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

class      lowerCAmelCase__  (			lowerCamelCase__    ):






		def __init__(	self	:							Optional[Any]							,    SCREAMING_SNAKE_CASE__	:							List[Any]							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=None							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=None						)  ->							int:
						__lowerCamelCase					      =			file_names
						__lowerCamelCase					      =			image_transform
						__lowerCamelCase					      =			label_to_id






		def __len__(	self	:							Any						)  ->							List[Any]:
						return len(self.file_names						)







		def __getitem__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							str						)  ->							int:
						__lowerCamelCase					      =			self.file_names[idx]
						__lowerCamelCase					      =			PIL.Image.open(SCREAMING_SNAKE_CASE_						)
						__lowerCamelCase					      =			raw_image.convert('''RGB'''						)
						if self.image_transform is not None:
										__lowerCamelCase					      =			self.image_transform(SCREAMING_SNAKE_CASE_						)
						__lowerCamelCase					      =			extract_label(SCREAMING_SNAKE_CASE_						)
						if self.label_to_id is not None:
										__lowerCamelCase					      =			self.label_to_id[label]
						return {"image": image, "label": label}





def 				__magic_name__				(     __lowerCAmelCase			:		Optional[int]	,						__lowerCAmelCase			:		str							)    ->     Optional[int]:
				# Initialize accelerator
				__lowerCamelCase					      =			Accelerator(cpu=args.cpu	,						mixed_precision=args.mixed_precision							)

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

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

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

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

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

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

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

				# Instantiate dataloaders.
				__lowerCamelCase					      =			DataLoader(_UpperCAmelCase	,						shuffle=_UpperCAmelCase	,						batch_size=_UpperCAmelCase	,						num_workers=4							)
				__lowerCamelCase					      =			DataLoader(_UpperCAmelCase	,						shuffle=_UpperCAmelCase	,						batch_size=_UpperCAmelCase	,						num_workers=4							)

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

				# 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					      =			model.to(accelerator.device							)

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

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

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

				# Instantiate learning rate scheduler
				__lowerCamelCase					      =			OneCycleLR(optimizer=_UpperCAmelCase	,						max_lr=_UpperCAmelCase	,						epochs=_UpperCAmelCase	,						steps_per_epoch=len(_UpperCAmelCase							)							)

				# 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      ,					__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase      ,					__lowerCamelCase					      =			accelerator.prepare(
				    _UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase	,						_UpperCAmelCase							)

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

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

								__lowerCamelCase					      =			accurate.item() / num_elems
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}: {100 * eval_metric:.2f}'''							)




def 				__magic_name__				(     )    ->     int:
				__lowerCamelCase					      =			argparse.ArgumentParser(description='''Simple example of training script.'''							)
				parser.add_argument('''--data_dir'''	,						required=_UpperCAmelCase	,						help='''The data folder on disk.'''							)
				parser.add_argument(
				    '''--mixed_precision'''	,						type=_UpperCAmelCase	,						default=_UpperCAmelCase	,						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=_UpperCAmelCase	,						default=_UpperCAmelCase	,						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					      =			parser.parse_args()
				__lowerCamelCase					      =			{'''lr''': 3E-2, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 64, '''image_size''': 224}
				training_function(_UpperCAmelCase	,						_UpperCAmelCase							)


if __name__ == "__main__":
					main()





from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


SCREAMING_SNAKE_CASE__     :    int      							=					logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__     :    List[str]      							=					{
    "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/config.json",
    # See all M2M100 models at https://huggingface.co/models?filter=m2m_100
}

class      lowerCAmelCase__  (			__lowercase    ):
		a__ :		Union[str, Any]   						=	"""m2m_100"""
		a__ :		Optional[int]   						=	["""past_key_values"""]
		a__ :		List[Any]   						=	{"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}






		def __init__(	self	:							int							,    SCREAMING_SNAKE_CASE__	:							Tuple=12_81_12							,    SCREAMING_SNAKE_CASE__	:							Any=10_24							,    SCREAMING_SNAKE_CASE__	:							int=12							,    SCREAMING_SNAKE_CASE__	:							List[str]=40_96							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=16							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=12							,    SCREAMING_SNAKE_CASE__	:							int=40_96							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]=16							,    SCREAMING_SNAKE_CASE__	:							Dict=0.05							,    SCREAMING_SNAKE_CASE__	:							List[Any]=0.05							,    SCREAMING_SNAKE_CASE__	:							Dict=True							,    SCREAMING_SNAKE_CASE__	:							Tuple=True							,    SCREAMING_SNAKE_CASE__	:							Optional[Any]="relu"							,    SCREAMING_SNAKE_CASE__	:							Dict=10_24							,    SCREAMING_SNAKE_CASE__	:							Optional[int]=0.1							,    SCREAMING_SNAKE_CASE__	:							Tuple=0.1							,    SCREAMING_SNAKE_CASE__	:							str=0.0							,    SCREAMING_SNAKE_CASE__	:							Any=0.02							,    SCREAMING_SNAKE_CASE__	:							int=2							,    SCREAMING_SNAKE_CASE__	:							List[Any]=True							,    SCREAMING_SNAKE_CASE__	:							Any=1							,    SCREAMING_SNAKE_CASE__	:							Union[str, Any]=0							,    SCREAMING_SNAKE_CASE__	:							str=2							,    **SCREAMING_SNAKE_CASE__	:							Tuple							,    )  ->							Tuple:
						__lowerCamelCase					      =			vocab_size
						__lowerCamelCase					      =			max_position_embeddings
						__lowerCamelCase					      =			d_model
						__lowerCamelCase					      =			encoder_ffn_dim
						__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			encoder_attention_heads
						__lowerCamelCase					      =			decoder_ffn_dim
						__lowerCamelCase					      =			decoder_layers
						__lowerCamelCase					      =			decoder_attention_heads
						__lowerCamelCase					      =			dropout
						__lowerCamelCase					      =			attention_dropout
						__lowerCamelCase					      =			activation_dropout
						__lowerCamelCase					      =			activation_function
						__lowerCamelCase					      =			init_std
						__lowerCamelCase					      =			encoder_layerdrop
						__lowerCamelCase					      =			decoder_layerdrop
						__lowerCamelCase					      =			use_cache
						__lowerCamelCase					      =			encoder_layers
						__lowerCamelCase					      =			scale_embedding  # scale factor will be sqrt(d_model) if True

