


'''simple docstring'''







import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaForSemanticSegmentation,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE       :   List[str]        = logging.get_logger(__name__)



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					List[str]						       =       MobileNetVaConfig(layer_norm_eps=0.001		)

				if "quant" in model_name:
								raise ValueError("Quantized models are not supported."		)

				__magic_name__				:					Dict						       =       re.match(R"^.*mobilenet_v2_([^_]*)_([^_]*)$"     ,   UpperCamelCase__		)
				if matches:
								__magic_name__				:					Tuple						       =       float(matches[1]		)
								__magic_name__				:					List[Any]						       =       int(matches[2]		)

				if model_name.startswith("deeplabv3_"		):
								__magic_name__				:					Any						       =       8
								__magic_name__				:					Any						       =       21
								__magic_name__				:					Optional[Any]						       =       "pascal-voc-id2label.json"
				else:
								# The TensorFlow version of MobileNetV2 predicts 1001 classes instead
								# of the usual 1000. The first class (index 0) is "background".
								__magic_name__				:					Optional[Any]						       =       1001
								__magic_name__				:					str						       =       "imagenet-1k-id2label.json"

				__magic_name__				:					Any						       =       "huggingface/label-files"
				__magic_name__				:					str						       =       json.load(open(hf_hub_download(UpperCamelCase__     ,   UpperCamelCase__     ,   repo_type="dataset"		)     ,   "r"		)		)

				if config.num_labels == 1001:
								__magic_name__				:					Optional[Any]						       =       {int(UpperCamelCase__		) + 1: v for k, v in idalabel.items()}
								__magic_name__				:					Any						       =       "background"
				else:
								__magic_name__				:					Optional[Any]						       =       {int(UpperCamelCase__		): v for k, v in idalabel.items()}

				__magic_name__				:					Any						       =       idalabel
				__magic_name__				:					List[str]						       =       {v: k for k, v in idalabel.items()}

				return config



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				__magic_name__				:					List[Any]						       =       "http://images.cocodataset.org/val2017/000000039769.jpg"
				__magic_name__				:					Union[str, Any]						       =       Image.open(requests.get(UpperCamelCase__     ,   stream=UpperCamelCase__		).raw		)
				return im



@torch.no_grad()
def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=False		):






				"""simple docstring"""



				__magic_name__				:					Dict						       =       get_mobilenet_va_config(UpperCamelCase__		)

				# Load 🤗 model
				if model_name.startswith("deeplabv3_"		):
								__magic_name__				:					str						       =       MobileNetVaForSemanticSegmentation(UpperCamelCase__		).eval()
				else:
								__magic_name__				:					Any						       =       MobileNetVaForImageClassification(UpperCamelCase__		).eval()

				# Load weights from TensorFlow checkpoint
				load_tf_weights_in_mobilenet_va(UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		)

				# Check outputs on an image, prepared by MobileNetV2ImageProcessor
				__magic_name__				:					List[str]						       =       MobileNetVaImageProcessor(
				    crop_size={"width": config.image_size, "height": config.image_size}     ,   size={"shortest_edge": config.image_size + 32}     ,   )
				__magic_name__				:					Any						       =       image_processor(images=prepare_img()     ,   return_tensors="pt"		)
				__magic_name__				:					Dict						       =       model(**UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       outputs.logits

				if model_name.startswith("deeplabv3_"		):
								assert logits.shape == (1, 21, 65, 65)

								if model_name == "deeplabv3_mobilenet_v2_1.0_513":
												__magic_name__				:					Any						       =       torch.tensor(
												    [
												        [[17.5790, 17.7581, 18.3355], [18.3257, 18.4230, 18.8973], [18.6169, 18.8650, 19.2187]],
												        [[-2.1595, -2.0977, -2.3741], [-2.4226, -2.3028, -2.6835], [-2.7819, -2.5991, -2.7706]],
												        [[4.2058, 4.8317, 4.7638], [4.4136, 5.0361, 4.9383], [4.5028, 4.9644, 4.8734]],
												    ]		)

								else:
												raise ValueError(F"""Unknown model name: {model_name}"""		)

								assert torch.allclose(logits[0, :3, :3, :3]     ,   UpperCamelCase__     ,   atol=1E-4		)
				else:
								assert logits.shape == (1, 1001)

