

"""simple docstring"""




import argparse
import os
import re

import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, ToTensor

from accelerate import Accelerator
def   __SCREAMING_SNAKE_CASE		(		lowercase__ ):
					"""simple docstring"""
					A     =   fname.split(os.path.sep )[-1]
					return re.search(r"^(.*)_\d+\.jpg$"     ,				lowercase__ ).groups()[0]





class      __UpperCamelCase					(  _A		):


		def __init__(self					:						Optional[int]	,	__SCREAMING_SNAKE_CASE					:						str	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]=None	,	__SCREAMING_SNAKE_CASE					:						List[Any]=None):
							A     =   file_names
							A     =   image_transform
							A     =   label_to_id


		def __len__(self					:						str):
							return len(self.file_names)


		def __getitem__(self					:						int	,	__SCREAMING_SNAKE_CASE					:						Union[str, Any]):
							A     =   self.file_names[idx]
							A     =   PIL.Image.open(__SCREAMING_SNAKE_CASE)
							A     =   raw_image.convert("RGB")
							if self.image_transform is not None:
												A     =   self.image_transform(__SCREAMING_SNAKE_CASE)
							A     =   extract_label(__SCREAMING_SNAKE_CASE)
							if self.label_to_id is not None:
												A     =   self.label_to_id[label]
							return {"image": image, "label": label}

def   __SCREAMING_SNAKE_CASE		(		lowercase__     ,				lowercase__ ):
					"""simple docstring"""
					# Initialize accelerator
					A     =   Accelerator(cpu=args.cpu     ,				mixed_precision=args.mixed_precision )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					A     =   config["lr"]
					A     =   int(config["num_epochs"] )
					A     =   int(config["seed"] )
					A     =   int(config["batch_size"] )
					A     =   config["image_size"]
					if not isinstance(lowercase__     ,				(list, tuple) ):
										A     =   (image_size, image_size)

					# Grab all the image filenames
					A     =   [os.path.join(args.data_dir     ,				lowercase__ ) for fname in os.listdir(args.data_dir ) if fname.endswith(".jpg" )]

					# Build the label correspondences
					A     =   [extract_label(lowercase__ ) for fname in file_names]
					A     =   list(set(lowercase__ ) )
					id_to_label.sort()
					A     =   {lbl: i for i, lbl in enumerate(lowercase__ )}

					# Set the seed before splitting the data.
					np.random.seed(lowercase__ )
					torch.manual_seed(lowercase__ )
					torch.cuda.manual_seed_all(lowercase__ )

					# Split our filenames between train and validation
					A     =   np.random.permutation(len(lowercase__ ) )
					A     =   int(0.8 * len(lowercase__ ) )
					A     =   random_perm[:cut]
					A     =   random_perm[cut:]

					# For training we use a simple RandomResizedCrop
					A     =   Compose([RandomResizedCrop(lowercase__     ,				scale=(0.5, 1.0) ), ToTensor()] )
					A     =   PetsDataset(
					    [file_names[i] for i in train_split]     ,				image_transform=lowercase__     ,				label_to_id=lowercase__ )

					# For evaluation, we use a deterministic Resize
					A     =   Compose([Resize(lowercase__ ), ToTensor()] )
					A     =   PetsDataset([file_names[i] for i in eval_split]     ,				image_transform=lowercase__     ,				label_to_id=lowercase__ )

					# Instantiate dataloaders.
					A     =   DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__     ,				num_workers=4 )
					A     =   DataLoader(lowercase__     ,				shuffle=lowercase__     ,				batch_size=lowercase__     ,				num_workers=4 )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					A     =   create_model("resnet50d"     ,				pretrained=lowercase__     ,				num_classes=len(lowercase__ ) )

					# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
					# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
					# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
					A     =   model.to(accelerator.device )

					# Freezing the base model
					for param in model.parameters():
										A     =   False
					for param in model.get_classifier().parameters():
										A     =   True

					# We normalize the batches of images to be a bit faster.
					A     =   torch.tensor(model.default_cfg["mean"] )[None, :, None, None].to(accelerator.device )
					A     =   torch.tensor(model.default_cfg["std"] )[None, :, None, None].to(accelerator.device )

					# Instantiate optimizer
					A     =   torch.optim.Adam(params=model.parameters()     ,				lr=lr / 25 )

					# Instantiate learning rate scheduler
					A     =   OneCycleLR(optimizer=lowercase__     ,				max_lr=lowercase__     ,				epochs=lowercase__     ,				steps_per_epoch=len(lowercase__ ) )

					# Prepare everything
					# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
					# prepare method.
					A     ,       A     ,       A     ,       A     ,       A     =   accelerator.prepare(
					    lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__     ,				lowercase__ )

