

"""simple docstring"""





import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase    : Any				 =						logging.get_logger()

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_		,	A_		,	A_		,	A_ = True					):
	print(f'Converting {name}...'					)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				lowerCAmelCase__      :		Tuple    							=      timm.create_model('''levit_128s'''		,	pretrained=A_					)
			else:
				lowerCAmelCase__      :		List[Any]    							=      timm.create_model('''levit_128'''		,	pretrained=A_					)
		if hidden_sizes == 1_92:
			lowerCAmelCase__      :		List[Any]    							=      timm.create_model('''levit_192'''		,	pretrained=A_					)
		if hidden_sizes == 2_56:
			lowerCAmelCase__      :		List[Any]    							=      timm.create_model('''levit_256'''		,	pretrained=A_					)
		if hidden_sizes == 3_84:
			lowerCAmelCase__      :		str    							=      timm.create_model('''levit_384'''		,	pretrained=A_					)

		from_model.eval()
		lowerCAmelCase__      :		Optional[Any]    							=      LevitForImageClassificationWithTeacher(A_					).eval()
		lowerCAmelCase__      :		str    							=      OrderedDict()

		lowerCAmelCase__      :		int    							=      from_model.state_dict()
		lowerCAmelCase__      :		Dict    							=      list(from_model.state_dict().keys()					)
		lowerCAmelCase__      :		Tuple    							=      list(our_model.state_dict().keys()					)
		print(len(A_					)		,	len(A_					)					)
		for i in range(len(A_					)					):
			lowerCAmelCase__      :		str    							=      weights[og_keys[i]]
		our_model.load_state_dict(A_					)

		lowerCAmelCase__      :		str    							=      torch.randn((2, 3, 2_24, 2_24)					)
		lowerCAmelCase__      :		Any    							=      from_model(A_					)
		lowerCAmelCase__      :		int    							=      our_model(A_					).logits

	assert torch.allclose(A_		,	A_					), "The model logits don't match the original one."

	lowerCAmelCase__      :		Tuple    							=      name
	print(A_					)

	if push_to_hub:
		our_model.save_pretrained(save_directory / checkpoint_name					)
		lowerCAmelCase__      :		str    							=      LevitImageProcessor()
		image_processor.save_pretrained(save_directory / checkpoint_name					)

		print(f'Pushed {checkpoint_name}'					)

def     __SCREAMING_SNAKE_CASE    ( A_		,	A_ = None		,	A_ = True					):
	lowerCAmelCase__      :		Any    							=      '''imagenet-1k-id2label.json'''
	lowerCAmelCase__      :		str    							=      10_00
	lowerCAmelCase__      :		Optional[Any]    							=      (1, num_labels)

	lowerCAmelCase__      :		List[Any]    							=      '''huggingface/label-files'''
	lowerCAmelCase__      :		str    							=      num_labels
	lowerCAmelCase__      :		List[Any]    							=      json.load(open(hf_hub_download(A_		,	A_		,	repo_type='''dataset'''					)		,	'''r'''					)					)
	lowerCAmelCase__      :		Optional[int]    							=      {int(A_					): v for k, v in idalabel.items()}

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

	lowerCAmelCase__      :		Optional[int]    							=      partial(A_		,	num_labels=A_		,	idalabel=A_		,	labelaid=A_					)

	lowerCAmelCase__      :		Union[str, Any]    							=      {
	    '''levit-128S''': 1_28,
	    '''levit-128''': 1_28,
	    '''levit-192''': 1_92,
	    '''levit-256''': 2_56,
	    '''levit-384''': 3_84,
	}

