




"""simple docstring"""






import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)







def      _snake_case			(				lowercase__		):
				_lowerCamelCase     :			Dict    =    VideoMAEConfig()

				set_architecture_configs(lowercase__							,    lowercase__		)

				if "finetuned" not in model_name:
								_lowerCamelCase     :			Tuple    =    False

				if "finetuned" in model_name:
								_lowerCamelCase     :			Optional[Any]    =    'huggingface/label-files'
								if "kinetics" in model_name:
												_lowerCamelCase     :			Union[str, Any]    =    400
												_lowerCamelCase     :			str    =    'kinetics400-id2label.json'
								elif "ssv2" in model_name:
												_lowerCamelCase     :			List[Any]    =    174
												_lowerCamelCase     :			Optional[Any]    =    'something-something-v2-id2label.json'
								else:
												raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'		)
								_lowerCamelCase     :			Optional[int]    =    json.load(open(hf_hub_download(lowercase__							,    lowercase__							,    repo_type='dataset'		)							,    'r'		)		)
								_lowerCamelCase     :			List[Any]    =    {int(lowercase__		): v for k, v in idalabel.items()}
								_lowerCamelCase     :			List[Any]    =    idalabel
								_lowerCamelCase     :			List[str]    =    {v: k for k, v in idalabel.items()}

				return config







def      _snake_case			(				lowercase__							,    lowercase__		):
				if "small" in model_name:
								_lowerCamelCase     :			List[str]    =    384
								_lowerCamelCase     :			Dict    =    1536
								_lowerCamelCase     :			int    =    12
								_lowerCamelCase     :			List[Any]    =    16
								_lowerCamelCase     :			Any    =    12
								_lowerCamelCase     :			int    =    3
								_lowerCamelCase     :			Dict    =    192
								_lowerCamelCase     :			Optional[Any]    =    768
				elif "large" in model_name:
								_lowerCamelCase     :			Optional[Any]    =    1024
								_lowerCamelCase     :			Tuple    =    4096
								_lowerCamelCase     :			int    =    24
								_lowerCamelCase     :			Union[str, Any]    =    16
								_lowerCamelCase     :			Tuple    =    12
								_lowerCamelCase     :			List[str]    =    8
								_lowerCamelCase     :			List[Any]    =    512
								_lowerCamelCase     :			Any    =    2048
				elif "huge" in model_name:
								_lowerCamelCase     :			Dict    =    1280
								_lowerCamelCase     :			Any    =    5120
								_lowerCamelCase     :			str    =    32
								_lowerCamelCase     :			Union[str, Any]    =    16
								_lowerCamelCase     :			str    =    12
								_lowerCamelCase     :			Union[str, Any]    =    8
								_lowerCamelCase     :			Tuple    =    640
								_lowerCamelCase     :			Any    =    2560
				elif "base" not in model_name:
								raise ValueError('Model name should include either "small", "base", "large", or "huge"'		)







def      _snake_case			(				lowercase__		):
				if "encoder." in name:
								_lowerCamelCase     :			Any    =    name.replace('encoder.'							,    ''		)
				if "cls_token" in name:
								_lowerCamelCase     :			Any    =    name.replace('cls_token'							,    'videomae.embeddings.cls_token'		)
				if "decoder_pos_embed" in name:
								_lowerCamelCase     :			Dict    =    name.replace('decoder_pos_embed'							,    'decoder.decoder_pos_embed'		)
				if "pos_embed" in name and "decoder" not in name:
								_lowerCamelCase     :			Tuple    =    name.replace('pos_embed'							,    'videomae.embeddings.position_embeddings'		)
				if "patch_embed.proj" in name:
								_lowerCamelCase     :			Any    =    name.replace('patch_embed.proj'							,    'videomae.embeddings.patch_embeddings.projection'		)
				if "patch_embed.norm" in name:
								_lowerCamelCase     :			List[Any]    =    name.replace('patch_embed.norm'							,    'videomae.embeddings.norm'		)
				if "decoder.blocks" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('decoder.blocks'							,    'decoder.decoder_layers'		)
				if "blocks" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('blocks'							,    'videomae.encoder.layer'		)
				if "attn.proj" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('attn.proj'							,    'attention.output.dense'		)
				if "attn" in name and "bias" not in name:
								_lowerCamelCase     :			List[Any]    =    name.replace('attn'							,    'attention.self'		)
				if "attn" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('attn'							,    'attention.attention'		)
				if "norm1" in name:
								_lowerCamelCase     :			List[Any]    =    name.replace('norm1'							,    'layernorm_before'		)
				if "norm2" in name:
								_lowerCamelCase     :			str    =    name.replace('norm2'							,    'layernorm_after'		)
				if "mlp.fc1" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('mlp.fc1'							,    'intermediate.dense'		)
				if "mlp.fc2" in name:
								_lowerCamelCase     :			Optional[int]    =    name.replace('mlp.fc2'							,    'output.dense'		)
				if "decoder_embed" in name:
								_lowerCamelCase     :			Optional[int]    =    name.replace('decoder_embed'							,    'decoder.decoder_embed'		)
				if "decoder_norm" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('decoder_norm'							,    'decoder.decoder_norm'		)
				if "decoder_pred" in name:
								_lowerCamelCase     :			Optional[Any]    =    name.replace('decoder_pred'							,    'decoder.decoder_pred'		)
				if "norm.weight" in name and "decoder" not in name and "fc" not in name:
								_lowerCamelCase     :			int    =    name.replace('norm.weight'							,    'videomae.layernorm.weight'		)
				if "norm.bias" in name and "decoder" not in name and "fc" not in name:
								_lowerCamelCase     :			Union[str, Any]    =    name.replace('norm.bias'							,    'videomae.layernorm.bias'		)
				if "head" in name and "decoder" not in name:
								_lowerCamelCase     :			Any    =    name.replace('head'							,    'classifier'		)

