



"""simple docstring"""





from copy import deepcopy

import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader

from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import DistributedType, is_torch_version, set_seed
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Dict					, __UpperCamelCase      :			Any					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Dict    ):


						'''simple docstring'''






						for param, grad_param in zip(model_a.parameters()					, model_b.parameters()    ):
												if not param.requires_grad:
																		continue
												if not did_step:
																		# Grads should not be in sync
																		assert (
																		    torch.allclose(param.grad					, grad_param.grad    ) is False
																		), F'Gradients in sync when they should not be at iteration {iteration}:\nmodel_a grad ({param.grad}) == model_b grad ({grad_param.grad})'
												else:
																		# Grads should be in sync
																		assert (
																		    torch.allclose(param.grad					, grad_param.grad    ) is True
																		), F'Gradients not in sync when they should be at iteration {iteration}:\nmodel_a grad ({param.grad}) != model_b grad ({grad_param.grad})'
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			str=True    ):


						'''simple docstring'''






						model.train()
						snake_case_   : str						=			model(__UpperCamelCase    )
						snake_case_   : Tuple						=			F.mse_loss(__UpperCamelCase					, target.to(output.device    )    )
						if not do_backward:
												loss /= accelerator.gradient_accumulation_steps
												loss.backward()
						else:
												accelerator.backward(__UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			str=False    ):


						'''simple docstring'''






						set_seed(4_2    )
						snake_case_   : int						=			RegressionModel()
						snake_case_   : Dict						=			deepcopy(__UpperCamelCase    )
						snake_case_   : List[str]						=			RegressionDataset(length=8_0    )
						snake_case_   : Union[str, Any]						=			DataLoader(__UpperCamelCase					, batch_size=1_6    )
						model.to(accelerator.device    )
						if sched:
												snake_case_   : Any						=			AdamW(params=model.parameters()					, lr=1E-3    )
												snake_case_   : str						=			AdamW(params=ddp_model.parameters()					, lr=1E-3    )
												snake_case_   : Tuple						=			LambdaLR(__UpperCamelCase					, lr_lambda=lambda __UpperCamelCase    : epoch**0.65    )
												snake_case_   : Optional[Any]						=			LambdaLR(__UpperCamelCase					, lr_lambda=lambda __UpperCamelCase    : epoch**0.65    )
						# Make a copy of `model`
						if sched:
												snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[Any]						=			accelerator.prepare(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
						else:
												snake_case_			,		snake_case_   : str						=			accelerator.prepare(__UpperCamelCase					, __UpperCamelCase    )
						if sched:
												return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
						return model, ddp_model, dataloader
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[Any]    ):


						'''simple docstring'''






						snake_case_			,		snake_case_			,		snake_case_   : int						=			get_training_setup(__UpperCamelCase    )
						# Use a single batch
						snake_case_			,		snake_case_   : List[Any]						=			next(iter(__UpperCamelCase    )    ).values()
						for iteration in range(3    ):
												# Gather the distributed inputs and targs for the base model
												snake_case_			,		snake_case_   : List[Any]						=			accelerator.gather((ddp_input, ddp_target)    )
												snake_case_			,		snake_case_   : Dict						=			input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(__UpperCamelCase    ):
																								step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												else:
																		# Sync grads
																		step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )

												# Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
												check_model_parameters(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												for param, ddp_param in zip(model.parameters()					, ddp_model.parameters()    ):
																		if not param.requires_grad:
																								continue
																		assert torch.allclose(
																		    param.grad					, ddp_param.grad    ), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

												# Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration    )
												snake_case_   : Union[str, Any]						=			ddp_input[torch.randperm(len(__UpperCamelCase    )    )]
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]    ):


						'''simple docstring'''






						snake_case_			,		snake_case_			,		snake_case_   : Optional[int]						=			get_training_setup(__UpperCamelCase    )
						# Use a single batch
						snake_case_			,		snake_case_   : str						=			next(iter(__UpperCamelCase    )    ).values()
						for iteration in range(3    ):
												# Gather the distributed inputs and targs for the base model
												snake_case_			,		snake_case_   : Optional[Any]						=			accelerator.gather((ddp_input, ddp_target)    )
												snake_case_			,		snake_case_   : Optional[Any]						=			input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												# Do "gradient accumulation" (noop)
												if iteration % 2 == 0:
																		# Accumulate grads locally
																		with accelerator.no_sync(__UpperCamelCase    ):
																								step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												else:
																		# Sync grads
																		step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()					, ddp_model.parameters()    ):
																		if not param.requires_grad:
																								continue
																		if iteration % 2 == 0:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad					, ddp_param.grad    ) is False
																								), F'Gradients in sync when they should not be:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'
																		else:
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad					, ddp_param.grad    ) is True
																								), F'Gradients not in sync when they should be:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration    )
												snake_case_   : Union[str, Any]						=			ddp_input[torch.randperm(len(__UpperCamelCase    )    )]
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Optional[int]=False					, __UpperCamelCase      :			Optional[Any]=False    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			Accelerator(
						    split_batches=__UpperCamelCase					, dispatch_batches=__UpperCamelCase					, gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						snake_case_			,		snake_case_			,		snake_case_   : List[str]						=			get_training_setup(__UpperCamelCase    )
						for iteration, batch in enumerate(__UpperCamelCase    ):
												snake_case_			,		snake_case_   : int						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												snake_case_			,		snake_case_   : Any						=			accelerator.gather((ddp_input, ddp_target)    )
												snake_case_			,		snake_case_   : int						=			input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												# Do "gradient accumulation" (noop)
												with accelerator.accumulate(__UpperCamelCase    ):
																		step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )

