






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       ):
			_lowercase		:     Optional[int]        =				inspect.getfile(accelerate.test_utils       )
			_lowercase		:     Tuple        =				os.path.sep.join(mod_file.split(os.path.sep       )[:-1] + ['''scripts''', '''test_cli.py''']       )

			_lowercase		:     int        =				['''accelerate''', '''launch''']
			_lowercase		:     int        =				Path.home() / '''.cache/huggingface/accelerate'''
			_lowercase		:     int        =				'''default_config.yaml'''
			_lowercase		:     List[Any]        =				config_folder / config_file
			_lowercase		:     str        =				config_folder / '''_default_config.yaml'''

			_lowercase		:     str        =				Path('''tests/test_configs'''       )






			@classmethod
			def  SCREAMING_SNAKE_CASE__     (							cls)   ->							List[str]:
						if cls.config_path.is_file():
									cls.config_path.rename(cls.changed_path)






			@classmethod
			def  SCREAMING_SNAKE_CASE__     (							cls)   ->							str:
						if cls.changed_path.is_file():
									cls.changed_path.rename(cls.config_path)






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







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



class 		_snake_case	(	unittest.TestCase       ):
			_lowercase		:     Optional[Any]        =				'''test-tpu'''
			_lowercase		:     Optional[Any]        =				'''us-central1-a'''
			_lowercase		:     List[str]        =				'''ls'''
			_lowercase		:     Union[str, Any]        =				['''accelerate''', '''tpu-config''']
			_lowercase		:     Tuple        =				'''cd /usr/share'''
			_lowercase		:     Optional[Any]        =				'''tests/test_samples/test_command_file.sh'''
			_lowercase		:     List[Any]        =				'''Running gcloud compute tpus tpu-vm ssh'''






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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						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=a		,       )
						self.assertIn(
						    f'''{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all'''		,       a		,       )






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						SCREAMING_SNAKE_CASE							=			run_command(
						    self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--debug']		,       return_stdout=a)
						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'''		,       a		,       )






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






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






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						SCREAMING_SNAKE_CASE							=			run_command(
						    self.cmd
						    + ['--config_file', 'tests/test_configs/latest.yaml', '--command_file', self.command_file, '--debug']		,       return_stdout=a		,       )
						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'''		,       a		,       )






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						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=a		,       )
						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'''		,       a		,       )






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			run_command(
						    self.cmd + ['--config_file', 'tests/test_configs/latest.yaml', '--install_accelerate', '--debug']		,       return_stdout=a		,       )
						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'''		,       a		,       )







			def  SCREAMING_SNAKE_CASE__     (							self)   ->							str:
						SCREAMING_SNAKE_CASE							=			run_command(
						    self.cmd
						    + [
						        '--config_file',
						        'tests/test_configs/latest.yaml',
						        '--install_accelerate',
						        '--accelerate_version',
						        '12.0.0',
						        '--debug',
						    ]		,       return_stdout=a		,       )
						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'''		,       a		,       )








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     ):
							print(f"""Converting {name}..."""     )

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

														from_model.eval()
														a__      =LevitForImageClassificationWithTeacher(__a     ).eval()
														a__      =OrderedDict()

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

														a__      =torch.randn((2, 3, 224, 224)     )
														a__      =from_model(__a     )
														a__      =our_model(__a     ).logits

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

							a__      =name
							print(__a     )

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

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






def   _lowercase(					__a				:						Path    ,     __a				:						str = None    ,     __a				:						bool = True     ):
							a__      ='imagenet-1k-id2label.json'
							a__      =1000
							a__      =(1, num_labels)

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

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

							a__      =partial(__a    ,     num_labels=__a    ,     idalabel=__a    ,     labelaid=__a     )

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

							a__      ={
							    '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]    ,     __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:						Any   				=       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:						Union[str, Any]   				=       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)

