
"""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        _lowerCamelCase ( __a,							__a = 16,							__a = "bert-base-cased"    ):
		SCREAMING_SNAKE_CASE_						 = AutoTokenizer.from_pretrained(__a    )
		SCREAMING_SNAKE_CASE_						 = load_dataset('''glue''',							'''mrpc'''    )

		def tokenize_function(__a    ):
				# max_length=None => use the model max length (it's actually the default)
				SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = 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
		SCREAMING_SNAKE_CASE_						 = 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=128,							return_tensors='''pt'''    )
				return tokenizer.pad(__a,							padding='''longest''',							return_tensors='''pt'''    )

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

		return train_dataloader, eval_dataloader



def        _lowerCamelCase ( __a,							__a,							__a,							__a    ):
		model.eval()
		SCREAMING_SNAKE_CASE_						 = 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():
						SCREAMING_SNAKE_CASE_						 = model(**__a    )
				SCREAMING_SNAKE_CASE_						 = outputs.logits.argmax(dim=-1    )
				# It is slightly faster to call this once, than multiple times
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = 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:
								SCREAMING_SNAKE_CASE_						 = predictions[: len(eval_dataloader.dataset    ) - samples_seen]
								SCREAMING_SNAKE_CASE_						 = references[: len(eval_dataloader.dataset    ) - samples_seen]
						else:
								samples_seen += references.shape[0]
				metric.add_batch(
				    predictions=__a,							references=__a,							)

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



def        _lowerCamelCase ( __a,							__a    ):
		# Initialize accelerator
		SCREAMING_SNAKE_CASE_						 = Accelerator()

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

		set_seed(__a    )
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = get_dataloaders(__a,							__a,							__a    )

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

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

		if accelerator.state.deepspeed_plugin is not None:
				SCREAMING_SNAKE_CASE_						 = accelerator.state.deepspeed_plugin.deepspeed_config[
				    '''gradient_accumulation_steps'''
				]
		else:
				SCREAMING_SNAKE_CASE_						 = 1
		SCREAMING_SNAKE_CASE_						 = (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
		):
				SCREAMING_SNAKE_CASE_						 = get_linear_schedule_with_warmup(
				    optimizer=__a,							num_warmup_steps=0,							num_training_steps=__a,							)
		else:
				SCREAMING_SNAKE_CASE_						 = 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.
		SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = accelerator.prepare(
		    __a,							__a,							__a,							__a,							__a    )

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

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

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

						overall_step += 1
				SCREAMING_SNAKE_CASE_						 = F'epoch_{epoch}'
				SCREAMING_SNAKE_CASE_						 = os.path.join(args.output_dir,							__a    )
				accelerator.save_state(__a    )
				SCREAMING_SNAKE_CASE_						 = evaluation_loop(__a,							__a,							__a,							__a    )
				SCREAMING_SNAKE_CASE_						 = accuracy
				SCREAMING_SNAKE_CASE_						 = lr_scheduler.get_lr()[0]
				SCREAMING_SNAKE_CASE_						 = optimizer.param_groups[0]['''lr''']
				SCREAMING_SNAKE_CASE_						 = epoch
				SCREAMING_SNAKE_CASE_						 = 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        _lowerCamelCase ( ):
		SCREAMING_SNAKE_CASE_						 = 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.''',							)
		SCREAMING_SNAKE_CASE_						 = parser.parse_args()
		SCREAMING_SNAKE_CASE_						 = {'''lr''': 2E-5, '''num_epochs''': args.num_epochs, '''seed''': 42, '''batch_size''': 16}

		training_function(__a,							__a    )


if __name__ == "__main__":
						main()

"""simple docstring"""




import argparse
import json

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

from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling



def        _lowerCamelCase ( __a    ):
		SCREAMING_SNAKE_CASE_						 = [2, 2, 6, 2] if '''tiny''' in model_name else [2, 2, 18, 2]
		SCREAMING_SNAKE_CASE_						 = True if '''large''' in model_name or '''huge''' in model_name else False
		SCREAMING_SNAKE_CASE_						 = True if '''large''' in model_name or '''huge''' in model_name else False
		SCREAMING_SNAKE_CASE_						 = True if '''large''' in model_name or '''huge''' in model_name else False