				return name







def      _snake_case			(				lowercase__							,    lowercase__		):
				for key in orig_state_dict.copy().keys():
								_lowerCamelCase     :			List[str]    =    orig_state_dict.pop(lowercase__		)

								if key.startswith('encoder.'		):
												_lowerCamelCase     :			int    =    key.replace('encoder.'							,    ''		)

								if "qkv" in key:
												_lowerCamelCase     :			str    =    key.split('.'		)
												if key.startswith('decoder.blocks'		):
																_lowerCamelCase     :			Any    =    config.decoder_hidden_size
																_lowerCamelCase     :			Optional[int]    =    int(key_split[2]		)
																_lowerCamelCase     :			List[str]    =    'decoder.decoder_layers.'
																if "weight" in key:
																				_lowerCamelCase     :			Optional[Any]    =    val[:dim, :]
																				_lowerCamelCase     :			Any    =    val[dim : dim * 2, :]
																				_lowerCamelCase     :			Union[str, Any]    =    val[-dim:, :]
												else:
																_lowerCamelCase     :			int    =    config.hidden_size
																_lowerCamelCase     :			Optional[int]    =    int(key_split[1]		)
																_lowerCamelCase     :			Tuple    =    'videomae.encoder.layer.'
																if "weight" in key:
																				_lowerCamelCase     :			List[str]    =    val[:dim, :]
																				_lowerCamelCase     :			Optional[int]    =    val[dim : dim * 2, :]
																				_lowerCamelCase     :			List[Any]    =    val[-dim:, :]
								else:
												_lowerCamelCase     :			int    =    val

				return orig_state_dict







def      _snake_case			(				):
				_lowerCamelCase     :			Optional[Any]    =    hf_hub_download(
				    repo_id='hf-internal-testing/spaghetti-video'							,    filename='eating_spaghetti.npy'							,    repo_type='dataset'		)
				_lowerCamelCase     :			Any    =    np.load(lowercase__		)
				return list(lowercase__		)







def      _snake_case			(				lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		):
				_lowerCamelCase     :			str    =    get_videomae_config(lowercase__		)

				if "finetuned" in model_name:
								_lowerCamelCase     :			str    =    VideoMAEForVideoClassification(lowercase__		)
				else:
								_lowerCamelCase     :			Union[str, Any]    =    VideoMAEForPreTraining(lowercase__		)

				# download original checkpoint, hosted on Google Drive
				_lowerCamelCase     :			Any    =    'pytorch_model.bin'
				gdown.cached_download(lowercase__							,    lowercase__							,    quiet=lowercase__		)
				_lowerCamelCase     :			Optional[int]    =    torch.load(lowercase__							,    map_location='cpu'		)
				if "model" in files:
								_lowerCamelCase     :			Any    =    files['model']
				else:
								_lowerCamelCase     :			Dict    =    files['module']
				_lowerCamelCase     :			Union[str, Any]    =    convert_state_dict(lowercase__							,    lowercase__		)

				model.load_state_dict(lowercase__		)
				model.eval()

