



"""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  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase							)       ->  str:







  '''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  (__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase=True							)       ->  Any:







  '''simple docstring'''


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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase       ,     __lowerCAmelCase=False							)       ->  Optional[Any]:







  '''simple docstring'''


  set_seed(42							)
  lowercase_	    =       RegressionModel()
  lowercase_	    =       deepcopy(__lowerCAmelCase							)
  lowercase_	    =       RegressionDataset(length=80							)
  lowercase_	    =       DataLoader(__lowerCAmelCase       ,     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(__lowerCAmelCase       ,     lr_lambda=lambda __lowerCAmelCase							: epoch**0.65							)
    lowercase_	    =       LambdaLR(__lowerCAmelCase       ,     lr_lambda=lambda __lowerCAmelCase							: epoch**0.65							)
  # Make a copy of `model`
  if sched:
    lowercase_	    =       accelerator.prepare(__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase							)
  else:
    lowercase_	    =       accelerator.prepare(__lowerCAmelCase       ,     __lowerCAmelCase							)
  if sched:
    return (model, opt, sched, dataloader, ddp_model, ddp_opt, ddp_sched)
  return model, ddp_model, dataloader


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  int:







  '''simple docstring'''


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

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


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  Union[str, Any]:







  '''simple docstring'''


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

    # 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							)
    lowercase_	    =       ddp_input[torch.randperm(len(__lowerCAmelCase							)							)]


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







  '''simple docstring'''


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

    # 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(__lowerCAmelCase							) - 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							)
    lowercase_	    =       ddp_input[torch.randperm(len(__lowerCAmelCase							)							)]
  GradientState._reset_state()


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase=False       ,     __lowerCAmelCase=False							)       ->  int:







  '''simple docstring'''


  lowercase_	    =       Accelerator(
      split_batches=__lowerCAmelCase       ,     dispatch_batches=__lowerCAmelCase       ,     gradient_accumulation_steps=2							)
  # Test that context manager behaves properly
  lowercase_	    =       get_training_setup(__lowerCAmelCase       ,     __lowerCAmelCase							)
  for iteration, batch in enumerate(__lowerCAmelCase							):
    lowercase_	    =       batch.values()
    # Gather the distributed inputs and targs for the base model
    lowercase_	    =       accelerator.gather((ddp_input, ddp_target)							)
    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(__lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase       ,     __lowerCAmelCase							)
    opt.step()

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


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







  '''simple docstring'''


  lowercase_	    =       Accelerator()

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

        # 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(__lowerCAmelCase       ,     __lowerCAmelCase							)


def 	_SCREAMING_SNAKE_CASE  (__lowerCAmelCase							)       ->  int:







  '''simple docstring'''


  main()


if __name__ == "__main__":
       main()


'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
  import PIL


__a: List[str]          =   logging.get_logger(__name__)



class 			UpperCAmelCase  (						a__					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    ["pixel_values"]

       def __init__(						self	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = PILImageResampling.BICUBIC	,   __lowerCAmelCase = True	,   __lowerCAmelCase = 1 / 255	,   __lowerCAmelCase = True	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = True	,   **__lowerCAmelCase	,   )  ->  None:
        super().__init__(**__lowerCAmelCase )
        lowercase__				:    Optional[int]  =					size if size is not None else {'''height''': 384, '''width''': 384}
        lowercase__				:    Optional[Any]  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )

        lowercase__				:    Dict  =					do_resize
        lowercase__				:    int  =					size
        lowercase__				:    int  =					resample
        lowercase__				:    Tuple  =					do_rescale
        lowercase__				:    int  =					rescale_factor
        lowercase__				:    int  =					do_normalize
        lowercase__				:    Optional[int]  =					image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowercase__				:    Optional[int]  =					image_std if image_std is not None else OPENAI_CLIP_STD
        lowercase__				:    Tuple  =					do_convert_rgb

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = PILImageResampling.BICUBIC	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  np.ndarray:
        lowercase__				:    Union[str, Any]  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )
        if "height" not in size or "width" not in size:
         raise ValueError(F"""The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}""" )
        lowercase__				:    Any  =					(size['''height'''], size['''width'''])
        return resize(__lowerCAmelCase	,   size=__lowerCAmelCase	,   resample=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  str:
        return rescale(__lowerCAmelCase	,   scale=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   **__lowerCAmelCase	,   )  ->  np.ndarray:
        return normalize(__lowerCAmelCase	,   mean=__lowerCAmelCase	,   std=__lowerCAmelCase	,   data_format=__lowerCAmelCase	,   **__lowerCAmelCase )


       def _lowerCAmelCase(						self	,   __lowerCAmelCase	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = None	,   __lowerCAmelCase = ChannelDimension.FIRST	,   **__lowerCAmelCase	,   )  ->  PIL.Image.Image:
        lowercase__				:    Union[str, Any]  =					do_resize if do_resize is not None else self.do_resize
        lowercase__				:    Any  =					resample if resample is not None else self.resample
        lowercase__				:    int  =					do_rescale if do_rescale is not None else self.do_rescale
        lowercase__				:    List[Any]  =					rescale_factor if rescale_factor is not None else self.rescale_factor
        lowercase__				:    List[Any]  =					do_normalize if do_normalize is not None else self.do_normalize
        lowercase__				:    Dict  =					image_mean if image_mean is not None else self.image_mean
        lowercase__				:    Dict  =					image_std if image_std is not None else self.image_std
        lowercase__				:    Dict  =					do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowercase__				:    Optional[int]  =					size if size is not None else self.size
        lowercase__				:    int  =					get_size_dict(__lowerCAmelCase	,   default_to_square=__lowerCAmelCase )

        lowercase__				:    str  =					make_list_of_images(__lowerCAmelCase )

        if not valid_images(__lowerCAmelCase ):
         raise ValueError(
             '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
             '''torch.Tensor, tf.Tensor or jax.ndarray.''' )

        if do_resize and size is None or resample is None:
         raise ValueError('''Size and resample must be specified if do_resize is True.''' )

        if do_rescale and rescale_factor is None:
         raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )

        if do_normalize and (image_mean is None or image_std is None):
         raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
         lowercase__				:    Optional[Any]  =					[convert_to_rgb(__lowerCAmelCase ) for image in images]

        # All transformations expect numpy arrays.
        lowercase__				:    Any  =					[to_numpy_array(__lowerCAmelCase ) for image in images]

        if do_resize:
         lowercase__				:    Tuple  =					[self.resize(image=__lowerCAmelCase	,   size=__lowerCAmelCase	,   resample=__lowerCAmelCase ) for image in images]

        if do_rescale:
         lowercase__				:    List[str]  =					[self.rescale(image=__lowerCAmelCase	,   scale=__lowerCAmelCase ) for image in images]

        if do_normalize:
         lowercase__				:    Tuple  =					[self.normalize(image=__lowerCAmelCase	,   mean=__lowerCAmelCase	,   std=__lowerCAmelCase ) for image in images]

        lowercase__				:    List[str]  =					[to_channel_dimension_format(__lowerCAmelCase	,   __lowerCAmelCase ) for image in images]

        lowercase__				:    Optional[Any]  =					BatchFeature(data={'''pixel_values''': images}	,   tensor_type=__lowerCAmelCase )

        return encoded_outputs