					# Now we train the model
					for epoch in range(lowercase__ ):
										model.train()
										for step, batch in enumerate(lowercase__ ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															A     =   {k: v.to(accelerator.device ) for k, v in batch.items()}
															A     =   (batch["image"] - mean) / std
															A     =   model(lowercase__ )
															A     =   torch.nn.functional.cross_entropy(lowercase__     ,				batch["label"] )
															accelerator.backward(lowercase__ )
															optimizer.step()
															lr_scheduler.step()
															optimizer.zero_grad()

										model.eval()
										A     =   0
										A     =   0
										for _, batch in enumerate(lowercase__ ):
															# We could avoid this line since we set the accelerator with `device_placement=True`.
															A     =   {k: v.to(accelerator.device ) for k, v in batch.items()}
															A     =   (batch["image"] - mean) / std
															with torch.no_grad():
																				A     =   model(lowercase__ )
															A     =   outputs.argmax(dim=-1 )
															A     ,       A     =   accelerator.gather_for_metrics((predictions, batch["label"]) )
															A     =   predictions == references
															num_elems += accurate_preds.shape[0]
															accurate += accurate_preds.long().sum()

										A     =   accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(F"""epoch {epoch}: {100 * eval_metric:.2f}""" )
def   __SCREAMING_SNAKE_CASE		(		):
					"""simple docstring"""
					A     =   argparse.ArgumentParser(description="Simple example of training script." )
					parser.add_argument("--data_dir"     ,				required=lowercase__     ,				help="The data folder on disk." )
					parser.add_argument(
					    "--mixed_precision"     ,				type=lowercase__     ,				default=lowercase__     ,				choices=["no", "fp16", "bf16", "fp8"]     ,				help="Whether to use mixed precision. Choose"
					    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
					    "and an Nvidia Ampere GPU."     ,				)
					parser.add_argument(
					    "--checkpointing_steps"     ,				type=lowercase__     ,				default=lowercase__     ,				help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch."     ,				)
					parser.add_argument("--cpu"     ,				action="store_true"     ,				help="If passed, will train on the CPU." )
					A     =   parser.parse_args()
					A     =   {"lr": 3e-2, "num_epochs": 3, "seed": 42, "batch_size": 64, "image_size": 224}
					training_function(lowercase__     ,				lowercase__ )


if __name__ == "__main__":
	main()








"""simple docstring"""

import os
import sys
from pathlib import Path

import torch

from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
from utils import load_json


a__			:						Union[str, Any]							=			torch.cuda.is_available()
a__			:						Union[str, Any]							=			[""" Sam ate lunch today.""", """Sams lunch ingredients."""]
a__			:						List[Any]							=			["""A very interesting story about what I ate for lunch.""", """Avocado, celery, turkey, coffee"""]
a__			:						str							=			{
    """max_tokens_per_batch""": None,
    """supervise_forward""": True,
    """normalize_hidden""": True,
    """label_smoothing""": 0.2,
    """eval_max_gen_length""": None,
    """eval_beams""": 1,
    """val_metric""": """loss""",
    """save_top_k""": 1,
    """adafactor""": True,
    """early_stopping_patience""": 2,
    """logger_name""": """default""",
    """length_penalty""": 0.5,
    """cache_dir""": """""",
    """task""": """summarization""",
    """num_workers""": 2,
    """alpha_hid""": 0,
    """freeze_embeds""": True,
    """enc_only""": False,
    """tgt_suffix""": """""",
    """resume_from_checkpoint""": None,
    """sortish_sampler""": True,
    """student_decoder_layers""": 1,
    """val_check_interval""": 1.0,
    """output_dir""": """""",
    """fp16""": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
    """no_teacher""": False,
    """fp16_opt_level""": """O1""",
    """gpus""": 1 if CUDA_AVAILABLE else 0,
    """n_tpu_cores""": 0,
    """max_grad_norm""": 1.0,
    """do_train""": True,
    """do_predict""": True,
    """accumulate_grad_batches""": 1,
    """server_ip""": """""",
    """server_port""": """""",
    """seed""": 42,
    """model_name_or_path""": """sshleifer/bart-tiny-random""",
    """config_name""": """""",
    """tokenizer_name""": """facebook/bart-large""",
    """do_lower_case""": False,
    """learning_rate""": 0.3,
    """lr_scheduler""": """linear""",
    """weight_decay""": 0.0,
    """adam_epsilon""": 1E-08,
    """warmup_steps""": 0,
    """max_epochs""": 1,
    """train_batch_size""": 2,
    """eval_batch_size""": 2,
    """max_source_length""": 12,
    """max_target_length""": 12,
    """val_max_target_length""": 12,
    """test_max_target_length""": 12,
    """fast_dev_run""": False,
    """no_cache""": False,
    """n_train""": -1,
    """n_val""": -1,
    """n_test""": -1,
    """student_encoder_layers""": 1,
    """freeze_encoder""": False,
    """auto_scale_batch_size""": False,
    """overwrite_output_dir""": False,
    """student""": None,
}





