

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()
A									=  logging.get_logger()





def a(lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__      ,			lowercase__ = True						):





	'''simple docstring'''
	print(f"""Converting {name}..."""						)

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

		from_model.eval()
		snake_case_   		=			LevitForImageClassificationWithTeacher(lowercase__						).eval()
		snake_case_   		=			OrderedDict()

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

		snake_case_   		=			torch.randn((2, 3, 224, 224)						)
		snake_case_   		=			from_model(lowercase__						)
		snake_case_   		=			our_model(lowercase__						).logits

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

	snake_case_   		=			name
	print(lowercase__						)

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

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





def a(lowercase__      ,			lowercase__ = None      ,			lowercase__ = True						):





	'''simple docstring'''
	snake_case_   		=			'imagenet-1k-id2label.json'
	snake_case_   		=			1000
	snake_case_   		=			(1, num_labels)

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

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

	snake_case_   		=			partial(lowercase__      ,			num_labels=lowercase__      ,			idalabel=lowercase__      ,			labelaid=lowercase__						)

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

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


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

		A									=  parser.parse_args()
		A									=  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 ..utils import DummyObject, requires_backends


class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







class SCREAMING_SNAKE_CASE	(		metaclass=__snake_case				):






			"""simple docstring"""




			__A    	=	["""flax""", """transformers"""]


			def __init__(     self			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(self			,					['flax', 'transformers']				)


			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)







			@classmethod
			def       __lowerCAmelCase			(     cls			,					*__UpperCamelCase			,					**__UpperCamelCase				):

				"""simple docstring"""


				requires_backends(cls			,					['flax', 'transformers']				)
