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()
_lowerCAmelCase       =		logging.get_logger()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case = True						):
					print(f"""Converting {name}..."""						)

					with torch.no_grad():
										if hidden_sizes == 128:
															if name[-1] == "S":
																				_UpperCamelCase			     =     timm.create_model('''levit_128s'''  ,     pretrained=__snake_case						)
															else:
																				_UpperCamelCase			     =     timm.create_model('''levit_128'''  ,     pretrained=__snake_case						)
										if hidden_sizes == 192:
															_UpperCamelCase			     =     timm.create_model('''levit_192'''  ,     pretrained=__snake_case						)
										if hidden_sizes == 256:
															_UpperCamelCase			     =     timm.create_model('''levit_256'''  ,     pretrained=__snake_case						)
										if hidden_sizes == 384:
															_UpperCamelCase			     =     timm.create_model('''levit_384'''  ,     pretrained=__snake_case						)

										from_model.eval()
										_UpperCamelCase			     =     LevitForImageClassificationWithTeacher(__snake_case						).eval()
										_UpperCamelCase			     =     OrderedDict()

										_UpperCamelCase			     =     from_model.state_dict()
										_UpperCamelCase			     =     list(from_model.state_dict().keys()						)
										_UpperCamelCase			     =     list(our_model.state_dict().keys()						)
										print(len(__snake_case						)  ,     len(__snake_case						)						)
										for i in range(len(__snake_case						)						):
															_UpperCamelCase			     =     weights[og_keys[i]]
										our_model.load_state_dict(__snake_case						)

										_UpperCamelCase			     =     torch.randn((2, 3, 224, 224)						)
										_UpperCamelCase			     =     from_model(__snake_case						)
										_UpperCamelCase			     =     our_model(__snake_case						).logits

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

					_UpperCamelCase			     =     name
					print(__snake_case						)

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

										print(f"""Pushed {checkpoint_name}"""						)
def 					_snake_case	(  __snake_case  ,     __snake_case = None  ,     __snake_case = True						):
					_UpperCamelCase			     =     '''imagenet-1k-id2label.json'''
					_UpperCamelCase			     =     1000
					_UpperCamelCase			     =     (1, num_labels)

					_UpperCamelCase			     =     '''huggingface/label-files'''
					_UpperCamelCase			     =     num_labels
					_UpperCamelCase			     =     json.load(open(hf_hub_download(__snake_case  ,     __snake_case  ,     repo_type='''dataset'''						)  ,     '''r'''						)						)
					_UpperCamelCase			     =     {int(__snake_case						): v for k, v in idalabel.items()}

					_UpperCamelCase			     =     idalabel
					_UpperCamelCase			     =     {v: k for k, v in idalabel.items()}

					_UpperCamelCase			     =     partial(__snake_case  ,     num_labels=__snake_case  ,     idalabel=__snake_case  ,     labelaid=__snake_case						)

					_UpperCamelCase			     =     {
					    '''levit-128S''': 128,
					    '''levit-128''': 128,
					    '''levit-192''': 192,
					    '''levit-256''': 256,
					    '''levit-384''': 384,
					}

					_UpperCamelCase			     =     {
					    '''levit-128S''': ImageNetPreTrainedConfig(
					        hidden_sizes=[128, 256, 384]  ,     num_attention_heads=[4, 6, 8]  ,     depths=[2, 3, 4]  ,     key_dim=[16, 16, 16]  ,     drop_path_rate=0  ,     ),
					    '''levit-128''': ImageNetPreTrainedConfig(
					        hidden_sizes=[128, 256, 384]  ,     num_attention_heads=[4, 8, 12]  ,     depths=[4, 4, 4]  ,     key_dim=[16, 16, 16]  ,     drop_path_rate=0  ,     ),
					    '''levit-192''': ImageNetPreTrainedConfig(
					        hidden_sizes=[192, 288, 384]  ,     num_attention_heads=[3, 5, 6]  ,     depths=[4, 4, 4]  ,     key_dim=[32, 32, 32]  ,     drop_path_rate=0  ,     ),
					    '''levit-256''': ImageNetPreTrainedConfig(
					        hidden_sizes=[256, 384, 512]  ,     num_attention_heads=[4, 6, 8]  ,     depths=[4, 4, 4]  ,     key_dim=[32, 32, 32]  ,     drop_path_rate=0  ,     ),
					    '''levit-384''': ImageNetPreTrainedConfig(
					        hidden_sizes=[384, 512, 768]  ,     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]  ,     __snake_case  ,     names_to_config[model_name]  ,     __snake_case  ,     __snake_case						)
					else:
										for model_name, config in names_to_config.items():
															convert_weight_and_push(names_to_hidden_sizes[model_name]  ,     __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case						)
					return config, expected_shape