				# verify model on basic input
				_lowerCamelCase     :			Any    =    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]							,    image_std=[0.5, 0.5, 0.5]		)
				_lowerCamelCase     :			Tuple    =    prepare_video()
				_lowerCamelCase     :			List[Any]    =    image_processor(lowercase__							,    return_tensors='pt'		)

				if "finetuned" not in model_name:
								_lowerCamelCase     :			Tuple    =    hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'							,    filename='bool_masked_pos.pt'		)
								_lowerCamelCase     :			List[Any]    =    torch.load(lowercase__		)

				_lowerCamelCase     :			Any    =    model(**lowercase__		)
				_lowerCamelCase     :			Union[str, Any]    =    outputs.logits

				_lowerCamelCase     :			Optional[int]    =    [
				    'videomae-small-finetuned-kinetics',
				    'videomae-small-finetuned-ssv2',
				    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
				    'videomae-base-short',
				    'videomae-base-short-finetuned-kinetics',
				    'videomae-base',
				    'videomae-base-finetuned-kinetics',
				    'videomae-large',
				    'videomae-large-finetuned-kinetics',
				    'videomae-huge-finetuned-kinetics',
				    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
				    'videomae-base-short-ssv2',
				    'videomae-base-short-finetuned-ssv2',
				    'videomae-base-ssv2',
				    'videomae-base-finetuned-ssv2',
				]

				# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
				if model_name == "videomae-small-finetuned-kinetics":
								_lowerCamelCase     :			Optional[Any]    =    torch.Size([1, 400]		)
								_lowerCamelCase     :			int    =    torch.tensor([-0.9_2_9_1, -0.4_0_6_1, -0.9_3_0_7]		)
				elif model_name == "videomae-small-finetuned-ssv2":
								_lowerCamelCase     :			Optional[int]    =    torch.Size([1, 174]		)
								_lowerCamelCase     :			int    =    torch.tensor([0.2_6_7_1, -0.4_6_8_9, -0.8_2_3_5]		)
				elif model_name == "videomae-base":
								_lowerCamelCase     :			int    =    torch.Size([1, 1408, 1536]		)
								_lowerCamelCase     :			Dict    =    torch.tensor([[0.7_7_3_9, 0.7_9_6_8, 0.7_0_8_9], [0.6_7_0_1, 0.7_4_8_7, 0.6_2_0_9], [0.4_2_8_7, 0.5_1_5_8, 0.4_7_7_3]]		)
				elif model_name == "videomae-base-short":
								_lowerCamelCase     :			Any    =    torch.Size([1, 1408, 1536]		)
								_lowerCamelCase     :			Optional[Any]    =    torch.tensor([[0.7_9_9_4, 0.9_6_1_2, 0.8_5_0_8], [0.7_4_0_1, 0.8_9_5_8, 0.8_3_0_2], [0.5_8_6_2, 0.7_4_6_8, 0.7_3_2_5]]		)
								# we verified the loss both for normalized and unnormalized targets for this one
								_lowerCamelCase     :			str    =    torch.tensor([0.5_1_4_2]		) if config.norm_pix_loss else torch.tensor([0.6_4_6_9]		)
				elif model_name == "videomae-large":
								_lowerCamelCase     :			Any    =    torch.Size([1, 1408, 1536]		)
								_lowerCamelCase     :			int    =    torch.tensor([[0.7_1_4_9, 0.7_9_9_7, 0.6_9_6_6], [0.6_7_6_8, 0.7_8_6_9, 0.6_9_4_8], [0.5_1_3_9, 0.6_2_2_1, 0.5_6_0_5]]		)
				elif model_name == "videomae-large-finetuned-kinetics":
								_lowerCamelCase     :			List[str]    =    torch.Size([1, 400]		)
								_lowerCamelCase     :			Optional[int]    =    torch.tensor([0.0_7_7_1, 0.0_0_1_1, -0.3_6_2_5]		)
				elif model_name == "videomae-huge-finetuned-kinetics":
								_lowerCamelCase     :			Any    =    torch.Size([1, 400]		)
								_lowerCamelCase     :			Any    =    torch.tensor([0.2_4_3_3, 0.1_6_3_2, -0.4_8_9_4]		)
				elif model_name == "videomae-base-short-finetuned-kinetics":
								_lowerCamelCase     :			Union[str, Any]    =    torch.Size([1, 400]		)
								_lowerCamelCase     :			Tuple    =    torch.tensor([0.6_5_8_8, 0.0_9_9_0, -0.2_4_9_3]		)
				elif model_name == "videomae-base-finetuned-kinetics":
								_lowerCamelCase     :			Dict    =    torch.Size([1, 400]		)
								_lowerCamelCase     :			List[str]    =    torch.tensor([0.3_6_6_9, -0.0_6_8_8, -0.2_4_2_1]		)
				elif model_name == "videomae-base-short-ssv2":
								_lowerCamelCase     :			Any    =    torch.Size([1, 1408, 1536]		)
								_lowerCamelCase     :			Optional[Any]    =    torch.tensor([[0.4_7_1_2, 0.5_2_9_6, 0.5_7_8_6], [0.2_2_7_8, 0.2_7_2_9, 0.4_0_2_6], [0.0_3_5_2, 0.0_7_3_0, 0.2_5_0_6]]		)
				elif model_name == "videomae-base-short-finetuned-ssv2":
								_lowerCamelCase     :			Union[str, Any]    =    torch.Size([1, 174]		)
								_lowerCamelCase     :			Dict    =    torch.tensor([-0.0_5_3_7, -0.1_5_3_9, -0.3_2_6_6]		)
				elif model_name == "videomae-base-ssv2":
								_lowerCamelCase     :			Optional[int]    =    torch.Size([1, 1408, 1536]		)
								_lowerCamelCase     :			str    =    torch.tensor([[0.8_1_3_1, 0.8_7_2_7, 0.8_5_4_6], [0.7_3_6_6, 0.9_3_7_7, 0.8_8_7_0], [0.5_9_3_5, 0.8_8_7_4, 0.8_5_6_4]]		)
				elif model_name == "videomae-base-finetuned-ssv2":
								_lowerCamelCase     :			Tuple    =    torch.Size([1, 174]		)
								_lowerCamelCase     :			Dict    =    torch.tensor([0.1_9_6_1, -0.8_3_3_7, -0.6_3_8_9]		)
				else:
								raise ValueError(f'''Model name not supported. Should be one of {model_names}'''		)