												# DDP model and model should only be in sync when not (iteration % 2 == 0)
												for param, ddp_param in zip(model.parameters()					, ddp_model.parameters()    ):
																		if not param.requires_grad:
																								continue
																		if ((iteration + 1) % 2 == 0) or (iteration == len(__UpperCamelCase    ) - 1):
																								# Grads should be in sync
																								assert (
																								    torch.allclose(param.grad					, ddp_param.grad    ) is True
																								), F'Gradients not in sync when they should be at iteration {iteration}:\nModel grad ({param.grad}) != DDP grad ({ddp_param.grad})'
																		else:
																								# Grads should not be in sync
																								assert (
																								    torch.allclose(param.grad					, ddp_param.grad    ) is False
																								), F'Gradients in sync when they should not be at iteration {iteration}:\nModel grad ({param.grad}) == DDP grad ({ddp_param.grad})'

        # Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration    )
												snake_case_   : str						=			ddp_input[torch.randperm(len(__UpperCamelCase    )    )]
						GradientState._reset_state()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[Any]=False					, __UpperCamelCase      :			str=False    ):


						'''simple docstring'''






						snake_case_   : Dict						=			Accelerator(
						    split_batches=__UpperCamelCase					, dispatch_batches=__UpperCamelCase					, gradient_accumulation_steps=2    )
						# Test that context manager behaves properly
						snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_			,		snake_case_   : List[str]						=			get_training_setup(__UpperCamelCase					, __UpperCamelCase    )
						for iteration, batch in enumerate(__UpperCamelCase    ):
												snake_case_			,		snake_case_   : List[str]						=			batch.values()
												# Gather the distributed inputs and targs for the base model
												snake_case_			,		snake_case_   : str						=			accelerator.gather((ddp_input, ddp_target)    )
												snake_case_			,		snake_case_   : Any						=			input.to(accelerator.device    ), target.to(accelerator.device    )
												# Perform our initial ground truth step in non "DDP"
												model.train()
												ddp_model.train()
												step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												opt.step()

												if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(__UpperCamelCase    )):
																		if split_batches:
																								sched.step()
																		else:
																								for _ in range(accelerator.num_processes    ):
																														sched.step()
												opt.zero_grad()
												# Perform gradient accumulation under wrapper
												with accelerator.accumulate(__UpperCamelCase    ):
																		step_model(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
																		ddp_opt.step()
																		ddp_sched.step()
																		ddp_opt.zero_grad()

												# Learning rates should be the same
												assert (
												    opt.param_groups[0]["lr"] == ddp_opt.param_groups[0]["lr"]
												), F'Learning rates found in each optimizer did not align\nopt: {opt.param_groups[0]["lr"]}\nDDP opt: {ddp_opt.param_groups[0]["lr"]}\n'
												snake_case_   : Tuple						=			(((iteration + 1) % 2) == 0) or ((iteration + 1) == len(__UpperCamelCase    ))
												if accelerator.num_processes > 1:
																		check_model_parameters(__UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase					, __UpperCamelCase    )
												# Shuffle ddp_input on each iteration
												torch.manual_seed(1_3_3_7 + iteration    )
						GradientState._reset_state()
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : int						=			Accelerator()

