
"""simple docstring"""




import warnings
from typing import List
from unittest.mock import Mock

import torch
from torch.utils.data import DataLoader, IterableDataset, TensorDataset

from accelerate.accelerator import Accelerator
from accelerate.utils.dataclasses import DistributedType





class  snake_case     (							__lowerCamelCase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = data







							def __iter__(self    ):

									"""simple docstring"""







									for element in self.data:
											yield element







def        _lowerCamelCase ( __a=True    ):
		SCREAMING_SNAKE_CASE_						 = Accelerator(even_batches=lowerCamelCase_    )
		assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
		return accelerator



def        _lowerCamelCase ( __a,							__a,							__a,							__a = False    ):
		if iterable:
				SCREAMING_SNAKE_CASE_						 = DummyIterableDataset(torch.as_tensor(range(lowerCamelCase_    )    )    )
		else:
				SCREAMING_SNAKE_CASE_						 = TensorDataset(torch.as_tensor(range(lowerCamelCase_    )    )    )

		SCREAMING_SNAKE_CASE_						 = DataLoader(lowerCamelCase_,							batch_size=lowerCamelCase_    )
		SCREAMING_SNAKE_CASE_						 = accelerator.prepare(lowerCamelCase_    )

		return dl



def        _lowerCamelCase ( __a,							__a,							__a,							__a,							__a,							):
		SCREAMING_SNAKE_CASE_						 = create_dataloader(accelerator=lowerCamelCase_,							dataset_size=lowerCamelCase_,							batch_size=lowerCamelCase_    )

		SCREAMING_SNAKE_CASE_						 = [len(batch[0]    ) for batch in dl]

		if accelerator.process_index == 0:
				assert batch_sizes == process_0_expected_batch_sizes
		elif accelerator.process_index == 1:
				assert batch_sizes == process_1_expected_batch_sizes



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = create_accelerator()

		# without padding, we would expect a different number of batches
		verify_dataloader_batch_sizes(
		    lowerCamelCase_,							dataset_size=3,							batch_size=1,							process_0_expected_batch_sizes=[1, 1],							process_1_expected_batch_sizes=[1, 1],							)

		# without padding, we would expect the same number of batches, but different sizes
		verify_dataloader_batch_sizes(
		    lowerCamelCase_,							dataset_size=7,							batch_size=2,							process_0_expected_batch_sizes=[2, 2],							process_1_expected_batch_sizes=[2, 2],							)



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = create_accelerator(even_batches=lowerCamelCase_    )

		verify_dataloader_batch_sizes(
		    lowerCamelCase_,							dataset_size=3,							batch_size=1,							process_0_expected_batch_sizes=[1, 1],							process_1_expected_batch_sizes=[1],							)

		verify_dataloader_batch_sizes(
		    lowerCamelCase_,							dataset_size=7,							batch_size=2,							process_0_expected_batch_sizes=[2, 2],							process_1_expected_batch_sizes=[2, 1],							)



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = create_accelerator(even_batches=lowerCamelCase_    )

		SCREAMING_SNAKE_CASE_						 = torch.nn.Linear(1,							1    )
		SCREAMING_SNAKE_CASE_						 = accelerator.prepare(lowerCamelCase_    )

		SCREAMING_SNAKE_CASE_						 = create_dataloader(lowerCamelCase_,							dataset_size=3,							batch_size=1    )

		SCREAMING_SNAKE_CASE_						 = []
		with accelerator.join_uneven_inputs([ddp_model]    ):
				for batch_idx, batch in enumerate(lowerCamelCase_    ):
						SCREAMING_SNAKE_CASE_						 = ddp_model(batch[0].float()    )
						SCREAMING_SNAKE_CASE_						 = output.sum()
						loss.backward()
						batch_idxs.append(lowerCamelCase_    )

		accelerator.wait_for_everyone()

		if accelerator.process_index == 0:
				assert batch_idxs == [0, 1]
		elif accelerator.process_index == 1:
				assert batch_idxs == [0]



def        _lowerCamelCase ( __a    ):
		with warnings.catch_warnings(record=lowerCamelCase_    ) as w:
				with accelerator.join_uneven_inputs([Mock()]    ):
						pass

				assert issubclass(w[-1].category,							lowerCamelCase_    )
				assert "only supported for multi-GPU" in str(w[-1].message    )



