






"""simple docstring"""
import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCamelCase_							      = 16
lowerCamelCase_							      = 32

def 							snake_case (  A__      ,A__ = 16      ,A__ = "bert-base-cased"		):
			UpperCAmelCase_	:					str     	=   AutoTokenizer.from_pretrained(A__		)
			UpperCAmelCase_	:					Dict     	=   load_dataset("glue"      ,"mrpc"		)

			def tokenize_function(A__		):
						# max_length=None => use the model max length (it's actually the default)
						UpperCAmelCase_	:					str     	=   tokenizer(examples["sentence1"]      ,examples["sentence2"]      ,truncation=A__      ,max_length=A__		)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			UpperCAmelCase_	:					Optional[int]     	=   datasets.map(
			    A__      ,batched=A__      ,remove_columns=["idx", "sentence1", "sentence2"]      ,load_from_cache_file=A__		)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			UpperCAmelCase_	:					List[Any]     	=   tokenized_datasets.rename_column("label"      ,"labels"		)

			def collate_fn(A__		):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						if accelerator.distributed_type == DistributedType.TPU:
									return tokenizer.pad(A__      ,padding="max_length"      ,max_length=1_28      ,return_tensors="pt"		)
						return tokenizer.pad(A__      ,padding="longest"      ,return_tensors="pt"		)

			# Instantiate dataloaders.
			UpperCAmelCase_	:					int     	=   DataLoader(
			    tokenized_datasets["train"]      ,shuffle=A__      ,collate_fn=A__      ,batch_size=A__		)
			UpperCAmelCase_	:					List[str]     	=   DataLoader(
			    tokenized_datasets["validation"]      ,shuffle=A__      ,collate_fn=A__      ,batch_size=A__		)

			return train_dataloader, eval_dataloader

def 							snake_case (  A__      ,A__      ,A__      ,A__		):
			model.eval()
			UpperCAmelCase_	:					Optional[Any]     	=   0
			for step, batch in enumerate(A__		):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device		)
						with torch.no_grad():
									UpperCAmelCase_	:					Optional[Any]     	=   model(**A__		)
						UpperCAmelCase_	:					Optional[Any]     	=   outputs.logits.argmax(dim=-1		)
						# It is slightly faster to call this once, than multiple times
						UpperCAmelCase_	,      UpperCAmelCase_	:					int     	=   accelerator.gather(
						    (predictions, batch["labels"])		)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
									if step == len(A__		) - 1:
												UpperCAmelCase_	:					List[Any]     	=   predictions[: len(eval_dataloader.dataset		) - samples_seen]
												UpperCAmelCase_	:					Union[str, Any]     	=   references[: len(eval_dataloader.dataset		) - samples_seen]
									else:
												samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=A__      ,references=A__      ,)

			UpperCAmelCase_	:					Union[str, Any]     	=   metric.compute()
			return eval_metric["accuracy"]

def 							snake_case (  A__      ,A__		):
			# Initialize accelerator
			UpperCAmelCase_	:					List[Any]     	=   Accelerator()

			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			UpperCAmelCase_	:					Any     	=   config["lr"]
			UpperCAmelCase_	:					Union[str, Any]     	=   int(config["num_epochs"]		)
			UpperCAmelCase_	:					Union[str, Any]     	=   int(config["seed"]		)
			UpperCAmelCase_	:					str     	=   int(config["batch_size"]		)
			UpperCAmelCase_	:					Optional[Any]     	=   args.model_name_or_path

			set_seed(A__		)
			UpperCAmelCase_	,      UpperCAmelCase_	:					Tuple     	=   get_dataloaders(A__      ,A__      ,A__		)

			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			UpperCAmelCase_	:					str     	=   AutoModelForSequenceClassification.from_pretrained(A__      ,return_dict=A__		)

