




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   :						List[str]				=			logging.get_logger()







def 		lowercase__   (		__A:	int							,__A:	str							,__A:	LevitConfig							,__A:	Path							,__A:	bool = True				):





					'''simple docstring'''






					print(F'''Converting {name}...'''				)

					with torch.no_grad():
										if hidden_sizes == 1_2_8:
															if name[-1] == "S":
																				__magic_name__							:	int    							=							timm.create_model('''levit_128s'''							,pretrained=__A				)
															else:
																				__magic_name__							:	str    							=							timm.create_model('''levit_128'''							,pretrained=__A				)
										if hidden_sizes == 1_9_2:
															__magic_name__							:	Optional[Any]    							=							timm.create_model('''levit_192'''							,pretrained=__A				)
										if hidden_sizes == 2_5_6:
															__magic_name__							:	str    							=							timm.create_model('''levit_256'''							,pretrained=__A				)
										if hidden_sizes == 3_8_4:
															__magic_name__							:	str    							=							timm.create_model('''levit_384'''							,pretrained=__A				)

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

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

										__magic_name__							:	Optional[Any]    							=							torch.randn((2, 3, 2_2_4, 2_2_4)				)
										__magic_name__							:	int    							=							from_model(__A				)
										__magic_name__							:	int    							=							our_model(__A				).logits

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

					__magic_name__							:	Dict    							=							name
					print(__A				)

					if push_to_hub:
										our_model.save_pretrained(save_directory / checkpoint_name				)
										__magic_name__							:	List[str]    							=							LevitImageProcessor()
										image_processor.save_pretrained(save_directory / checkpoint_name				)

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







def 		lowercase__   (		__A:	Path							,__A:	str = None							,__A:	bool = True				):





					'''simple docstring'''






					__magic_name__							:	Dict    							=							'''imagenet-1k-id2label.json'''
					__magic_name__							:	Tuple    							=							1_0_0_0
					__magic_name__							:	Dict    							=							(1, num_labels)

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

					__magic_name__							:	Optional[Any]    							=							idalabel
					__magic_name__							:	Tuple    							=							{v: k for k, v in idalabel.items()}

					__magic_name__							:	int    							=							partial(__A							,num_labels=__A							,idalabel=__A							,labelaid=__A				)

					__magic_name__							:	Optional[int]    							=							{
					    '''levit-128S''': 1_2_8,
					    '''levit-128''': 1_2_8,
					    '''levit-192''': 1_9_2,
					    '''levit-256''': 2_5_6,
					    '''levit-384''': 3_8_4,
					}

					__magic_name__							:	str    							=							{
					    '''levit-128S''': ImageNetPreTrainedConfig(
					        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]							,num_attention_heads=[4, 6, 8]							,depths=[2, 3, 4]							,key_dim=[1_6, 1_6, 1_6]							,drop_path_rate=0							,),
					    '''levit-128''': ImageNetPreTrainedConfig(
					        hidden_sizes=[1_2_8, 2_5_6, 3_8_4]							,num_attention_heads=[4, 8, 1_2]							,depths=[4, 4, 4]							,key_dim=[1_6, 1_6, 1_6]							,drop_path_rate=0							,),
					    '''levit-192''': ImageNetPreTrainedConfig(
					        hidden_sizes=[1_9_2, 2_8_8, 3_8_4]							,num_attention_heads=[3, 5, 6]							,depths=[4, 4, 4]							,key_dim=[3_2, 3_2, 3_2]							,drop_path_rate=0							,),
					    '''levit-256''': ImageNetPreTrainedConfig(
					        hidden_sizes=[2_5_6, 3_8_4, 5_1_2]							,num_attention_heads=[4, 6, 8]							,depths=[4, 4, 4]							,key_dim=[3_2, 3_2, 3_2]							,drop_path_rate=0							,),
					    '''levit-384''': ImageNetPreTrainedConfig(
					        hidden_sizes=[3_8_4, 5_1_2, 7_6_8]							,num_attention_heads=[6, 9, 1_2]							,depths=[4, 4, 4]							,key_dim=[3_2, 3_2, 3_2]							,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__":
	__lowerCamelCase   :						Dict				=			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   :						str				=			parser.parse_args()
	__lowerCamelCase   :						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)