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = True
		SCREAMING_SNAKE_CASE_						 = False
		SCREAMING_SNAKE_CASE_						 = create_accelerator(even_batches=lowerCamelCase_    )
		SCREAMING_SNAKE_CASE_						 = torch.nn.Linear(1,							1    )
		SCREAMING_SNAKE_CASE_						 = accelerator.prepare(lowerCamelCase_    )
		SCREAMING_SNAKE_CASE_						 = create_dataloader(lowerCamelCase_,							dataset_size=3,							batch_size=1    )
		SCREAMING_SNAKE_CASE_						 = create_dataloader(lowerCamelCase_,							dataset_size=3,							batch_size=1    )

		with accelerator.join_uneven_inputs([ddp_model],							even_batches=lowerCamelCase_    ):
				SCREAMING_SNAKE_CASE_						 = train_dl.batch_sampler.even_batches
				SCREAMING_SNAKE_CASE_						 = valid_dl.batch_sampler.even_batches

		assert train_dl_overridden_value == overridden_even_batches
		assert valid_dl_overridden_value == overridden_even_batches
		assert train_dl.batch_sampler.even_batches == default_even_batches
		assert valid_dl.batch_sampler.even_batches == default_even_batches



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = True
		SCREAMING_SNAKE_CASE_						 = False
		SCREAMING_SNAKE_CASE_						 = create_accelerator(even_batches=lowerCamelCase_    )
		SCREAMING_SNAKE_CASE_						 = torch.nn.Linear(1,							1    )
		SCREAMING_SNAKE_CASE_						 = accelerator.prepare(lowerCamelCase_    )
		create_dataloader(lowerCamelCase_,							dataset_size=3,							batch_size=1,							iterable=lowerCamelCase_    )
		SCREAMING_SNAKE_CASE_						 = create_dataloader(lowerCamelCase_,							dataset_size=3,							batch_size=1    )

		with warnings.catch_warnings():
				warnings.filterwarnings('''ignore'''    )
				try:
						with accelerator.join_uneven_inputs([ddp_model],							even_batches=lowerCamelCase_    ):
								SCREAMING_SNAKE_CASE_						 = batch_dl.batch_sampler.even_batches
				except AttributeError:
						# ensure attribute error is not raised when processing iterable dl
						raise AssertionError

		assert batch_dl_overridden_value == overridden_even_batches
		assert batch_dl.batch_sampler.even_batches == default_even_batches



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = create_accelerator()
		SCREAMING_SNAKE_CASE_						 = torch.nn.Linear(1,							1    )
		SCREAMING_SNAKE_CASE_						 = accelerator.prepare(lowerCamelCase_    )
		create_dataloader(lowerCamelCase_,							dataset_size=3,							batch_size=1,							iterable=lowerCamelCase_    )

		with warnings.catch_warnings(record=lowerCamelCase_    ) as w:
				with accelerator.join_uneven_inputs([ddp_model],							even_batches=lowerCamelCase_    ):
						pass

				assert issubclass(w[-1].category,							lowerCamelCase_    )
				assert "only supported for map-style datasets" in str(w[-1].message    )



def        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = create_accelerator()

		accelerator.print('''Test that even_batches variable ensures uniform batches across processes'''    )
		test_default_ensures_even_batch_sizes()

		accelerator.print('''Run tests with even_batches disabled'''    )
		test_can_disable_even_batches()

		accelerator.print('''Test joining uneven inputs'''    )
		test_can_join_uneven_inputs()

		accelerator.print('''Test overriding even_batches when joining uneven inputs'''    )
		test_join_can_override_even_batches()

		accelerator.print('''Test overriding even_batches for mixed dataloader types'''    )
		test_join_can_override_for_mixed_type_dataloaders()

