






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


lowercase    				=   False




class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




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




	@property
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								return 12




	@property
	def 				_UpperCamelCase     (			self   )			->       Dict:
								return 12




	@property
	def 				_UpperCamelCase     (			self   )			->       Tuple:
								return 32




	@property
	def 				_UpperCamelCase     (			self   )			->       Union[str, Any]:
								torch.manual_seed(0   )
								snake_case_           =				VQModel(
								    block_out_channels=[32, 64]			,     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   )			->       Dict:
								snake_case_           =				CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'   )
								return tokenizer




	@property
	def 				_UpperCamelCase     (			self   )			->       Tuple:
								torch.manual_seed(0   )
								snake_case_           =				CLIPTextConfig(
								    bos_token_id=0			,     eos_token_id=2			,     hidden_size=self.text_embedder_hidden_size			,     intermediate_size=37			,     layer_norm_eps=1E-05			,     num_attention_heads=4			,     num_hidden_layers=5			,     pad_token_id=1			,     vocab_size=10_00			,     )
								return CLIPTextModel(a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       List[str]:
								torch.manual_seed(0   )

								snake_case_           =				12
								snake_case_           =				12

								snake_case_           =				{
								    'attention_bias': True,
								    'cross_attention_dim': 32,
								    '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': 32,
								    'sample_size': width,
								    'activation_fn': 'geglu-approximate',
								}

								snake_case_           =				TransformeraDModel(**a   )
								return model




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								snake_case_           =				'cpu'

								snake_case_           =				self.dummy_vqvae
								snake_case_           =				self.dummy_text_encoder
								snake_case_           =				self.dummy_tokenizer
								snake_case_           =				self.dummy_transformer
								snake_case_           =				VQDiffusionScheduler(self.num_embed   )
								snake_case_           =				LearnedClassifierFreeSamplingEmbeddings(learnable=a   )

								snake_case_           =				VQDiffusionPipeline(
								    vqvae=a			,     text_encoder=a			,     tokenizer=a			,     transformer=a			,     scheduler=a			,     learned_classifier_free_sampling_embeddings=a			,     )
								snake_case_           =				pipe.to(a   )
								pipe.set_progress_bar_config(disable=a   )

								snake_case_           =				'teddy bear playing in the pool'

								snake_case_           =				torch.Generator(device=a   ).manual_seed(0   )
								snake_case_           =				pipe([prompt]			,     generator=a			,     num_inference_steps=2			,     output_type='np'   )
								snake_case_           =				output.images

								snake_case_           =				torch.Generator(device=a   ).manual_seed(0   )
								snake_case_           =				pipe(
								    [prompt]			,     generator=a			,     output_type='np'			,     return_dict=a			,     num_inference_steps=2   )[0]

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

								assert image.shape == (1, 24, 24, 3)

								snake_case_           =				np.array([0.6_551, 0.6_168, 0.5_008, 0.5_676, 0.5_659, 0.4_295, 0.6_073, 0.5_599, 0.4_992]   )

								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   )			->       int:
								snake_case_           =				'cpu'

								snake_case_           =				self.dummy_vqvae
								snake_case_           =				self.dummy_text_encoder
								snake_case_           =				self.dummy_tokenizer
								snake_case_           =				self.dummy_transformer
								snake_case_           =				VQDiffusionScheduler(self.num_embed   )
								snake_case_           =				LearnedClassifierFreeSamplingEmbeddings(
								    learnable=a			,     hidden_size=self.text_embedder_hidden_size			,     length=tokenizer.model_max_length   )

								snake_case_           =				VQDiffusionPipeline(
								    vqvae=a			,     text_encoder=a			,     tokenizer=a			,     transformer=a			,     scheduler=a			,     learned_classifier_free_sampling_embeddings=a			,     )
								snake_case_           =				pipe.to(a   )
								pipe.set_progress_bar_config(disable=a   )

								snake_case_           =				'teddy bear playing in the pool'

								snake_case_           =				torch.Generator(device=a   ).manual_seed(0   )
								snake_case_           =				pipe([prompt]			,     generator=a			,     num_inference_steps=2			,     output_type='np'   )
								snake_case_           =				output.images

								snake_case_           =				torch.Generator(device=a   ).manual_seed(0   )
								snake_case_           =				pipe(
								    [prompt]			,     generator=a			,     output_type='np'			,     return_dict=a			,     num_inference_steps=2   )[0]

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

								assert image.shape == (1, 24, 24, 3)

								snake_case_           =				np.array([0.6_693, 0.6_075, 0.4_959, 0.5_701, 0.5_583, 0.4_333, 0.6_171, 0.5_684, 0.4_988]   )

								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 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




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






	def 				_UpperCamelCase     (			self   )			->       Dict:
								snake_case_           =				load_numpy(
								    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
								    '/vq_diffusion/teddy_bear_pool_classifier_free_sampling.npy'   )

								snake_case_           =				VQDiffusionPipeline.from_pretrained('microsoft/vq-diffusion-ithq'   )
								snake_case_           =				pipeline.to(a   )
								pipeline.set_progress_bar_config(disable=a   )

								# requires GPU generator for gumbel softmax
								# don't use GPU generator in tests though
								snake_case_           =				torch.Generator(device=a   ).manual_seed(0   )
								snake_case_           =				pipeline(
								    'teddy bear playing in the pool'			,     num_images_per_prompt=1			,     generator=a			,     output_type='np'			,     )

								snake_case_           =				output.images[0]

								assert image.shape == (2_56, 2_56, 3)
								assert np.abs(expected_image - image   ).max() < 2.0













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 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					0
	lowerCAmelCase						=					False
	lowerCAmelCase						=					3.0






class 					UpperCamelCase_ (     unittest.TestCase							):



	'''simple docstring'''




	def 				_UpperCamelCase     (			self   )			->       List[Any]:
								# 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 				_UpperCamelCase     (			self   )			->       str:
								# If no defaults are changed, `to_kwargs` returns an empty dict.
								snake_case_           =				GradScalerKwargs(init_scale=10_24			,     growth_factor=2   )
								AcceleratorState._reset_state()
								snake_case_           =				Accelerator(mixed_precision='fp16'			,     kwargs_handlers=[scaler_handler]   )
								print(accelerator.use_fpaa   )
								snake_case_           =				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			,     20_00   )
								self.assertEqual(scaler._enabled			,     a   )






	@require_multi_gpu
	def 				_UpperCamelCase     (			self   )			->       Optional[Any]:
								snake_case_           =				['torchrun', F'''--nproc_per_node={torch.cuda.device_count()}''', inspect.getfile(self.__class__   )]
								execute_subprocess_async(a			,     env=os.environ.copy()   )


if __name__ == "__main__":
	lowercase    				=   DistributedDataParallelKwargs(bucket_cap_mb=15, find_unused_parameters=True)
	lowercase    				=   Accelerator(kwargs_handlers=[ddp_scaler])
	lowercase    				=   torch.nn.Linear(100, 200)
	lowercase    				=   accelerator.prepare(model)

	# Check the values changed in kwargs
	lowercase    				=   ""
	lowercase    				=   model.bucket_bytes_cap // (1024 * 1024)
	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)





