





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()
lowercase_		:				Optional[int]				=						logging.get_logger()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = True ):
				print(F'''Converting {name}...''' )

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

								from_model.eval()
								_snake_case :			List[str]  						=   LevitForImageClassificationWithTeacher(__A ).eval()
								_snake_case :			Optional[Any]  						=   OrderedDict()

								_snake_case :			Any  						=   from_model.state_dict()
								_snake_case :			Union[str, Any]  						=   list(from_model.state_dict().keys() )
								_snake_case :			List[str]  						=   list(our_model.state_dict().keys() )
								print(len(__A )	,			len(__A ) )
								for i in range(len(__A ) ):
												_snake_case :			int  						=   weights[og_keys[i]]
								our_model.load_state_dict(__A )

								_snake_case :			Dict  						=   torch.randn((2, 3, 2_24, 2_24) )
								_snake_case :			Optional[int]  						=   from_model(__A )
								_snake_case :			Union[str, Any]  						=   our_model(__A ).logits

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

				_snake_case :			List[Any]  						=   name
				print(__A )

				if push_to_hub:
								our_model.save_pretrained(save_directory / checkpoint_name )
								_snake_case :			Tuple  						=   LevitImageProcessor()
								image_processor.save_pretrained(save_directory / checkpoint_name )

								print(F'''Pushed {checkpoint_name}''' )

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase = None	,			__lowerCAmelCase = True ):
				_snake_case :			Tuple  						=   '''imagenet-1k-id2label.json'''
				_snake_case :			List[Any]  						=   10_00
				_snake_case :			List[str]  						=   (1, num_labels)

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

				_snake_case :			int  						=   idalabel
				_snake_case :			Union[str, Any]  						=   {v: k for k, v in idalabel.items()}

				_snake_case :			Optional[Any]  						=   partial(__A	,			num_labels=__A	,			idalabel=__A	,			labelaid=__A )

				_snake_case :			Dict  						=   {
				    '''levit-128S''': 1_28,
				    '''levit-128''': 1_28,
				    '''levit-192''': 1_92,
				    '''levit-256''': 2_56,
				    '''levit-384''': 3_84,
				}

				_snake_case :			Any  						=   {
				    '''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__":
		lowercase_		:				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''',
		)

		lowercase_		:				Dict				=						parser.parse_args()
		lowercase_		:				List[str]				=						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)












import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


lowercase_		:				Optional[Any]				=						pytest.mark.integration

@pytest.mark.parametrize('path'	,			['paws', 'csv'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_dataset(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			str  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.filterwarnings('ignore:inspect_metric is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.parametrize('path'	,			['accuracy'] )
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				inspect_metric(__lowerCAmelCase	,			__lowerCAmelCase )
				_snake_case :			Tuple  						=   path + '.py'
				assert script_name in os.listdir(__lowerCAmelCase )
				assert "__pycache__" not in os.listdir(__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, config_name, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Union[str, Any]  						=   get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )
				assert info.config_name == config_name
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_config_info(__lowerCAmelCase	,			config_name=__lowerCAmelCase )

@pytest.mark.parametrize(
    'path, expected'	,			[
        ('squad', 'plain_text'),
        ('acronym_identification', 'default'),
        ('lhoestq/squad', 'plain_text'),
        ('lhoestq/test', 'default'),
        ('lhoestq/demo1', 'lhoestq--demo1'),
        ('dalle-mini/wit', 'dalle-mini--wit'),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Optional[Any]  						=   get_dataset_config_names(__lowerCAmelCase )
				assert expected in config_names

@pytest.mark.parametrize(
    'path, expected_configs, expected_splits_in_first_config'	,			[
        ('squad', ['plain_text'], ['train', 'validation']),
        ('dalle-mini/wit', ['dalle-mini--wit'], ['train']),
        ('paws', ['labeled_final', 'labeled_swap', 'unlabeled_final'], ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			List[Any]  						=   get_dataset_infos(__lowerCAmelCase )
				assert list(infos.keys() ) == expected_configs
				_snake_case :			Any  						=   expected_configs[0]
				assert expected_config in infos
				_snake_case :			str  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits_in_first_config

@pytest.mark.parametrize(
    'path, expected_config, expected_splits'	,			[
        ('squad', 'plain_text', ['train', 'validation']),
        ('dalle-mini/wit', 'dalle-mini--wit', ['train']),
        ('paws', 'labeled_final', ['train', 'test', 'validation']),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				_snake_case :			Any  						=   get_dataset_infos(__lowerCAmelCase )
				assert expected_config in infos
				_snake_case :			Any  						=   infos[expected_config]
				assert info.config_name == expected_config
				assert list(info.splits.keys() ) == expected_splits

@pytest.mark.parametrize(
    'path, config_name, expected_exception'	,			[
        ('paws', None, ValueError),
    ]	,			)
def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				with pytest.raises(__lowerCAmelCase ):
								get_dataset_split_names(__lowerCAmelCase	,			config_name=__lowerCAmelCase )






