




"""simple docstring"""






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModelWithProjection,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import (
    DiffusionPipeline,
    UnCLIPImageVariationPipeline,
    UnCLIPScheduler,
    UNetaDConditionModel,
    UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps

from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()






class lowerCAmelCase__	(     UpperCamelCase__,	unittest.TestCase						):






			'''simple docstring'''



			lowerCamelCase__      =			UnCLIPImageVariationPipeline
			lowerCamelCase__      =			IMAGE_VARIATION_PARAMS - {"""height""", """width""", """guidance_scale"""}
			lowerCamelCase__      =			IMAGE_VARIATION_BATCH_PARAMS

			lowerCamelCase__      =			[
			    """generator""",
			    """return_dict""",
			    """decoder_num_inference_steps""",
			    """super_res_num_inference_steps""",
			]
			lowerCamelCase__      =			False
			@property
			def 					A_    (  self  ):
							return 32
			@property
			def 					A_    (  self  ):
							return 32
			@property
			def 					A_    (  self  ):
							return self.time_input_dim
			@property
			def 					A_    (  self  ):
							return self.time_input_dim * 4
			@property
			def 					A_    (  self  ):
							return 100
			@property
			def 					A_    (  self  ):
							_lowerCamelCase     :			Dict    =    CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'  )
							return tokenizer
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			Tuple    =    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(__a  )
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )
							_lowerCamelCase     :			List[Any]    =    CLIPVisionConfig(
							    hidden_size=self.text_embedder_hidden_size  ,      projection_dim=self.text_embedder_hidden_size  ,      num_hidden_layers=5  ,      num_attention_heads=4  ,      image_size=32  ,      intermediate_size=37  ,      patch_size=1  ,      )
							return CLIPVisionModelWithProjection(__a  )
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )

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

							_lowerCamelCase     :			int    =    UnCLIPTextProjModel(**__a  )
							return model
			@property
			def 					A_    (  self  ):
							torch.manual_seed(0  )

							_lowerCamelCase     :			int    =    {
							    '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',
							}

							_lowerCamelCase     :			int    =    UNetaDConditionModel(**__a  )
							return model
			@property
			def 					A_    (  self  ):
							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  ):
							torch.manual_seed(0  )

							_lowerCamelCase     :			List[Any]    =    UNetaDModel(**self.dummy_super_res_kwargs  )
							return model
			@property
			def 					A_    (  self  ):
							# seeded differently to get different unet than `self.dummy_super_res_first`
							torch.manual_seed(1  )

							_lowerCamelCase     :			Optional[int]    =    UNetaDModel(**self.dummy_super_res_kwargs  )
							return model
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    self.dummy_decoder
							_lowerCamelCase     :			Optional[Any]    =    self.dummy_text_proj
							_lowerCamelCase     :			List[str]    =    self.dummy_text_encoder
							_lowerCamelCase     :			int    =    self.dummy_tokenizer
							_lowerCamelCase     :			Union[str, Any]    =    self.dummy_super_res_first
							_lowerCamelCase     :			Any    =    self.dummy_super_res_last

							_lowerCamelCase     :			Dict    =    UnCLIPScheduler(
							    variance_type='learned_range'  ,      prediction_type='epsilon'  ,      num_train_timesteps=1000  ,      )

							_lowerCamelCase     :			Tuple    =    UnCLIPScheduler(
							    variance_type='fixed_small_log'  ,      prediction_type='epsilon'  ,      num_train_timesteps=1000  ,      )

							_lowerCamelCase     :			int    =    CLIPImageProcessor(crop_size=32  ,      size=32  )

							_lowerCamelCase     :			List[Any]    =    self.dummy_image_encoder

							return {
							    "decoder": decoder,
							    "text_encoder": text_encoder,
							    "tokenizer": tokenizer,
							    "text_proj": text_proj,
							    "feature_extractor": feature_extractor,
							    "image_encoder": image_encoder,
							    "super_res_first": super_res_first,
							    "super_res_last": super_res_last,
							    "decoder_scheduler": decoder_scheduler,
							    "super_res_scheduler": super_res_scheduler,
							}
			def 					A_    (  self  ,      lowercase  ,      lowercase=0  ,      lowercase=True  ):
							_lowerCamelCase     :			Tuple    =    floats_tensor((1, 3, 32, 32)  ,      rng=random.Random(__a  )  ).to(__a  )
							if str(__a  ).startswith('mps'  ):
											_lowerCamelCase     :			Dict    =    torch.manual_seed(__a  )
							else:
											_lowerCamelCase     :			str    =    torch.Generator(device=__a  ).manual_seed(__a  )

							if pil_image:
											_lowerCamelCase     :			Optional[Any]    =    input_image * 0.5 + 0.5
											_lowerCamelCase     :			str    =    input_image.clamp(0  ,      1  )
											_lowerCamelCase     :			Union[str, Any]    =    input_image.cpu().permute(0  ,      2  ,      3  ,      1  ).float().numpy()
											_lowerCamelCase     :			Union[str, Any]    =    DiffusionPipeline.numpy_to_pil(__a  )[0]

							return {
							    "image": input_image,
							    "generator": generator,
							    "decoder_num_inference_steps": 2,
							    "super_res_num_inference_steps": 2,
							    "output_type": "np",
							}
			def 					A_    (  self  ):
							_lowerCamelCase     :			int    =    'cpu'

