



"""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


__A   :							List[str]													=   16
__A   :							Any													=   32







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Accelerator					,     _SCREAMING_SNAKE_CASE   :						int = 16					,     _SCREAMING_SNAKE_CASE   :						str = "bert-base-cased"				):






				'''simple docstring'''



				_UpperCAmelCase					=    AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    load_dataset('''glue'''					,     '''mrpc'''				)

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

				# Apply the method we just defined to all the examples in all the splits of the dataset
				_UpperCAmelCase					=    datasets.map(
				    _SCREAMING_SNAKE_CASE					,     batched=_SCREAMING_SNAKE_CASE					,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']					,     load_from_cache_file=_SCREAMING_SNAKE_CASE				)

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				_UpperCAmelCase					=    tokenized_datasets.rename_column('''label'''					,     '''labels'''				)

				def collate_fn(_SCREAMING_SNAKE_CASE   :						str				):
								# 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(_SCREAMING_SNAKE_CASE					,     padding='''max_length'''					,     max_length=128					,     return_tensors='''pt'''				)
								return tokenizer.pad(_SCREAMING_SNAKE_CASE					,     padding='''longest'''					,     return_tensors='''pt'''				)

				# Instantiate dataloaders.
				_UpperCAmelCase					=    DataLoader(
				    tokenized_datasets['''train''']					,     shuffle=_SCREAMING_SNAKE_CASE					,     collate_fn=_SCREAMING_SNAKE_CASE					,     batch_size=_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    DataLoader(
				    tokenized_datasets['''validation''']					,     shuffle=_SCREAMING_SNAKE_CASE					,     collate_fn=_SCREAMING_SNAKE_CASE					,     batch_size=_SCREAMING_SNAKE_CASE				)

				return train_dataloader, eval_dataloader







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						str					,     _SCREAMING_SNAKE_CASE   :						List[str]					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



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

				_UpperCAmelCase					=    metric.compute()
				return eval_metric["accuracy"]







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Any					,     _SCREAMING_SNAKE_CASE   :						Tuple				):






				'''simple docstring'''



				_UpperCAmelCase					=    Accelerator()

				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				_UpperCAmelCase					=    config['''lr''']
				_UpperCAmelCase					=    int(config['''num_epochs''']				)
				_UpperCAmelCase					=    int(config['''seed''']				)
				_UpperCAmelCase					=    int(config['''batch_size''']				)
				_UpperCAmelCase					=    args.model_name_or_path

				set_seed(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase		,						_UpperCAmelCase					=    get_dataloaders(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				_UpperCAmelCase					=    AutoModelForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE					,     return_dict=_SCREAMING_SNAKE_CASE				)

				# Instantiate optimizer
				_UpperCAmelCase					=    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or '''optimizer''' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_UpperCAmelCase					=    optimizer_cls(params=model.parameters()					,     lr=_SCREAMING_SNAKE_CASE				)

				if accelerator.state.deepspeed_plugin is not None:
								_UpperCAmelCase					=    accelerator.state.deepspeed_plugin.deepspeed_config[
								    '''gradient_accumulation_steps'''
								]
				else:
								_UpperCAmelCase					=    1
				_UpperCAmelCase					=    (len(_SCREAMING_SNAKE_CASE				) * 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					=    get_linear_schedule_with_warmup(
								    optimizer=_SCREAMING_SNAKE_CASE					,     num_warmup_steps=0					,     num_training_steps=_SCREAMING_SNAKE_CASE					,     )
				else:
								_UpperCAmelCase					=    DummyScheduler(_SCREAMING_SNAKE_CASE					,     total_num_steps=_SCREAMING_SNAKE_CASE					,     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					=    accelerator.prepare(
				    _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)

