




'''simple docstring'''


import argparse
import json
import sys
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import torch
import torch.nn as nn
from huggingface_hub import cached_download, hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, VanConfig, VanForImageClassification
from transformers.models.deprecated.van.modeling_van import VanLayerScaling
from transformers.utils import logging


logging.set_verbosity_info()
UpperCAmelCase_	:     str							     =	logging.get_logger(__name__)





@dataclass
class 					lowercase__						:





							'''simple docstring'''


							A_							:   nn.Module
							A_							:   List[nn.Module]   			=						field(default_factory=_snake_case      )
							A_							:   list   			=						field(default_factory=_snake_case      )






							def UpperCAmelCase_			(					self					,      __snake_case					,      __snake_case					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Tuple  	=   len(list(m.modules()						)						) == 1 or isinstance(__snake_case					,      nn.Convad						) or isinstance(__snake_case					,      nn.BatchNormad						)
											if has_not_submodules:
															if not isinstance(__snake_case					,      __snake_case						):
																			self.traced.append(__snake_case						)






							def __call__(					self					,      __snake_case						):
											for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook						)						)
											self.module(__snake_case						)
											[x.remove() for x in self.handles]
											return self






							@property
							def UpperCAmelCase_			(					self						):
											# check the len of the state_dict keys to see if we have learnable params
											return list(filter(lambda __snake_case						: len(list(x.state_dict().keys()						)						) > 0					,      self.traced						)						)





@dataclass
class 					lowercase__						:





							'''simple docstring'''


							A_							:   nn.Module
							A_							:   nn.Module
							A_							:   int   			=						0
							A_							:   List   			=						field(default_factory=_snake_case      )
							A_							:   List   			=						field(default_factory=_snake_case      )






							def __call__(					self					,      __snake_case						):
											_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   Tracker(self.dest						)(__snake_case						).parametrized
											_SCREAMING_SNAKE_CASE				: int  	=   Tracker(self.src						)(__snake_case						).parametrized

											_SCREAMING_SNAKE_CASE				: Dict  	=   list(filter(lambda __snake_case						: type(__snake_case						) not in self.src_skip					,      __snake_case						)						)
											_SCREAMING_SNAKE_CASE				: List[str]  	=   list(filter(lambda __snake_case						: type(__snake_case						) not in self.dest_skip					,      __snake_case						)						)

											if len(__snake_case						) != len(__snake_case						):
															raise Exception(
															    f"""Numbers of operations are different. Source module has {len(__snake_case						)} operations while"""
															    f""" destination module has {len(__snake_case						)}."""						)

											for dest_m, src_m in zip(__snake_case					,      __snake_case						):
															dest_m.load_state_dict(src_m.state_dict()						)
															if self.verbose == 1:
																			print(f"""Transfered from={src_m} to={dest_m}"""						)







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Tuple  	=   from_model.state_dict()
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   our_model.state_dict()
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   our_model.config
				_SCREAMING_SNAKE_CASE				: List[str]  	=   []
				for stage_idx in range(len(config.hidden_sizes  )  ):
								for block_id in range(config.depths[stage_idx]  ):
												_SCREAMING_SNAKE_CASE				: str  	=   f"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
												_SCREAMING_SNAKE_CASE				: Tuple  	=   f"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)  )
												_SCREAMING_SNAKE_CASE				: int  	=   f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												_SCREAMING_SNAKE_CASE				: Dict  	=   f"""van.encoder.stages.{stage_idx}.layers.{block_id}.mlp_scaling.weight"""

												all_keys.append((from_key, to_key)  )

				for from_key, to_key in all_keys:
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   from_state_dict.pop(SCREAMING_SNAKE_CASE__  )

				our_model.load_state_dict(SCREAMING_SNAKE_CASE__  )
				return our_model







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__ = True							,   ):







				"""simple docstring"""





				print(f"""Downloading weights for {name}..."""  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   cached_download(SCREAMING_SNAKE_CASE__  )
				print(f"""Converting {name}..."""  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   torch.load(SCREAMING_SNAKE_CASE__  )["""state_dict"""]
				from_model.load_state_dict(SCREAMING_SNAKE_CASE__  )
				from_model.eval()
				with torch.no_grad():
								_SCREAMING_SNAKE_CASE				: Tuple  	=   VanForImageClassification(SCREAMING_SNAKE_CASE__  ).eval()
								_SCREAMING_SNAKE_CASE				: Dict  	=   ModuleTransfer(src=SCREAMING_SNAKE_CASE__							,   dest=SCREAMING_SNAKE_CASE__  )
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   torch.randn((1, 3, 224, 224)  )
								module_transfer(SCREAMING_SNAKE_CASE__  )
								_SCREAMING_SNAKE_CASE				: Any  	=   copy_parameters(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )

				if not torch.allclose(from_model(SCREAMING_SNAKE_CASE__  )							,   our_model(SCREAMING_SNAKE_CASE__  ).logits  ):
								raise ValueError("""The model logits don't match the original one."""  )