		accelerator.print('''Test overriding even_batches raises a warning for iterable dataloaders'''    )
		test_join_raises_warning_for_iterable_when_overriding_even_batches()

		accelerator.print('''Test join with non DDP distributed raises warning'''    )
		SCREAMING_SNAKE_CASE_						 = accelerator.state.distributed_type
		SCREAMING_SNAKE_CASE_						 = DistributedType.FSDP
		test_join_raises_warning_for_non_ddp_distributed(lowerCamelCase_    )
		SCREAMING_SNAKE_CASE_						 = original_state


if __name__ == "__main__":
						main()

"""simple docstring"""




import gc
import random
import unittest

import numpy as np
import torch

from diffusers import (
    DDIMScheduler,
    KandinskyVaaControlnetPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
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     (							__lowercase       ,			unittest.TestCase   ):
							UpperCAmelCase__						     =      KandinskyVaaControlnetPipeline
							UpperCAmelCase__						     =      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
							UpperCAmelCase__						     =      ['''image_embeds''', '''negative_image_embeds''', '''hint''']
							UpperCAmelCase__						     =      [
							    '''generator''',
							    '''height''',
							    '''width''',
							    '''latents''',
							    '''guidance_scale''',
							    '''num_inference_steps''',
							    '''return_dict''',
							    '''guidance_scale''',
							    '''num_images_per_prompt''',
							    '''output_type''',
							    '''return_dict''',
							]
							UpperCAmelCase__						     =      False

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 32

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 32

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.time_input_dim

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return self.time_input_dim * 4

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return 1_00

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )

									SCREAMING_SNAKE_CASE_						 = {
									    '''in_channels''': 8,
									    # Out channels is double in channels because predicts mean and variance
									    '''out_channels''': 8,
									    '''addition_embed_type''': '''image_hint''',
									    '''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''': '''image_proj''',
									    '''cross_attention_dim''': self.cross_attention_dim,
									    '''attention_head_dim''': 4,
									    '''resnet_time_scale_shift''': '''scale_shift''',
									    '''class_embed_type''': None,
									}

									SCREAMING_SNAKE_CASE_						 = UNetaDConditionModel(**SCREAMING_SNAKE_CASE_    )
									return model

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									return {
									    "block_out_channels": [32, 32, 64, 64],
									    "down_block_types": [
									        "DownEncoderBlock2D",
									        "DownEncoderBlock2D",
									        "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", "UpDecoderBlock2D", "UpDecoderBlock2D"],
									    "vq_embed_dim": 4,
									}

							@property
							def _lowercase					(self    ):

									"""simple docstring"""







									torch.manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = VQModel(**self.dummy_movq_kwargs    )
									return model

							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = self.dummy_unet
									SCREAMING_SNAKE_CASE_						 = self.dummy_movq

									SCREAMING_SNAKE_CASE_						 = DDIMScheduler(
									    num_train_timesteps=10_00       ,       beta_schedule='''linear'''       ,       beta_start=0.0_00_85       ,       beta_end=0.0_12       ,       clip_sample=SCREAMING_SNAKE_CASE_       ,       set_alpha_to_one=SCREAMING_SNAKE_CASE_       ,       steps_offset=1       ,       prediction_type='''epsilon'''       ,       thresholding=SCREAMING_SNAKE_CASE_       ,       )

									SCREAMING_SNAKE_CASE_						 = {
									    '''unet''': unet,
									    '''scheduler''': scheduler,
									    '''movq''': movq,
									}
									return components

							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_=0    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, self.text_embedder_hidden_size)       ,       rng=random.Random(seed + 1    )    ).to(
									    SCREAMING_SNAKE_CASE_    )