								if model_name == "mobilenet_v2_1.4_224":
												__magic_name__				:					List[str]						       =       torch.tensor([0.0181, -1.0015, 0.4688]		)
								elif model_name == "mobilenet_v2_1.0_224":
												__magic_name__				:					str						       =       torch.tensor([0.2445, -1.1993, 0.1905]		)
								elif model_name == "mobilenet_v2_0.75_160":
												__magic_name__				:					Dict						       =       torch.tensor([0.2482, 0.4136, 0.6669]		)
								elif model_name == "mobilenet_v2_0.35_96":
												__magic_name__				:					List[Any]						       =       torch.tensor([0.1451, -0.4624, 0.7192]		)
								else:
												__magic_name__				:					int						       =       None

								if expected_logits is not None:
												assert torch.allclose(logits[0, :3]     ,   UpperCamelCase__     ,   atol=1E-4		)

				Path(UpperCamelCase__		).mkdir(exist_ok=UpperCamelCase__		)
				print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""		)
				model.save_pretrained(UpperCamelCase__		)
				print(F"""Saving image processor to {pytorch_dump_folder_path}"""		)
				image_processor.save_pretrained(UpperCamelCase__		)

				if push_to_hub:
								print("Pushing to the hub..."		)
								__magic_name__				:					Optional[Any]						       =       "google/" + model_name
								image_processor.push_to_hub(UpperCamelCase__		)
								model.push_to_hub(UpperCamelCase__		)


if __name__ == "__main__":
		_SCREAMING_SNAKE_CASE       :   Any        = argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    "--model_name",
		    default="mobilenet_v2_1.0_224",
		    type=str,
		    help="Name of the MobileNetV2 model you\'d like to convert. Should in the form \'mobilenet_v2_<depth>_<size>\'.",
		)
		parser.add_argument(
		    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
		)
		parser.add_argument(
		    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
		)
		parser.add_argument(
		    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
		)

		_SCREAMING_SNAKE_CASE       :   List[str]        = parser.parse_args()
		convert_movilevit_checkpoint(
		    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)








'''simple docstring'''







import logging
import os
import time
from argparse import Namespace
from pathlib import Path

import datasets
import torch
from accelerate import Accelerator, DistributedType
from accelerate.utils import ProjectConfiguration
from arguments import TrainingArguments
from datasets import load_dataset
from huggingface_hub import Repository
from torch.optim import AdamW
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from torch.utils.data.datapipes.iter.combinatorics import ShufflerIterDataPipe

import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, get_scheduler, set_seed






class 		_snake_case    (				snake_case_		):






						'''simple docstring'''







						def __init__(  self:	List[Any]      ,							__UpperCamelCase:	str      ,							__UpperCamelCase:	Optional[Any]      ,							__UpperCamelCase:	Optional[int]=False      ,							__UpperCamelCase:	Optional[int]=1024      ,							__UpperCamelCase:	Union[str, Any]=1024      ,							__UpperCamelCase:	Optional[int]=3.6      ,							__UpperCamelCase:	str=False      ,							)							->	Optional[int]:
										__magic_name__				:					Union[str, Any]						       =       tokenizer
										__magic_name__				:					Any						       =       tokenizer.bos_token_id
										__magic_name__				:					Optional[int]						       =       dataset
										__magic_name__				:					List[Any]						       =       seq_length
										__magic_name__				:					Union[str, Any]						       =       0
										__magic_name__				:					Dict						       =       infinite
										__magic_name__				:					Any						       =       0
										__magic_name__				:					int						       =       tokenized

										if self.tokenized:
														__magic_name__				:					Dict						       =       seq_length * num_of_sequences
														__magic_name__				:					Optional[Any]						       =       "input_ids"
										else:
														__magic_name__				:					str						       =       seq_length * chars_per_token * num_of_sequences
														__magic_name__				:					Tuple						       =       "content"







