


"""simple docstring"""


import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion_safe import StableDiffusionPipelineSafe as StableDiffusionPipeline
from diffusers.utils import floats_tensor, nightly, torch_device
from diffusers.utils.testing_utils import require_torch_gpu
class snake_case      (  unittest.TestCase):







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





						'''simple docstring'''







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







		@property
		def  a_ (					self      :       Optional[Any]		)       ->						Optional[Any]:





						'''simple docstring'''







						_A					  =			1
						_A					  =			3
						_A					  =			(32, 32)

						_A					  =			floats_tensor((batch_size, num_channels) + sizes    ,   rng=random.Random(0		)		).to(a__		)
						return image







		@property
		def  a_ (					self      :       Any		)       ->						Tuple:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			UNetaDConditionModel(
						    block_out_channels=(32, 64)    ,   layers_per_block=2    ,   sample_size=32    ,   in_channels=4    ,   out_channels=4    ,   down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")    ,   up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")    ,   cross_attention_dim=32    ,   )
						return model







		@property
		def  a_ (					self      :       str		)       ->						Union[str, Any]:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			AutoencoderKL(
						    block_out_channels=[32, 64]    ,   in_channels=3    ,   out_channels=3    ,   down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]    ,   up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]    ,   latent_channels=4    ,   )
						return model







		@property
		def  a_ (					self      :       str		)       ->						Dict:





						'''simple docstring'''







						torch.manual_seed(0		)
						_A					  =			CLIPTextConfig(
						    bos_token_id=0    ,   eos_token_id=2    ,   hidden_size=32    ,   intermediate_size=37    ,   layer_norm_eps=1E-0_5    ,   num_attention_heads=4    ,   num_hidden_layers=5    ,   pad_token_id=1    ,   vocab_size=10_00    ,   )
						return CLIPTextModel(a__		)







		@property
		def  a_ (					self      :       List[Any]		)       ->						int:





						'''simple docstring'''







						def extract(*a__      :       List[Any]    ,   **a__      :       str		):
										class snake_case      :







												def __init__(					self      :       int		)       ->						List[Any]:





																'''simple docstring'''







																_A					  =			torch.ones([0]		)







												def  a_ (					self      :       int    ,   a__      :       Dict		)       ->						Dict:





																'''simple docstring'''







																self.pixel_values.to(a__		)
																return self





										return Out()

						return extract







		def  a_ (					self      :       List[str]		)       ->						List[str]:





						'''simple docstring'''







						_A					  =			"cpu"  # ensure determinism for the device-dependent torch.Generator
						_A					  =			self.dummy_cond_unet
						_A					  =			DDIMScheduler(
						    beta_start=0.0_0_0_8_5    ,   beta_end=0.0_1_2    ,   beta_schedule="scaled_linear"    ,   clip_sample=a__    ,   set_alpha_to_one=a__    ,   )

						_A					  =			self.dummy_vae
						_A					  =			self.dummy_text_encoder
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						# make sure here that pndm scheduler skips prk
						_A					  =			StableDiffusionPipeline(
						    unet=a__    ,   scheduler=a__    ,   vae=a__    ,   text_encoder=a__    ,   tokenizer=a__    ,   safety_checker=a__    ,   feature_extractor=self.dummy_extractor    ,   )
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"A painting of a squirrel eating a burger"

						_A					  =			torch.Generator(device=a__		).manual_seed(0		)
						_A					  =			sd_pipe([prompt]    ,   generator=a__    ,   guidance_scale=6.0    ,   num_inference_steps=2    ,   output_type="np"		)
						_A					  =			output.images

						_A					  =			torch.Generator(device=a__		).manual_seed(0		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=6.0    ,   num_inference_steps=2    ,   output_type="np"    ,   return_dict=a__    ,   )[0]

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

						assert image.shape == (1, 64, 64, 3)
						_A					  =			np.array([0.5_7_5_6, 0.6_1_1_8, 0.5_0_0_5, 0.5_0_4_1, 0.5_4_7_1, 0.4_7_2_6, 0.4_9_7_6, 0.4_8_6_5, 0.4_8_6_4]		)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







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





						'''simple docstring'''