						snake_case_   : Tuple						=			RegressionDataset(length=8_0    )
						snake_case_   : Optional[int]						=			DataLoader(__UpperCamelCase					, batch_size=1_6    )
						snake_case_   : Any						=			RegressionDataset(length=9_6    )
						snake_case_   : Union[str, Any]						=			DataLoader(__UpperCamelCase					, batch_size=1_6    )
						snake_case_			,		snake_case_   : Optional[Any]						=			accelerator.prepare(__UpperCamelCase					, __UpperCamelCase    )
						assert accelerator.gradient_state.active_dataloader is None
						for iteration, _ in enumerate(__UpperCamelCase    ):
												assert id(accelerator.gradient_state.active_dataloader    ) == id(__UpperCamelCase    )
												if iteration < len(__UpperCamelCase    ) - 1:
																		assert not accelerator.gradient_state.end_of_dataloader
																		if iteration == 1:
																								for batch_num, _ in enumerate(__UpperCamelCase    ):
																														assert id(accelerator.gradient_state.active_dataloader    ) == id(__UpperCamelCase    )
																														if batch_num < len(__UpperCamelCase    ) - 1:
																																				assert not accelerator.gradient_state.end_of_dataloader
																														else:
																																				assert accelerator.gradient_state.end_of_dataloader
												else:
																		assert accelerator.gradient_state.end_of_dataloader
						assert accelerator.gradient_state.active_dataloader is None
def 	__lowerCAmelCase						(		):


						'''simple docstring'''






						snake_case_   : Any						=			Accelerator()
						snake_case_   : Optional[int]						=			accelerator.state
						if state.local_process_index == 0:
												print("""**Test `accumulate` gradient accumulation with dataloader break**"""    )
						test_dataloader_break()
						if state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print("""**Test NOOP `no_sync` context manager**"""    )
												test_noop_sync(__UpperCamelCase    )
						if state.distributed_type in (DistributedType.MULTI_GPU, DistributedType.MULTI_CPU):
												if state.local_process_index == 0:
																		print("""**Test Distributed `no_sync` context manager**"""    )
												test_distributed_sync(__UpperCamelCase    )
						if state.distributed_type == DistributedType.MULTI_GPU:
												for split_batch in [True, False]:
																		for dispatch_batches in [True, False]:
																								if state.local_process_index == 0:
																														print(
																														    """**Test `accumulate` gradient accumulation, """					, F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'					, )
																								test_gradient_accumulation(__UpperCamelCase					, __UpperCamelCase    )

    # Currently will break on torch 2.0 +, need to investigate why
						if is_torch_version("""<"""					, """2.0"""    ) or state.distributed_type == DistributedType.NO:
												if state.local_process_index == 0:
																		print(
																		    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """					, """`split_batches=False`, `dispatch_batches=False`**"""					, )
												test_gradient_accumulation_with_opt_and_scheduler()
												if state.distributed_type == DistributedType.MULTI_GPU:
																		for split_batch in [True, False]:
																								for dispatch_batches in [True, False]:
																														if not split_batch and not dispatch_batches:
																																				continue
																														if state.local_process_index == 0:
																																				print(
																																				    """**Test `accumulate` gradient accumulation with optimizer and scheduler, """					, F'`split_batches={split_batch}` and `dispatch_batches={dispatch_batches}`**'					, )
																														test_gradient_accumulation_with_opt_and_scheduler(__UpperCamelCase					, __UpperCamelCase    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]    ):


						'''simple docstring'''






						main()


if __name__ == "__main__":
					main()






"""simple docstring"""





import gc
import unittest

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

from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings
from diffusers.utils import load_numpy, slow, torch_device
from diffusers.utils.testing_utils import require_torch_gpu


__lowerCAmelCase       :					Optional[Any]      					=					False



class    _lowerCAmelCase    (				unittest.TestCase		):





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

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





		@property
		def    UpperCAmelCase__							(					self   ) ->						Any:







								'''simple docstring'''

								return 1_2





		@property
		def    UpperCAmelCase__							(					self   ) ->						Dict:







								'''simple docstring'''

								return 1_2





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







								'''simple docstring'''

								return 3_2





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







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : List[Any]						=			VQModel(
								    block_out_channels=[3_2, 6_4]							,    in_channels=3							,    out_channels=3							,    down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""]							,    up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""]							,    latent_channels=3							,    num_vq_embeddings=self.num_embed							,    vq_embed_dim=3							,    )
								return model





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







								'''simple docstring'''

								snake_case_   : int						=			CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""   )
								return tokenizer





		@property
		def    UpperCAmelCase__							(					self   ) ->						int:







								'''simple docstring'''

								torch.manual_seed(0   )
								snake_case_   : Dict						=			CLIPTextConfig(
								    bos_token_id=0							,    eos_token_id=2							,    hidden_size=self.text_embedder_hidden_size							,    intermediate_size=3_7							,    layer_norm_eps=1E-05							,    num_attention_heads=4							,    num_hidden_layers=5							,    pad_token_id=1							,    vocab_size=1_0_0_0							,    )
								return CLIPTextModel(_lowercase   )