							_lowerCamelCase     :			Optional[int]    =    self.get_dummy_components()

							_lowerCamelCase     :			Union[str, Any]    =    self.pipeline_class(**__a  )
							_lowerCamelCase     :			Union[str, Any]    =    pipe.to(__a  )

							pipe.set_progress_bar_config(disable=__a  )

							_lowerCamelCase     :			List[str]    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )

							_lowerCamelCase     :			Tuple    =    pipe(**__a  )
							_lowerCamelCase     :			Dict    =    output.images

							_lowerCamelCase     :			List[Any]    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )

							_lowerCamelCase     :			str    =    pipe(
							    **__a  ,      return_dict=__a  ,      )[0]

							_lowerCamelCase     :			Optional[int]    =    image[0, -3:, -3:, -1]
							_lowerCamelCase     :			str    =    image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase     :			List[Any]    =    np.array(
							    [
							        0.99_97,
							        0.00_02,
							        0.99_97,
							        0.99_97,
							        0.99_69,
							        0.00_23,
							        0.99_97,
							        0.99_69,
							        0.99_70,
							    ]  )

							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  ):
							_lowerCamelCase     :			Dict    =    'cpu'

							_lowerCamelCase     :			Tuple    =    self.get_dummy_components()

							_lowerCamelCase     :			int    =    self.pipeline_class(**__a  )
							_lowerCamelCase     :			Union[str, Any]    =    pipe.to(__a  )

							pipe.set_progress_bar_config(disable=__a  )

							_lowerCamelCase     :			str    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )

							_lowerCamelCase     :			int    =    pipe(**__a  )
							_lowerCamelCase     :			Union[str, Any]    =    output.images

							_lowerCamelCase     :			int    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )

							_lowerCamelCase     :			Dict    =    pipe(
							    **__a  ,      return_dict=__a  ,      )[0]

							_lowerCamelCase     :			Union[str, Any]    =    image[0, -3:, -3:, -1]
							_lowerCamelCase     :			Optional[int]    =    image_from_tuple[0, -3:, -3:, -1]

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

							_lowerCamelCase     :			Any    =    np.array([0.99_97, 0.00_03, 0.99_97, 0.99_97, 0.99_70, 0.00_24, 0.99_97, 0.99_71, 0.99_71]  )

							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  ):
							_lowerCamelCase     :			Optional[Any]    =    'cpu'

							_lowerCamelCase     :			Dict    =    self.get_dummy_components()

							_lowerCamelCase     :			List[str]    =    self.pipeline_class(**__a  )
							_lowerCamelCase     :			Any    =    pipe.to(__a  )

							pipe.set_progress_bar_config(disable=__a  )

							_lowerCamelCase     :			Union[str, Any]    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )
							_lowerCamelCase     :			Tuple    =    [
							    pipeline_inputs['image'],
							    pipeline_inputs['image'],
							]

							_lowerCamelCase     :			int    =    pipe(**__a  )
							_lowerCamelCase     :			Union[str, Any]    =    output.images

							_lowerCamelCase     :			Any    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )
							_lowerCamelCase     :			List[str]    =    [
							    tuple_pipeline_inputs['image'],
							    tuple_pipeline_inputs['image'],
							]

							_lowerCamelCase     :			List[Any]    =    pipe(
							    **__a  ,      return_dict=__a  ,      )[0]

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

							assert image.shape == (2, 64, 64, 3)

							_lowerCamelCase     :			Dict    =    np.array(
							    [
							        0.99_97,
							        0.99_89,
							        0.00_08,
							        0.00_21,
							        0.99_60,
							        0.00_18,
							        0.00_14,
							        0.00_02,
							        0.99_33,
							    ]  )

							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  ):
							_lowerCamelCase     :			Tuple    =    torch.device('cpu'  )






							class lowerCAmelCase__	:






										'''simple docstring'''



										lowerCamelCase__      =			1

							_lowerCamelCase     :			Dict    =    self.get_dummy_components()

							_lowerCamelCase     :			List[str]    =    self.pipeline_class(**__a  )
							_lowerCamelCase     :			Any    =    pipe.to(__a  )

							pipe.set_progress_bar_config(disable=__a  )