						_A					  =			"cpu"  # ensure determinism for the device-dependent torch.Generator
						_A					  =			self.dummy_cond_unet
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						_A					  =			self.dummy_vae
						_A					  =			self.dummy_text_encoder
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						# make sure here that pndm scheduler skips prk
						_A					  =			StableDiffusionPipeline(
						    unet=a__    ,   scheduler=a__    ,   vae=a__    ,   text_encoder=a__    ,   tokenizer=a__    ,   safety_checker=a__    ,   feature_extractor=self.dummy_extractor    ,   )
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"A painting of a squirrel eating a burger"
						_A					  =			torch.Generator(device=a__		).manual_seed(0		)
						_A					  =			sd_pipe([prompt]    ,   generator=a__    ,   guidance_scale=6.0    ,   num_inference_steps=2    ,   output_type="np"		)

						_A					  =			output.images

						_A					  =			torch.Generator(device=a__		).manual_seed(0		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=6.0    ,   num_inference_steps=2    ,   output_type="np"    ,   return_dict=a__    ,   )[0]

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

						assert image.shape == (1, 64, 64, 3)
						_A					  =			np.array([0.5_1_2_5, 0.5_7_1_6, 0.4_8_2_8, 0.5_0_6_0, 0.5_6_5_0, 0.4_7_6_8, 0.5_1_8_5, 0.4_8_9_5, 0.4_9_9_3]		)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2
						assert np.abs(image_from_tuple_slice.flatten() - expected_slice		).max() < 1E-2







		def  a_ (					self      :       Optional[int]		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			StableDiffusionPipeline.from_pretrained(
						    "hf-internal-testing/tiny-stable-diffusion-lms-pipe"    ,   safety_checker=a__		)
						assert isinstance(a__    ,   a__		)
						assert isinstance(pipe.scheduler    ,   a__		)
						assert pipe.safety_checker is None

						_A					  =			pipe("example prompt"    ,   num_inference_steps=2		).images[0]
						assert image is not None

						# check that there's no error when saving a pipeline with one of the models being None
						with tempfile.TemporaryDirectory() as tmpdirname:
										pipe.save_pretrained(a__		)
										_A					  =			StableDiffusionPipeline.from_pretrained(a__		)

						# sanity check that the pipeline still works
						assert pipe.safety_checker is None
						_A					  =			pipe("example prompt"    ,   num_inference_steps=2		).images[0]
						assert image is not None







		@unittest.skipIf(torch_device != "cuda"    ,   "This test requires a GPU"		)
		def  a_ (					self      :       Union[str, Any]		)       ->						int:





						'''simple docstring'''







						_A					  =			self.dummy_cond_unet
						_A					  =			PNDMScheduler(skip_prk_steps=a__		)
						_A					  =			self.dummy_vae
						_A					  =			self.dummy_text_encoder
						_A					  =			CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"		)

						# put models in fp16
						_A					  =			unet.half()
						_A					  =			vae.half()
						_A					  =			bert.half()

						# make sure here that pndm scheduler skips prk
						_A					  =			StableDiffusionPipeline(
						    unet=a__    ,   scheduler=a__    ,   vae=a__    ,   text_encoder=a__    ,   tokenizer=a__    ,   safety_checker=a__    ,   feature_extractor=self.dummy_extractor    ,   )
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"A painting of a squirrel eating a burger"
						_A					  =			sd_pipe([prompt]    ,   num_inference_steps=2    ,   output_type="np"		).images

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





@nightly
@require_torch_gpu
class snake_case      (  unittest.TestCase):







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





						'''simple docstring'''







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







		def  a_ (					self      :       Dict		)       ->						str:





						'''simple docstring'''







						_A					  =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"    ,   safety_checker=a__		)
						_A					  =			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			(
						    "portrait of girl with smokey eyes makeup in abandoned hotel, grange clothes, redshift, wide high angle"
						    " coloured polaroid photograph with flash, kodak film, hyper real, stunning moody cinematography, with"
						    " anamorphic lenses, by maripol, fallen angels by wong kar - wai, style of suspiria and neon demon and"
						    " children from bahnhof zoo, detailed "
						)
						_A					  =			40_03_66_03_46
						_A					  =			7

