"""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__      						=  logging.get_logger()





def    lowerCAmelCase__    (			_UpperCamelCase       :				int ,			_UpperCamelCase       :				str ,			_UpperCamelCase       :				LevitConfig ,			_UpperCamelCase       :				Path ,			_UpperCamelCase       :				bool = True					)					->				Tuple:





						"""simple docstring"""






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

						with torch.no_grad():
												if hidden_sizes == 1_2_8:
																		if name[-1] == "S":
																								snake_case								=			timm.create_model('levit_128s' ,			pretrained=_UpperCamelCase					)
																		else:
																								snake_case								=			timm.create_model('levit_128' ,			pretrained=_UpperCamelCase					)
												if hidden_sizes == 1_9_2:
																		snake_case								=			timm.create_model('levit_192' ,			pretrained=_UpperCamelCase					)
												if hidden_sizes == 2_5_6:
																		snake_case								=			timm.create_model('levit_256' ,			pretrained=_UpperCamelCase					)
												if hidden_sizes == 3_8_4:
																		snake_case								=			timm.create_model('levit_384' ,			pretrained=_UpperCamelCase					)

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

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

												snake_case								=			torch.randn((2, 3, 2_2_4, 2_2_4)					)
												snake_case								=			from_model(_UpperCamelCase					)
												snake_case								=			our_model(_UpperCamelCase					).logits

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

						snake_case								=			name
						print(_UpperCamelCase					)

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

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





def    lowerCAmelCase__    (			_UpperCamelCase       :				Path ,			_UpperCamelCase       :				str = None ,			_UpperCamelCase       :				bool = True					)					->				Optional[Any]:





						"""simple docstring"""






						snake_case								=			'imagenet-1k-id2label.json'
						snake_case								=			1_0_0_0
						snake_case								=			(1, num_labels)

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

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

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

						snake_case								=			{
						    'levit-128S': 1_2_8,
						    'levit-128': 1_2_8,
						    'levit-192': 1_9_2,
						    'levit-256': 2_5_6,
						    'levit-384': 3_8_4,
						}

						snake_case								=			{
						    'levit-128S': ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,			num_attention_heads=[4, 6, 8] ,			depths=[2, 3, 4] ,			key_dim=[1_6, 1_6, 1_6] ,			drop_path_rate=0 ,			),
						    'levit-128': ImageNetPreTrainedConfig(
						        hidden_sizes=[1_2_8, 2_5_6, 3_8_4] ,			num_attention_heads=[4, 8, 1_2] ,			depths=[4, 4, 4] ,			key_dim=[1_6, 1_6, 1_6] ,			drop_path_rate=0 ,			),
						    'levit-192': ImageNetPreTrainedConfig(
						        hidden_sizes=[1_9_2, 2_8_8, 3_8_4] ,			num_attention_heads=[3, 5, 6] ,			depths=[4, 4, 4] ,			key_dim=[3_2, 3_2, 3_2] ,			drop_path_rate=0 ,			),
						    'levit-256': ImageNetPreTrainedConfig(
						        hidden_sizes=[2_5_6, 3_8_4, 5_1_2] ,			num_attention_heads=[4, 6, 8] ,			depths=[4, 4, 4] ,			key_dim=[3_2, 3_2, 3_2] ,			drop_path_rate=0 ,			),
						    'levit-384': ImageNetPreTrainedConfig(
						        hidden_sizes=[3_8_4, 5_1_2, 7_6_8] ,			num_attention_heads=[6, 9, 1_2] ,			depths=[4, 4, 4] ,			key_dim=[3_2, 3_2, 3_2] ,			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__":
					SCREAMING_SNAKE_CASE__      						=  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__      						=  parser.parse_args()
					SCREAMING_SNAKE_CASE__      						=  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)



"""simple docstring"""
import enum
import os
from hashlib import shaaaa
from typing import Optional

from .. import config
from .logging import get_logger


SCREAMING_SNAKE_CASE__      						=  get_logger(__name__)



class   lowerCAmelCase_    (			enum.Enum					):



				"""simple docstring"""
				_lowerCAmelCase     :    Union[str, Any]			  =						"""all_checks"""
				_lowerCAmelCase     :    Optional[Any]			  =						"""basic_checks"""
				_lowerCAmelCase     :    Optional[int]			  =						"""no_checks"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict ,			_UpperCamelCase       :				str=None					)					->				Dict:





						"""simple docstring"""






						if expected_checksums is None:
												logger.info('Unable to verify checksums.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreDownloadedFiles(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedDownloadedFile(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
						snake_case								=			' for ' + verification_name if verification_name is not None else ''
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingChecksumError(
												    f"""Checksums didn't match{for_verification_name}:\n"""
												    f"""{bad_urls}\n"""
												    'Set `verification_mode=\'no_checks\'` to skip checksums verification and ignore this error'					)
						logger.info('All the checksums matched successfully' + for_verification_name					)



class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





class   lowerCAmelCase_    (			lowerCAmelCase					):



				"""simple docstring"""





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[dict] ,			_UpperCamelCase       :				dict					)					->				Tuple:





						"""simple docstring"""






						if expected_splits is None:
												logger.info('Unable to verify splits sizes.'					)
												return
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise ExpectedMoreSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						if len(set(_UpperCamelCase					) - set(_UpperCamelCase					)					) > 0:
												raise UnexpectedSplits(str(set(_UpperCamelCase					) - set(_UpperCamelCase					)					)					)
						snake_case								=			[
						    {'expected': expected_splits[name], 'recorded': recorded_splits[name]}
						    for name in expected_splits
						    if expected_splits[name].num_examples != recorded_splits[name].num_examples
						]
						if len(_UpperCamelCase					) > 0:
												raise NonMatchingSplitsSizesError(str(_UpperCamelCase					)					)
						logger.info('All the splits matched successfully.'					)





def    lowerCAmelCase__    (			_UpperCamelCase       :				str ,			_UpperCamelCase       :				bool = True					)					->				dict:





						"""simple docstring"""






						if record_checksum:
												snake_case								=			shaaaa()
												with open(_UpperCamelCase ,			'rb'					) as f:
																		for chunk in iter(lambda: f.read(1 << 2_0					) ,			B''					):
																								m.update(_UpperCamelCase					)
																		snake_case								=			m.hexdigest()
						else:
												snake_case								=			None
						return {"num_bytes": os.path.getsize(_UpperCamelCase					), "checksum": checksum}





def    lowerCAmelCase__    (			_UpperCamelCase       :				Optional[int]					)					->				int:





						"""simple docstring"""






						if dataset_size and config.IN_MEMORY_MAX_SIZE:
												return dataset_size < config.IN_MEMORY_MAX_SIZE
						else:
												return False


