

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_		)     -> Dict:



       """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		)     -> Union[str, Any]:



       """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		)     -> Optional[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_		)     -> List[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_		)     -> Dict:



       """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		)     -> Tuple:



       """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		)     -> List[Any]:



       """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       ( )     -> Optional[Any]:



       """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()








import glob
import os
import random
from string import ascii_lowercase, digits

import cva
import numpy as np

# Parrameters
_lowerCamelCase			:	List[str]							 =     (720, 1280)  # Height, Width
_lowerCamelCase			:	Optional[Any]							 =     (0.4, 0.6)  # if height or width lower than this scale, drop it.
_lowerCamelCase			:	List[Any]							 =     1 / 100
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	List[str]							 =     """"""
_lowerCamelCase			:	Union[str, Any]							 =     250







def SCREAMING_SNAKE_CASE       ( )     -> None:



       """simple docstring"""

       A__				,							A__   				=					get_dataset(lowercase_				,       lowercase_		)
       for index in range(lowercase_		):
              A__   				=					random.sample(range(len(lowercase_		)		)				,       4		)
              A__				,							A__				,							A__   				=					update_image_and_anno(
                  lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       filter_scale=lowercase_				,       )

              # Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
              A__   				=					random_chars(32		)
              A__   				=					path.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              A__   				=					f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
              cva.imwrite(f"""{file_root}.jpg"""				,       lowercase_				,       [cva.IMWRITE_JPEG_QUALITY, 85]		)
              print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}"""		)
              A__   				=					[]
              for anno in new_annos:
                     A__   				=					anno[3] - anno[1]
                     A__   				=					anno[4] - anno[2]
                     A__   				=					anno[1] + width / 2
                     A__   				=					anno[2] + height / 2
                     A__   				=					f"""{anno[0]} {x_center} {y_center} {width} {height}"""
                     annos_list.append(lowercase_		)
              with open(f"""{file_root}.txt"""				,       '''w'''		) as outfile:
                     outfile.write('''\n'''.join(line for line in annos_list		)		)







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> tuple[list, list]:



       """simple docstring"""

       A__   				=					[]
       A__   				=					[]
       for label_file in glob.glob(os.path.join(lowercase_				,       '''*.txt'''		)		):
              A__   				=					label_file.split(os.sep		)[-1].rsplit('''.'''				,       1		)[0]
              with open(lowercase_		) as in_file:
                     A__   				=					in_file.readlines()
              A__   				=					os.path.join(lowercase_				,       f"""{label_name}.jpg"""		)

              A__   				=					[]
              for obj_list in obj_lists:
                     A__   				=					obj_list.rstrip('''\n'''		).split(''' '''		)
                     A__   				=					float(obj[1]		) - float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) - float(obj[4]		) / 2
                     A__   				=					float(obj[1]		) + float(obj[3]		) / 2
                     A__   				=					float(obj[2]		) + float(obj[4]		) / 2

                     boxes.append([int(obj[0]		), xmin, ymin, xmax, ymax]		)
              if not boxes:
                     continue
              img_paths.append(lowercase_		)
              labels.append(lowercase_		)
       return img_paths, labels







def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_				,       lowercase_ = 0.0				,       )     -> tuple[list, list, str]:



       """simple docstring"""

       A__   				=					np.zeros([output_size[0], output_size[1], 3]				,       dtype=np.uinta		)
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
       A__   				=					int(scale_x * output_size[1]		)
       A__   				=					int(scale_y * output_size[0]		)

       A__   				=					[]
       A__   				=					[]
       for i, index in enumerate(lowercase_		):
              A__   				=					all_img_list[index]
              path_list.append(lowercase_		)
              A__   				=					all_annos[index]
              A__   				=					cva.imread(lowercase_		)
              if i == 0:  # top-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					bbox[2] * scale_y
                            A__   				=					bbox[3] * scale_x
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 1:  # top-right
                     A__   				=					cva.resize(lowercase_				,       (output_size[1] - divid_point_x, divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					bbox[2] * scale_y
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					bbox[4] * scale_y
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              elif i == 2:  # bottom-left
                     A__   				=					cva.resize(lowercase_				,       (divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					bbox[1] * scale_x
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					bbox[3] * scale_x
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)
              else:  # bottom-right
                     A__   				=					cva.resize(
                         lowercase_				,       (output_size[1] - divid_point_x, output_size[0] - divid_point_y)		)
                     A__   				=					img
                     for bbox in img_annos:
                            A__   				=					scale_x + bbox[1] * (1 - scale_x)
                            A__   				=					scale_y + bbox[2] * (1 - scale_y)
                            A__   				=					scale_x + bbox[3] * (1 - scale_x)
                            A__   				=					scale_y + bbox[4] * (1 - scale_y)
                            new_anno.append([bbox[0], xmin, ymin, xmax, ymax]		)

    # Remove bounding box small than scale of filter
       if filter_scale > 0:
              A__   				=					[
                  anno
                  for anno in new_anno
                  if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
              ]

       return output_img, new_anno, path_list[0]







def SCREAMING_SNAKE_CASE       ( lowercase_		)     -> str:



       """simple docstring"""

       assert number_char > 1, "The number of character should greater than 1"
       A__   				=					ascii_lowercase + digits
       return "".join(random.choice(lowercase_		) for _ in range(lowercase_		)		)


if __name__ == "__main__":
  main()
  print("""DONE ✅""")


