






import gc
import unittest

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

from diffusers import PriorTransformer, UnCLIPPipeline, UnCLIPScheduler, UNetaDConditionModel, UNetaDModel
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import load_numpy, nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()



class 			a_					(    lowerCAmelCase_     ,						unittest.TestCase    ):
	A						       =   UnCLIPPipeline
	A						       =   TEXT_TO_IMAGE_PARAMS - {
	    """negative_prompt""",
	    """height""",
	    """width""",
	    """negative_prompt_embeds""",
	    """guidance_scale""",
	    """prompt_embeds""",
	    """cross_attention_kwargs""",
	}
	A						       =   TEXT_TO_IMAGE_BATCH_PARAMS
	A						       =   [
	    """generator""",
	    """return_dict""",
	    """prior_num_inference_steps""",
	    """decoder_num_inference_steps""",
	    """super_res_num_inference_steps""",
	]
	A						       =   False





	@property
	def 	A_(       self			)     ->				str:




				"""simple docstring"""



				return 32





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




				"""simple docstring"""



				return 32





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




				"""simple docstring"""



				return self.time_input_dim





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




				"""simple docstring"""



				return self.time_input_dim * 4





	@property
	def 	A_(       self			)     ->				List[str]:




				"""simple docstring"""



				return 100





	@property
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'			)
				return tokenizer





	@property
	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	CLIPTextConfig(
				    bos_token_id=0			,    eos_token_id=2			,    hidden_size=self.text_embedder_hidden_size			,    projection_dim=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=1000			,    )
				return CLIPTextModelWithProjection(__SCREAMING_SNAKE_CASE			)





	@property
	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				torch.manual_seed(0			)

				SCREAMING_SNAKE_CASE_			=	{
				    'num_attention_heads': 2,
				    'attention_head_dim': 12,
				    'embedding_dim': self.text_embedder_hidden_size,
				    'num_layers': 1,
				}

				SCREAMING_SNAKE_CASE_			=	PriorTransformer(**__SCREAMING_SNAKE_CASE			)
				return model





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




				"""simple docstring"""



				torch.manual_seed(0			)

				SCREAMING_SNAKE_CASE_			=	{
				    'clip_embeddings_dim': self.text_embedder_hidden_size,
				    'time_embed_dim': self.time_embed_dim,
				    'cross_attention_dim': self.cross_attention_dim,
				}

				SCREAMING_SNAKE_CASE_			=	UnCLIPTextProjModel(**__SCREAMING_SNAKE_CASE			)
				return model





	@property
	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				torch.manual_seed(0			)

				SCREAMING_SNAKE_CASE_			=	{
				    'sample_size': 32,
				    # RGB in channels
				    'in_channels': 3,
				    # Out channels is double in channels because predicts mean and variance
				    'out_channels': 6,
				    '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,
				    'cross_attention_dim': self.cross_attention_dim,
				    'attention_head_dim': 4,
				    'resnet_time_scale_shift': 'scale_shift',
				    'class_embed_type': 'identity',
				}

				SCREAMING_SNAKE_CASE_			=	UNetaDConditionModel(**__SCREAMING_SNAKE_CASE			)
				return model





	@property
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				return {
				    "sample_size": 64,
				    "layers_per_block": 1,
				    "down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
				    "up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
				    "block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
				    "in_channels": 6,
				    "out_channels": 3,
				}





	@property
	def 	A_(       self			)     ->				Optional[int]:




				"""simple docstring"""



				torch.manual_seed(0			)

				SCREAMING_SNAKE_CASE_			=	UNetaDModel(**self.dummy_super_res_kwargs			)
				return model





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




				"""simple docstring"""



				torch.manual_seed(1			)

				SCREAMING_SNAKE_CASE_			=	UNetaDModel(**self.dummy_super_res_kwargs			)
				return model





