




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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)   ->       Union[str, 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 SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_=True						)   ->       str:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_=False						)   ->       int:



       """simple docstring"""

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







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

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

              # Since `no_sync` is a noop, `ddp_model` and `model` grads should always be in sync
              check_model_parameters(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              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_337 + iteration						)
              A__       =    ddp_input[torch.randperm(len(lowercase_						)						)]







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Optional[Any]:



       """simple docstring"""

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

              # 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_337 + iteration						)
              A__       =    ddp_input[torch.randperm(len(lowercase_						)						)]







def SCREAMING_SNAKE_CASE				(       lowercase_=False ,				lowercase_=False						)   ->       Dict:



       """simple docstring"""

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

              # 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(lowercase_						) - 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_337 + iteration						)
              A__       =    ddp_input[torch.randperm(len(lowercase_						)						)]
       GradientState._reset_state()







def SCREAMING_SNAKE_CASE				(       lowercase_=False ,				lowercase_=False						)   ->       int:



       """simple docstring"""

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

              if ((iteration + 1) % 2 == 0) or ((iteration + 1) == len(lowercase_						)):
                     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(lowercase_						):
                     step_model(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
                     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"""
              A__       =    (((iteration + 1) % 2) == 0) or ((iteration + 1) == len(lowercase_						))
              if accelerator.num_processes > 1:
                     check_model_parameters(lowercase_ ,				lowercase_ ,				lowercase_ ,				lowercase_						)
              # Shuffle ddp_input on each iteration
              torch.manual_seed(1_337 + iteration						)
       GradientState._reset_state()







def SCREAMING_SNAKE_CASE				(       )   ->       Optional[Any]:



       """simple docstring"""

       A__       =    Accelerator()

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



       """simple docstring"""

       A__       =    Accelerator()
       A__       =    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(lowercase_						)
       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(lowercase_						)
       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(lowercase_ ,				lowercase_						)

    # 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(lowercase_ ,				lowercase_						)







def SCREAMING_SNAKE_CASE				(       lowercase_						)   ->       Tuple:



       """simple docstring"""

       main()


if __name__ == "__main__":
  main()










"""simple docstring"""




import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device


_snake_case          =   False






class        UpperCamelCase			(	unittest.TestCase      ):
 pass







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


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


 def _lowercase	(     self		:						Tuple							)					->       List[Any]:
   _a  :       Tuple             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       List[str]             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)

   _a  :       Optional[Any]             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   with tempfile.TemporaryDirectory() as tmpdirname:
     pipe.save_pretrained(UpperCAmelCase__							)
     _a  :       Dict             =	VersatileDiffusionPipeline.from_pretrained(UpperCAmelCase__							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       Optional[Any]             =	generator.manual_seed(0							)
   _a  :       str             =	pipe.dual_guided(
       prompt="""first prompt"""							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=2							,						output_type="""numpy"""							,						).images

   assert np.abs(image - new_image							).sum() < 1E-5, "Models don't have the same forward pass"







 def _lowercase	(     self		:						Optional[int]							)					->       Optional[int]:
   _a  :       Optional[int]             =	VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion"""							,						torch_dtype=torch.floataa							)
   pipe.to(UpperCAmelCase__							)
   pipe.set_progress_bar_config(disable=UpperCAmelCase__							)

   _a  :       int             =	"""cyberpunk 2077"""
   _a  :       Tuple             =	load_image(
       """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg"""							)
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Any             =	pipe.dual_guided(
       prompt=UpperCAmelCase__							,						image=UpperCAmelCase__							,						text_to_image_strength=0.7_5							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							,						).images

   _a  :       List[str]             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[int]             =	np.array([0.1_4_4_8, 0.1_6_1_9, 0.1_7_4_1, 0.1_0_8_6, 0.1_1_4_7, 0.1_1_2_8, 0.1_1_9_9, 0.1_1_6_5, 0.1_0_0_1]							)

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

   _a  :       int             =	"""A painting of a squirrel eating a burger """
   _a  :       Tuple             =	torch.manual_seed(0							)
   _a  :       Union[str, Any]             =	pipe.text_to_image(
       prompt=UpperCAmelCase__							,						generator=UpperCAmelCase__							,						guidance_scale=7.5							,						num_inference_steps=50							,						output_type="""numpy"""							).images

   _a  :       int             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       int             =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8]							)

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

   _a  :       str             =	pipe.image_variation(UpperCAmelCase__							,						generator=UpperCAmelCase__							,						output_type="""numpy"""							).images

   _a  :       str             =	image[0, 253:256, 253:256, -1]

   assert image.shape == (1, 512, 512, 3)
   _a  :       Optional[Any]             =	np.array([0.3_0_7_6, 0.3_1_2_3, 0.3_2_8_4, 0.3_7_8_2, 0.3_7_7_0, 0.3_8_9_4, 0.4_2_9_7, 0.4_3_3_1, 0.4_4_5_6]							)

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