						# without safety guidance (sld_guidance_scale = 0)
						_A					  =			torch.manual_seed(a__		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=a__    ,   num_inference_steps=50    ,   output_type="np"    ,   width=5_12    ,   height=5_12    ,   sld_guidance_scale=0    ,   )

						_A					  =			output.images
						_A					  =			image[0, -3:, -3:, -1]
						_A					  =			[0.2_2_7_8, 0.2_2_3_1, 0.2_2_4_9, 0.2_3_3_3, 0.2_3_0_3, 0.1_8_8_5, 0.2_2_7_3, 0.2_1_4_4, 0.2_1_7_6]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2

						# without safety guidance (strong configuration)
						_A					  =			torch.manual_seed(a__		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=a__    ,   num_inference_steps=50    ,   output_type="np"    ,   width=5_12    ,   height=5_12    ,   sld_guidance_scale=20_00    ,   sld_warmup_steps=7    ,   sld_threshold=0.0_2_5    ,   sld_momentum_scale=0.5    ,   sld_mom_beta=0.7    ,   )

						_A					  =			output.images
						_A					  =			image[0, -3:, -3:, -1]
						_A					  =			[0.2_3_8_3, 0.2_2_7_6, 0.2_3_6, 0.2_1_9_2, 0.2_1_8_6, 0.2_0_5_3, 0.1_9_7_1, 0.1_9_0_1, 0.1_7_1_9]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2







		def  a_ (					self      :       str		)       ->						Tuple:





						'''simple docstring'''







						_A					  =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"    ,   safety_checker=a__		)
						_A					  =			LMSDiscreteScheduler.from_config(sd_pipe.scheduler.config		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			"padme amidala taking a bath artwork, safe for work, no nudity"
						_A					  =			27_34_97_17_55
						_A					  =			7

						_A					  =			torch.manual_seed(a__		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=a__    ,   num_inference_steps=50    ,   output_type="np"    ,   width=5_12    ,   height=5_12    ,   sld_guidance_scale=0    ,   )

						_A					  =			output.images
						_A					  =			image[0, -3:, -3:, -1]
						_A					  =			[0.3_5_0_2, 0.3_6_2_2, 0.3_3_9_6, 0.3_6_4_2, 0.3_4_7_8, 0.3_3_1_8, 0.3_5, 0.3_3_4_8, 0.3_2_9_7]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2

						_A					  =			torch.manual_seed(a__		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=a__    ,   num_inference_steps=50    ,   output_type="np"    ,   width=5_12    ,   height=5_12    ,   sld_guidance_scale=20_00    ,   sld_warmup_steps=7    ,   sld_threshold=0.0_2_5    ,   sld_momentum_scale=0.5    ,   sld_mom_beta=0.7    ,   )

						_A					  =			output.images
						_A					  =			image[0, -3:, -3:, -1]
						_A					  =			[0.5_5_3_1, 0.5_2_0_6, 0.4_8_9_5, 0.5_1_5_6, 0.5_1_8_2, 0.4_7_5_1, 0.4_8_0_2, 0.4_8_0_3, 0.4_4_4_3]

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2







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





						'''simple docstring'''







						_A					  =			StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5"		)
						_A					  =			sd_pipe.to(a__		)
						sd_pipe.set_progress_bar_config(disable=a__		)

						_A					  =			(
						    "the four horsewomen of the apocalypse, painting by tom of finland, gaston bussiere, craig mullins, j. c."
						    " leyendecker"
						)
						_A					  =			10_44_35_52_34
						_A					  =			12

						_A					  =			torch.manual_seed(a__		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=a__    ,   num_inference_steps=50    ,   output_type="np"    ,   width=5_12    ,   height=5_12    ,   sld_guidance_scale=0    ,   )

						_A					  =			output.images
						_A					  =			image[0, -3:, -3:, -1]
						_A					  =			np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]		)

