


'''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 _SCREAMING_SNAKE_CASE      (A  , A  , A  , A       )       ->       Optional[int]:






       """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      (A  , A  , A  , A  , A=True       )       ->       Any:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A  , A=False       )       ->       Optional[int]:






       """simple docstring"""







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

def _SCREAMING_SNAKE_CASE      (A       )       ->       int:






       """simple docstring"""







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

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

def _SCREAMING_SNAKE_CASE      (A       )       ->       Optional[Any]:






       """simple docstring"""







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

              # 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       )
              lowercase__      			= ddp_input[torch.randperm(len(A       )       )]

def _SCREAMING_SNAKE_CASE      (A=False  , A=False       )       ->       Tuple:






       """simple docstring"""







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

              # 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(A       ) - 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       )
              lowercase__      			= ddp_input[torch.randperm(len(A       )       )]
       GradientState._reset_state()

def _SCREAMING_SNAKE_CASE      (A=False  , A=False       )       ->       str:






       """simple docstring"""







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

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

def _SCREAMING_SNAKE_CASE      ()       ->       str:






       """simple docstring"""







       lowercase__      			= Accelerator()

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






       """simple docstring"""







       lowercase__      			= Accelerator()
       lowercase__      			= 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(A       )
       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(A       )
       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(A  , A       )

    # 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(A  , A       )

def _SCREAMING_SNAKE_CASE      (A       )       ->       List[str]:






       """simple docstring"""







       main()


if __name__ == "__main__":
 main()








'''simple docstring'''

import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel

from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class   __lowerCAmelCase						(lowercase_  ,     unittest.TestCase     ):





      '''simple docstring'''






      lowerCAmelCase__					:     Dict  					=							ShapEImgaImgPipeline
      lowerCAmelCase__					:     List[str]  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							["""image"""]
      lowerCAmelCase__					:     Any  					=							[
          """num_images_per_prompt""",
          """num_inference_steps""",
          """generator""",
          """latents""",
          """guidance_scale""",
          """frame_size""",
          """output_type""",
          """return_dict""",
      ]
      lowerCAmelCase__					:     Tuple  					=							False



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             return 32



      @property
      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             return self.time_input_dim * 4



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             return 8



      @property
      def       UpperCamelCase__		(self    :    Any ):

             '''simple docstring'''







             torch.manual_seed(0 )
             lowercase__      			= CLIPVisionConfig(
                 hidden_size=self.text_embedder_hidden_size   ,						image_size=64   ,						projection_dim=self.text_embedder_hidden_size   ,						intermediate_size=37   ,						num_attention_heads=4   ,						num_channels=3   ,						num_hidden_layers=5   ,						patch_size=1   ,						)

             lowercase__      			= CLIPVisionModel(UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    List[Any] ):

             '''simple docstring'''







             lowercase__      			= CLIPImageProcessor(
                 crop_size=224   ,						do_center_crop=UpperCamelCase   ,						do_normalize=UpperCamelCase   ,						do_resize=UpperCamelCase   ,						image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73]   ,						image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11]   ,						resample=3   ,						size=224   ,						)

             return image_processor



      @property
      def       UpperCamelCase__		(self    :    str ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''num_attention_heads''': 2,
                 '''attention_head_dim''': 16,
                 '''embedding_dim''': self.time_input_dim,
                 '''num_embeddings''': 32,
                 '''embedding_proj_dim''': self.text_embedder_hidden_size,
                 '''time_embed_dim''': self.time_embed_dim,
                 '''num_layers''': 1,
                 '''clip_embed_dim''': self.time_input_dim * 2,
                 '''additional_embeddings''': 0,
                 '''time_embed_act_fn''': '''gelu''',
                 '''norm_in_type''': '''layer''',
                 '''embedding_proj_norm_type''': '''layer''',
                 '''encoder_hid_proj_type''': None,
                 '''added_emb_type''': None,
             }

             lowercase__      			= PriorTransformer(**UpperCamelCase )
             return model



      @property
      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             torch.manual_seed(0 )

             lowercase__      			= {
                 '''param_shapes''': (
                     (self.renderer_dim, 93),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                     (self.renderer_dim, 8),
                 ),
                 '''d_latent''': self.time_input_dim,
                 '''d_hidden''': self.renderer_dim,
                 '''n_output''': 12,
                 '''background''': (
                     0.1,
                     0.1,
                     0.1,
                 ),
             }
             lowercase__      			= ShapERenderer(**UpperCamelCase )
             return model