			# Instantiate optimizer
			UpperCAmelCase_	:					Dict     	=   (
			    AdamW
			    if accelerator.state.deepspeed_plugin is None
			    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
			    else DummyOptim
			)
			UpperCAmelCase_	:					int     	=   optimizer_cls(params=model.parameters()      ,lr=A__		)

			if accelerator.state.deepspeed_plugin is not None:
						UpperCAmelCase_	:					str     	=   accelerator.state.deepspeed_plugin.deepspeed_config[
						    "gradient_accumulation_steps"
						]
			else:
						UpperCAmelCase_	:					Tuple     	=   1
			UpperCAmelCase_	:					int     	=   (len(A__		) * num_epochs) // gradient_accumulation_steps

			# Instantiate scheduler
			if (
			    accelerator.state.deepspeed_plugin is None
			    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
			):
						UpperCAmelCase_	:					List[Any]     	=   get_linear_schedule_with_warmup(
						    optimizer=A__      ,num_warmup_steps=0      ,num_training_steps=A__      ,)
			else:
						UpperCAmelCase_	:					List[str]     	=   DummyScheduler(A__      ,total_num_steps=A__      ,warmup_num_steps=0		)

			# 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.
			UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	,      UpperCAmelCase_	:					Any     	=   accelerator.prepare(
			    A__      ,A__      ,A__      ,A__      ,A__		)

			# We need to keep track of how many total steps we have iterated over
			UpperCAmelCase_	:					Optional[Any]     	=   0
			# We also need to keep track of the stating epoch so files are named properly
			UpperCAmelCase_	:					int     	=   0
			UpperCAmelCase_	:					Optional[Any]     	=   evaluate.load("glue"      ,"mrpc"		)
			UpperCAmelCase_	:					Optional[Any]     	=   num_epochs

			if args.partial_train_epoch is not None:
						UpperCAmelCase_	:					Optional[int]     	=   args.partial_train_epoch

			if args.resume_from_checkpoint:
						accelerator.load_state(args.resume_from_checkpoint		)
						UpperCAmelCase_	:					List[str]     	=   args.resume_from_checkpoint.split("epoch_"		)[1]
						UpperCAmelCase_	:					str     	=   ""
						for char in epoch_string:
									if char.isdigit():
												state_epoch_num += char
									else:
												break
						UpperCAmelCase_	:					List[str]     	=   int(A__		) + 1
						UpperCAmelCase_	:					List[str]     	=   evaluation_loop(A__      ,A__      ,A__      ,A__		)
						accelerator.print("resumed checkpoint performance:"      ,A__		)
						accelerator.print("resumed checkpoint's scheduler's lr:"      ,lr_scheduler.get_lr()[0]		)
						accelerator.print("resumed optimizers's lr:"      ,optimizer.param_groups[0]["lr"]		)
						with open(os.path.join(args.output_dir      ,F"""state_{starting_epoch-1}.json"""		)      ,"r"		) as f:
									UpperCAmelCase_	:					Optional[int]     	=   json.load(A__		)
									assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["lr"] == lr_scheduler.get_lr()[0]
									), "Scheduler learning rate mismatch, loading from checkpoint failed"
									assert (
									    resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
									), "Optimizer learning rate mismatch, loading from checkpoint failed"
									assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
									return

    # Now we train the model
			UpperCAmelCase_	:					int     	=   {}
			for epoch in range(A__      ,A__		):
						model.train()
						for step, batch in enumerate(A__		):
									UpperCAmelCase_	:					str     	=   model(**A__		)
									UpperCAmelCase_	:					Any     	=   outputs.loss
									UpperCAmelCase_	:					Any     	=   loss / gradient_accumulation_steps
									accelerator.backward(A__		)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

									overall_step += 1
						UpperCAmelCase_	:					Any     	=   F"""epoch_{epoch}"""
						UpperCAmelCase_	:					Optional[Any]     	=   os.path.join(args.output_dir      ,A__		)
						accelerator.save_state(A__		)
						UpperCAmelCase_	:					Optional[int]     	=   evaluation_loop(A__      ,A__      ,A__      ,A__		)
						UpperCAmelCase_	:					List[Any]     	=   accuracy
						UpperCAmelCase_	:					str     	=   lr_scheduler.get_lr()[0]
						UpperCAmelCase_	:					Dict     	=   optimizer.param_groups[0]["lr"]
						UpperCAmelCase_	:					List[str]     	=   epoch
						UpperCAmelCase_	:					str     	=   overall_step
						accelerator.print(F"""epoch {epoch}:"""      ,A__		)

