





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






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

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

								from_model.eval()
								_a								= LevitForImageClassificationWithTeacher(lowercase						).eval()
								_a								= OrderedDict()

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

								_a								= torch.randn((2, 3, 224, 224)						)
								_a								= from_model(lowercase						)
								_a								= our_model(lowercase						).logits

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

				_a								= name
				print(lowercase						)

				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  _lowerCamelCase				( lowercase						:      Path    ,							lowercase						:      str = None    ,							lowercase						:      bool = True						)	->	List[Any]:
				_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(lowercase    ,							lowercase    ,							repo_type="dataset"						)    ,							"r"						)						)
				_a								= {int(lowercase						): v for k, v in idalabel.items()}

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

				_a								= partial(lowercase    ,							num_labels=lowercase    ,							idalabel=lowercase    ,							labelaid=lowercase						)

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


if __name__ == "__main__":
					lowerCAmelCase_					:    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',
					)

					lowerCAmelCase_					:    List[str]      	=			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)







'''simple docstring'''







import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      int    ,							lowercase						:      int=1024    ,							lowercase						:      int=1024    ,							lowercase						:      Tuple=False    ,							**lowercase						:      Optional[int]						)	->	Union[str, Any]:
				_a								= AutoTokenizer.from_pretrained(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="train"    ,							**lowercase						)
				_a								= tok.pad_token_id

				def get_lens(lowercase						:      Optional[int]						):
								_a								= tqdm(
								    DataLoader(lowercase    ,							batch_size=512    ,							num_workers=8    ,							shuffle=lowercase    ,							collate_fn=ds.collate_fn						)    ,							desc=str(ds.len_file						)    ,							)
								_a								= []
								for batch in dl:
												_a								= batch["input_ids"].ne(lowercase						).sum(1						).tolist()
												_a								= batch["labels"].ne(lowercase						).sum(1						).tolist()
												if consider_target:
																for src, tgt in zip(lowercase    ,							lowercase						):
																				max_lens.append(max(lowercase    ,							lowercase						)						)
												else:
																max_lens.extend(lowercase						)
								return max_lens

				_a								= get_lens(lowercase						)
				_a								= SeqaSeqDataset(lowercase    ,							lowercase    ,							lowercase    ,							lowercase    ,							type_path="val"    ,							**lowercase						)
				_a								= get_lens(lowercase						)
				pickle_save(lowercase    ,							train_ds.len_file						)
				pickle_save(lowercase    ,							val_ds.len_file						)


if __name__ == "__main__":
					fire.Fire(save_len_file)
