




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_		:						List[str]             =    logging.get_logger()






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase = True):
			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=_UpperCAmelCase)
									else:
												SCREAMING_SNAKE_CASE						= timm.create_model('levit_128'							,       pretrained=_UpperCAmelCase)
						if hidden_sizes == 192:
									SCREAMING_SNAKE_CASE						= timm.create_model('levit_192'							,       pretrained=_UpperCAmelCase)
						if hidden_sizes == 256:
									SCREAMING_SNAKE_CASE						= timm.create_model('levit_256'							,       pretrained=_UpperCAmelCase)
						if hidden_sizes == 384:
									SCREAMING_SNAKE_CASE						= timm.create_model('levit_384'							,       pretrained=_UpperCAmelCase)

						from_model.eval()
						SCREAMING_SNAKE_CASE						= LevitForImageClassificationWithTeacher(_UpperCAmelCase).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(_UpperCAmelCase)							,       len(_UpperCAmelCase))
						for i in range(len(_UpperCAmelCase)):
									SCREAMING_SNAKE_CASE						= weights[og_keys[i]]
						our_model.load_state_dict(_UpperCAmelCase)

						SCREAMING_SNAKE_CASE						= torch.randn((2, 3, 224, 224))
						SCREAMING_SNAKE_CASE						= from_model(_UpperCAmelCase)
						SCREAMING_SNAKE_CASE						= our_model(_UpperCAmelCase).logits

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

			SCREAMING_SNAKE_CASE						= name
			print(_UpperCAmelCase)

			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 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase = None							,       _UpperCAmelCase = True):
			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(_UpperCAmelCase							,       _UpperCAmelCase							,       repo_type='dataset')							,       'r'))
			SCREAMING_SNAKE_CASE						= {int(_UpperCAmelCase): 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(_UpperCAmelCase							,       num_labels=_UpperCAmelCase							,       idalabel=_UpperCAmelCase							,       labelaid=_UpperCAmelCase)

			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]							,       _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_		:						List[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_		:						Tuple             =    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)












# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run

import doctest
import sys
import warnings
from os.path import abspath, dirname, join

import _pytest

from transformers.testing_utils import HfDoctestModule, HfDocTestParser


# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
a_		:						List[str]             =    abspath(join(dirname(__file__), 'src'))
sys.path.insert(1, git_repo_path)

# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='ignore', category=FutureWarning)






def 				lowerCamelCase__				(_UpperCAmelCase):
			config.addinivalue_line(
			    'markers'							,       'is_pt_tf_cross_test: mark test to run only when PT and TF interactions are tested')
			config.addinivalue_line(
			    'markers'							,       'is_pt_flax_cross_test: mark test to run only when PT and FLAX interactions are tested')
			config.addinivalue_line('markers'							,       'is_pipeline_test: mark test to run only when pipelines are tested')
			config.addinivalue_line('markers'							,       'is_staging_test: mark test to run only in the staging environment')
			config.addinivalue_line('markers'							,       'accelerate_tests: mark test that require accelerate')
			config.addinivalue_line('markers'							,       'tool_tests: mark the tool tests that are run on their specific schedule')






def 				lowerCamelCase__				(_UpperCAmelCase):
			from transformers.testing_utils import pytest_addoption_shared

			pytest_addoption_shared(_UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase):
			from transformers.testing_utils import pytest_terminal_summary_main

			SCREAMING_SNAKE_CASE						= terminalreporter.config.getoption('--make-reports')
			if make_reports:
						pytest_terminal_summary_main(_UpperCAmelCase							,       id=_UpperCAmelCase)






def 				lowerCamelCase__				(_UpperCAmelCase							,       _UpperCAmelCase):
			# If no tests are collected, pytest exists with code 5, which makes the CI fail.
			if exitstatus == 5:
						SCREAMING_SNAKE_CASE						= 0


# Doctest custom flag to ignore output.
a_		:						List[Any]             =    doctest.register_optionflag('IGNORE_RESULT')

a_		:						Any             =    doctest.OutputChecker

class 		_snake_case  (  A__					):






			def    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a		,					a)      ->       List[Any]:
						if IGNORE_RESULT & optionflags:
									return True
						return OutputChecker.check_output(self		,					a		,					a		,					a)



a_		:						int             =    CustomOutputChecker
a_		:						List[str]             =    HfDoctestModule







a_		:						Optional[int]             =    HfDocTestParser