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	self.dummy_prior
				SCREAMING_SNAKE_CASE_			=	self.dummy_decoder
				SCREAMING_SNAKE_CASE_			=	self.dummy_text_proj
				SCREAMING_SNAKE_CASE_			=	self.dummy_text_encoder
				SCREAMING_SNAKE_CASE_			=	self.dummy_tokenizer
				SCREAMING_SNAKE_CASE_			=	self.dummy_super_res_first
				SCREAMING_SNAKE_CASE_			=	self.dummy_super_res_last

				SCREAMING_SNAKE_CASE_			=	UnCLIPScheduler(
				    variance_type='fixed_small_log'			,    prediction_type='sample'			,    num_train_timesteps=1000			,    clip_sample_range=5.0			,    )

				SCREAMING_SNAKE_CASE_			=	UnCLIPScheduler(
				    variance_type='learned_range'			,    prediction_type='epsilon'			,    num_train_timesteps=1000			,    )

				SCREAMING_SNAKE_CASE_			=	UnCLIPScheduler(
				    variance_type='fixed_small_log'			,    prediction_type='epsilon'			,    num_train_timesteps=1000			,    )

				SCREAMING_SNAKE_CASE_			=	{
				    'prior': prior,
				    'decoder': decoder,
				    'text_proj': text_proj,
				    'text_encoder': text_encoder,
				    'tokenizer': tokenizer,
				    'super_res_first': super_res_first,
				    'super_res_last': super_res_last,
				    'prior_scheduler': prior_scheduler,
				    'decoder_scheduler': decoder_scheduler,
				    'super_res_scheduler': super_res_scheduler,
				}

				return components





	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE=0			)     ->				Union[str, Any]:




				"""simple docstring"""



				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_			=	{
				    'prompt': 'horse',
				    'generator': generator,
				    'prior_num_inference_steps': 2,
				    'decoder_num_inference_steps': 2,
				    'super_res_num_inference_steps': 2,
				    'output_type': 'numpy',
				}
				return inputs





	def 	A_(       self			)     ->				str:




				"""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(
				    [
				        0.9_9_9_7,
				        0.9_9_8_8,
				        0.0_0_2_8,
				        0.9_9_9_7,
				        0.9_9_8_4,
				        0.9_9_6_5,
				        0.0_0_2_9,
				        0.9_9_8_6,
				        0.0_0_2_5,
				    ]			)

				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[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.device('cpu'			)



				class 			a_					:
					A						       =   1




				SCREAMING_SNAKE_CASE_			=	self.get_dummy_components()

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

				SCREAMING_SNAKE_CASE_			=	components['prior']
				SCREAMING_SNAKE_CASE_			=	components['decoder']
				SCREAMING_SNAKE_CASE_			=	components['super_res_first']
				SCREAMING_SNAKE_CASE_			=	components['tokenizer']
				SCREAMING_SNAKE_CASE_			=	components['text_encoder']

				SCREAMING_SNAKE_CASE_			=	torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	prior.dtype
				SCREAMING_SNAKE_CASE_			=	1

				SCREAMING_SNAKE_CASE_			=	(batch_size, prior.config.embedding_dim)
				SCREAMING_SNAKE_CASE_			=	pipe.prepare_latents(
				    __SCREAMING_SNAKE_CASE			,    dtype=__SCREAMING_SNAKE_CASE			,    device=__SCREAMING_SNAKE_CASE			,    generator=__SCREAMING_SNAKE_CASE			,    latents=__SCREAMING_SNAKE_CASE			,    scheduler=DummyScheduler()			)
				SCREAMING_SNAKE_CASE_			=	(batch_size, decoder.config.in_channels, decoder.config.sample_size, decoder.config.sample_size)
				SCREAMING_SNAKE_CASE_			=	pipe.prepare_latents(
				    __SCREAMING_SNAKE_CASE			,    dtype=__SCREAMING_SNAKE_CASE			,    device=__SCREAMING_SNAKE_CASE			,    generator=__SCREAMING_SNAKE_CASE			,    latents=__SCREAMING_SNAKE_CASE			,    scheduler=DummyScheduler()			)

