
'''simple docstring'''
import argparse
import json

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification


def     a   (    __a     )						->			Tuple:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				SwinConfig()
					UpperCamelCase__     :Optional[Any]							      =				swin_name.split('''_'''     )

					UpperCamelCase__     :Dict							      =				name_split[1]
					UpperCamelCase__     :List[str]							      =				int(name_split[4]     )
					UpperCamelCase__     :List[Any]							      =				int(name_split[3][-1]     )

					if model_size == "tiny":
										UpperCamelCase__     :Any							      =				96
										UpperCamelCase__     :Dict							      =				(2, 2, 6, 2)
										UpperCamelCase__     :Optional[int]							      =				(3, 6, 12, 24)
					elif model_size == "small":
										UpperCamelCase__     :List[str]							      =				96
										UpperCamelCase__     :List[str]							      =				(2, 2, 18, 2)
										UpperCamelCase__     :List[Any]							      =				(3, 6, 12, 24)
					elif model_size == "base":
										UpperCamelCase__     :Dict							      =				128
										UpperCamelCase__     :int							      =				(2, 2, 18, 2)
										UpperCamelCase__     :Union[str, Any]							      =				(4, 8, 16, 32)
					else:
										UpperCamelCase__     :Optional[int]							      =				192
										UpperCamelCase__     :Optional[int]							      =				(2, 2, 18, 2)
										UpperCamelCase__     :str							      =				(6, 12, 24, 48)

					if "in22k" in swin_name:
										UpperCamelCase__     :List[Any]							      =				21841
					else:
										UpperCamelCase__     :List[Any]							      =				1000
										UpperCamelCase__     :Optional[int]							      =				'''huggingface/label-files'''
										UpperCamelCase__     :int							      =				'''imagenet-1k-id2label.json'''
										UpperCamelCase__     :int							      =				json.load(open(hf_hub_download(__a				,      __a				,      repo_type='''dataset'''     )				,      '''r'''     )     )
										UpperCamelCase__     :int							      =				{int(__a     ): v for k, v in idalabel.items()}
										UpperCamelCase__     :Any							      =				idalabel
										UpperCamelCase__     :List[Any]							      =				{v: k for k, v in idalabel.items()}

					UpperCamelCase__     :Optional[Any]							      =				img_size
					UpperCamelCase__     :str							      =				num_classes
					UpperCamelCase__     :List[str]							      =				embed_dim
					UpperCamelCase__     :int							      =				depths
					UpperCamelCase__     :int							      =				num_heads
					UpperCamelCase__     :List[str]							      =				window_size

					return config


def     a   (    __a     )						->			Union[str, Any]:



					'''simple docstring'''






					if "patch_embed.proj" in name:
										UpperCamelCase__     :str							      =				name.replace('''patch_embed.proj'''				,      '''embeddings.patch_embeddings.projection'''     )
					if "patch_embed.norm" in name:
										UpperCamelCase__     :Union[str, Any]							      =				name.replace('''patch_embed.norm'''				,      '''embeddings.norm'''     )
					if "layers" in name:
										UpperCamelCase__     :Any							      =				'''encoder.''' + name
					if "attn.proj" in name:
										UpperCamelCase__     :List[Any]							      =				name.replace('''attn.proj'''				,      '''attention.output.dense'''     )
					if "attn" in name:
										UpperCamelCase__     :str							      =				name.replace('''attn'''				,      '''attention.self'''     )
					if "norm1" in name:
										UpperCamelCase__     :Union[str, Any]							      =				name.replace('''norm1'''				,      '''layernorm_before'''     )
					if "norm2" in name:
										UpperCamelCase__     :Union[str, Any]							      =				name.replace('''norm2'''				,      '''layernorm_after'''     )
					if "mlp.fc1" in name:
										UpperCamelCase__     :str							      =				name.replace('''mlp.fc1'''				,      '''intermediate.dense'''     )
					if "mlp.fc2" in name:
										UpperCamelCase__     :Tuple							      =				name.replace('''mlp.fc2'''				,      '''output.dense'''     )

					if name == "norm.weight":
										UpperCamelCase__     :List[Any]							      =				'''layernorm.weight'''
					if name == "norm.bias":
										UpperCamelCase__     :Tuple							      =				'''layernorm.bias'''

					if "head" in name:
										UpperCamelCase__     :int							      =				name.replace('''head'''				,      '''classifier'''     )
					else:
										UpperCamelCase__     :int							      =				'''swin.''' + name

					return name


def     a   (    __a				,      __a     )						->			Dict:



					'''simple docstring'''






					for key in orig_state_dict.copy().keys():
										UpperCamelCase__     :Dict							      =				orig_state_dict.pop(__a     )

										if "mask" in key:
															continue
										elif "qkv" in key:
															UpperCamelCase__     :Dict							      =				key.split('''.'''     )
															UpperCamelCase__     :List[str]							      =				int(key_split[1]     )
															UpperCamelCase__     :str							      =				int(key_split[3]     )
															UpperCamelCase__     :Tuple							      =				model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size

															if "weight" in key:
																				UpperCamelCase__     :Optional[int]							      =				val[:dim, :]
																				UpperCamelCase__     :int							      =				val[
																				    dim : dim * 2, :
																				]
																				UpperCamelCase__     :Dict							      =				val[-dim:, :]
															else:
																				UpperCamelCase__     :str							      =				val[
																				    :dim
																				]
																				UpperCamelCase__     :List[Any]							      =				val[
																				    dim : dim * 2
																				]
																				UpperCamelCase__     :Union[str, Any]							      =				val[
																				    -dim:
																				]
										else:
															UpperCamelCase__     :List[Any]							      =				val

					return orig_state_dict


def     a   (    __a				,      __a     )						->			Optional[int]:



					'''simple docstring'''






					UpperCamelCase__     :Union[str, Any]							      =				timm.create_model(__a				,      pretrained=__a     )
					timm_model.eval()

					UpperCamelCase__     :Dict							      =				get_swin_config(__a     )
					UpperCamelCase__     :Tuple							      =				SwinForImageClassification(__a     )
					model.eval()

					UpperCamelCase__     :str							      =				convert_state_dict(timm_model.state_dict()				,      __a     )
					model.load_state_dict(__a     )

					UpperCamelCase__     :str							      =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''

					UpperCamelCase__     :List[Any]							      =				AutoImageProcessor.from_pretrained('''microsoft/{}'''.format(swin_name.replace('''_'''				,      '''-'''     )     )     )
					UpperCamelCase__     :List[Any]							      =				Image.open(requests.get(__a				,      stream=__a     ).raw     )
					UpperCamelCase__     :Dict							      =				image_processor(images=__a				,      return_tensors='''pt'''     )

					UpperCamelCase__     :Optional[int]							      =				timm_model(inputs['''pixel_values''']     )
					UpperCamelCase__     :Union[str, Any]							      =				model(**__a     ).logits

					assert torch.allclose(__a				,      __a				,      atol=1e-3     )

					print(f'''Saving model {swin_name} to {pytorch_dump_folder_path}'''     )
					model.save_pretrained(__a     )

					print(f'''Saving image processor to {pytorch_dump_folder_path}'''     )
					image_processor.save_pretrained(__a     )


if __name__ == "__main__":
		__snake_case							   =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--swin_name''',
		    default='''swin_tiny_patch4_window7_224''',
		    type=str,
		    help='''Name of the Swin timm model you\'d like to convert.''',
		)
		parser.add_argument(
		    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
		)

		__snake_case							   =  parser.parse_args()
		convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)



'''simple docstring'''
from .integrations import (
    is_optuna_available,
    is_ray_available,
    is_sigopt_available,
    is_wandb_available,
    run_hp_search_optuna,
    run_hp_search_ray,
    run_hp_search_sigopt,
    run_hp_search_wandb,
)
from .trainer_utils import (
    HPSearchBackend,
    default_hp_space_optuna,
    default_hp_space_ray,
    default_hp_space_sigopt,
    default_hp_space_wandb,
)
from .utils import logging


__snake_case							   =  logging.get_logger(__name__)
class      lowercase			:

		"""simple docstring"""

		_a									=       42
		_a									=       None




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							raise NotImplementedError




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							if not self.is_available():
												raise RuntimeError(
												    F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.'''							)




		@classmethod
		def 					lowerCAmelCase__       (	cls							):

							'''simple docstring'''


							return F'''`pip install {cls.pip_package or cls.name}`'''



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'optuna'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_optuna_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_optuna(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_optuna(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'ray'
		_a									=       '\'ray[tune]\''




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_ray_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_ray(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_ray(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'sigopt'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_sigopt_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_sigopt(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_sigopt(UpperCamelCase_							)



class      lowercase			(							A__      ):

		"""simple docstring"""

		_a									=       'wandb'




		@staticmethod
		def 					lowerCAmelCase__       (	):

							'''simple docstring'''


							return is_wandb_available()




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							):

							'''simple docstring'''


							return run_hp_search_wandb(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_					,     **UpperCamelCase_							)




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_							):

							'''simple docstring'''


							return default_hp_space_wandb(UpperCamelCase_							)



__snake_case							   =  {
    HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}


def     a   (    )						->			str:



					'''simple docstring'''






					UpperCamelCase__     :List[Any]							      =				[backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
					if len(__a     ) > 0:
										UpperCamelCase__     :Tuple							      =				available_backends[0].name
										if len(__a     ) > 1:
															logger.info(
															    f'''{len(__a     )} hyperparameter search backends available. Using {name} as the default.'''     )
										return name
					raise RuntimeError(
					    '''No hyperparameter search backend available.\n'''
					    + '''\n'''.join(
					        f''' - To install {backend.name} run {backend.pip_install()}'''
					        for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values()     )     )