				# verify logits
				assert logits.shape == expected_shape
				if "finetuned" in model_name:
								assert torch.allclose(logits[0, :3]							,    lowercase__							,    atol=1E-4		)
				else:
								print('Logits:'							,    logits[0, :3, :3]		)
								assert torch.allclose(logits[0, :3, :3]							,    lowercase__							,    atol=1E-4		)
				print('Logits ok!'		)

				# verify loss, if applicable
				if model_name == "videomae-base-short":
								_lowerCamelCase     :			Dict    =    outputs.loss
								assert torch.allclose(lowercase__							,    lowercase__							,    atol=1E-4		)
								print('Loss ok!'		)

				if pytorch_dump_folder_path is not None:
								print(f'''Saving model and image processor to {pytorch_dump_folder_path}'''		)
								image_processor.save_pretrained(lowercase__		)
								model.save_pretrained(lowercase__		)

				if push_to_hub:
								print('Pushing to the hub...'		)
								model.push_to_hub(lowercase__							,    organization='nielsr'		)


if __name__ == "__main__":
			lowercase__     						=						argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    """--checkpoint_url""",
			    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
			    type=str,
			    help=(
			        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
			        """ download link."""
			    ),
			)
			parser.add_argument(
			    """--pytorch_dump_folder_path""",
			    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
			    type=str,
			    help="""Path to the output PyTorch model directory.""",
			)
			parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
			parser.add_argument(
			    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
			)

			lowercase__     						=						parser.parse_args()
			convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





"""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


lowercase__     						=						16
lowercase__     						=						32







def      _snake_case			(				lowercase__							,    lowercase__ = 16							,    lowercase__ = "bert-base-cased"		):
				_lowerCamelCase     :			List[Any]    =    AutoTokenizer.from_pretrained(lowercase__		)
				_lowerCamelCase     :			Tuple    =    load_dataset('glue'							,    'mrpc'		)

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

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

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

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

				# Instantiate dataloaders.
				_lowerCamelCase     :			List[str]    =    DataLoader(
				    tokenized_datasets['train']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)
				_lowerCamelCase     :			int    =    DataLoader(
				    tokenized_datasets['validation']							,    shuffle=lowercase__							,    collate_fn=lowercase__							,    batch_size=lowercase__		)

				return train_dataloader, eval_dataloader







def      _snake_case			(				lowercase__							,    lowercase__		):
				# Initialize accelerator
				_lowerCamelCase     :			Optional[int]    =    Accelerator()