				SCREAMING_SNAKE_CASE_			=	(
				    batch_size,
				    super_res_first.config.in_channels // 2,
				    super_res_first.config.sample_size,
				    super_res_first.config.sample_size,
				)
				SCREAMING_SNAKE_CASE_			=	pipe.prepare_latents(
				    __SCREAMING_SNAKE_CASE			,    dtype=__SCREAMING_SNAKE_CASE			,    device=__SCREAMING_SNAKE_CASE			,    generator=__SCREAMING_SNAKE_CASE			,    latents=__SCREAMING_SNAKE_CASE			,    scheduler=DummyScheduler()			)

				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	'this is a prompt example'

				SCREAMING_SNAKE_CASE_			=	torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	pipe(
				    [prompt]			,    generator=__SCREAMING_SNAKE_CASE			,    prior_num_inference_steps=2			,    decoder_num_inference_steps=2			,    super_res_num_inference_steps=2			,    prior_latents=__SCREAMING_SNAKE_CASE			,    decoder_latents=__SCREAMING_SNAKE_CASE			,    super_res_latents=__SCREAMING_SNAKE_CASE			,    output_type='np'			,    )
				SCREAMING_SNAKE_CASE_			=	output.images

				SCREAMING_SNAKE_CASE_			=	tokenizer(
				    __SCREAMING_SNAKE_CASE			,    padding='max_length'			,    max_length=tokenizer.model_max_length			,    return_tensors='pt'			,    )
				SCREAMING_SNAKE_CASE_			=	text_encoder(text_inputs.input_ids			)
				SCREAMING_SNAKE_CASE_			=	text_inputs.attention_mask

				SCREAMING_SNAKE_CASE_			=	torch.Generator(device=__SCREAMING_SNAKE_CASE			).manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	pipe(
				    generator=__SCREAMING_SNAKE_CASE			,    prior_num_inference_steps=2			,    decoder_num_inference_steps=2			,    super_res_num_inference_steps=2			,    prior_latents=__SCREAMING_SNAKE_CASE			,    decoder_latents=__SCREAMING_SNAKE_CASE			,    super_res_latents=__SCREAMING_SNAKE_CASE			,    text_model_output=__SCREAMING_SNAKE_CASE			,    text_attention_mask=__SCREAMING_SNAKE_CASE			,    output_type='np'			,    )[0]

				# make sure passing text embeddings manually is identical
				assert np.abs(image - image_from_text			).max() < 1e-4





	@skip_mps
	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch_device == 'cpu'

				self._test_attention_slicing_forward_pass(test_max_difference=__SCREAMING_SNAKE_CASE			,    expected_max_diff=0.0_1			)





	@skip_mps
	def 	A_(       self			)     ->				List[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch_device == 'cpu'
				SCREAMING_SNAKE_CASE_			=	True
				SCREAMING_SNAKE_CASE_			=	[
				    'prior_num_inference_steps',
				    'decoder_num_inference_steps',
				    'super_res_num_inference_steps',
				]

				self._test_inference_batch_single_identical(
				    test_max_difference=__SCREAMING_SNAKE_CASE			,    relax_max_difference=__SCREAMING_SNAKE_CASE			,    additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			,    )





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	[
				    'prior_num_inference_steps',
				    'decoder_num_inference_steps',
				    'super_res_num_inference_steps',
				]

				if torch_device == "mps":
							# TODO: MPS errors with larger batch sizes
							SCREAMING_SNAKE_CASE_			=	[2, 3]
							self._test_inference_batch_consistent(
							    batch_sizes=__SCREAMING_SNAKE_CASE			,    additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			,    )
				else:
							self._test_inference_batch_consistent(
							    additional_params_copy_to_batched_inputs=__SCREAMING_SNAKE_CASE			)





	@skip_mps
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				return super().test_dict_tuple_outputs_equivalent()





	@skip_mps
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				return super().test_save_load_local()





	@skip_mps
	def 	A_(       self			)     ->				List[Any]:




				"""simple docstring"""



				return super().test_save_load_optional_components()




@nightly
class 			a_					(    unittest.TestCase    ):





	def 	A_(       self			)     ->				str:




				"""simple docstring"""



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





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/unclip/karlo_v1_alpha_horse_cpu.npy'			)

				SCREAMING_SNAKE_CASE_			=	UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'			)
				pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	torch.manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	pipeline(
				    'horse'			,    num_images_per_prompt=1			,    generator=__SCREAMING_SNAKE_CASE			,    output_type='np'			,    )

				SCREAMING_SNAKE_CASE_			=	output.images[0]

				assert image.shape == (256, 256, 3)
				assert np.abs(expected_image - image			).max() < 1e-1




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





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




				"""simple docstring"""



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





	def 	A_(       self			)     ->				Any:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	load_numpy(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/unclip/karlo_v1_alpha_horse_fp16.npy'			)

				SCREAMING_SNAKE_CASE_			=	UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'			,    torch_dtype=torch.floataa			)
				SCREAMING_SNAKE_CASE_			=	pipeline.to(__SCREAMING_SNAKE_CASE			)
				pipeline.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)

				SCREAMING_SNAKE_CASE_			=	torch.Generator(device='cpu'			).manual_seed(0			)
				SCREAMING_SNAKE_CASE_			=	pipeline(
				    'horse'			,    generator=__SCREAMING_SNAKE_CASE			,    output_type='np'			,    )

				SCREAMING_SNAKE_CASE_			=	output.images[0]

				assert image.shape == (256, 256, 3)

				assert_mean_pixel_difference(__SCREAMING_SNAKE_CASE			,    __SCREAMING_SNAKE_CASE			)





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




				"""simple docstring"""



				torch.cuda.empty_cache()
				torch.cuda.reset_max_memory_allocated()
				torch.cuda.reset_peak_memory_stats()

				SCREAMING_SNAKE_CASE_			=	UnCLIPPipeline.from_pretrained('kakaobrain/karlo-v1-alpha'			,    torch_dtype=torch.floataa			)
				SCREAMING_SNAKE_CASE_			=	pipe.to(__SCREAMING_SNAKE_CASE			)
				pipe.set_progress_bar_config(disable=__SCREAMING_SNAKE_CASE			)
				pipe.enable_attention_slicing()
				pipe.enable_sequential_cpu_offload()

				SCREAMING_SNAKE_CASE_			=	pipe(
				    'horse'			,    num_images_per_prompt=1			,    prior_num_inference_steps=2			,    decoder_num_inference_steps=2			,    super_res_num_inference_steps=2			,    output_type='np'			,    )

				SCREAMING_SNAKE_CASE_			=	torch.cuda.max_memory_allocated()
				# make sure that less than 7 GB is allocated
				assert mem_bytes < 7 * 10**9






import os
import pickle
import unittest
from collections import UserDict, namedtuple

import torch

from accelerate.test_utils.testing import require_cuda, require_torch_min_version
from accelerate.test_utils.training import RegressionModel
from accelerate.utils import (
    convert_outputs_to_fpaa,
    extract_model_from_parallel,
    find_device,
    listify,
    patch_environment,
    recursively_apply,
    send_to_device,
)


SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					namedtuple("ExampleNamedTuple", "a b c")



class 			a_					(    unittest.TestCase    ):





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.randn(5			,    2			)
				SCREAMING_SNAKE_CASE_			=	torch.device('cuda'			) if torch.cuda.is_available() else torch.device('cpu'			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.cpu()			,    SCREAMING_SNAKE_CASE			)			)

				SCREAMING_SNAKE_CASE_			=	send_to_device((tensor, [tensor, tensor], 1)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta[1]			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta[1][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta[1][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta[2]			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device({'a': tensor, 'b': [tensor, tensor], 'c': 1}			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta['c']			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(ExampleNamedTuple(a=SCREAMING_SNAKE_CASE			,    b=[tensor, tensor]			,    c=1			)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.a.cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta.b			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta.b[0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta.b[1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta.c			,    1			)