				# We need to keep track of how many total steps we have iterated over
				_UpperCAmelCase					=    0
				# We also need to keep track of the stating epoch so files are named properly
				_UpperCAmelCase					=    0
				_UpperCAmelCase					=    evaluate.load('''glue'''					,     '''mrpc'''				)
				_UpperCAmelCase					=    num_epochs

				if args.partial_train_epoch is not None:
								_UpperCAmelCase					=    args.partial_train_epoch

				if args.resume_from_checkpoint:
								accelerator.load_state(args.resume_from_checkpoint				)
								_UpperCAmelCase					=    args.resume_from_checkpoint.split('''epoch_'''				)[1]
								_UpperCAmelCase					=    ''''''
								for char in epoch_string:
												if char.isdigit():
																state_epoch_num += char
												else:
																break
								_UpperCAmelCase					=    int(_SCREAMING_SNAKE_CASE				) + 1
								_UpperCAmelCase					=    evaluation_loop(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								accelerator.print('''resumed checkpoint performance:'''					,     _SCREAMING_SNAKE_CASE				)
								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					=    json.load(_SCREAMING_SNAKE_CASE				)
												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					=    {}
				for epoch in range(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				):
								model.train()
								for step, batch in enumerate(_SCREAMING_SNAKE_CASE				):
												_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE				)
												_UpperCAmelCase					=    outputs.loss
												_UpperCAmelCase					=    loss / gradient_accumulation_steps
												accelerator.backward(_SCREAMING_SNAKE_CASE				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1
								_UpperCAmelCase					=    f'epoch_{epoch}'
								_UpperCAmelCase					=    os.path.join(args.output_dir					,     _SCREAMING_SNAKE_CASE				)
								accelerator.save_state(_SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    evaluation_loop(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)
								_UpperCAmelCase					=    accuracy
								_UpperCAmelCase					=    lr_scheduler.get_lr()[0]
								_UpperCAmelCase					=    optimizer.param_groups[0]['''lr''']
								_UpperCAmelCase					=    epoch
								_UpperCAmelCase					=    overall_step
								accelerator.print(f'epoch {epoch}:'					,     _SCREAMING_SNAKE_CASE				)

								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(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)







def  lowercase	(      ):






				'''simple docstring'''



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

				training_function(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					main()








"""simple docstring"""


import argparse
import json

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

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Optional[Any]				):






				'''simple docstring'''



				_UpperCAmelCase					=    SwinConfig()
				_UpperCAmelCase					=    swin_name.split('''_'''				)

				_UpperCAmelCase					=    name_split[1]
				_UpperCAmelCase					=    int(name_split[4]				)
				_UpperCAmelCase					=    int(name_split[3][-1]				)

				if model_size == "tiny":
								_UpperCAmelCase					=    96
								_UpperCAmelCase					=    (2, 2, 6, 2)
								_UpperCAmelCase					=    (3, 6, 12, 24)
				elif model_size == "small":
								_UpperCAmelCase					=    96
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (3, 6, 12, 24)
				elif model_size == "base":
								_UpperCAmelCase					=    128
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (4, 8, 16, 32)
				else:
								_UpperCAmelCase					=    192
								_UpperCAmelCase					=    (2, 2, 18, 2)
								_UpperCAmelCase					=    (6, 12, 24, 48)

				if "in22k" in swin_name:
								_UpperCAmelCase					=    2_1841
				else:
								_UpperCAmelCase					=    1000
								_UpperCAmelCase					=    '''huggingface/label-files'''
								_UpperCAmelCase					=    '''imagenet-1k-id2label.json'''
								_UpperCAmelCase					=    json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     repo_type='''dataset'''				)					,     '''r'''				)				)
								_UpperCAmelCase					=    {int(_SCREAMING_SNAKE_CASE				): v for k, v in idalabel.items()}
								_UpperCAmelCase					=    idalabel
								_UpperCAmelCase					=    {v: k for k, v in idalabel.items()}

				_UpperCAmelCase					=    img_size
				_UpperCAmelCase					=    num_classes
				_UpperCAmelCase					=    embed_dim
				_UpperCAmelCase					=    depths
				_UpperCAmelCase					=    num_heads
				_UpperCAmelCase					=    window_size