									# create hint
									SCREAMING_SNAKE_CASE_						 = floats_tensor((1, 3, 64, 64)       ,       rng=random.Random(SCREAMING_SNAKE_CASE_    )    ).to(SCREAMING_SNAKE_CASE_    )

									if str(SCREAMING_SNAKE_CASE_    ).startswith('''mps'''    ):
											SCREAMING_SNAKE_CASE_						 = torch.manual_seed(SCREAMING_SNAKE_CASE_    )
									else:
											SCREAMING_SNAKE_CASE_						 = torch.Generator(device=SCREAMING_SNAKE_CASE_    ).manual_seed(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = {
									    '''image_embeds''': image_embeds,
									    '''negative_image_embeds''': negative_image_embeds,
									    '''hint''': hint,
									    '''generator''': generator,
									    '''height''': 64,
									    '''width''': 64,
									    '''guidance_scale''': 4.0,
									    '''num_inference_steps''': 2,
									    '''output_type''': '''np''',
									}
									return inputs







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = '''cpu'''

									SCREAMING_SNAKE_CASE_						 = self.get_dummy_components()

									SCREAMING_SNAKE_CASE_						 = self.pipeline_class(**SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = pipe.to(SCREAMING_SNAKE_CASE_    )

									pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )    )
									SCREAMING_SNAKE_CASE_						 = output.images

									SCREAMING_SNAKE_CASE_						 = pipe(
									    **self.get_dummy_inputs(SCREAMING_SNAKE_CASE_    )       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )[0]

									SCREAMING_SNAKE_CASE_						 = image[0, -3:, -3:, -1]
									SCREAMING_SNAKE_CASE_						 = image_from_tuple[0, -3:, -3:, -1]

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

									SCREAMING_SNAKE_CASE_						 = np.array(
									    [0.6_95_98_26, 0.86_82_79, 0.7_55_80_92, 0.68_76_94_67, 0.85_80_58_04, 0.65_97_74_96, 0.44_88_53_02, 0.5_95_91_11, 0.4_25_15_95]    )

									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 _lowercase					(self    ):

									"""simple docstring"""







									super().tearDown()
									gc.collect()
									torch.cuda.empty_cache()







							def _lowercase					(self    ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy'''    )

									SCREAMING_SNAKE_CASE_						 = load_image(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
									    '''/kandinskyv22/hint_image_cat.png'''    )
									SCREAMING_SNAKE_CASE_						 = torch.from_numpy(np.array(SCREAMING_SNAKE_CASE_    )    ).float() / 2_55.0
									SCREAMING_SNAKE_CASE_						 = hint.permute(2       ,       0       ,       1    ).unsqueeze(0    )

									SCREAMING_SNAKE_CASE_						 = KandinskyVaaPriorPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-prior'''       ,       torch_dtype=torch.floataa    )
									pipe_prior.to(SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = KandinskyVaaControlnetPipeline.from_pretrained(
									    '''kandinsky-community/kandinsky-2-2-controlnet-depth'''       ,       torch_dtype=torch.floataa    )
									SCREAMING_SNAKE_CASE_						 = pipeline.to(SCREAMING_SNAKE_CASE_    )
									pipeline.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE_    )

									SCREAMING_SNAKE_CASE_						 = '''A robot, 4k photo'''

									SCREAMING_SNAKE_CASE_						 = torch.Generator(device='''cuda'''    ).manual_seed(0    )
									SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = pipe_prior(
									    SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=5       ,       negative_prompt=''''''       ,       ).to_tuple()

									SCREAMING_SNAKE_CASE_						 = torch.Generator(device='''cuda'''    ).manual_seed(0    )
									SCREAMING_SNAKE_CASE_						 = pipeline(
									    image_embeds=SCREAMING_SNAKE_CASE_       ,       negative_image_embeds=SCREAMING_SNAKE_CASE_       ,       hint=SCREAMING_SNAKE_CASE_       ,       generator=SCREAMING_SNAKE_CASE_       ,       num_inference_steps=1_00       ,       output_type='''np'''       ,       )

									SCREAMING_SNAKE_CASE_						 = output.images[0]

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

									assert_mean_pixel_difference(SCREAMING_SNAKE_CASE_       ,       SCREAMING_SNAKE_CASE_    )