						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-7

						_A					  =			torch.manual_seed(a__		)
						_A					  =			sd_pipe(
						    [prompt]    ,   generator=a__    ,   guidance_scale=a__    ,   num_inference_steps=50    ,   output_type="np"    ,   width=5_12    ,   height=5_12    ,   sld_guidance_scale=20_00    ,   sld_warmup_steps=7    ,   sld_threshold=0.0_2_5    ,   sld_momentum_scale=0.5    ,   sld_mom_beta=0.7    ,   )

						_A					  =			output.images
						_A					  =			image[0, -3:, -3:, -1]
						_A					  =			np.array([0.5_8_1_8, 0.6_2_8_5, 0.6_8_3_5, 0.6_0_1_9, 0.6_2_5, 0.6_7_5_4, 0.6_0_9_6, 0.6_3_3_4, 0.6_5_6_1]		)
						assert image.shape == (1, 5_12, 5_12, 3)

						assert np.abs(image_slice.flatten() - expected_slice		).max() < 1E-2



"""simple docstring"""


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.


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      (  _UpperCamelCase):







		def __init__(					self      :       List[Any]    ,   a__      :       Any		)       ->						Any:





						'''simple docstring'''







						_A					  =			data







		def __iter__(					self      :       List[str]		)       ->						str:





						'''simple docstring'''







						for element in self.data:
										yield element






def      a__ (				__lowercase=True   )	->	Tuple:
				_A					  =			Accelerator(even_batches=__lowercase   )
				assert accelerator.num_processes == 2, "this script expects that two GPUs are available"
				return accelerator






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase = False   )	->	Union[str, Any]:
				if iterable:
								_A					  =			DummyIterableDataset(torch.as_tensor(range(__lowercase   )   )   )
				else:
								_A					  =			TensorDataset(torch.as_tensor(range(__lowercase   )   )   )

				_A					  =			DataLoader(__lowercase     ,			batch_size=__lowercase   )
				_A					  =			accelerator.prepare(__lowercase   )

				return dl






def      a__ (				__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			__lowercase     ,			)	->	Dict:
				_A					  =			create_dataloader(accelerator=__lowercase     ,			dataset_size=__lowercase     ,			batch_size=__lowercase   )

				_A					  =			[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      a__ (				)	->	List[str]:
				_A					  =			create_accelerator()

				# without padding, we would expect a different number of batches
				verify_dataloader_batch_sizes(
				    __lowercase     ,			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(
				    __lowercase     ,			dataset_size=7     ,			batch_size=2     ,			process_0_expected_batch_sizes=[2, 2]     ,			process_1_expected_batch_sizes=[2, 2]     ,			)






def      a__ (				)	->	List[Any]:
				_A					  =			create_accelerator(even_batches=__lowercase   )

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

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






def      a__ (				)	->	int:
				_A					  =			create_accelerator(even_batches=__lowercase   )

				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )

				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

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

				accelerator.wait_for_everyone()

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






def      a__ (				__lowercase   )	->	List[str]:
				with warnings.catch_warnings(record=__lowercase   ) as w:
								with accelerator.join_uneven_inputs([Mock()]   ):
												pass

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






def      a__ (				)	->	Tuple:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
								_A					  =			train_dl.batch_sampler.even_batches
								_A					  =			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      a__ (				)	->	int:
				_A					  =			True
				_A					  =			False
				_A					  =			create_accelerator(even_batches=__lowercase   )
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )
				_A					  =			create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1   )

				with warnings.catch_warnings():
								warnings.filterwarnings("ignore"   )
								try:
												with accelerator.join_uneven_inputs([ddp_model]     ,			even_batches=__lowercase   ):
																_A					  =			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      a__ (				)	->	Optional[Any]:
				_A					  =			create_accelerator()
				_A					  =			torch.nn.Linear(1     ,			1   )
				_A					  =			accelerator.prepare(__lowercase   )
				create_dataloader(__lowercase     ,			dataset_size=3     ,			batch_size=1     ,			iterable=__lowercase   )

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

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






def      a__ (				)	->	Optional[Any]:
				_A					  =			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"   )
				_A					  =			accelerator.state.distributed_type
				_A					  =			DistributedType.FSDP
				test_join_raises_warning_for_non_ddp_distributed(__lowercase   )
				_A					  =			original_state


if __name__ == "__main__":
		main()