						def __iter__(  self:	List[Any]     )							->	Dict:
										__magic_name__				:					Dict						       =       iter(self.dataset     )
										__magic_name__				:					Any						       =       True
										while more_examples:
														__magic_name__       ,     __magic_name__				:					Any						       =       [], 0
														while True:
																		if buffer_len >= self.max_buffer_size:
																						break
																		try:
																						buffer.append(next(__UpperCamelCase     )[self.content_field]     )
																						buffer_len += len(buffer[-1]     )
																		except StopIteration:
																						if self.infinite:
																										__magic_name__				:					Union[str, Any]						       =       iter(self.dataset     )
																										self.epoch += 1
																										logger.info(f"""Dataset epoch: {self.epoch}"""     )
																						else:
																										__magic_name__				:					Any						       =       False
																										break
														if self.tokenized:
																		__magic_name__				:					str						       =       buffer
														else:
																		__magic_name__				:					Any						       =       self.tokenizer(__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
														__magic_name__				:					List[str]						       =       []
														for tokenized_input in tokenized_inputs:
																		all_token_ids.extend(tokenized_input + [self.concat_token_id]     )
														for i in range(0      ,							len(__UpperCamelCase     )      ,							self.seq_length     ):
																		__magic_name__				:					Optional[int]						       =       all_token_ids[i : i + self.seq_length]
																		if len(__UpperCamelCase     ) == self.seq_length:
																						self.current_size += 1
																						yield torch.tensor(__UpperCamelCase     )







						def lowerCAmelCase__ (  self:	Tuple      ,							__UpperCamelCase:	str=1000     )							->	List[str]:
										return ShufflerIterDataPipe(self      ,							buffer_size=__UpperCamelCase     )






def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Optional[Any]						       =       args.model_ckpt.split("/"		)[-1]
				__magic_name__				:					Union[str, Any]						       =       logging.getLogger(__name__		)
				__magic_name__				:					Optional[int]						       =       Path(args.save_dir		) / "log/"
				log_dir.mkdir(exist_ok=UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       F"""debug_{accelerator.process_index}.log"""
				logging.basicConfig(
				    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"     ,   datefmt="%m/%d/%Y %H:%M:%S"     ,   level=logging.INFO     ,   handlers=[logging.FileHandler(log_dir / filename		), logging.StreamHandler()]     ,   )
				if accelerator.is_main_process:  # we only want to setup logging once
								accelerator.init_trackers(UpperCamelCase__     ,   vars(UpperCamelCase__		)		)
								__magic_name__				:					Union[str, Any]						       =       accelerator.trackers[0].run.name
								logger.setLevel(logging.INFO		)
								datasets.utils.logging.set_verbosity_info()
								transformers.utils.logging.set_verbosity_info()
				else:
								__magic_name__				:					Dict						       =       ""
								logger.setLevel(logging.ERROR		)
								datasets.utils.logging.set_verbosity_error()
								transformers.utils.logging.set_verbosity_error()
				return logger, run_name



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					Tuple						       =       {"streaming": True}
				__magic_name__				:					Any						       =       load_dataset(args.dataset_name_train     ,   split="train"     ,   **UpperCamelCase__		)
				__magic_name__				:					Any						       =       train_data.shuffle(buffer_size=args.shuffle_buffer     ,   seed=args.seed		)
				__magic_name__				:					Optional[int]						       =       load_dataset(args.dataset_name_valid     ,   split="train"     ,   **UpperCamelCase__		)
				__magic_name__				:					int						       =       ConstantLengthDataset(
				    UpperCamelCase__     ,   UpperCamelCase__     ,   infinite=UpperCamelCase__     ,   seq_length=args.seq_length     ,   tokenized=args.tokenized		)
				__magic_name__				:					List[str]						       =       ConstantLengthDataset(
				    UpperCamelCase__     ,   UpperCamelCase__     ,   infinite=UpperCamelCase__     ,   seq_length=args.seq_length     ,   tokenized=args.tokenized		)
				__magic_name__				:					Tuple						       =       train_dataset.shuffle(buffer_size=args.shuffle_buffer		)
				__magic_name__				:					List[str]						       =       DataLoader(UpperCamelCase__     ,   batch_size=args.train_batch_size     ,   shuffle=UpperCamelCase__		)
				__magic_name__				:					List[Any]						       =       DataLoader(UpperCamelCase__     ,   batch_size=args.valid_batch_size		)
				return train_dataloader, eval_dataloader



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=["bias", "ln_1.weight", "ln_2.weight", "ln_f.weight"]		):






				"""simple docstring"""



				__magic_name__       ,     __magic_name__				:					List[str]						       =       [], []
				for n, p in model.named_parameters():
								if any(nd in n for nd in no_decay		):
												params_without_wd.append(UpperCamelCase__		)
								else:
												params_with_wd.append(UpperCamelCase__		)
				return [
				    {"params": params_with_wd, "weight_decay": args.weight_decay},
				    {"params": params_without_wd, "weight_decay": 0.0},
				]