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

				set_seed(lowercase__		)
				_lowerCamelCase, _lowerCamelCase     :			Optional[int]    =    get_dataloaders(lowercase__							,    lowercase__							,    lowercase__		)

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

				# Instantiate optimizer
				_lowerCamelCase     :			Optional[int]    =    (
				    AdamW
				    if accelerator.state.deepspeed_plugin is None
				    or 'optimizer' not in accelerator.state.deepspeed_plugin.deepspeed_config
				    else DummyOptim
				)
				_lowerCamelCase     :			Union[str, Any]    =    optimizer_cls(params=model.parameters()							,    lr=lowercase__		)

				if accelerator.state.deepspeed_plugin is not None:
								_lowerCamelCase     :			str    =    accelerator.state.deepspeed_plugin.deepspeed_config[
								    'gradient_accumulation_steps'
								]
				else:
								_lowerCamelCase     :			Tuple    =    1
				_lowerCamelCase     :			List[Any]    =    (len(lowercase__		) * num_epochs) // gradient_accumulation_steps

				# Instantiate scheduler
				if (
				    accelerator.state.deepspeed_plugin is None
				    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
				):
								_lowerCamelCase     :			Tuple    =    get_linear_schedule_with_warmup(
								    optimizer=lowercase__							,    num_warmup_steps=0							,    num_training_steps=lowercase__							,    )
				else:
								_lowerCamelCase     :			Any    =    DummyScheduler(lowercase__							,    total_num_steps=lowercase__							,    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.
				_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase     :			Union[str, Any]    =    accelerator.prepare(
				    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__							,    lowercase__		)

				# We need to keep track of how many total steps we have iterated over
				_lowerCamelCase     :			Union[str, Any]    =    0
				# We also need to keep track of the stating epoch so files are named properly
				_lowerCamelCase     :			Dict    =    0

				# Now we train the model
				_lowerCamelCase     :			Dict    =    evaluate.load('glue'							,    'mrpc'		)
				_lowerCamelCase     :			Optional[int]    =    0
				_lowerCamelCase     :			str    =    {}
				for epoch in range(lowercase__							,    lowercase__		):
								model.train()
								for step, batch in enumerate(lowercase__		):
												_lowerCamelCase     :			List[Any]    =    model(**lowercase__		)
												_lowerCamelCase     :			int    =    outputs.loss
												_lowerCamelCase     :			Dict    =    loss / gradient_accumulation_steps
												accelerator.backward(lowercase__		)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

												overall_step += 1

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

								_lowerCamelCase     :			List[Any]    =    metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(f'''epoch {epoch}:'''							,    lowercase__		)
								_lowerCamelCase     :			Tuple    =    eval_metric['accuracy']

								if best_performance < eval_metric["accuracy"]:
												_lowerCamelCase     :			str    =    eval_metric['accuracy']

				if args.performance_lower_bound is not None:
								assert (
								    args.performance_lower_bound <= best_performance
								), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

				accelerator.wait_for_everyone()
				if accelerator.is_main_process:
								with open(os.path.join(args.output_dir							,    'all_results.json'		)							,    'w'		) as f:
												json.dump(lowercase__							,    lowercase__		)







def      _snake_case			(				):
				_lowerCamelCase     :			Any    =    argparse.ArgumentParser(description='Simple example of training script tracking peak GPU memory usage.'		)
				parser.add_argument(
				    '--model_name_or_path'							,    type=lowercase__							,    default='bert-base-cased'							,    help='Path to pretrained model or model identifier from huggingface.co/models.'							,    required=lowercase__							,    )
				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(
				    '--performance_lower_bound'							,    type=lowercase__							,    default=lowercase__							,    help='Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.'							,    )
				parser.add_argument(
				    '--num_epochs'							,    type=lowercase__							,    default=3							,    help='Number of train epochs.'							,    )
				_lowerCamelCase     :			Optional[Any]    =    parser.parse_args()
				_lowerCamelCase     :			str    =    {'lr': 2E-5, 'num_epochs': args.num_epochs, 'seed': 42, 'batch_size': 16}
				training_function(lowercase__							,    lowercase__		)


if __name__ == "__main__":
			main()