		if "large" in model_name or "xlarge" in model_name or "huge" in model_name:
				if "fl3" in model_name:
						SCREAMING_SNAKE_CASE_						 = [3, 3, 3, 3]
						SCREAMING_SNAKE_CASE_						 = [5, 5, 5, 5]
				elif "fl4" in model_name:
						SCREAMING_SNAKE_CASE_						 = [4, 4, 4, 4]
						SCREAMING_SNAKE_CASE_						 = [3, 3, 3, 3]

		if "tiny" in model_name or "small" in model_name or "base" in model_name:
				SCREAMING_SNAKE_CASE_						 = [3, 3, 3, 3]
				if "lrf" in model_name:
						SCREAMING_SNAKE_CASE_						 = [3, 3, 3, 3]
				else:
						SCREAMING_SNAKE_CASE_						 = [2, 2, 2, 2]

		if "tiny" in model_name:
				SCREAMING_SNAKE_CASE_						 = 96
		elif "small" in model_name:
				SCREAMING_SNAKE_CASE_						 = 96
		elif "base" in model_name:
				SCREAMING_SNAKE_CASE_						 = 128
		elif "large" in model_name:
				SCREAMING_SNAKE_CASE_						 = 192
		elif "xlarge" in model_name:
				SCREAMING_SNAKE_CASE_						 = 256
		elif "huge" in model_name:
				SCREAMING_SNAKE_CASE_						 = 352

		# set label information
		SCREAMING_SNAKE_CASE_						 = '''huggingface/label-files'''
		if "large" in model_name or "huge" in model_name:
				SCREAMING_SNAKE_CASE_						 = '''imagenet-22k-id2label.json'''
		else:
				SCREAMING_SNAKE_CASE_						 = '''imagenet-1k-id2label.json'''

		SCREAMING_SNAKE_CASE_						 = json.load(open(hf_hub_download(__a,							__a,							repo_type='''dataset'''    ),							'''r'''    )    )
		SCREAMING_SNAKE_CASE_						 = {int(__a    ): v for k, v in idalabel.items()}
		SCREAMING_SNAKE_CASE_						 = {v: k for k, v in idalabel.items()}

		SCREAMING_SNAKE_CASE_						 = FocalNetConfig(
		    embed_dim=__a,							depths=__a,							focal_levels=__a,							focal_windows=__a,							use_conv_embed=__a,							idalabel=__a,							labelaid=__a,							use_post_layernorm=__a,							use_layerscale=__a,							)

		return config



def        _lowerCamelCase ( __a    ):
		if "patch_embed.proj" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''patch_embed.proj''',							'''embeddings.patch_embeddings.projection'''    )
		if "patch_embed.norm" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''patch_embed.norm''',							'''embeddings.norm'''    )
		if "layers" in name:
				SCREAMING_SNAKE_CASE_						 = '''encoder.''' + name
		if "encoder.layers" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''encoder.layers''',							'''encoder.stages'''    )
		if "downsample.proj" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''downsample.proj''',							'''downsample.projection'''    )
		if "blocks" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''blocks''',							'''layers'''    )
		if "modulation.f.weight" in name or "modulation.f.bias" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''modulation.f''',							'''modulation.projection_in'''    )
		if "modulation.h.weight" in name or "modulation.h.bias" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''modulation.h''',							'''modulation.projection_context'''    )
		if "modulation.proj.weight" in name or "modulation.proj.bias" in name:
				SCREAMING_SNAKE_CASE_						 = name.replace('''modulation.proj''',							'''modulation.projection_out'''    )

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

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

		return name



def        _lowerCamelCase ( __a,							__a,							__a=False    ):
		# fmt: off
		SCREAMING_SNAKE_CASE_						 = {
		    '''focalnet-tiny''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_srf.pth''',
		    '''focalnet-tiny-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_tiny_lrf.pth''',
		    '''focalnet-small''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_srf.pth''',
		    '''focalnet-small-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_small_lrf.pth''',
		    '''focalnet-base''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_srf.pth''',
		    '''focalnet-base-lrf''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_base_lrf.pth''',
		    '''focalnet-large-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384.pth''',
		    '''focalnet-large-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_large_lrf_384_fl4.pth''',
		    '''focalnet-xlarge-lrf-fl3''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384.pth''',
		    '''focalnet-xlarge-lrf-fl4''': '''https://projects4jw.blob.core.windows.net/focalnet/release/classification/focalnet_xlarge_lrf_384_fl4.pth''',
		}
		# fmt: on