				return config







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				if "patch_embed.proj" in name:
								_UpperCAmelCase					=    name.replace('''patch_embed.proj'''					,     '''embeddings.patch_embeddings.projection'''				)
				if "patch_embed.norm" in name:
								_UpperCAmelCase					=    name.replace('''patch_embed.norm'''					,     '''embeddings.norm'''				)
				if "layers" in name:
								_UpperCAmelCase					=    '''encoder.''' + name
				if "attn.proj" in name:
								_UpperCAmelCase					=    name.replace('''attn.proj'''					,     '''attention.output.dense'''				)
				if "attn" in name:
								_UpperCAmelCase					=    name.replace('''attn'''					,     '''attention.self'''				)
				if "norm1" in name:
								_UpperCAmelCase					=    name.replace('''norm1'''					,     '''layernorm_before'''				)
				if "norm2" in name:
								_UpperCAmelCase					=    name.replace('''norm2'''					,     '''layernorm_after'''				)
				if "mlp.fc1" in name:
								_UpperCAmelCase					=    name.replace('''mlp.fc1'''					,     '''intermediate.dense'''				)
				if "mlp.fc2" in name:
								_UpperCAmelCase					=    name.replace('''mlp.fc2'''					,     '''output.dense'''				)

				if name == "norm.weight":
								_UpperCAmelCase					=    '''layernorm.weight'''
				if name == "norm.bias":
								_UpperCAmelCase					=    '''layernorm.bias'''

				if "head" in name:
								_UpperCAmelCase					=    name.replace('''head'''					,     '''classifier'''				)
				else:
								_UpperCAmelCase					=    '''swin.''' + name

				return name







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						Union[str, Any]					,     _SCREAMING_SNAKE_CASE   :						Dict				):






				'''simple docstring'''



				for key in orig_state_dict.copy().keys():
								_UpperCAmelCase					=    orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

								if "mask" in key:
												continue
								elif "qkv" in key:
												_UpperCAmelCase					=    key.split('''.'''				)
												_UpperCAmelCase					=    int(key_split[1]				)
												_UpperCAmelCase					=    int(key_split[3]				)
												_UpperCAmelCase					=    model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

												if "weight" in key:
																_UpperCAmelCase					=    val[:dim, :]
																_UpperCAmelCase					=    val[
																    dim : dim * 2, :
																]
																_UpperCAmelCase					=    val[-dim:, :]
												else:
																_UpperCAmelCase					=    val[
																    :dim
																]
																_UpperCAmelCase					=    val[
																    dim : dim * 2
																]
																_UpperCAmelCase					=    val[
																    -dim:
																]
								else:
												_UpperCAmelCase					=    val

				return orig_state_dict







def  lowercase	(      _SCREAMING_SNAKE_CASE   :						int					,     _SCREAMING_SNAKE_CASE   :						int				):






				'''simple docstring'''



				_UpperCAmelCase					=    timm.create_model(_SCREAMING_SNAKE_CASE					,     pretrained=_SCREAMING_SNAKE_CASE				)
				timm_model.eval()

				_UpperCAmelCase					=    get_swin_config(_SCREAMING_SNAKE_CASE				)
				_UpperCAmelCase					=    SwinForImageClassification(_SCREAMING_SNAKE_CASE				)
				model.eval()

				_UpperCAmelCase					=    convert_state_dict(timm_model.state_dict()					,     _SCREAMING_SNAKE_CASE				)
				model.load_state_dict(_SCREAMING_SNAKE_CASE				)

				_UpperCAmelCase					=    '''http://images.cocodataset.org/val2017/000000039769.jpg'''

				_UpperCAmelCase					=    AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''					,     '''-'''				)				)				)
				_UpperCAmelCase					=    Image.open(requests.get(_SCREAMING_SNAKE_CASE					,     stream=_SCREAMING_SNAKE_CASE				).raw				)
				_UpperCAmelCase					=    image_processor(images=_SCREAMING_SNAKE_CASE					,     return_tensors='''pt'''				)

				_UpperCAmelCase					=    timm_model(inputs['''pixel_values''']				)
				_UpperCAmelCase					=    model(**_SCREAMING_SNAKE_CASE				).logits

				assert torch.allclose(_SCREAMING_SNAKE_CASE					,     _SCREAMING_SNAKE_CASE					,     atol=1E-3				)

				print(f'Saving model {swin_name} to {pytorch_dump_folder_path}'				)
				model.save_pretrained(_SCREAMING_SNAKE_CASE				)

				print(f'Saving image processor to {pytorch_dump_folder_path}'				)
				image_processor.save_pretrained(_SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
					__A   :							int										=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--swin_name",
					    default="swin_tiny_patch4_window7_224",
					    type=str,
					    help="Name of the Swin timm model 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   :							Tuple										=   parser.parse_args()
					convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