						accelerator.wait_for_everyone()
						if accelerator.is_main_process:
									with open(os.path.join(args.output_dir      ,F"""state_{epoch}.json"""		)      ,"w"		) as f:
												json.dump(A__      ,A__		)

def 							snake_case (  ):
			UpperCAmelCase_	:					Optional[Any]     	=   argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."		)
			parser.add_argument(
			    "--model_name_or_path"      ,type=A__      ,default="bert-base-cased"      ,help="Path to pretrained model or model identifier from huggingface.co/models."      ,required=A__      ,)
			parser.add_argument(
			    "--output_dir"      ,type=A__      ,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=A__      ,default=A__      ,help="If the training should continue from a checkpoint folder."      ,)
			parser.add_argument(
			    "--partial_train_epoch"      ,type=A__      ,default=A__      ,help="If passed, the training will stop after this number of epochs."      ,)
			parser.add_argument(
			    "--num_epochs"      ,type=A__      ,default=2      ,help="Number of train epochs."      ,)
			UpperCAmelCase_	:					List[Any]     	=   parser.parse_args()
			UpperCAmelCase_	:					List[str]     	=   {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}

			training_function(A__      ,A__		)


if __name__ == "__main__":
			main()





import argparse

import requests
import torch
from PIL import Image

from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor




def 							a		(				snake_case__:   List[Any]   ):


				'''simple docstring'''
				if "cls_token" in name:
								lowercase_	    =   name.replace('''cls_token'''		,  '''vit.embeddings.cls_token'''   )
				if "mask_token" in name:
								lowercase_	    =   name.replace('''mask_token'''		,  '''decoder.mask_token'''   )
				if "decoder_pos_embed" in name:
								lowercase_	    =   name.replace('''decoder_pos_embed'''		,  '''decoder.decoder_pos_embed'''   )
				if "pos_embed" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''pos_embed'''		,  '''vit.embeddings.position_embeddings'''   )
				if "patch_embed.proj" in name:
								lowercase_	    =   name.replace('''patch_embed.proj'''		,  '''vit.embeddings.patch_embeddings.projection'''   )
				if "patch_embed.norm" in name:
								lowercase_	    =   name.replace('''patch_embed.norm'''		,  '''vit.embeddings.norm'''   )
				if "decoder_blocks" in name:
								lowercase_	    =   name.replace('''decoder_blocks'''		,  '''decoder.decoder_layers'''   )
				if "blocks" in name:
								lowercase_	    =   name.replace('''blocks'''		,  '''vit.encoder.layer'''   )
				if "attn.proj" in name:
								lowercase_	    =   name.replace('''attn.proj'''		,  '''attention.output.dense'''   )
				if "attn" in name:
								lowercase_	    =   name.replace('''attn'''		,  '''attention.self'''   )
				if "norm1" in name:
								lowercase_	    =   name.replace('''norm1'''		,  '''layernorm_before'''   )
				if "norm2" in name:
								lowercase_	    =   name.replace('''norm2'''		,  '''layernorm_after'''   )
				if "mlp.fc1" in name:
								lowercase_	    =   name.replace('''mlp.fc1'''		,  '''intermediate.dense'''   )
				if "mlp.fc2" in name:
								lowercase_	    =   name.replace('''mlp.fc2'''		,  '''output.dense'''   )
				if "decoder_embed" in name:
								lowercase_	    =   name.replace('''decoder_embed'''		,  '''decoder.decoder_embed'''   )
				if "decoder_norm" in name:
								lowercase_	    =   name.replace('''decoder_norm'''		,  '''decoder.decoder_norm'''   )
				if "decoder_pred" in name:
								lowercase_	    =   name.replace('''decoder_pred'''		,  '''decoder.decoder_pred'''   )
				if "norm.weight" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.weight'''		,  '''vit.layernorm.weight'''   )
				if "norm.bias" in name and "decoder" not in name:
								lowercase_	    =   name.replace('''norm.bias'''		,  '''vit.layernorm.bias'''   )

				return name




def 							a		(				snake_case__:   Optional[int]		,  snake_case__:   Any   ):


