from copy import deepcopy
from pathlib import Path
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union

import torch
from torch import nn
from torch.utils.data import Dataset

from .deepspeed import is_deepspeed_zeroa_enabled
from .generation.configuration_utils import GenerationConfig
from .trainer import Trainer
from .utils import logging


if TYPE_CHECKING:
	from .data.data_collator import DataCollator
	from .modeling_utils import PreTrainedModel
	from .tokenization_utils_base import PreTrainedTokenizerBase
	from .trainer_callback import TrainerCallback
	from .trainer_utils import EvalPrediction, PredictionOutput
	from .training_args import TrainingArguments


lowercase__    :       Tuple						=			logging.get_logger(__name__)




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = (None, None)	,    __SCREAMING_SNAKE_CASE = None	,    )		->Any:
								super().__init__(
								    model=__SCREAMING_SNAKE_CASE	,    args=__SCREAMING_SNAKE_CASE	,    data_collator=__SCREAMING_SNAKE_CASE	,    train_dataset=__SCREAMING_SNAKE_CASE	,    eval_dataset=__SCREAMING_SNAKE_CASE	,    tokenizer=__SCREAMING_SNAKE_CASE	,    model_init=__SCREAMING_SNAKE_CASE	,    compute_metrics=__SCREAMING_SNAKE_CASE	,    callbacks=__SCREAMING_SNAKE_CASE	,    optimizers=__SCREAMING_SNAKE_CASE	,    preprocess_logits_for_metrics=__SCREAMING_SNAKE_CASE	,    )

								# Override self.model.generation_config if a GenerationConfig is specified in args.
								# Priority: args.generation_config > model.generation_config > default GenerationConfig.
								if self.args.generation_config is not None:
															lowerCAmelCase				=	self.load_generation_config(self.args.generation_config       )
															lowerCAmelCase				=	gen_config


	@staticmethod
	def     SCREAMING_SNAKE_CASE_       (							__SCREAMING_SNAKE_CASE       )		->GenerationConfig:

								# GenerationConfig provided, nothing to do
								if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ):
															return deepcopy(__SCREAMING_SNAKE_CASE       )

								# str or Path
								lowerCAmelCase				=	Path(__SCREAMING_SNAKE_CASE       ) if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) else gen_config_arg
								lowerCAmelCase				=	None

								# Figuring if it is path pointing to a file, pointing to a directory or else a model id or URL
								# This step is required in order to determine config_file_name
								if pretrained_model_name.is_file():
															lowerCAmelCase				=	pretrained_model_name.name
															lowerCAmelCase				=	pretrained_model_name.parent
								# dir path
								elif pretrained_model_name.is_dir():
															pass
								# model id or URL
								else:
															lowerCAmelCase				=	gen_config_arg

								lowerCAmelCase				=	GenerationConfig.from_pretrained(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )
								return gen_config


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = "eval"	,    **__SCREAMING_SNAKE_CASE	,    )		->Dict[str, float]:
								lowerCAmelCase				=	gen_kwargs.copy()
								if gen_kwargs.get('''max_length'''       ) is None and gen_kwargs.get('''max_new_tokens'''       ) is None:
															lowerCAmelCase				=	self.args.generation_max_length
								lowerCAmelCase				=	(
								    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
								)
								lowerCAmelCase				=	gen_kwargs

								return super().evaluate(__SCREAMING_SNAKE_CASE	,    ignore_keys=__SCREAMING_SNAKE_CASE	,    metric_key_prefix=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    __SCREAMING_SNAKE_CASE = "test"	,    **__SCREAMING_SNAKE_CASE	,    )		->"PredictionOutput":
								lowerCAmelCase				=	gen_kwargs.copy()
								if gen_kwargs.get('''max_length'''       ) is None and gen_kwargs.get('''max_new_tokens'''       ) is None:
															lowerCAmelCase				=	self.args.generation_max_length
								lowerCAmelCase				=	(
								    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.args.generation_num_beams
								)
								lowerCAmelCase				=	gen_kwargs

								return super().predict(__SCREAMING_SNAKE_CASE	,    ignore_keys=__SCREAMING_SNAKE_CASE	,    metric_key_prefix=__SCREAMING_SNAKE_CASE       )