				_SCREAMING_SNAKE_CASE				: str  	=   name
				print(SCREAMING_SNAKE_CASE__  )

				if push_to_hub:
								our_model.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name							,   commit_message="""Add model"""							,   use_temp_dir=SCREAMING_SNAKE_CASE__							,   )

								# we can use the convnext one
								_SCREAMING_SNAKE_CASE				: Optional[int]  	=   AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""  )
								image_processor.push_to_hub(
								    repo_path_or_name=save_directory / checkpoint_name							,   commit_message="""Add image processor"""							,   use_temp_dir=SCREAMING_SNAKE_CASE__							,   )

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







def     snake_case_     (						SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__ = None							,   SCREAMING_SNAKE_CASE__ = True  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Tuple  	=   """imagenet-1k-id2label.json"""
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   1000

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   """huggingface/label-files"""
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   num_labels
				_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   repo_type="""dataset"""  )							,   """r"""  )  )
				_SCREAMING_SNAKE_CASE				: Tuple  	=   {int(SCREAMING_SNAKE_CASE__  ): v for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE				: Any  	=   idalabel
				_SCREAMING_SNAKE_CASE				: List[Any]  	=   {v: k for k, v in idalabel.items()}

				_SCREAMING_SNAKE_CASE				: List[Any]  	=   partial(SCREAMING_SNAKE_CASE__							,   num_labels=SCREAMING_SNAKE_CASE__							,   idalabel=SCREAMING_SNAKE_CASE__							,   labelaid=SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: str  	=   {
				    """van-tiny""": ImageNetPreTrainedConfig(
				        hidden_sizes=[32, 64, 160, 256]							,   depths=[3, 3, 5, 2]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
				    """van-small""": ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 128, 320, 512]							,   depths=[2, 2, 4, 2]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
				    """van-base""": ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 128, 320, 512]							,   depths=[3, 3, 12, 3]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
				    """van-large""": ImageNetPreTrainedConfig(
				        hidden_sizes=[64, 128, 320, 512]							,   depths=[3, 5, 27, 3]							,   mlp_ratios=[8, 8, 4, 4]							,   ),
				}

				_SCREAMING_SNAKE_CASE				: Tuple  	=   {
				    """van-tiny""": van_tiny,
				    """van-small""": van_small,
				    """van-base""": van_base,
				    """van-large""": van_large,
				}

				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   {
				    """van-tiny""": (
				        """https://huggingface.co/Visual-Attention-Network/VAN-Tiny-original/resolve/main/van_tiny_754.pth.tar"""
				    ),
				    """van-small""": (
				        """https://huggingface.co/Visual-Attention-Network/VAN-Small-original/resolve/main/van_small_811.pth.tar"""
				    ),
				    """van-base""": (
				        """https://huggingface.co/Visual-Attention-Network/VAN-Base-original/resolve/main/van_base_828.pth.tar"""
				    ),
				    """van-large""": (
				        """https://huggingface.co/Visual-Attention-Network/VAN-Large-original/resolve/main/van_large_839.pth.tar"""
				    ),
				}

				if model_name:
								convert_weight_and_push(
								    SCREAMING_SNAKE_CASE__							,   names_to_config[model_name]							,   checkpoint=names_to_original_checkpoints[model_name]							,   from_model=names_to_original_models[model_name]()							,   save_directory=SCREAMING_SNAKE_CASE__							,   push_to_hub=SCREAMING_SNAKE_CASE__							,   )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   checkpoint=names_to_original_checkpoints[model_name]							,   from_model=names_to_original_models[model_name]()							,   save_directory=SCREAMING_SNAKE_CASE__							,   push_to_hub=SCREAMING_SNAKE_CASE__							,   )


if __name__ == "__main__":
				UpperCAmelCase_	:     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 resnet* architecture,'
				        ' currently: van-tiny/small/base/large. If `None`, all of them will the converted.'
				    ),
				)
				parser.add_argument(
				    '--pytorch_dump_folder_path',
				    default=None,
				    type=Path,
				    required=True,
				    help='Path to the output PyTorch model directory.',
				)
				parser.add_argument(
				    '--van_dir',
				    required=True,
				    type=Path,
				    help=(
				        'A path to VAN\'s original implementation directory. You can download from here:'
				        ' https://github.com/Visual-Attention-Network/VAN-Classification'
				    ),
				)
				parser.add_argument(
				    '--push_to_hub',
				    default=True,
				    type=bool,
				    required=False,
				    help='If True, push model and image processor to the hub.',
				)