				'''simple docstring'''
				for key in orig_state_dict.copy().keys():
								lowercase_	    =   orig_state_dict.pop(snake_case__   )

								if "qkv" in key:
												lowercase_	    =   key.split('''.'''   )
												lowercase_	    =   int(key_split[1]   )
												if "decoder_blocks" in key:
																lowercase_	    =   config.decoder_hidden_size
																lowercase_	    =   '''decoder.decoder_layers.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]
												else:
																lowercase_	    =   config.hidden_size
																lowercase_	    =   '''vit.encoder.layer.'''
																if "weight" in key:
																				lowercase_	    =   val[:dim, :]
																				lowercase_	    =   val[dim : dim * 2, :]
																				lowercase_	    =   val[-dim:, :]
																elif "bias" in key:
																				lowercase_	    =   val[:dim]
																				lowercase_	    =   val[dim : dim * 2]
																				lowercase_	    =   val[-dim:]

								else:
												lowercase_	    =   val

				return orig_state_dict




def 							a		(				snake_case__:   str		,  snake_case__:   int   ):


				'''simple docstring'''
				lowercase_	    =   ViTMAEConfig()
				if "large" in checkpoint_url:
								lowercase_	    =   1_024
								lowercase_	    =   4_096
								lowercase_	    =   24
								lowercase_	    =   16
				elif "huge" in checkpoint_url:
								lowercase_	    =   14
								lowercase_	    =   1_280
								lowercase_	    =   5_120
								lowercase_	    =   32
								lowercase_	    =   16

				lowercase_	    =   ViTMAEForPreTraining(snake_case__   )

				lowercase_	    =   torch.hub.load_state_dict_from_url(snake_case__		,  map_location='''cpu'''   )['''model''']

				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )

				lowercase_	    =   convert_state_dict(snake_case__		,  snake_case__   )

				model.load_state_dict(snake_case__   )
				model.eval()

				lowercase_	    =   '''https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg'''

				lowercase_	    =   Image.open(requests.get(snake_case__		,  stream=snake_case__   ).raw   )
				lowercase_	    =   ViTMAEImageProcessor(size=config.image_size   )
				lowercase_	    =   image_processor(images=snake_case__		,  return_tensors='''pt'''   )

				# forward pass
				torch.manual_seed(2   )
				lowercase_	    =   model(**snake_case__   )
				lowercase_	    =   outputs.logits

				if "large" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-0.7_3_0_9, -0.7_1_2_8, -1.0_1_6_9], [-1.0_1_6_1, -0.9_0_5_8, -1.1_8_7_8], [-1.0_4_7_8, -0.9_4_1_1, -1.1_9_1_1]]   )
				elif "huge" in checkpoint_url:
								lowercase_	    =   torch.tensor(
								    [[-1.1_5_9_9, -0.9_1_9_9, -1.2_2_2_1], [-1.1_9_5_2, -0.9_2_6_9, -1.2_3_0_7], [-1.2_1_4_3, -0.9_3_3_7, -1.2_2_6_2]]   )
				else:
								lowercase_	    =   torch.tensor(
								    [[-0.9_1_9_2, -0.8_4_8_1, -1.1_2_5_9], [-1.1_3_4_9, -1.0_0_3_4, -1.2_5_9_9], [-1.1_7_5_7, -1.0_4_2_9, -1.2_7_2_6]]   )

				# verify logits
				assert torch.allclose(logits[0, :3, :3]		,  snake_case__		,  atol=1e-4   )

				print(F'''Saving model to {pytorch_dump_folder_path}'''   )
				model.save_pretrained(snake_case__   )

				print(F'''Saving image processor to {pytorch_dump_folder_path}'''   )
				image_processor.save_pretrained(snake_case__   )


if __name__ == "__main__":
						__a         =   argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--checkpoint_url',
						    default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
						    type=str,
						    help='URL of the checkpoint you\'d like to convert.',
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
						)

						__a         =   parser.parse_args()
						convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)