if __name__ == "__main__":
					_lowerCAmelCase       =		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",
					)

					_lowerCAmelCase       =		parser.parse_args()
					_lowerCAmelCase       =		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"""







# Lint as: python3
import itertools
import os
import re


a_							=    re.compile(r"""([A-Z]+)([A-Z][a-z])""")
a_							=    re.compile(r"""([a-z\d])([A-Z])""")

a_							=    re.compile(r"""(?<!_)_(?!_)""")
a_							=    re.compile(r"""(_{2,})""")

a_							=    r"""^\w+(\.\w+)*$"""

a_							=    r"""<>:/\|?*"""

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					str	       =    _uppercase_uppercase_re.sub(r'\1_\2'      ,							__a						)
			_lowerCamelCase					:					Tuple	       =    _lowercase_uppercase_re.sub(r'\1_\2'      ,							__a						)
			return name.lower()

def     UpperCAmelCase_					(				__a   :			Optional[int]						):




			'''simple docstring'''



			_lowerCamelCase					:					Dict	       =    _single_underscore_re.split(__a						)
			_lowerCamelCase					:					Tuple	       =    [_multiple_underscores_re.split(__a						) for n in name]
			return "".join(n.capitalize() for n in itertools.chain.from_iterable(__a						) if n != ''						)

def     UpperCAmelCase_					(				__a   :			List[Any]						):




			'''simple docstring'''



			if os.path.basename(__a						) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"						)
			return camelcase_to_snakecase(__a						)

def     UpperCAmelCase_					(				__a   :			Union[str, Any]      ,							__a   :			Optional[int]						):




			'''simple docstring'''



			if os.path.basename(__a						) != name:
						raise ValueError(f"Should be a dataset name, not a path: {name}"						)
			if not re.match(_split_re      ,							__a						):
						raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'."						)
			return f"{filename_prefix_for_name(__a						)}-{split}"

def     UpperCAmelCase_					(				__a   :			Any      ,							__a   :			Union[str, Any]      ,							__a   :			List[Any]      ,							__a   :			List[str]=None						):




			'''simple docstring'''



			_lowerCamelCase					:					List[Any]	       =    filename_prefix_for_split(__a      ,							__a						)
			if filetype_suffix:
						prefix += f".{filetype_suffix}"
			_lowerCamelCase					:					List[str]	       =    os.path.join(__a      ,							__a						)
			return f"{filepath}*"



def     UpperCAmelCase_					(				__a   :			str      ,							__a   :			List[Any]      ,							__a   :			List[str]      ,							__a   :			Tuple=None      ,							__a   :			Tuple=None						):




			'''simple docstring'''



			_lowerCamelCase					:					Tuple	       =    filename_prefix_for_split(__a      ,							__a						)
			_lowerCamelCase					:					List[str]	       =    os.path.join(__a      ,							__a						)

			if shard_lengths:
						_lowerCamelCase					:					Union[str, Any]	       =    len(__a						)
						_lowerCamelCase					:					str	       =    [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__a						)]
						if filetype_suffix:
									_lowerCamelCase					:					int	       =    [filename + f".{filetype_suffix}" for filename in filenames]
						return filenames
			else:
						_lowerCamelCase					:					int	       =    prefix
						if filetype_suffix:
									filename += f".{filetype_suffix}"
						return [filename]





