


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 : Tuple		=				logging.get_logger()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase = True  ):


			'''simple docstring'''







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

			with torch.no_grad():
						if hidden_sizes == 1_28:
									if name[-1] == "S":
												__lowercase 					=   timm.create_model('''levit_128s'''       ,			pretrained=_UpperCamelCase  )
									else:
												__lowercase 					=   timm.create_model('''levit_128'''       ,			pretrained=_UpperCamelCase  )
						if hidden_sizes == 1_92:
									__lowercase 					=   timm.create_model('''levit_192'''       ,			pretrained=_UpperCamelCase  )
						if hidden_sizes == 2_56:
									__lowercase 					=   timm.create_model('''levit_256'''       ,			pretrained=_UpperCamelCase  )
						if hidden_sizes == 3_84:
									__lowercase 					=   timm.create_model('''levit_384'''       ,			pretrained=_UpperCamelCase  )

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

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

						__lowercase 					=   torch.randn((2, 3, 2_24, 2_24)  )
						__lowercase 					=   from_model(_UpperCamelCase  )
						__lowercase 					=   our_model(_UpperCamelCase  ).logits

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

			__lowercase 					=   name
			print(_UpperCamelCase  )

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

						print(F'Pushed {checkpoint_name}'  )


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase = None       ,			_UpperCamelCase = True  ):


			'''simple docstring'''







			__lowercase 					=   '''imagenet-1k-id2label.json'''
			__lowercase 					=   10_00
			__lowercase 					=   (1, num_labels)

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

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

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

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

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


if __name__ == "__main__":
	a : str		=				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 : List[Any]		=				parser.parse_args()
	a : 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)






import unittest

from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax

from .test_modeling_common_flax import FlaxModelTesterMixin


if is_flax_available():
	import jax
@require_flax
class 	lowerCamelCase_       (				lowerCAmelCase__     ,     unittest.TestCase				):



					'''simple docstring'''







					__UpperCAmelCase            =				FlaxAutoencoderKL






					@property
					def A      (				self						)   ->			Optional[int]:







								'''simple docstring'''

								__lowercase 					=   4
								__lowercase 					=   3
								__lowercase 					=   (3_2, 3_2)

								__lowercase 					=   jax.random.PRNGKey(0						)
								__lowercase 					=   jax.random.uniform(snake_case_ ,       ((batch_size, num_channels) + sizes)						)

								return {"sample": image, "prng_key": prng_key}






					def A      (				self						)   ->			Any:







								'''simple docstring'''

								__lowercase 					=   {
								    '''block_out_channels''': [3_2, 6_4],
								    '''in_channels''': 3,
								    '''out_channels''': 3,
								    '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
								    '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
								    '''latent_channels''': 4,
								}
								__lowercase 					=   self.dummy_input
								return init_dict, inputs_dict