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				logger.info(F"""Step {step}: {metrics}"""		)
				if accelerator.is_main_process:
								accelerator.log(UpperCamelCase__     ,   UpperCamelCase__		)



def 						_UpperCamelCase							( UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__		):






				"""simple docstring"""



				__magic_name__				:					str						       =       accelerator.unwrap_model(UpperCamelCase__		).config
				__magic_name__				:					str						       =       4 if args.gradient_checkpointing else 3
				__magic_name__				:					Any						       =       args.train_batch_size * accelerator.state.num_processes * args.gradient_accumulation_steps
				__magic_name__				:					int						       =       24 * checkpoint_factor * batch_size * args.seq_length * config_model.n_layer * (config_model.n_embd**2)
				__magic_name__				:					Optional[Any]						       =       factor * (
				    1.0
				    + (args.seq_length / (6.0 * config_model.n_embd))
				    + (tokenizer.vocab_size / (16.0 * config_model.n_layer * config_model.n_embd))
				)
				__magic_name__				:					Union[str, Any]						       =       flops_per_iteration / (elapsed_time * accelerator.state.num_processes * (10**12))
				return tflops



def 						_UpperCamelCase							( UpperCamelCase__		):






				"""simple docstring"""



				model.eval()
				__magic_name__				:					str						       =       []
				for step, batch in enumerate(UpperCamelCase__		):
								with torch.no_grad():
												__magic_name__				:					Any						       =       model(UpperCamelCase__     ,   labels=UpperCamelCase__		)
								__magic_name__				:					str						       =       outputs.loss.repeat(args.valid_batch_size		)
								losses.append(accelerator.gather(UpperCamelCase__		)		)
								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				__magic_name__				:					Tuple						       =       torch.cat(UpperCamelCase__		)
				__magic_name__				:					Tuple						       =       losses[: eval_dataloader.dataset.current_size].mean()
				try:
								__magic_name__				:					int						       =       torch.exp(UpperCamelCase__		)
				except OverflowError:
								__magic_name__				:					List[Any]						       =       float("inf"		)
				return loss.item(), perplexity.item()


# Settings
_SCREAMING_SNAKE_CASE       :   Optional[int]        = HfArgumentParser(TrainingArguments)
_SCREAMING_SNAKE_CASE       :   Any        = parser.parse_args()

# Accelerator
_SCREAMING_SNAKE_CASE       :   List[str]        = ProjectConfiguration(project_dir=args.save_dir, logging_dir="log")
_SCREAMING_SNAKE_CASE       :   List[Any]        = Accelerator(log_with=["wandb", "tensorboard"], project_config=config)
_SCREAMING_SNAKE_CASE       :   List[Any]        = {str(k): str(v) for k, v in accelerator.state.__dict__.items()}

_SCREAMING_SNAKE_CASE       :   Optional[Any]        = Namespace(**vars(args), **acc_state)
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = accelerator.state.num_processes * args.train_batch_size
set_seed(args.seed)

# Clone model repository
if accelerator.is_main_process:
		_SCREAMING_SNAKE_CASE       :   Tuple        = Repository(args.save_dir, clone_from=args.model_ckpt)

# Logging
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = setup_logging(args)
logger.info(accelerator.state)

# Checkout new branch on repo
if accelerator.is_main_process:
		hf_repo.git_checkout(run_name, create_branch_ok=True)

# Load model and tokenizer
_SCREAMING_SNAKE_CASE       :   Any        = AutoModelForCausalLM.from_pretrained(args.save_dir)
if args.gradient_checkpointing:
		model.gradient_checkpointing_enable()
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = AutoTokenizer.from_pretrained(args.save_dir)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Dict        = create_dataloaders(args)

# Prepare the optimizer and learning rate scheduler
_SCREAMING_SNAKE_CASE       :   Tuple        = AdamW(get_grouped_params(model, args), lr=args.learning_rate)
_SCREAMING_SNAKE_CASE       :   str        = get_scheduler(
    name=args.lr_scheduler_type,
    optimizer=optimizer,
    num_warmup_steps=args.num_warmup_steps,
    num_training_steps=args.max_train_steps,
)
accelerator.register_for_checkpointing(lr_scheduler)



def 						_UpperCamelCase							( ):