		@property
		def    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								torch.manual_seed(0   )

								snake_case_   : Union[str, Any]						=			1_2
								snake_case_   : Tuple						=			1_2

								snake_case_   : Tuple						=			{
								    """attention_bias""": True,
								    """cross_attention_dim""": 3_2,
								    """attention_head_dim""": height * width,
								    """num_attention_heads""": 1,
								    """num_vector_embeds""": self.num_embed,
								    """num_embeds_ada_norm""": self.num_embeds_ada_norm,
								    """norm_num_groups""": 3_2,
								    """sample_size""": width,
								    """activation_fn""": """geglu-approximate""",
								}

								snake_case_   : Optional[Any]						=			TransformeraDModel(**_lowercase   )
								return model





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								snake_case_   : str						=			"""cpu"""

								snake_case_   : List[str]						=			self.dummy_vqvae
								snake_case_   : Any						=			self.dummy_text_encoder
								snake_case_   : Tuple						=			self.dummy_tokenizer
								snake_case_   : int						=			self.dummy_transformer
								snake_case_   : int						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : Dict						=			LearnedClassifierFreeSamplingEmbeddings(learnable=_lowercase   )

								snake_case_   : Optional[Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : int						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : List[Any]						=			"""teddy bear playing in the pool"""

								snake_case_   : Dict						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : List[Any]						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Optional[int]						=			output.images

								snake_case_   : List[Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Dict						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

								snake_case_   : List[Any]						=			image[0, -3:, -3:, -1]
								snake_case_   : Any						=			image_from_tuple[0, -3:, -3:, -1]

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : Dict						=			np.array([0.6551, 0.6168, 0.5008, 0.5676, 0.5659, 0.4295, 0.6073, 0.5599, 0.4992]   )

								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    UpperCAmelCase__							(					self   ) ->						str:







								'''simple docstring'''

								snake_case_   : int						=			"""cpu"""

								snake_case_   : List[Any]						=			self.dummy_vqvae
								snake_case_   : Optional[int]						=			self.dummy_text_encoder
								snake_case_   : List[Any]						=			self.dummy_tokenizer
								snake_case_   : Union[str, Any]						=			self.dummy_transformer
								snake_case_   : str						=			VQDiffusionScheduler(self.num_embed   )
								snake_case_   : List[Any]						=			LearnedClassifierFreeSamplingEmbeddings(
								    learnable=_lowercase							,    hidden_size=self.text_embedder_hidden_size							,    length=tokenizer.model_max_length   )

								snake_case_   : Union[str, Any]						=			VQDiffusionPipeline(
								    vqvae=_lowercase							,    text_encoder=_lowercase							,    tokenizer=_lowercase							,    transformer=_lowercase							,    scheduler=_lowercase							,    learned_classifier_free_sampling_embeddings=_lowercase							,    )
								snake_case_   : Any						=			pipe.to(_lowercase   )
								pipe.set_progress_bar_config(disable=_lowercase   )

								snake_case_   : Tuple						=			"""teddy bear playing in the pool"""

								snake_case_   : str						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Tuple						=			pipe([prompt]							,    generator=_lowercase							,    num_inference_steps=2							,    output_type="""np"""   )
								snake_case_   : Dict						=			output.images

								snake_case_   : Union[str, Any]						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Any						=			pipe(
								    [prompt]							,    generator=_lowercase							,    output_type="""np"""							,    return_dict=_lowercase							,    num_inference_steps=2   )[0]

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

								assert image.shape == (1, 2_4, 2_4, 3)

								snake_case_   : int						=			np.array([0.6693, 0.6075, 0.4959, 0.5701, 0.5583, 0.4333, 0.6171, 0.5684, 0.4988]   )

								assert np.abs(image_slice.flatten() - expected_slice   ).max() < 2.0
								assert np.abs(image_from_tuple_slice.flatten() - expected_slice   ).max() < 1E-2




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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						List[Any]:







								'''simple docstring'''

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





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







								'''simple docstring'''

								snake_case_   : List[Any]						=			load_numpy(
								    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
								    """/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy"""   )

								snake_case_   : str						=			VQDiffusionPipeline.from_pretrained("""microsoft/vq-diffusion-ithq"""   )
								snake_case_   : Optional[Any]						=			pipeline.to(_lowercase   )
								pipeline.set_progress_bar_config(disable=_lowercase   )

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								snake_case_   : Any						=			torch.Generator(device=_lowercase   ).manual_seed(0   )
								snake_case_   : Optional[int]						=			pipeline(
								    """teddy bear playing in the pool"""							,    num_images_per_prompt=1							,    generator=_lowercase							,    output_type="""np"""							,    )

								snake_case_   : Union[str, Any]						=			output.images[0]

								assert image.shape == (2_5_6, 2_5_6, 3)
								assert np.abs(expected_image - image   ).max() < 2.0