							_lowerCamelCase     :			int    =    torch.Generator(device=__a  ).manual_seed(0  )
							_lowerCamelCase     :			Any    =    pipe.decoder.dtype
							_lowerCamelCase     :			Any    =    1

							_lowerCamelCase     :			Tuple    =    (
							    batch_size,
							    pipe.decoder.config.in_channels,
							    pipe.decoder.config.sample_size,
							    pipe.decoder.config.sample_size,
							)
							_lowerCamelCase     :			str    =    pipe.prepare_latents(
							    __a  ,      dtype=__a  ,      device=__a  ,      generator=__a  ,      latents=__a  ,      scheduler=DummyScheduler()  )

							_lowerCamelCase     :			Tuple    =    (
							    batch_size,
							    pipe.super_res_first.config.in_channels // 2,
							    pipe.super_res_first.config.sample_size,
							    pipe.super_res_first.config.sample_size,
							)
							_lowerCamelCase     :			Union[str, Any]    =    pipe.prepare_latents(
							    __a  ,      dtype=__a  ,      device=__a  ,      generator=__a  ,      latents=__a  ,      scheduler=DummyScheduler()  )

							_lowerCamelCase     :			int    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )

							_lowerCamelCase     :			str    =    pipe(
							    **__a  ,      decoder_latents=__a  ,      super_res_latents=__a  ).images

							_lowerCamelCase     :			Dict    =    self.get_dummy_inputs(__a  ,      pil_image=__a  )
							# Don't pass image, instead pass embedding
							_lowerCamelCase     :			List[Any]    =    pipeline_inputs.pop('image'  )
							_lowerCamelCase     :			Optional[int]    =    pipe.image_encoder(__a  ).image_embeds

							_lowerCamelCase     :			List[str]    =    pipe(
							    **__a  ,      decoder_latents=__a  ,      super_res_latents=__a  ,      image_embeddings=__a  ,      ).images

							# make sure passing text embeddings manually is identical
							assert np.abs(img_out_a - img_out_a  ).max() < 1E-4
			@skip_mps
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    torch_device == 'cpu'

							# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
							_lowerCamelCase     :			Optional[int]    =    1E-2

							self._test_attention_slicing_forward_pass(
							    test_max_difference=__a  ,      expected_max_diff=__a  )
			@skip_mps
			def 					A_    (  self  ):
							_lowerCamelCase     :			Union[str, Any]    =    torch_device == 'cpu'
							_lowerCamelCase     :			Dict    =    True
							_lowerCamelCase     :			int    =    [
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]

							self._test_inference_batch_single_identical(
							    test_max_difference=__a  ,      relax_max_difference=__a  ,      additional_params_copy_to_batched_inputs=__a  ,      )
			def 					A_    (  self  ):
							_lowerCamelCase     :			List[str]    =    [
							    'decoder_num_inference_steps',
							    'super_res_num_inference_steps',
							]

							if torch_device == "mps":
											# TODO: MPS errors with larger batch sizes
											_lowerCamelCase     :			Tuple    =    [2, 3]
											self._test_inference_batch_consistent(
											    batch_sizes=__a  ,      additional_params_copy_to_batched_inputs=__a  ,      )
							else:
											self._test_inference_batch_consistent(
											    additional_params_copy_to_batched_inputs=__a  )
			@skip_mps
			def 					A_    (  self  ):
							return super().test_dict_tuple_outputs_equivalent()
			@skip_mps
			def 					A_    (  self  ):
							return super().test_save_load_local()






			@skip_mps
			def 					A_    (  self  ):
							return super().test_save_load_optional_components()






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






			'''simple docstring'''



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






			def 					A_    (  self  ):
							_lowerCamelCase     :			str    =    load_image(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png'  )
							_lowerCamelCase     :			List[str]    =    load_numpy(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
							    '/unclip/karlo_v1_alpha_cat_variation_fp16.npy'  )

							_lowerCamelCase     :			Tuple    =    UnCLIPImageVariationPipeline.from_pretrained(
							    'kakaobrain/karlo-v1-alpha-image-variations'  ,      torch_dtype=torch.floataa  )
							_lowerCamelCase     :			Any    =    pipeline.to(__a  )
							pipeline.set_progress_bar_config(disable=__a  )

							_lowerCamelCase     :			Optional[int]    =    torch.Generator(device='cpu'  ).manual_seed(0  )
							_lowerCamelCase     :			str    =    pipeline(
							    __a  ,      generator=__a  ,      output_type='np'  ,      )

							_lowerCamelCase     :			Optional[int]    =    output.images[0]

