



"""simple docstring"""
import inspect
import os
import unittest
from dataclasses import dataclass

import torch

from accelerate import Accelerator, DistributedDataParallelKwargs, GradScalerKwargs
from accelerate.state import AcceleratorState
from accelerate.test_utils import execute_subprocess_async, require_cuda, require_multi_gpu
from accelerate.utils import KwargsHandler




@dataclass
class 			snake_case_						(						a_				):
							__lowerCAmelCase    					=					0
							__lowerCAmelCase    					=					False
							__lowerCAmelCase    					=					3.0




class 			snake_case_						(						unittest.TestCase				):






							def snake_case_  (   self      ):
									# If no defaults are changed, `to_kwargs` returns an empty dict.
									self.assertDictEqual(MockClass().to_kwargs()			,     {}      )
									self.assertDictEqual(MockClass(a=2      ).to_kwargs()			,     {"a": 2}      )
									self.assertDictEqual(MockClass(a=2			,     b=a_      ).to_kwargs()			,     {"a": 2, "b": True}      )
									self.assertDictEqual(MockClass(a=2			,     c=2.25      ).to_kwargs()			,     {"a": 2, "c": 2.25}      )






							@require_cuda
							def snake_case_  (   self      ):
									# If no defaults are changed, `to_kwargs` returns an empty dict.
									a_     :      Any 						=     GradScalerKwargs(init_scale=1_0_2_4			,     growth_factor=2      )
									AcceleratorState._reset_state()
									a_     :      Union[str, Any] 						=     Accelerator(mixed_precision="fp16"			,     kwargs_handlers=[scaler_handler]      )
									print(accelerator.use_fpaa      )
									a_     :      Union[str, Any] 						=     accelerator.scaler

									# Check the kwargs have been applied
									self.assertEqual(scaler._init_scale			,     1_024.0      )
									self.assertEqual(scaler._growth_factor			,     2.0      )

									# Check the other values are at the default
									self.assertEqual(scaler._backoff_factor			,     0.5      )
									self.assertEqual(scaler._growth_interval			,     2_0_0_0      )
									self.assertEqual(scaler._enabled			,     a_      )






							@require_multi_gpu
							def snake_case_  (   self      ):
									a_     :      str 						=     ["torchrun", F"""--nproc_per_node={torch.cuda.device_count()}""", inspect.getfile(self.__class__      )]
									execute_subprocess_async(a_			,     env=os.environ.copy()      )


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_				= DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
					SCREAMING_SNAKE_CASE_				= Accelerator(kwargs_handlers=[ddp_scaler])
					SCREAMING_SNAKE_CASE_				= torch.nn.Linear(1_00, 2_00)
					SCREAMING_SNAKE_CASE_				= accelerator.prepare(model)

					# Check the values changed in kwargs
					SCREAMING_SNAKE_CASE_				= """"""
					SCREAMING_SNAKE_CASE_				= model.bucket_bytes_cap // (10_24 * 10_24)
					if observed_bucket_cap_map != 15:
										error_msg += F"Kwargs badly passed, should have `15` but found {observed_bucket_cap_map}.\n"
					if model.find_unused_parameters is not True:
										error_msg += F"Kwargs badly passed, should have `True` but found {model.find_unused_parameters}.\n"

					# Check the values of the defaults
					if model.dim != 0:
										error_msg += F"Default value not respected, should have `0` but found {model.dim}.\n"
					if model.broadcast_buffers is not True:
										error_msg += F"Default value not respected, should have `True` but found {model.broadcast_buffers}.\n"
					if model.gradient_as_bucket_view is not False:
										error_msg += F"Default value not respected, should have `False` but found {model.gradient_as_bucket_view}.\n"

					# Raise error at the end to make sure we don't stop at the first failure.
					if len(error_msg) > 0:
										raise ValueError(error_msg)




"""simple docstring"""
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast

from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCLIPConfig, MultilingualCLIP
from diffusers.utils import floats_tensor, load_image, 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 			snake_case_						(						a_     ,unittest.TestCase				):
							__lowerCAmelCase    					=					KandinskyImgaImgPipeline
							__lowerCAmelCase    					=					["prompt", "image_embeds", "negative_image_embeds", "image"]
							__lowerCAmelCase    					=					[
							    "prompt",
							    "negative_prompt",
							    "image_embeds",
							    "negative_image_embeds",
							    "image",
							]
							__lowerCAmelCase    					=					[
							    "generator",
							    "height",
							    "width",
							    "strength",
							    "guidance_scale",
							    "negative_prompt",
							    "num_inference_steps",
							    "return_dict",
							    "guidance_scale",
							    "num_images_per_prompt",
							    "output_type",
							    "return_dict",
							]
							__lowerCAmelCase    					=					False






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return 3_2






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim






							@property
							def snake_case_  (   self      ):
									return self.time_input_dim * 4






							@property
							def snake_case_  (   self      ):
									return 1_0_0






							@property
							def snake_case_  (   self      ):
									a_     :      List[str] 						=     XLMRobertaTokenizerFast.from_pretrained("YiYiXu/tiny-random-mclip-base"      )
									return tokenizer






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      List[Any] 						=     MCLIPConfig(
									    numDims=self.cross_attention_dim			,     transformerDimensions=self.text_embedder_hidden_size			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=3_7			,     num_attention_heads=4			,     num_hidden_layers=5			,     vocab_size=1_0_0_5			,     )

									a_     :      str 						=     MultilingualCLIP(a_      )
									a_     :      Any 						=     text_encoder.eval()

									return text_encoder






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )

									a_     :      Union[str, 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,
									}

									a_     :      Dict 						=     UNetaDConditionModel(**a_      )
									return model