	lowerCAmelCase__      :		Tuple    							=      {
	    '''levit-128S''': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]		,	num_attention_heads=[4, 6, 8]		,	depths=[2, 3, 4]		,	key_dim=[16, 16, 16]		,	drop_path_rate=0		,	),
	    '''levit-128''': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]		,	num_attention_heads=[4, 8, 12]		,	depths=[4, 4, 4]		,	key_dim=[16, 16, 16]		,	drop_path_rate=0		,	),
	    '''levit-192''': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]		,	num_attention_heads=[3, 5, 6]		,	depths=[4, 4, 4]		,	key_dim=[32, 32, 32]		,	drop_path_rate=0		,	),
	    '''levit-256''': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]		,	num_attention_heads=[4, 6, 8]		,	depths=[4, 4, 4]		,	key_dim=[32, 32, 32]		,	drop_path_rate=0		,	),
	    '''levit-384''': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]		,	num_attention_heads=[6, 9, 12]		,	depths=[4, 4, 4]		,	key_dim=[32, 32, 32]		,	drop_path_rate=0.1		,	),
	}

	if model_name:
		convert_weight_and_push(
		    names_to_hidden_sizes[model_name]		,	A_		,	names_to_config[model_name]		,	A_		,	A_					)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]		,	A_		,	A_		,	A_		,	A_					)
	return config, expected_shape


if __name__ == "__main__":
							__UpperCamelCase    : int				 =						argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '''--model_name''',
							    default=None,
							    type=str,
							    help='''The name of the model you wish to convert, it must be one of the supported Levit* architecture,''',
							)
							parser.add_argument(
							    '''--pytorch_dump_folder_path''',
							    default='''levit-dump-folder/''',
							    type=Path,
							    required=False,
							    help='''Path to the output PyTorch model directory.''',
							)
							parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')
							parser.add_argument(
							    '''--no-push_to_hub''',
							    dest='''push_to_hub''',
							    action='''store_false''',
							    help='''Do not push model and image processor to the hub''',
							)

							__UpperCamelCase    : List[Any]				 =						parser.parse_args()
							__UpperCamelCase    : Path				 =						args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)







"""simple docstring"""





import os
from argparse import ArgumentParser
from typing import List

import torch.utils.data

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node


__UpperCamelCase    : str				 =						4
__UpperCamelCase    : List[str]				 =						3







class 							SCREAMING_SNAKE_CASE   (						a_			):






					"""simple docstring"""



					pass

def     __SCREAMING_SNAKE_CASE    ( A_					):
	for shard in shards:
		for i in range(A_					):
			yield {"i": i, "shard": shard}

def     __SCREAMING_SNAKE_CASE    ( ):
	lowerCAmelCase__      :		List[str]    							=      int(os.environ['''RANK''']					)
	lowerCAmelCase__      :		Union[str, Any]    							=      int(os.environ['''WORLD_SIZE''']					)

	lowerCAmelCase__      :		Tuple    							=      ArgumentParser()
	parser.add_argument('''--streaming'''		,	type=A_					)
	parser.add_argument('''--local_rank'''		,	type=A_					)
	parser.add_argument('''--num_workers'''		,	type=A_		,	default=0					)
	lowerCAmelCase__      :		Tuple    							=      parser.parse_args()
	lowerCAmelCase__      :		Tuple    							=      args.streaming
	lowerCAmelCase__      :		Any    							=      args.num_workers

	lowerCAmelCase__      :		Dict    							=      {'''shards''': [f'shard_{shard_idx}' for shard_idx in range(A_					)]}
	lowerCAmelCase__      :		Optional[int]    							=      IterableDataset.from_generator(A_		,	gen_kwargs=A_					)
	if not streaming:
		lowerCAmelCase__      :		Dict    							=      Dataset.from_list(list(A_					)					)

	lowerCAmelCase__      :		Optional[Any]    							=      split_dataset_by_node(A_		,	rank=A_		,	world_size=A_					)
	lowerCAmelCase__      :		List[Any]    							=      torch.utils.data.DataLoader(A_		,	num_workers=A_					)

	lowerCAmelCase__      :		Optional[int]    							=      NUM_SHARDS * NUM_ITEMS_PER_SHARD
	lowerCAmelCase__      :		List[str]    							=      full_size // world_size
	expected_local_size += int(rank < (full_size % world_size)					)

	lowerCAmelCase__      :		int    							=      sum(1 for _ in dataloader					)
	if local_size != expected_local_size:
		raise FailedTestError(f'local_size {local_size} != expected_local_size {expected_local_size}'					)


if __name__ == "__main__":
							main()




