase			)

						def 						a__ (					self		:			List[Any]	,						_lowerCamelCase		:			Tuple	,						_lowerCamelCase		:			str	,						_lowerCamelCase		:			str=None			):
													_UpperCAmelCase  :			Union[str, Any]	       =		self.config.num_train_timesteps // self.num_inference_steps

													for i in range(_lowerCamelCase			):
																				_UpperCAmelCase  :			Union[str, Any]	       =		self.betas[timestep + i]
																				if sample.device.type == "mps":
																											# randn does not work reproducibly on mps
																											_UpperCAmelCase  :			Dict	       =		randn_tensor(sample.shape	,						dtype=sample.dtype	,						generator=_lowerCamelCase			)
																											_UpperCAmelCase  :			Any	       =		noise.to(sample.device			)
																				else:
																											_UpperCAmelCase  :			Union[str, Any]	       =		randn_tensor(sample.shape	,						generator=_lowerCamelCase	,						device=sample.device	,						dtype=sample.dtype			)

																				# 10. Algorithm 1 Line 10 https://arxiv.org/pdf/2201.09865.pdf
																				_UpperCAmelCase  :			int	       =		(1 - beta) ** 0.5 * sample + beta**0.5 * noise

													return sample

						def 						a__ (					self		:			List[str]	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			torch.FloatTensor	,						_lowerCamelCase		:			torch.IntTensor	,						):
													raise NotImplementedError("Use `DDPMScheduler.add_noise()` to train for sampling with RePaint."			)






						def __len__(					self		:			int			):
													return self.config.num_train_timesteps






import gc
import random
import unittest

import numpy as np
import torch
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()
class    _UpperCamelCase(   SCREAMING_SNAKE_CASE		,						unittest.TestCase				):
						__A:						Dict				   =      KandinskyPipeline
						__A:						List[Any]				   =      [
						    """prompt""",
						    """image_embeds""",
						    """negative_image_embeds""",
						]
						__A:						Dict				   =      ["""prompt""", """negative_prompt""", """image_embeds""", """negative_image_embeds"""]
						__A:						List[str]				   =      [
						    """generator""",
						    """height""",
						    """width""",
						    """latents""",
						    """guidance_scale""",
						    """negative_prompt""",
						    """num_inference_steps""",
						    """return_dict""",
						    """guidance_scale""",
						    """num_images_per_prompt""",
						    """output_type""",
						    """return_dict""",
						]
						__A:						List[str]				   =      False

						@property
						def 						a__ (					self		:			List[Any]			):
													return 32

						@property
						def 						a__ (					self		:			Tuple			):
													return 32

						@property
						def 						a__ (					self		:			List[Any]			):
													return self.time_input_dim

						@property
						def 						a__ (					self		:			List[Any]			):
													return self.time_input_dim * 4

						@property
						def 						a__ (					self		:			Dict			):
													return 1_00

						@property
						def 						a__ (					self		:			Tuple			):
													_UpperCAmelCase  :			Optional[int]	       =		XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"			)
													return tokenizer

						@property
						def 						a__ (					self		:			Tuple			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			int	       =		MCLIPConfig(
													    numDims=self.cross_attention_dim	,						transformerDimensions=self.text_embedder_hidden_size	,						hidden_size=self.text_embedder_hidden_size	,						intermediate_size=37	,						num_attention_heads=4	,						num_hidden_layers=5	,						vocab_size=10_05	,						)

													_UpperCAmelCase  :			Optional[Any]	       =		MultilingualCLIP(_lowerCamelCase			)
													_UpperCAmelCase  :			str	       =		text_encoder.eval()

													return text_encoder

						@property
						def 						a__ (					self		:			Any			):
													torch.manual_seed(0			)

													_UpperCAmelCase  :			Any	       =		{
													    "in_channels": 4,
													    # Out channels is double in channels because predicts mean and variance
													    "out_channels": 8,
													    "addition_embed_type": "text_image",
													    "down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
													    "up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
													    "mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
													    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
													    "layers_per_block": 1,
													    "encoder_hid_dim": self.text_embedder_hidden_size,
													    "encoder_hid_dim_type": "text_image_proj",
													    "cross_attention_dim": self.cross_attention_dim,
													    "attention_head_dim": 4,
													    "resnet_time_scale_shift": "scale_shift",
													    "class_embed_type": None,
													}

													_UpperCAmelCase  :			Optional[Any]	       =		UNetaDConditionModel(**_lowerCamelCase			)
													return model

						@property
						def 						a__ (					self		:			Optional[Any]			):
													return {
													    "block_out_channels": [32, 64],
													    "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
													    "in_channels": 3,
													    "latent_channels": 4,
													    "layers_per_block": 1,
													    "norm_num_groups": 8,
													    "norm_type": "spatial",
													    "num_vq_embeddings": 12,
													    "out_channels": 3,
													    "up_block_types": [
													        "AttnUpDecoderBlock2D",
													        "UpDecoderBlock2D",
													    ],
													    "vq_embed_dim": 4,
													}

						@property
						def 						a__ (					self		:			Tuple			):
													torch.manual_seed(0			)
													_UpperCAmelCase  :			Dict	       =		VQModel(**self.dummy_movq_kwargs			)
													return model

						def 						a__ (					self		:			List[Any]			):
													_UpperCAmelCase  :			Union[str, Any]	       =		self.dummy_text_encoder
													_UpperCAmelCase  :			Any	       =		self.dummy_tokenizer
													_UpperCAmelCase  :			List[Any]	       =		self.dummy_unet
													_UpperCAmelCase  :			Union[str, Any]	       =		self.dummy_movq