      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= self.dummy_prior
             lowercase__      			= self.dummy_image_encoder
             lowercase__      			= self.dummy_image_processor
             lowercase__      			= self.dummy_renderer

             lowercase__      			= HeunDiscreteScheduler(
                 beta_schedule='''exp'''   ,						num_train_timesteps=1024   ,						prediction_type='''sample'''   ,						use_karras_sigmas=UpperCamelCase   ,						clip_sample=UpperCamelCase   ,						clip_sample_range=1.0   ,						)
             lowercase__      			= {
                 '''prior''': prior,
                 '''image_encoder''': image_encoder,
                 '''image_processor''': image_processor,
                 '''renderer''': renderer,
                 '''scheduler''': scheduler,
             }

             return components



      def       UpperCamelCase__		(self    :    Optional[int]   ,						UpperCamelCase    :    Optional[int]   ,						UpperCamelCase    :    str=0 ):

             '''simple docstring'''







             lowercase__      			= floats_tensor((1, 3, 64, 64)   ,						rng=random.Random(UpperCamelCase ) ).to(UpperCamelCase )

             if str(UpperCamelCase ).startswith('''mps''' ):
                    lowercase__      			= torch.manual_seed(UpperCamelCase )
             else:
                    lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(UpperCamelCase )
             lowercase__      			= {
                 '''image''': input_image,
                 '''generator''': generator,
                 '''num_inference_steps''': 1,
                 '''frame_size''': 32,
                 '''output_type''': '''np''',
             }
             return inputs



      def       UpperCamelCase__		(self    :    Tuple ):

             '''simple docstring'''







             lowercase__      			= '''cpu'''

             lowercase__      			= self.get_dummy_components()

             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )

             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= pipe(**self.get_dummy_inputs(UpperCamelCase ) )
             lowercase__      			= output.images[0]
             lowercase__      			= image[0, -3:, -3:, -1]

             assert image.shape == (20, 32, 32, 3)

             lowercase__      			= np.array(
                 [
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                     0.00_03_92_16,
                 ] )

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



      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







             self._test_inference_batch_consistent(batch_sizes=[1, 2] )



      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= torch_device == '''cpu'''
             lowercase__      			= True
             self._test_inference_batch_single_identical(
                 batch_size=2   ,						test_max_difference=UpperCamelCase   ,						relax_max_difference=UpperCamelCase   ,						)






      def       UpperCamelCase__		(self    :    int ):

             '''simple docstring'''







             lowercase__      			= self.get_dummy_components()
             lowercase__      			= self.pipeline_class(**UpperCamelCase )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= 1
             lowercase__      			= 2

             lowercase__      			= self.get_dummy_inputs(UpperCamelCase )

             for key in inputs.keys():
                    if key in self.batch_params:
                           lowercase__      			= batch_size * [inputs[key]]

             lowercase__      			= pipe(**UpperCamelCase   ,						num_images_per_prompt=UpperCamelCase )[0]

             assert images.shape[0] == batch_size * num_images_per_prompt






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





      '''simple docstring'''






      def       UpperCamelCase__		(self    :    Optional[Any] ):

             '''simple docstring'''







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






      def       UpperCamelCase__		(self    :    Union[str, Any] ):

             '''simple docstring'''







             lowercase__      			= load_image(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''' )
             lowercase__      			= load_numpy(
                 '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
                 '''/shap_e/test_shap_e_img2img_out.npy''' )
             lowercase__      			= ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''' )
             lowercase__      			= pipe.to(UpperCamelCase )
             pipe.set_progress_bar_config(disable=UpperCamelCase )

             lowercase__      			= torch.Generator(device=UpperCamelCase ).manual_seed(0 )

             lowercase__      			= pipe(
                 UpperCamelCase   ,						generator=UpperCamelCase   ,						guidance_scale=3.0   ,						num_inference_steps=64   ,						frame_size=64   ,						output_type='''np'''   ,						).images[0]

             assert images.shape == (20, 64, 64, 3)

             assert_mean_pixel_difference(UpperCamelCase   ,						UpperCamelCase )