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

							assert_mean_pixel_difference(__a  ,      __a  ,      15  )
'''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_				(					snake_case_       : Dict					,    snake_case_       : Union[str, Any]					,    snake_case_       : Optional[Any]					,    snake_case_       : List[Any]			)    ->      List[Any]:


     '''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_				(					snake_case_       : Any					,    snake_case_       : Tuple					,    snake_case_       : Any					,    snake_case_       : List[Any]					,    snake_case_       : str=True			)    ->      Optional[Any]:


     '''simple docstring'''




     model.train()
     UpperCAmelCase_           =   model(snake_case_			)
     UpperCAmelCase_           =   F.mse_loss(snake_case_					,    target.to(output.device			)			)
     if not do_backward:
          loss /= accelerator.gradient_accumulation_steps
          loss.backward()
     else:
          accelerator.backward(snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]					,    snake_case_       : Any=False			)    ->      Dict:


     '''simple docstring'''




     set_seed(42			)
     UpperCAmelCase_           =   RegressionModel()
     UpperCAmelCase_           =   deepcopy(snake_case_			)
     UpperCAmelCase_           =   RegressionDataset(length=80			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     model.to(accelerator.device			)
     if sched:
          UpperCAmelCase_           =   AdamW(params=model.parameters()					,    lr=1E-3			)
          UpperCAmelCase_           =   AdamW(params=ddp_model.parameters()					,    lr=1E-3			)
          UpperCAmelCase_           =   LambdaLR(snake_case_					,    lr_lambda=lambda snake_case_			: epoch**0.65			)
          UpperCAmelCase_           =   LambdaLR(snake_case_					,    lr_lambda=lambda snake_case_			: epoch**0.65			)
     # Make a copy of `model`
     if sched:
          UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_    , UpperCAmelCase_           =   accelerator.prepare(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
     else:
          UpperCAmelCase_    , UpperCAmelCase_           =   accelerator.prepare(snake_case_					,    snake_case_			)
     if sched:
          return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
     return model, ddp_model, dataloader





def 	lowerCAmelCase_				(					snake_case_       : Any			)    ->      int:


     '''simple docstring'''




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

          # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
          check_model_parameters(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
          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(13_37 + iteration			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]





def 	lowerCAmelCase_				(					snake_case_       : Tuple			)    ->      str:


     '''simple docstring'''




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

          # 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(13_37 + iteration			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]





def 	lowerCAmelCase_				(					snake_case_       : Optional[int]=False					,    snake_case_       : str=False			)    ->      List[str]:


     '''simple docstring'''




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

          # 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(snake_case_			) - 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(13_37 + iteration			)
          UpperCAmelCase_           =   ddp_input[torch.randperm(len(snake_case_			)			)]
     GradientState._reset_state()





def 	lowerCAmelCase_				(					snake_case_       : Optional[Any]=False					,    snake_case_       : Tuple=False			)    ->      Union[str, Any]:


     '''simple docstring'''




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

          if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(snake_case_			)):
               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(snake_case_			):
               step_model(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
               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"""
          UpperCAmelCase_           =   (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(snake_case_			))
          if accelerator.num_processes > 1:
               check_model_parameters(snake_case_					,    snake_case_					,    snake_case_					,    snake_case_			)
          # Shuffle ddp_input on each iteration
          torch.manual_seed(13_37 + iteration			)
     GradientState._reset_state()





def 	lowerCAmelCase_				(					)    ->      List[Any]:


     '''simple docstring'''




     UpperCAmelCase_           =   Accelerator()

     UpperCAmelCase_           =   RegressionDataset(length=80			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     UpperCAmelCase_           =   RegressionDataset(length=96			)
     UpperCAmelCase_           =   DataLoader(snake_case_					,    batch_size=16			)
     UpperCAmelCase_    , UpperCAmelCase_           =   accelerator.prepare(snake_case_					,    snake_case_			)
     assert accelerator.gradient_state.active_dataloader is None
     for iteration, _ in enumerate(snake_case_			):
          assert id(accelerator.gradient_state.active_dataloader			) == id(snake_case_			)
          if iteration < len(snake_case_			) - 1:
               assert not accelerator.gradient_state.end_of_dataloader
               if iteration == 1:
                    for batch_num, _ in enumerate(snake_case_			):
                         assert id(accelerator.gradient_state.active_dataloader			) == id(snake_case_			)
                         if batch_num < len(snake_case_			) - 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_				(					)    ->      str:


     '''simple docstring'''




     UpperCAmelCase_           =   Accelerator()
     UpperCAmelCase_           =   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(snake_case_			)
     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(snake_case_			)
     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(snake_case_					,    snake_case_			)

    # 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(snake_case_					,    snake_case_			)





def 	lowerCAmelCase_				(					snake_case_       : Dict			)    ->      int:


     '''simple docstring'''




     main()


if __name__ == "__main__":
    main()