							@property
							def snake_case_  (   self      ):
									return {
									    "block_out_channels": [3_2, 6_4],
									    "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": 1_2,
									    "out_channels": 3,
									    "up_block_types": [
									        "AttnUpDecoderBlock2D",
									        "UpDecoderBlock2D",
									    ],
									    "vq_embed_dim": 4,
									}






							@property
							def snake_case_  (   self      ):
									torch.manual_seed(0      )
									a_     :      int 						=     VQModel(**self.dummy_movq_kwargs      )
									return model






							def snake_case_  (   self      ):
									a_     :      Dict 						=     self.dummy_text_encoder
									a_     :      Dict 						=     self.dummy_tokenizer
									a_     :      Optional[int] 						=     self.dummy_unet
									a_     :      Dict 						=     self.dummy_movq

									a_     :      List[str] 						=     {
									    "num_train_timesteps": 1_0_0_0,
									    "beta_schedule": "linear",
									    "beta_start": 0.00_085,
									    "beta_end": 0.012,
									    "clip_sample": False,
									    "set_alpha_to_one": False,
									    "steps_offset": 0,
									    "prediction_type": "epsilon",
									    "thresholding": False,
									}

									a_     :      List[Any] 						=     DDIMScheduler(**a_      )

									a_     :      Union[str, Any] 						=     {
									    "text_encoder": text_encoder,
									    "tokenizer": tokenizer,
									    "unet": unet,
									    "scheduler": scheduler,
									    "movq": movq,
									}

									return components






							def snake_case_  (   self			,     a_			,     a_=0      ):
									a_     :      Optional[int] 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Tuple 						=     floats_tensor((1, self.cross_attention_dim)			,     rng=random.Random(seed + 1      )      ).to(a_      )
									# create init_image
									a_     :      Optional[int] 						=     floats_tensor((1, 3, 6_4, 6_4)			,     rng=random.Random(a_      )      ).to(a_      )
									a_     :      Any 						=     image.cpu().permute(0			,     2			,     3			,     1      )[0]
									a_     :      int 						=     Image.fromarray(np.uinta(a_      )      ).convert("RGB"      ).resize((2_5_6, 2_5_6)      )

									if str(a_      ).startswith("mps"      ):
											a_     :      Any 						=     torch.manual_seed(a_      )
									else:
											a_     :      Any 						=     torch.Generator(device=a_      ).manual_seed(a_      )
									a_     :      List[Any] 						=     {
									    "prompt": "horse",
									    "image": init_image,
									    "image_embeds": image_embeds,
									    "negative_image_embeds": negative_image_embeds,
									    "generator": generator,
									    "height": 6_4,
									    "width": 6_4,
									    "num_inference_steps": 1_0,
									    "guidance_scale": 7.0,
									    "strength": 0.2,
									    "output_type": "np",
									}
									return inputs






							def snake_case_  (   self      ):
									a_     :      Optional[Any] 						=     "cpu"

									a_     :      List[Any] 						=     self.get_dummy_components()

									a_     :      Union[str, Any] 						=     self.pipeline_class(**a_      )
									a_     :      Tuple 						=     pipe.to(a_      )

									pipe.set_progress_bar_config(disable=a_      )

									a_     :      Union[str, Any] 						=     pipe(**self.get_dummy_inputs(a_      )      )
									a_     :      Any 						=     output.images

									a_     :      str 						=     pipe(
									    **self.get_dummy_inputs(a_      )			,     return_dict=a_			,     )[0]

									a_     :      List[str] 						=     image[0, -3:, -3:, -1]
									a_     :      Optional[Any] 						=     image_from_tuple[0, -3:, -3:, -1]

									assert image.shape == (1, 6_4, 6_4, 3)

									a_     :      Optional[int] 						=     np.array(
									    [0.61_474_943, 0.6_073_539, 0.43_308_544, 0.5_928_269, 0.47_493_595, 0.46_755_973, 0.4_613_838, 0.45_368_797, 0.50_119_233]      )
									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 			snake_case_						(						unittest.TestCase				):






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






							def snake_case_  (   self      ):
									a_     :      Optional[int] 						=     load_numpy(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
									    "/kandinsky/kandinsky_img2img_frog.npy"      )

									a_     :      Union[str, Any] 						=     load_image(
									    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png"      )
									a_     :      Optional[Any] 						=     "A red cartoon frog, 4k"

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

									a_     :      int 						=     KandinskyImgaImgPipeline.from_pretrained(
									    "kandinsky-community/kandinsky-2-1"			,     torch_dtype=torch.floataa      )
									a_     :      List[Any] 						=     pipeline.to(a_      )

									pipeline.set_progress_bar_config(disable=a_      )

									a_     :      int 						=     torch.Generator(device="cpu"      ).manual_seed(0      )
									a_						,				a_     :      Optional[int] 						=     pipe_prior(
									    a_			,     generator=a_			,     num_inference_steps=5			,     negative_prompt=""			,     ).to_tuple()

									a_     :      List[Any] 						=     pipeline(
									    a_			,     image=a_			,     image_embeds=a_			,     negative_image_embeds=a_			,     generator=a_			,     num_inference_steps=1_0_0			,     height=7_6_8			,     width=7_6_8			,     strength=0.2			,     output_type="np"			,     )

									a_     :      int 						=     output.images[0]

									assert image.shape == (7_6_8, 7_6_8, 3)

									assert_mean_pixel_difference(a_			,     a_      )