				"""simple docstring"""



				return optimizer.param_groups[0]["lr"]


# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   List[Any]        = accelerator.prepare(
    model, optimizer, train_dataloader, eval_dataloader
)

# 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)
				_SCREAMING_SNAKE_CASE       :   Union[str, Any]        = os.path.basename(args.resume_from_checkpoint)
		else:
				# Get the most recent checkpoint
				_SCREAMING_SNAKE_CASE       :   Any        = [f.name for f in os.scandir(args.save_dir) if f.is_dir() and "step" in str(f)]
				dirs.sort(key=os.path.getctime)
				_SCREAMING_SNAKE_CASE       :   str        = dirs[-1]  # Sorts folders by date modified, most recent checkpoint is the last
		# Extract the step of the checkpoint to continue from there
		_SCREAMING_SNAKE_CASE       :   List[str]        = os.path.splitext(path)[0]
		_SCREAMING_SNAKE_CASE       :   Optional[Any]        = int(training_difference.replace("step_", ""))

# Train model
model.train()
_SCREAMING_SNAKE_CASE       :   Optional[Any]        = 0
_SCREAMING_SNAKE_CASE       :   int        = time.time()
_SCREAMING_SNAKE_CASE       :   Optional[int]        = 0
for step, batch in enumerate(train_dataloader, start=1):
		if args.resume_from_checkpoint and step < resume_step:
				continue  # we need to skip steps until we reach the resumed step
		_SCREAMING_SNAKE_CASE       :   Tuple        = model(batch, labels=batch, use_cache=False).loss
		_SCREAMING_SNAKE_CASE       :   int        = accelerator.gather(loss.repeat(args.train_batch_size)).mean()
		loss_tracking += avg_loss.item() / args.gradient_accumulation_steps
		log_metrics(step, {"samples": step * samples_per_step, "loss_per_step/train": loss.item()})
		_SCREAMING_SNAKE_CASE       :   str        = loss / args.gradient_accumulation_steps
		if step % args.gradient_accumulation_steps != 0:
				# Prevent backward from doing gradient all_reduce in every step
				if accelerator.distributed_type == DistributedType.MULTI_GPU:
						with model.no_sync():
								accelerator.backward(loss)
				else:
						accelerator.backward(loss)
		else:
				_SCREAMING_SNAKE_CASE       :   str        = get_lr()
				accelerator.backward(loss)
				accelerator.clip_grad_norm_(model.parameters(), 1.0)
				optimizer.step()
				lr_scheduler.step()
				optimizer.zero_grad()
				_SCREAMING_SNAKE_CASE       :   Tuple        = time.time() - t_start
				_SCREAMING_SNAKE_CASE       :   List[str]        = compute_tflops(elapsed_time, accelerator, args)
				log_metrics(
				    step,
				    {
				        "steps": completed_steps,
				        "loss/train": loss_tracking,
				        "lr": lr,
				        "tflops": tflops,
				        "time_per_iteration": elapsed_time,
				    },
				)
				_SCREAMING_SNAKE_CASE       :   int        = time.time()
				_SCREAMING_SNAKE_CASE       :   Tuple        = 0
				completed_steps += 1
		if step % args.save_checkpoint_steps == 0:
				logger.info("Evaluating and saving model checkpoint")
				_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   str        = evaluate(args)
				log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
				accelerator.wait_for_everyone()
				_SCREAMING_SNAKE_CASE       :   Dict        = os.path.join(args.save_dir, f"step_{step}")
				accelerator.save_state(save_dir)
				if accelerator.is_main_process:
						hf_repo.push_to_hub(commit_message=f"step {step}")
				model.train()
		if completed_steps >= args.max_train_steps:
				break

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
_SCREAMING_SNAKE_CASE ,				_SCREAMING_SNAKE_CASE       :   Optional[int]        = evaluate(args)
log_metrics(step, {"loss/eval": eval_loss, "perplexity": perplexity})
accelerator.wait_for_everyone()
_SCREAMING_SNAKE_CASE       :   Optional[int]        = accelerator.unwrap_model(model)
unwrapped_model.save_pretrained(args.save_dir, save_function=accelerator.save)
_SCREAMING_SNAKE_CASE       :   List[Any]        = os.path.join(args.save_dir, f"step_{step}")
accelerator.save_state(save_dir)
if accelerator.is_main_process:
		hf_repo.push_to_hub(commit_message="final model")