		SCREAMING_SNAKE_CASE_						 = model_name_to_url[model_name]
		print('''Checkpoint URL: ''',							__a    )
		SCREAMING_SNAKE_CASE_						 = torch.hub.load_state_dict_from_url(__a,							map_location='''cpu'''    )['''model''']

		# rename keys
		for key in state_dict.copy().keys():
				SCREAMING_SNAKE_CASE_						 = state_dict.pop(__a    )
				SCREAMING_SNAKE_CASE_						 = val

		SCREAMING_SNAKE_CASE_						 = get_focalnet_config(__a    )
		SCREAMING_SNAKE_CASE_						 = FocalNetForImageClassification(__a    )
		model.eval()

		# load state dict
		model.load_state_dict(__a    )

		# verify conversion
		SCREAMING_SNAKE_CASE_						 = '''http://images.cocodataset.org/val2017/000000039769.jpg'''

		SCREAMING_SNAKE_CASE_						 = BitImageProcessor(
		    do_resize=__a,							size={'''shortest_edge''': 256},							resample=PILImageResampling.BILINEAR,							do_center_crop=__a,							crop_size=224,							do_normalize=__a,							image_mean=__a,							image_std=__a,							)
		SCREAMING_SNAKE_CASE_						 = Image.open(requests.get(__a,							stream=__a    ).raw    )
		SCREAMING_SNAKE_CASE_						 = processor(images=__a,							return_tensors='''pt'''    )

		SCREAMING_SNAKE_CASE_						 = transforms.Compose(
		    [
		        transforms.Resize(256    ),
		        transforms.CenterCrop(224    ),
		        transforms.ToTensor(),
		        transforms.Normalize(mean=[0.4_8_5, 0.4_5_6, 0.4_0_6],							std=[0.2_2_9, 0.2_2_4, 0.2_2_5]    ),
		    ]    )

		SCREAMING_SNAKE_CASE_						 = image_transforms(__a    ).unsqueeze(0    )

		# verify pixel_values
		assert torch.allclose(inputs.pixel_values,							__a,							atol=1E-4    )

		SCREAMING_SNAKE_CASE_						 = model(**__a    )

		SCREAMING_SNAKE_CASE_						 = outputs.logits.argmax(-1    ).item()
		print('''Predicted class:''',							model.config.idalabel[predicted_class_idx]    )

		print('''First values of logits:''',							outputs.logits[0, :3]    )

		if model_name == "focalnet-tiny":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([0.2_1_6_6, -0.4_3_6_8, 0.2_1_9_1]    )
		elif model_name == "focalnet-tiny-lrf":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([1.1_6_6_9, 0.0_1_2_5, -0.1_6_9_5]    )
		elif model_name == "focalnet-small":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([0.4_9_1_7, -0.0_4_3_0, 0.1_3_4_1]    )
		elif model_name == "focalnet-small-lrf":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([-0.2_5_8_8, -0.5_3_4_2, -0.2_3_3_1]    )
		elif model_name == "focalnet-base":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([-0.1_6_5_5, -0.4_0_9_0, -0.1_7_3_0]    )
		elif model_name == "focalnet-base-lrf":
				SCREAMING_SNAKE_CASE_						 = torch.tensor([0.5_3_0_6, -0.0_4_8_3, -0.3_9_2_8]    )
		assert torch.allclose(outputs.logits[0, :3],							__a,							atol=1E-4    )
		print('''Looks ok!'''    )

		if pytorch_dump_folder_path is not None:
				print(F'Saving model and processor of {model_name} to {pytorch_dump_folder_path}'    )
				model.save_pretrained(__a    )
				processor.save_pretrained(__a    )

		if push_to_hub:
				print(F'Pushing model and processor of {model_name} to the hub...'    )
				model.push_to_hub(F'{model_name}'    )
				processor.push_to_hub(F'{model_name}'    )


if __name__ == "__main__":
						lowerCAmelCase__							=				argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='focalnet-tiny',
						    type=str,
						    help='Name of the FocalNet 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.'
						)
						parser.add_argument(
						    '--push_to_hub',
						    action='store_true',
						    help='Whether to push the model and processor to the hub.',
						)

						lowerCAmelCase__							=				parser.parse_args()
						convert_focalnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)