
"""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()
_snake_case          =						logging.get_logger()






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					int     ,  SCREAMING_SNAKE_CASE:					str     ,  SCREAMING_SNAKE_CASE:					LevitConfig     ,  SCREAMING_SNAKE_CASE:					Path     ,  SCREAMING_SNAKE_CASE:					bool = True						):






							"""simple docstring"""



							print(f"""Converting {name}..."""						)

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

														from_model.eval()
														_lowerCAmelCase  	=			LevitForImageClassificationWithTeacher(SCREAMING_SNAKE_CASE						).eval()
														_lowerCAmelCase  	=			OrderedDict()

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

														_lowerCAmelCase  	=			torch.randn((2, 3, 224, 224)						)
														_lowerCAmelCase  	=			from_model(SCREAMING_SNAKE_CASE						)
														_lowerCAmelCase  	=			our_model(SCREAMING_SNAKE_CASE						).logits

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

							_lowerCAmelCase  	=			name
							print(SCREAMING_SNAKE_CASE						)

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

														print(f"""Pushed {checkpoint_name}"""						)






def 	__snake_case  (    SCREAMING_SNAKE_CASE:					Path     ,  SCREAMING_SNAKE_CASE:					str = None     ,  SCREAMING_SNAKE_CASE:					bool = True						):






							"""simple docstring"""



							_lowerCAmelCase  	=			'imagenet-1k-id2label.json'
							_lowerCAmelCase  	=			1000
							_lowerCAmelCase  	=			(1, num_labels)

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

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

							_lowerCAmelCase  	=			partial(SCREAMING_SNAKE_CASE     ,  num_labels=SCREAMING_SNAKE_CASE     ,  idalabel=SCREAMING_SNAKE_CASE     ,  labelaid=SCREAMING_SNAKE_CASE						)

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

							_lowerCAmelCase  	=			{
							    '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]     ,  SCREAMING_SNAKE_CASE     ,  names_to_config[model_name]     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE     ,  SCREAMING_SNAKE_CASE						)
							return config, expected_shape


if __name__ == "__main__":
							_snake_case          =						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''',
							)

							_snake_case          =						parser.parse_args()
							_snake_case          =						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"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case          =						logging.get_logger(__name__)

_snake_case          =						{
    '''abeja/gpt-neox-japanese-2.7b''': '''https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json''',
}
class        _SCREAMING_SNAKE_CASE  (  UpperCAmelCase							):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE_:							Dict							=					"gpt_neox_japanese"


				def __init__(					self	:			Optional[Any]				,     UpperCAmelCase_	:			Any=32_000				,     UpperCAmelCase_	:			Dict=2_560				,     UpperCAmelCase_	:			Union[str, Any]=32				,     UpperCAmelCase_	:			Any=32				,     UpperCAmelCase_	:			Optional[Any]=4				,     UpperCAmelCase_	:			int="gelu"				,     UpperCAmelCase_	:			Union[str, Any]=1.00				,     UpperCAmelCase_	:			Any=10_000				,     UpperCAmelCase_	:			Optional[int]=2_048				,     UpperCAmelCase_	:			Optional[Any]=0.02				,     UpperCAmelCase_	:			Tuple=1E-5				,     UpperCAmelCase_	:			int=True				,     UpperCAmelCase_	:			Tuple=31_996				,     UpperCAmelCase_	:			List[Any]=31_999				,     UpperCAmelCase_	:			str=0.1				,     UpperCAmelCase_	:			List[Any]=0.0				,     **UpperCAmelCase_	:			str				,     )		->  int:







											"""simple docstring"""




											super().__init__(bos_token_id=UpperCAmelCase_				,     eos_token_id=UpperCAmelCase_				,     **UpperCAmelCase_  )
											_lowerCAmelCase  	=			vocab_size
											_lowerCAmelCase  	=			max_position_embeddings
											_lowerCAmelCase  	=			hidden_size
											_lowerCAmelCase  	=			num_hidden_layers
											_lowerCAmelCase  	=			num_attention_heads
											_lowerCAmelCase  	=			intermediate_multiple_size
											_lowerCAmelCase  	=			hidden_act
											_lowerCAmelCase  	=			rotary_pct
											_lowerCAmelCase  	=			rotary_emb_base
											_lowerCAmelCase  	=			initializer_range
											_lowerCAmelCase  	=			layer_norm_eps
											_lowerCAmelCase  	=			use_cache
											_lowerCAmelCase  	=			attention_dropout
											_lowerCAmelCase  	=			hidden_dropout