	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE = None	,    **__SCREAMING_SNAKE_CASE	,    )		->Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]:

								if not self.args.predict_with_generate or prediction_loss_only:
															return super().prediction_step(
															    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE	,    prediction_loss_only=__SCREAMING_SNAKE_CASE	,    ignore_keys=__SCREAMING_SNAKE_CASE       )

								lowerCAmelCase				=	'''labels''' in inputs
								lowerCAmelCase				=	self._prepare_inputs(__SCREAMING_SNAKE_CASE       )

								# XXX: adapt synced_gpus for fairscale as well
								# Priority (handled in generate):
								# gen_kwargs > model.generation_config > default GenerationConfig()

								if len(__SCREAMING_SNAKE_CASE       ) == 0 and hasattr(self	,    '''_gen_kwargs'''       ):
															lowerCAmelCase				=	self._gen_kwargs.copy()

								if gen_kwargs.get('''max_length'''       ) is None and gen_kwargs.get('''max_new_tokens'''       ) is None:
															lowerCAmelCase				=	self.model.config.max_length
								lowerCAmelCase				=	(
								    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''       ) is not None else self.model.config.num_beams
								)
								lowerCAmelCase				=	True if is_deepspeed_zeroa_enabled() else False
								lowerCAmelCase				=	(
								    gen_kwargs['''synced_gpus'''] if gen_kwargs.get('''synced_gpus'''       ) is not None else default_synced_gpus
								)

								# If the `decoder_input_ids` was created from `labels`, evict the former, so that the model can freely generate
								# (otherwise, it would continue generating from the padded `decoder_input_ids`)
								if (
								    "labels" in inputs
								    and "decoder_input_ids" in inputs
								    and inputs["labels"].shape == inputs["decoder_input_ids"].shape
								):
															lowerCAmelCase				=	{k: v for k, v in inputs.items() if k != '''decoder_input_ids'''}
								lowerCAmelCase				=	self.model.generate(**__SCREAMING_SNAKE_CASE	,    **__SCREAMING_SNAKE_CASE       )

								# Temporary hack to ensure the generation config is not initialized for each iteration of the evaluation loop
								# TODO: remove this hack when the legacy code that initializes generation_config from a model config is
								# removed in https://github.com/huggingface/transformers/blob/98d88b23f54e5a23e741833f1e973fdf600cc2c5/src/transformers/generation/utils.py#L1183
								if self.model.generation_config._from_model_config:
															lowerCAmelCase				=	False

								# Retrieves GenerationConfig from model.generation_config
								lowerCAmelCase				=	self.model.generation_config
								# in case the batch is shorter than max length, the output should be padded
								if generated_tokens.shape[-1] < gen_config.max_length:
															lowerCAmelCase				=	self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE	,    gen_config.max_length       )
								elif gen_config.max_new_tokens is not None and generated_tokens.shape[-1] < gen_config.max_new_tokens + 1:
															lowerCAmelCase				=	self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE	,    gen_config.max_new_tokens + 1       )

								with torch.no_grad():
															if has_labels:
																						with self.compute_loss_context_manager():
																													lowerCAmelCase				=	model(**__SCREAMING_SNAKE_CASE       )
																						if self.label_smoother is not None:
																													lowerCAmelCase				=	self.label_smoother(__SCREAMING_SNAKE_CASE	,    inputs['''labels''']       ).mean().detach()
																						else:
																													lowerCAmelCase				=	(outputs['''loss'''] if isinstance(__SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       ) else outputs[0]).mean().detach()
															else:
																						lowerCAmelCase				=	None

