





'''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  _lowerCamelCase				( lowercase						:      Any						)	->	int:
				_a								= fname.split(os.path.sep						)[-1]
				return re.search(r"^(.*)_\d+\.jpg$"    ,							lowercase						).groups()[0]
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				def __init__(  self     :					Optional[Any]					,				__a     :					List[Any]					,				__a     :					List[str]=None					,				__a     :					List[Any]=None					):
								_a								= file_names
								_a								= image_transform
								_a								= label_to_id




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




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






def  _lowerCamelCase				( lowercase						:      List[Any]    ,							lowercase						:      int						)	->	str:
				# Initialize accelerator
				if args.with_tracking:
								_a								= Accelerator(
								    cpu=args.cpu    ,							mixed_precision=args.mixed_precision    ,							log_with="all"    ,							project_dir=args.project_dir						)
				else:
								_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)

				# Parse out whether we are saving every epoch or after a certain number of batches
				if hasattr(args.checkpointing_steps    ,							"isdigit"						):
								if args.checkpointing_steps == "epoch":
												_a								= args.checkpointing_steps
								elif args.checkpointing_steps.isdigit():
												_a								= int(args.checkpointing_steps						)
								else:
												raise ValueError(
												    F'Argument `checkpointing_steps` must be either a number or `epoch`. `{args.checkpointing_steps}` passed.'						)
				else:
								_a								= None

				# We need to initialize the trackers we use, and also store our configuration
				if args.with_tracking:
								_a								= os.path.split(lowercase						)[-1].split("."						)[0]
								accelerator.init_trackers(lowercase    ,							lowercase						)

				# 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						)
				# We need to keep track of how many total steps we have iterated over
				_a								= 0
				# We also need to keep track of the starting epoch so files are named properly
				_a								= 0

				# Potentially load in the weights and states from a previous save
				if args.resume_from_checkpoint:
								if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
												accelerator.print(F'Resumed from checkpoint: {args.resume_from_checkpoint}'						)
												accelerator.load_state(args.resume_from_checkpoint						)
												_a								= os.path.basename(args.resume_from_checkpoint						)
								else:
												# Get the most recent checkpoint
												_a								= [f.name for f in os.scandir(os.getcwd()						) if f.is_dir()]
												dirs.sort(key=os.path.getctime						)
												_a								= dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
								# Extract `epoch_{i}` or `step_{i}`
								_a								= os.path.splitext(lowercase						)[0]

								if "epoch" in training_difference:
												_a								= int(training_difference.replace("epoch_"    ,							""						)						) + 1
												_a								= None
								else:
												_a								= int(training_difference.replace("step_"    ,							""						)						)
												_a								= resume_step // len(lowercase						)
												resume_step -= starting_epoch * len(lowercase						)

    # Now we train the model
				for epoch in range(lowercase    ,							lowercase						):
								model.train()
								if args.with_tracking:
												_a								= 0
								if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
												# We need to skip steps until we reach the resumed step
												_a								= accelerator.skip_first_batches(lowercase    ,							lowercase						)
												overall_step += resume_step
								else:
												# After the first iteration though, we need to go back to the original dataloader
												_a								= train_dataloader
								for batch in active_dataloader:
												# 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"]						)
												# We keep track of the loss at each epoch
												if args.with_tracking:
																total_loss += loss.detach().float()
												accelerator.backward(lowercase						)
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()
												overall_step += 1
												if isinstance(lowercase    ,							lowercase						):
																_a								= F'step_{overall_step}'
																if overall_step % checkpointing_steps == 0:
																				if args.output_dir is not None:
																								_a								= os.path.join(args.output_dir    ,							lowercase						)
																				accelerator.save_state(lowercase						)
								model.eval()
								_a								= 0
								_a								= 0
								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
												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}'						)
								if args.with_tracking:
												accelerator.log(
												    {
												        "accuracy": 100 * eval_metric,
												        "train_loss": total_loss.item() / len(lowercase						),
												        "epoch": epoch,
												    }    ,							step=lowercase    ,							)
								if checkpointing_steps == "epoch":
												_a								= F'epoch_{epoch}'
												if args.output_dir is not None:
																_a								= os.path.join(args.output_dir    ,							lowercase						)
												accelerator.save_state(lowercase						)

				if args.with_tracking:
								accelerator.end_training()






def  _lowerCamelCase				( )	->	List[str]:
				_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("--fp16"    ,							action="store_true"    ,							help="If passed, will use FP16 training."						)
				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("--cpu"    ,							action="store_true"    ,							help="If passed, will train on the CPU."						)
				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(
				    "--output_dir"    ,							type=lowercase    ,							default="."    ,							help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."    ,							)
				parser.add_argument(
				    "--resume_from_checkpoint"    ,							type=lowercase    ,							default=lowercase    ,							help="If the training should continue from a checkpoint folder."    ,							)
				parser.add_argument(
				    "--with_tracking"    ,							action="store_true"    ,							help="Whether to load in all available experiment trackers from the environment and use them for logging."    ,							)
				parser.add_argument(
				    "--project_dir"    ,							type=lowercase    ,							default="logs"    ,							help="Location on where to store experiment tracking logs` and relevent project information"    ,							)
				_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 argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def 		__magic_name__    (	__UpperCAmelCase	)		->					str:

					'''simple docstring'''

					snake_case_									=				[
					    '''encoder.version''',
					    '''decoder.version''',
					    '''model.encoder.version''',
					    '''model.decoder.version''',
					    '''decoder.output_projection.weight''',
					    '''_float_tensor''',
					    '''encoder.embed_positions._float_tensor''',
					    '''decoder.embed_positions._float_tensor''',
					]
					for k in ignore_keys:
										state_dict.pop(__UpperCAmelCase,      __UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					Optional[int]:

					'''simple docstring'''

					snake_case_									=				list(s_dict.keys()	)
					for key in keys:
										if "transformer_layers" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)
										elif "subsample" in key:
															snake_case_									=				s_dict.pop(__UpperCAmelCase	)


def 		__magic_name__    (	__UpperCAmelCase	)		->					int:

					'''simple docstring'''

					snake_case_			,snake_case_									=				emb.weight.shape
					snake_case_									=				nn.Linear(__UpperCAmelCase,      __UpperCAmelCase,      bias=__UpperCAmelCase	)
					snake_case_									=				emb.weight.data
					return lin_layer


def 		__magic_name__    (	__UpperCAmelCase,      __UpperCAmelCase	)		->					Dict:

					'''simple docstring'''

					snake_case_									=				torch.load(__UpperCAmelCase,      map_location='''cpu'''	)
					snake_case_									=				mam_aaa['''args''']
					snake_case_									=				mam_aaa['''model''']
					snake_case_									=				state_dict['''decoder.output_projection.weight''']

					remove_ignore_keys_(__UpperCAmelCase	)
					rename_keys(__UpperCAmelCase	)

					snake_case_									=				state_dict['''decoder.embed_tokens.weight'''].shape[0]

					snake_case_									=				args.share_decoder_input_output_embed

					snake_case_									=				[int(__UpperCAmelCase	) for i in args.conv_kernel_sizes.split(''','''	)]
					snake_case_									=				SpeechaTextConfig(
					    vocab_size=__UpperCAmelCase,      max_source_positions=args.max_source_positions,      max_target_positions=args.max_target_positions,      encoder_layers=args.encoder_layers,      decoder_layers=args.decoder_layers,      encoder_attention_heads=args.encoder_attention_heads,      decoder_attention_heads=args.decoder_attention_heads,      encoder_ffn_dim=args.encoder_ffn_embed_dim,      decoder_ffn_dim=args.decoder_ffn_embed_dim,      d_model=args.encoder_embed_dim,      dropout=args.dropout,      attention_dropout=args.attention_dropout,      activation_dropout=args.activation_dropout,      activation_function='''relu''',      num_conv_layers=len(__UpperCAmelCase	),      conv_channels=args.conv_channels,      conv_kernel_sizes=__UpperCAmelCase,      input_feat_per_channel=args.input_feat_per_channel,      input_channels=args.input_channels,      tie_word_embeddings=__UpperCAmelCase,      num_beams=5,      max_length=200,      use_cache=__UpperCAmelCase,      decoder_start_token_id=2,      early_stopping=__UpperCAmelCase,      )

					snake_case_									=				SpeechaTextForConditionalGeneration(__UpperCAmelCase	)
					snake_case_			,snake_case_									=				model.model.load_state_dict(__UpperCAmelCase,      strict=__UpperCAmelCase	)
					if len(__UpperCAmelCase	) > 0 and not set(__UpperCAmelCase	) <= {
					    "encoder.embed_positions.weights",
					    "decoder.embed_positions.weights",
					}:
										raise ValueError(
										    '''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,'''
										    F" but all the following weights are missing {missing}"	)

					if tie_embeds:
										snake_case_									=				make_linear_from_emb(model.model.decoder.embed_tokens	)
					else:
										snake_case_									=				lm_head_weights

					model.save_pretrained(__UpperCAmelCase	)


if __name__ == "__main__":
							a  :				Any									=  argparse.ArgumentParser()
							# Required parameters
							parser.add_argument('--fairseq_path', type=str, help='Path to the fairseq model (.pt) file.')
							parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
							a  :				List[Any]									=  parser.parse_args()
							convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)