						super().__init__(
						    pad_token_id=SCREAMING_SNAKE_CASE__							,    bos_token_id=SCREAMING_SNAKE_CASE__							,    eos_token_id=SCREAMING_SNAKE_CASE__							,    is_encoder_decoder=SCREAMING_SNAKE_CASE__							,    decoder_start_token_id=SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__							,    )





class      lowerCAmelCase__  (			__lowercase    ):






		@property
		def 				__A (	self	:							List[Any]						)  ->							Mapping[str, Mapping[int, str]]:
						__lowerCamelCase					      =			OrderedDict(
						    [
						        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
						        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
						    ]						)

						if self.use_past:
										__lowerCamelCase					      =			{0: '''batch'''}
										__lowerCamelCase					      =			{0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
						else:
										__lowerCamelCase					      =			{0: '''batch''', 1: '''decoder_sequence'''}
										__lowerCamelCase					      =			{0: '''batch''', 1: '''decoder_sequence'''}

						if self.use_past:
										self.fill_with_past_key_values_(SCREAMING_SNAKE_CASE__							,    direction='''inputs'''						)
						return common_inputs






		def 				__A (	self	:							Any							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						# Copied from OnnxConfig.generate_dummy_inputs
						# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
						# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
						__lowerCamelCase					      =			compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__							,    fixed_dimension=OnnxConfig.default_fixed_batch							,    num_token_to_add=0						)

						# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
						__lowerCamelCase					      =			tokenizer.num_special_tokens_to_add(SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			compute_effective_axis_dimension(
						    SCREAMING_SNAKE_CASE__							,    fixed_dimension=OnnxConfig.default_fixed_sequence							,    num_token_to_add=SCREAMING_SNAKE_CASE__						)

						# Generate dummy inputs according to compute batch and sequence
						__lowerCamelCase					      =			[''' '''.join([tokenizer.unk_token]						) * seq_length] * batch_size
						__lowerCamelCase					      =			dict(tokenizer(SCREAMING_SNAKE_CASE__							,    return_tensors=SCREAMING_SNAKE_CASE__						)						)
						return common_inputs






		def 				__A (	self	:							Union[str, Any]							,    SCREAMING_SNAKE_CASE__	:							PreTrainedTokenizer							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							int = -1							,    SCREAMING_SNAKE_CASE__	:							bool = False							,    SCREAMING_SNAKE_CASE__	:							Optional[TensorType] = None							,    )  ->							Mapping[str, Any]:
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)

						# Generate decoder inputs
						__lowerCamelCase					      =			seq_length if not self.use_past else 1
						__lowerCamelCase					      =			self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
						    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
						__lowerCamelCase					      =			{f'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
						__lowerCamelCase					      =			dict(**SCREAMING_SNAKE_CASE__							,    **SCREAMING_SNAKE_CASE__						)

						if self.use_past:
										if not is_torch_available():
														raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''						)
										else:
														import torch
										__lowerCamelCase      ,					__lowerCamelCase					      =			common_inputs['''input_ids'''].shape
										__lowerCamelCase					      =			common_inputs['''decoder_input_ids'''].shape[1]
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_attention_heads
										__lowerCamelCase					      =			(
										    batch,
										    num_encoder_attention_heads,
										    encoder_seq_length,
										    self._config.hidden_size // num_encoder_attention_heads,
										)
										__lowerCamelCase					      =			decoder_seq_length + 3
										__lowerCamelCase					      =			(
										    batch,
										    num_decoder_attention_heads,
										    decoder_past_length,
										    self._config.hidden_size // num_decoder_attention_heads,
										)

										__lowerCamelCase					      =			torch.cat(
										    [common_inputs['''decoder_attention_mask'''], torch.ones(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)]							,    dim=1						)

										__lowerCamelCase					      =			[]
										# If the number of encoder and decoder layers are present in the model configuration, both are considered
										__lowerCamelCase      ,					__lowerCamelCase					      =			self.num_layers
										__lowerCamelCase					      =			min(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						)
										__lowerCamelCase					      =			max(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						) - min_num_layers
										__lowerCamelCase					      =			'''encoder''' if num_encoder_layers > num_decoder_layers else '''decoder'''

										for _ in range(SCREAMING_SNAKE_CASE__						):
														common_inputs["past_key_values"].append(
														    (
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														        torch.zeros(SCREAMING_SNAKE_CASE__						),
														    )						)
										# TODO: test this.
										__lowerCamelCase					      =			encoder_shape if remaining_side_name == '''encoder''' else decoder_shape
										for _ in range(SCREAMING_SNAKE_CASE__							,    SCREAMING_SNAKE_CASE__						):
														common_inputs["past_key_values"].append((torch.zeros(SCREAMING_SNAKE_CASE__						), torch.zeros(SCREAMING_SNAKE_CASE__						))						)
						return common_inputs







		a__ :		Dict   						=	_generate_dummy_inputs_for_default_and_seqaseq_lm