								if self.args.prediction_loss_only:
															return loss, None, None

								if has_labels:
															lowerCAmelCase				=	inputs['''labels''']
															if labels.shape[-1] < gen_config.max_length:
																						lowerCAmelCase				=	self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE	,    gen_config.max_length       )
															elif gen_config.max_new_tokens is not None and labels.shape[-1] < gen_config.max_new_tokens + 1:
																						lowerCAmelCase				=	self._pad_tensors_to_max_len(__SCREAMING_SNAKE_CASE	,    gen_config.max_new_tokens + 1       )
								else:
															lowerCAmelCase				=	None

								return loss, generated_tokens, labels




	def     SCREAMING_SNAKE_CASE_       (							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE       )		->Optional[int]:
								if self.tokenizer is not None and hasattr(self.tokenizer	,    '''pad_token_id'''       ):
															# If PAD token is not defined at least EOS token has to be defined
															lowerCAmelCase				=	(
															    self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id
															)
								else:
															if self.model.config.pad_token_id is not None:
																						lowerCAmelCase				=	self.model.config.pad_token_id
															else:
																						raise ValueError('''Pad_token_id must be set in the configuration of the model, in order to pad tensors'''       )

								lowerCAmelCase				=	pad_token_id * torch.ones(
								    (tensor.shape[0], max_length)	,    dtype=tensor.dtype	,    device=tensor.device       )
								lowerCAmelCase				=	tensor
								return padded_tensor




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




class      lowercase_						(						UpperCamelCase_		):



	"""simple docstring"""





	def __init__(							self	,    __SCREAMING_SNAKE_CASE	,    __SCREAMING_SNAKE_CASE=None	,    __SCREAMING_SNAKE_CASE=None       )		->Optional[Any]:
								lowerCAmelCase				=	file_names
								lowerCAmelCase				=	image_transform
								lowerCAmelCase				=	label_to_id


	def __len__(							self       )		->Optional[int]:
								return len(self.file_names       )




	def __getitem__(							self	,    __SCREAMING_SNAKE_CASE       )		->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      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			Any:
							# 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(snake_case__     ,						(list, tuple)							):
														lowerCAmelCase				=	(image_size, image_size)

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

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

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

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

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

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

							# Instantiate dataloaders.
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						num_workers=4							)
							lowerCAmelCase				=	DataLoader(snake_case__     ,						shuffle=snake_case__     ,						batch_size=snake_case__     ,						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=snake_case__     ,						num_classes=len(snake_case__							)							)

							# 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 / 2_5							)

							# Instantiate learning rate scheduler
							lowerCAmelCase				=	OneCycleLR(optimizer=snake_case__     ,						max_lr=snake_case__     ,						epochs=snake_case__     ,						steps_per_epoch=len(snake_case__							)							)

							# 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(
							    snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)

							# Now we train the model
							for epoch in range(snake_case__							):
														model.train()
														for step, batch in enumerate(snake_case__							):
																					# 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(snake_case__							)
																					lowerCAmelCase				=	torch.nn.functional.cross_entropy(snake_case__     ,						batch['''label''']							)
																					accelerator.backward(snake_case__							)
																					optimizer.step()
																					lr_scheduler.step()
																					optimizer.zero_grad()

														model.eval()
														lowerCAmelCase				=	0
														lowerCAmelCase				=	0
														for _, batch in enumerate(snake_case__							):
																					# 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(snake_case__							)
																					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}: {1_0_0 * eval_metric:.2f}"							)

def      SCREAMING_SNAKE_CASE_  (		)  ->			List[str]:
							lowerCAmelCase				=	argparse.ArgumentParser(description='''Simple example of training script.'''							)
							parser.add_argument('''--data_dir'''     ,						required=snake_case__     ,						help='''The data folder on disk.'''							)
							parser.add_argument(
							    '''--mixed_precision'''     ,						type=snake_case__     ,						default=snake_case__     ,						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=snake_case__     ,						default=snake_case__     ,						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''': 4_2, '''batch_size''': 6_4, '''image_size''': 2_2_4}
							training_function(snake_case__     ,						snake_case__							)


if __name__ == "__main__":
	main()