				SCREAMING_SNAKE_CASE_			=	send_to_device(UserDict({'a': tensor, 'b': [tensor, tensor], 'c': 1}			)			,    SCREAMING_SNAKE_CASE			)
				self.assertIsInstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['a'].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertIsInstance(resulta['b']			,    SCREAMING_SNAKE_CASE			)
				self.assertTrue(torch.equal(resulta['b'][0].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertTrue(torch.equal(resulta['b'][1].cpu()			,    SCREAMING_SNAKE_CASE			)			)
				self.assertEqual(resulta['c']			,    1			)





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				with self.assertRaises(SCREAMING_SNAKE_CASE			) as cm:
							SCREAMING_SNAKE_CASE_			=	recursively_apply(torch.tensor			,    (torch.tensor(1			), 1)			,    error_on_other_type=SCREAMING_SNAKE_CASE			)
				self.assertEqual(
				    str(cm.exception			)			,    'Unsupported types (<class \'int\'>) passed to `tensor`. Only nested list/tuple/dicts of objects that are valid for `is_torch_tensor` should be passed.'			,    )





	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	torch.tensor([1, 2, 3, 4, 5]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [1, 2, 3, 4, 5]			)

				SCREAMING_SNAKE_CASE_			=	torch.tensor([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)
				self.assertEqual(listify(SCREAMING_SNAKE_CASE			)			,    [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]			)

				SCREAMING_SNAKE_CASE_			=	torch.tensor([[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)
				self.assertEqual(
				    listify(SCREAMING_SNAKE_CASE			)			,    [[[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]], [[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]]]			)





	def 	A_(       self			)     ->				Tuple:




				"""simple docstring"""



				with patch_environment(aa=1			,    BB=2			):
							self.assertEqual(os.environ.get('AA'			)			,    '1'			)
							self.assertEqual(os.environ.get('BB'			)			,    '2'			)

				self.assertNotIn('AA'			,    os.environ			)
				self.assertNotIn('BB'			,    os.environ			)





	def 	A_(       self			)     ->				Dict:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			,    keep_fpaa_wrapper=SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	pickle.dumps(SCREAMING_SNAKE_CASE			)





	@require_cuda
	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	model.forward
				SCREAMING_SNAKE_CASE_			=	torch.cuda.amp.autocast(dtype=torch.floataa			)(model.forward			)
				SCREAMING_SNAKE_CASE_			=	convert_outputs_to_fpaa(model.forward			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(model.forward			,    backend='inductor'			)
				SCREAMING_SNAKE_CASE_			=	torch.randn(4			,    10			).cuda()
				SCREAMING_SNAKE_CASE_			=	model(SCREAMING_SNAKE_CASE			)





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




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)

				self.assertEqual(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			)





	@require_torch_min_version(version='2.0'			)
	def 	A_(       self			)     ->				int:




				"""simple docstring"""



				SCREAMING_SNAKE_CASE_			=	RegressionModel()
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)

				# could also do a test with DistributedDataParallel, but difficult to run on CPU or single GPU
				SCREAMING_SNAKE_CASE_			=	torch.nn.parallel.DataParallel(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	torch.compile(SCREAMING_SNAKE_CASE			)
				SCREAMING_SNAKE_CASE_			=	extract_model_from_parallel(SCREAMING_SNAKE_CASE			)

				self.assertEqual(compiled_model._orig_mod			,    compiled_model_unwrapped._orig_mod			)





	def 	A_(       self			)     ->				Optional[Any]:




				"""simple docstring"""



				self.assertEqual(find_device([1, 'a', torch.tensor([1, 2, 3]			)]			)			,    torch.device('cpu'			)			)
				self.assertEqual(find_device({'a': 1, 'b': torch.tensor([1, 2, 3]			)}			)			,    torch.device('cpu'			)			)
				self.assertIsNone(find_device([1, 'a']			)			)