from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


__lowerCamelCase   :						List[str]				=			{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Dict				=			[
	    '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''RobertaPreLayerNormForCausalLM''',
	    '''RobertaPreLayerNormForMaskedLM''',
	    '''RobertaPreLayerNormForMultipleChoice''',
	    '''RobertaPreLayerNormForQuestionAnswering''',
	    '''RobertaPreLayerNormForSequenceClassification''',
	    '''RobertaPreLayerNormForTokenClassification''',
	    '''RobertaPreLayerNormModel''',
	    '''RobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
	    '''TFRobertaPreLayerNormForCausalLM''',
	    '''TFRobertaPreLayerNormForMaskedLM''',
	    '''TFRobertaPreLayerNormForMultipleChoice''',
	    '''TFRobertaPreLayerNormForQuestionAnswering''',
	    '''TFRobertaPreLayerNormForSequenceClassification''',
	    '''TFRobertaPreLayerNormForTokenClassification''',
	    '''TFRobertaPreLayerNormMainLayer''',
	    '''TFRobertaPreLayerNormModel''',
	    '''TFRobertaPreLayerNormPreTrainedModel''',
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	__lowerCamelCase   :						Optional[Any]				=			[
	    '''FlaxRobertaPreLayerNormForCausalLM''',
	    '''FlaxRobertaPreLayerNormForMaskedLM''',
	    '''FlaxRobertaPreLayerNormForMultipleChoice''',
	    '''FlaxRobertaPreLayerNormForQuestionAnswering''',
	    '''FlaxRobertaPreLayerNormForSequenceClassification''',
	    '''FlaxRobertaPreLayerNormForTokenClassification''',
	    '''FlaxRobertaPreLayerNormModel''',
	    '''FlaxRobertaPreLayerNormPreTrainedModel''',
	]


if TYPE_CHECKING:
	from .configuration_roberta_prelayernorm import (
	    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    RobertaPreLayerNormConfig,
	    RobertaPreLayerNormOnnxConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_roberta_prelayernorm import (
		    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    RobertaPreLayerNormForCausalLM,
		    RobertaPreLayerNormForMaskedLM,
		    RobertaPreLayerNormForMultipleChoice,
		    RobertaPreLayerNormForQuestionAnswering,
		    RobertaPreLayerNormForSequenceClassification,
		    RobertaPreLayerNormForTokenClassification,
		    RobertaPreLayerNormModel,
		    RobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_roberta_prelayernorm import (
		    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFRobertaPreLayerNormForCausalLM,
		    TFRobertaPreLayerNormForMaskedLM,
		    TFRobertaPreLayerNormForMultipleChoice,
		    TFRobertaPreLayerNormForQuestionAnswering,
		    TFRobertaPreLayerNormForSequenceClassification,
		    TFRobertaPreLayerNormForTokenClassification,
		    TFRobertaPreLayerNormMainLayer,
		    TFRobertaPreLayerNormModel,
		    TFRobertaPreLayerNormPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_roberta_prelayernorm import (
		    FlaxRobertaPreLayerNormForCausalLM,
		    FlaxRobertaPreLayerNormForMaskedLM,
		    FlaxRobertaPreLayerNormForMultipleChoice,
		    FlaxRobertaPreLayerNormForQuestionAnswering,
		    FlaxRobertaPreLayerNormForSequenceClassification,
		    FlaxRobertaPreLayerNormForTokenClassification,
		    FlaxRobertaPreLayerNormModel,
		    FlaxRobertaPreLayerNormPreTrainedModel,
		)

else:
	import sys

	__lowerCamelCase   :						Dict				=			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



