

'''simple docstring'''

import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     inspect.getfile(accelerate.test_utils		)
				_a    	=     os.path.sep.join(mod_file.split(os.path.sep		)[:-1] + ["""scripts""", """test_cli.py"""]		)

				_a    	=     ["""accelerate""", """launch"""]
				_a    	=     Path.home() / """.cache/huggingface/accelerate"""
				_a    	=     """default_config.yaml"""
				_a    	=     config_folder / config_file
				_a    	=     config_folder / """_default_config.yaml"""

				_a    	=     Path("""tests/test_configs"""		)



				@classmethod
				def   a__					(    cls       )     ->							Any:
											if cls.config_path.is_file():
																		cls.config_path.rename(cls.changed_path       )



				@classmethod
				def   a__					(    cls       )     ->							Dict:
											if cls.changed_path.is_file():
																		cls.changed_path.rename(cls.config_path       )



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				self.base_cmd
											if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
																		cmd += ["--multi_gpu"]
											execute_subprocess_async(cmd + [self.test_file_path],     env=os.environ.copy()       )



				def   a__					(    self       )     ->							List[str]:
											for config in sorted(self.test_config_path.glob('**/*.yaml'       )       ):
																		with self.subTest(config_file=_lowercase       ):
																									execute_subprocess_async(
																									    self.base_cmd + ['--config_file', str(_lowercase       ), self.test_file_path],     env=os.environ.copy()       )





				def   a__					(    self       )     ->							Tuple:
											execute_subprocess_async(['accelerate', 'test'],     env=os.environ.copy()       )






class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""
				_a    	=     """test-tpu"""
				_a    	=     """us-central1-a"""
				_a    	=     """ls"""
				_a    	=     ["""accelerate""", """tpu-config"""]
				_a    	=     """cd /usr/share"""
				_a    	=     """tests/test_samples/test_command_file.sh"""
				_a    	=     """Running gcloud compute tpus tpu-vm ssh"""



				def   a__					(    self       )     ->							Optional[int]:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd
											    + ['--command', self.command, '--tpu_zone', self.tpu_zone, '--tpu_name', self.tpu_name, '--debug'],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd
											    + [
											        '--config_file',
											        'tests/test_configs/0_12_0.yaml',
											        '--command',
											        self.command,
											        '--tpu_zone',
											        self.tpu_zone,
											        '--tpu_name',
											        self.tpu_name,
											        '--debug',
											    ],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug'],     return_stdout=_lowercase       )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--command', self.command, '--debug'],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd
											    + [
											        '--config_file',
											        'tests/test_configs/latest.yaml',
											        '--command',
											        self.command,
											        '--command',
											        'echo "Hello World"',
											        '--debug',
											    ],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd
											    + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug'],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							int:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd
											    + [
											        '--config_file',
											        'tests/test_configs/0_12_0.yaml',
											        '--command_file',
											        self.command_file,
											        '--tpu_zone',
											        self.tpu_zone,
											        '--tpu_name',
											        self.tpu_name,
											        '--debug',
											    ],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all""",     _lowercase,     )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug'],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all""",     _lowercase,     )





				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				run_command(
											    self.cmd
											    + [
											        '--config_file',
											        'tests/test_configs/latest.yaml',
											        '--install_accelerate',
											        '--accelerate_version',
											        '12.0.0',
											        '--debug',
											    ],     return_stdout=_lowercase,     )
											self.assertIn(
											    f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all""",     _lowercase,     )








'''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()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

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

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

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

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

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

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

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

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




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

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

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

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

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

							SCREAMING_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]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				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",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				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)





