
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     UpperCAmelCase__							(       __magic_name__						:     int  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     fname.split(os.path.sep  )[-1]
					return re.search(R'''^(.*)_\d+\.jpg$'''							,  __magic_name__  ).groups()[0]


class 		__magic_name__   (						snake_case    ):





						def __init__(							self    :	int						,  lowerCamelCase__    :	Tuple						,  lowerCamelCase__    :	Union[str, Any]=None						,  lowerCamelCase__    :	int=None				):
											lowerCAmelCase		:					Dict  	=     file_names
											lowerCAmelCase		:					List[str]  	=     image_transform
											lowerCAmelCase		:					List[str]  	=     label_to_id





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





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



def     UpperCAmelCase__							(       __magic_name__						:     Dict							,  __magic_name__						:     str  ):


					'''simple docstring'''

					lowerCAmelCase		:					Union[str, Any]  	=     Accelerator(cpu=args.cpu							,  mixed_precision=args.mixed_precision  )

					# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
					lowerCAmelCase		:					Tuple  	=     config['''lr''']
					lowerCAmelCase		:					Tuple  	=     int(config['''num_epochs''']  )
					lowerCAmelCase		:					str  	=     int(config['''seed''']  )
					lowerCAmelCase		:					Union[str, Any]  	=     int(config['''batch_size''']  )
					lowerCAmelCase		:					Union[str, Any]  	=     config['''image_size''']
					if not isinstance(__magic_name__							,  (list, tuple)  ):
										lowerCAmelCase		:					Optional[int]  	=     (image_size, image_size)

					# Grab all the image filenames
					lowerCAmelCase		:					Optional[Any]  	=     [os.path.join(args.data_dir							,  __magic_name__  ) for fname in os.listdir(args.data_dir  ) if fname.endswith('''.jpg'''  )]

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

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

					# Split our filenames between train and validation
					lowerCAmelCase		:					Any  	=     np.random.permutation(len(__magic_name__  )  )
					lowerCAmelCase		:					int  	=     int(0.8 * len(__magic_name__  )  )
					lowerCAmelCase		:					List[str]  	=     random_perm[:cut]
					lowerCAmelCase		:					str  	=     random_perm[cut:]

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

					# For evaluation, we use a deterministic Resize
					lowerCAmelCase		:					List[str]  	=     Compose([Resize(__magic_name__  ), ToTensor()]  )
					lowerCAmelCase		:					Optional[Any]  	=     PetsDataset([file_names[i] for i in eval_split]							,  image_transform=__magic_name__							,  label_to_id=__magic_name__  )

					# Instantiate dataloaders.
					lowerCAmelCase		:					List[Any]  	=     DataLoader(__magic_name__							,  shuffle=__magic_name__							,  batch_size=__magic_name__							,  num_workers=4  )
					lowerCAmelCase		:					List[str]  	=     DataLoader(__magic_name__							,  shuffle=__magic_name__							,  batch_size=__magic_name__							,  num_workers=4  )

					# Instantiate the model (we build the model here so that the seed also control new weights initialization)
					lowerCAmelCase		:					Any  	=     create_model('''resnet50d'''							,  pretrained=__magic_name__							,  num_classes=len(__magic_name__  )  )

					# 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).
					lowerCAmelCase		:					Dict  	=     model.to(accelerator.device  )

					# Freezing the base model
					for param in model.parameters():
										lowerCAmelCase		:					List[str]  	=     False
					for param in model.get_classifier().parameters():
										lowerCAmelCase		:					Any  	=     True

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

					# Instantiate optimizer
					lowerCAmelCase		:					Union[str, Any]  	=     torch.optim.Adam(params=model.parameters()							,  lr=lr / 25  )

					# Instantiate learning rate scheduler
					lowerCAmelCase		:					Union[str, Any]  	=     OneCycleLR(optimizer=__magic_name__							,  max_lr=__magic_name__							,  epochs=__magic_name__							,  steps_per_epoch=len(__magic_name__  )  )

					# 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.
					lowerCAmelCase		:					Optional[Any]  	=     accelerator.prepare(
					    __magic_name__							,  __magic_name__							,  __magic_name__							,  __magic_name__							,  __magic_name__  )

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

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

										lowerCAmelCase		:					Union[str, Any]  	=     accurate.item() / num_elems
										# Use accelerator.print to print only on the main process.
										accelerator.print(f'''epoch {epoch}: {1_00 * eval_metric:.2f}'''  )
def     UpperCAmelCase__							(       ):


					'''simple docstring'''

					lowerCAmelCase		:					str  	=     argparse.ArgumentParser(description='''Simple example of training script.'''  )
					parser.add_argument('''--data_dir'''							,  required=__magic_name__							,  help='''The data folder on disk.'''  )
					parser.add_argument(
					    '''--mixed_precision'''							,  type=__magic_name__							,  default=__magic_name__							,  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=__magic_name__							,  default=__magic_name__							,  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.'''  )
					lowerCAmelCase		:					Optional[int]  	=     parser.parse_args()
					lowerCAmelCase		:					Optional[Any]  	=     {'''lr''': 3e-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 2_24}
					training_function(__magic_name__							,  __magic_name__  )


if __name__ == "__main__":
		main()


import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard


class 		__magic_name__   (						unittest.TestCase    ):





		def       _A							(							self    :	Dict				):
							lowerCAmelCase		:					List[Any]  	=     {
							    '''model_details''': {
							        '''Organization''': '''testing''',
							        '''Model date''': '''today''',
							        '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
							        '''Architecture''': '''Convolutional Neural Network.''',
							    },
							    '''metrics''': '''BLEU and ROUGE-1''',
							    '''evaluation_data''': {
							        '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
							        '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
							    },
							    '''training_data''': {
							        '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
							        '''Preprocessing''': (
							            '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
							            ''' https://arxiv.org/pdf/1810.03993.pdf'''
							        ),
							    },
							    '''quantitative_analyses''': {'''BLEU''': 5_5.1, '''ROUGE-1''': 7_6},
							}





		def       _A							(							self    :	str				):
							lowerCAmelCase		:					Dict  	=     ModelCard.from_dict(self.inputs_dict				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''model_details'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''intended_use'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''factors'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''metrics'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''evaluation_data'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''training_data'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''quantitative_analyses'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''ethical_considerations'''				)				)
							self.assertTrue(hasattr(lowerCamelCase__						,  '''caveats_and_recommendations'''				)				)





		def       _A							(							self    :	Optional[Any]				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)
							lowerCAmelCase		:					Dict  	=     json.loads(modelcard.to_json_string()				)
							for key, value in self.inputs_dict.items():
												self.assertEqual(obj[key]						,  lowerCamelCase__				)





		def       _A							(							self    :	Tuple				):
							lowerCAmelCase		:					Tuple  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												lowerCAmelCase		:					str  	=     os.path.join(lowerCamelCase__						,  '''modelcard.json'''				)
												model_card_first.to_json_file(lowerCamelCase__				)
												lowerCAmelCase		:					Dict  	=     ModelCard.from_json_file(lowerCamelCase__				)

							self.assertEqual(model_card_second.to_dict()						,  model_card_first.to_dict()				)





		def       _A							(							self    :	Union[str, Any]				):
							lowerCAmelCase		:					int  	=     ModelCard.from_dict(self.inputs_dict				)

							with tempfile.TemporaryDirectory() as tmpdirname:
												model_card_first.save_pretrained(lowerCamelCase__				)
												lowerCAmelCase		:					Any  	=     ModelCard.from_pretrained(lowerCamelCase__				)

							self.assertEqual(model_card_second.to_dict()						,  model_card_first.to_dict()				)