													_UpperCAmelCase  :			str	       =		DDIMScheduler(
													    num_train_timesteps=10_00	,						beta_schedule="linear"	,						beta_start=0.0_00_85	,						beta_end=0.0_12	,						clip_sample=_lowerCamelCase	,						set_alpha_to_one=_lowerCamelCase	,						steps_offset=1	,						prediction_type="epsilon"	,						thresholding=_lowerCamelCase	,						)

													_UpperCAmelCase  :			Optional[Any]	       =		{
													    "text_encoder": text_encoder,
													    "tokenizer": tokenizer,
													    "unet": unet,
													    "scheduler": scheduler,
													    "movq": movq,
													}
													return components

						def 						a__ (					self		:			int	,						_lowerCamelCase		:			Any	,						_lowerCamelCase		:			List[str]=0			):
													_UpperCAmelCase  :			Tuple	       =		floats_tensor((1, self.cross_attention_dim)	,						rng=random.Random(_lowerCamelCase			)			).to(_lowerCamelCase			)
													_UpperCAmelCase  :			Tuple	       =		floats_tensor((1, self.cross_attention_dim)	,						rng=random.Random(seed + 1			)			).to(_lowerCamelCase			)
													if str(_lowerCamelCase			).startswith("mps"			):
																				_UpperCAmelCase  :			Union[str, Any]	       =		torch.manual_seed(_lowerCamelCase			)
													else:
																				_UpperCAmelCase  :			Optional[Any]	       =		torch.Generator(device=_lowerCamelCase			).manual_seed(_lowerCamelCase			)
													_UpperCAmelCase  :			int	       =		{
													    "prompt": "horse",
													    "image_embeds": image_embeds,
													    "negative_image_embeds": negative_image_embeds,
													    "generator": generator,
													    "height": 64,
													    "width": 64,
													    "guidance_scale": 4.0,
													    "num_inference_steps": 2,
													    "output_type": "np",
													}
													return inputs






						def 						a__ (					self		:			List[Any]			):
													_UpperCAmelCase  :			Optional[Any]	       =		"cpu"

													_UpperCAmelCase  :			Dict	       =		self.get_dummy_components()

													_UpperCAmelCase  :			Any	       =		self.pipeline_class(**_lowerCamelCase			)
													_UpperCAmelCase  :			Optional[int]	       =		pipe.to(_lowerCamelCase			)

													pipe.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Dict	       =		pipe(**self.get_dummy_inputs(_lowerCamelCase			)			)
													_UpperCAmelCase  :			int	       =		output.images

													_UpperCAmelCase  :			Tuple	       =		pipe(
													    **self.get_dummy_inputs(_lowerCamelCase			)	,						return_dict=_lowerCamelCase	,						)[0]

													_UpperCAmelCase  :			Any	       =		image[0, -3:, -3:, -1]
													_UpperCAmelCase  :			Optional[Any]	       =		image_from_tuple[0, -3:, -3:, -1]

													assert image.shape == (1, 64, 64, 3)

													_UpperCAmelCase  :			Dict	       =		np.array(
													    [0.32_86_63, 1.0, 0.23_21_68_73, 1.0, 0.92_71_75_64, 0.4_63_90_46, 0.96_89_47_77, 0.31_71_33_78, 0.6_29_39_53]			)

													assert (
													    np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2
													), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
													assert (
													    np.abs(image_from_tuple_slice.flatten() - expected_slice			).max() < 1E-2
													), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""







@slow
@require_torch_gpu
class    _UpperCamelCase(   unittest.TestCase				):

						def 						a__ (					self		:			int			):
													# clean up the VRAM after each test
													super().tearDown()
													gc.collect()
													torch.cuda.empty_cache()






						def 						a__ (					self		:			str			):
													_UpperCAmelCase  :			List[str]	       =		load_numpy(
													    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
													    "/kandinsky/kandinsky_text2img_cat_fp16.npy"			)

													_UpperCAmelCase  :			Dict	       =		KandinskyPriorPipeline.from_pretrained(
													    "kandinsky-community/kandinsky-2-1-prior"	,						torch_dtype=torch.floataa			)
													pipe_prior.to(_lowerCamelCase			)

													_UpperCAmelCase  :			str	       =		KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1"	,						torch_dtype=torch.floataa			)
													_UpperCAmelCase  :			Union[str, Any]	       =		pipeline.to(_lowerCamelCase			)
													pipeline.set_progress_bar_config(disable=_lowerCamelCase			)

													_UpperCAmelCase  :			Tuple	       =		"red cat, 4k photo"

													_UpperCAmelCase  :			List[Any]	       =		torch.Generator(device="cuda"			).manual_seed(0			)
													_UpperCAmelCase       ,_UpperCAmelCase  :			int	       =		pipe_prior(
													    _lowerCamelCase	,						generator=_lowerCamelCase	,						num_inference_steps=5	,						negative_prompt=""	,						).to_tuple()

													_UpperCAmelCase  :			Tuple	       =		torch.Generator(device="cuda"			).manual_seed(0			)
													_UpperCAmelCase  :			Dict	       =		pipeline(
													    _lowerCamelCase	,						image_embeds=_lowerCamelCase	,						negative_image_embeds=_lowerCamelCase	,						generator=_lowerCamelCase	,						num_inference_steps=1_00	,						output_type="np"	,						)

													_UpperCAmelCase  :			Tuple	       =		output.images[0]

													assert image.shape == (5_12, 5_12, 3)

													assert_mean_pixel_difference(_lowerCamelCase	,						_lowerCamelCase			)