def 					__UpperCamelCase (     a__:			Path			,						a__:			list      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      '''\n'''.join(a__      )
					Path(a__      ).open('''w'''      ).writelines(a__      )





def 					__UpperCamelCase (     a__:			int      )					->      List[str]:




					"""simple docstring"""



					for split in ["train", "val", "test"]:
										_dump_articles(os.path.join(a__			,						f'''{split}.source'''      )			,						a__      )
										_dump_articles(os.path.join(a__			,						f'''{split}.target'''      )			,						a__      )
					return tmp_dir


class       _snake_case  (    UpperCamelCase							):







						@classmethod
						def 							_UpperCamelCase     (cls				)      ->    List[str]:
											return cls







						@require_torch_multi_gpu
						def 							_UpperCamelCase     (self				)      ->    Dict:
											__lowercase        =      {
											    '''no_teacher''': True,
											    '''freeze_encoder''': True,
											    '''gpus''': 2,
											    '''overwrite_output_dir''': True,
											    '''sortish_sampler''': True,
											}
											self._test_distiller_cli_fork(UpperCAmelCase_  ,				check_contents=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_=True				)      ->    Tuple:
											__lowercase        =      {
											    '''label_smoothing''': 0.0,
											    '''early_stopping_patience''': -1,
											    '''train_batch_size''': 1,
											    '''eval_batch_size''': 2,
											    '''max_epochs''': 2,
											    '''alpha_mlm''': 0.2,
											    '''alpha_ce''': 0.8,
											    '''do_predict''': True,
											    '''model_name_or_path''': '''sshleifer/tinier_bart''',
											    '''teacher''': CHEAP_ARGS['''model_name_or_path'''],
											    '''val_check_interval''': 0.5,
											}
											default_updates.update(UpperCAmelCase_				)
											__lowercase        =      CHEAP_ARGS.copy()
											__lowercase        =      make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()				)
											__lowercase        =      self.get_auto_remove_tmp_dir()
											args_d.update(data_dir=UpperCAmelCase_  ,				output_dir=UpperCAmelCase_  ,				**UpperCAmelCase_				)

											def convert(UpperCAmelCase_  ,				UpperCAmelCase_				):
																if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
																					return ""
																if v is False or v is None:
																					return ""
																if v is True:  # or len(str(v))==0:
																					return f'''--{k}'''
																return f'''--{k}={v}'''

											__lowercase        =      [x for x in (convert(UpperCAmelCase_  ,				UpperCAmelCase_				) for k, v in args_d.items()) if len(UpperCAmelCase_				)]
											__lowercase        =      [sys.executable, f'''{self.test_file_dir}/distillation.py'''] + cli_args
											execute_subprocess_async(UpperCAmelCase_  ,				env=self.get_env()				)

											__lowercase        =      os.listdir(UpperCAmelCase_				)
											__lowercase        =      {os.path.basename(UpperCAmelCase_				) for p in contents}
											__lowercase        =      [p for p in contents if p.endswith('''ckpt'''				)]
											assert len(UpperCAmelCase_				) > 0

											self.assertIn('''test_generations.txt'''  ,				UpperCAmelCase_				)
											self.assertIn('''test_results.txt'''  ,				UpperCAmelCase_				)

											# get the following from the module, (we don't have access to `model` here)
											__lowercase        =      os.path.join(UpperCAmelCase_  ,				'''metrics.json'''				)
											__lowercase        =      '''rouge2'''

											__lowercase        =      load_json(UpperCAmelCase_				)
											# {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
											print(UpperCAmelCase_				)
											__lowercase        =      metrics['''val'''][-1]
											self.assertGreaterEqual(last_step_stats['''val_avg_gen_time''']  ,				0.0_1				)
											self.assertIsInstance(last_step_stats[f'''val_avg_{val_metric}''']  ,				UpperCAmelCase_				)
											self.assertEqual(len(metrics['''test''']				)  ,				1				)
											__lowercase        =      int(args_d['''max_epochs'''] * (1 / args_d['''val_check_interval''']) / 2 + 1				)
											self.assertEqual(len(metrics['''val''']				)  ,				UpperCAmelCase_				)