				UpperCAmelCase_	:     str							     =	parser.parse_args()
				UpperCAmelCase_	:     Path							     =	args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				UpperCAmelCase_	:     List[str]							     =	args.van_dir
				# append the path to the parents to maskformer dir
				sys.path.append(str(van_dir.parent))
				from van.models.van import van_base, van_large, van_small, van_tiny

				convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)








'''simple docstring'''


import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: str  	=   set()
				# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
				for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
								_SCREAMING_SNAKE_CASE				: Any  	=   getattr(transformers.models.auto							,   SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
								if module is None:
												continue
								# all mappings in a single auto modeling file
								_SCREAMING_SNAKE_CASE				: str  	=   [
								    x
								    for x in dir(SCREAMING_SNAKE_CASE__  )
								    if x.endswith("""_MAPPING_NAMES"""  )
								    and (x.startswith("""MODEL_"""  ) or x.startswith("""TF_MODEL_"""  ) or x.startswith("""FLAX_MODEL_"""  ))
								]
								for name in mapping_names:
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												if mapping is not None:
																for v in mapping.values():
																				if isinstance(SCREAMING_SNAKE_CASE__							,   (list, tuple)  ):
																								model_names.update(SCREAMING_SNAKE_CASE__  )
																				elif isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																								model_names.add(SCREAMING_SNAKE_CASE__  )

				return sorted(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set(get_all_model_names()  )

				with open("""tests/utils/tiny_model_summary.json"""  ) as fp:
								_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   json.load(SCREAMING_SNAKE_CASE__  )
				_SCREAMING_SNAKE_CASE				: str  	=   set()
				for model_base_name in tiny_model_info:
								tiny_models_names.update(tiny_model_info[model_base_name]["""model_classes"""]  )

				# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
				_SCREAMING_SNAKE_CASE				: str  	=   model_names.difference(SCREAMING_SNAKE_CASE__  )
				for model_name in copy.copy(SCREAMING_SNAKE_CASE__  ):
								if not model_name.startswith("""TF"""  ) and f"""TF{model_name}""" in not_on_hub:
												tiny_models_names.remove(SCREAMING_SNAKE_CASE__  )
								elif model_name.startswith("""TF"""  ) and model_name[2:] in not_on_hub:
												tiny_models_names.remove(SCREAMING_SNAKE_CASE__  )

				return sorted(SCREAMING_SNAKE_CASE__  )







def     snake_case_     (						SCREAMING_SNAKE_CASE__  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   COMPOSITE_MODELS.values()

				# All tiny model base names on Hub
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   get_all_model_names()
				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   hf_api.list_models(
				    filter=ModelFilter(
				        author="""hf-internal-testing"""							,   )  )
				_SCREAMING_SNAKE_CASE				: Optional[Any]  	=   set()
				for x in models:
								_SCREAMING_SNAKE_CASE				: Tuple  	=   x.modelId
								_SCREAMING_SNAKE_CASE	, _SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model.split("""/"""  )
								if not model.startswith("""tiny-random-"""  ):
												continue
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.replace("""tiny-random-"""							,   """"""  )
								if not model[0].isupper():
												continue
								if model not in model_names and model not in special_models:
												continue
								_models.add(SCREAMING_SNAKE_CASE__  )

				_SCREAMING_SNAKE_CASE				: Optional[int]  	=   sorted(_models  )
				# All tiny model names on Hub
				_SCREAMING_SNAKE_CASE				: Dict  	=   {}
				for model in models:
								_SCREAMING_SNAKE_CASE				: str  	=   f"""hf-internal-testing/tiny-random-{model}"""
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   model.split("""-"""  )[0]
								try:
												_SCREAMING_SNAKE_CASE				: Tuple  	=   hf_api.repo_info(SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   {
												    """tokenizer_classes""": set(),
												    """processor_classes""": set(),
												    """model_classes""": set(),
												    """sha""": repo_info.sha,
												}
								except Exception:
												continue
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Optional[int]  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE__							,   use_fast=SCREAMING_SNAKE_CASE__  )
												content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: str  	=   AutoImageProcessor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["processor_classes"].add(img_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: Any  	=   AutoFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__  )
												if not isinstance(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  ):
																content["processor_classes"].add(feat_p.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: int  	=   getattr(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__  )
												_SCREAMING_SNAKE_CASE				: Tuple  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass
								try:
												time.sleep(1  )
												_SCREAMING_SNAKE_CASE				: List[Any]  	=   getattr(SCREAMING_SNAKE_CASE__							,   f"""TF{model}"""  )
												_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   model_class.from_pretrained(SCREAMING_SNAKE_CASE__  )
												content["model_classes"].add(m.__class__.__name__  )
								except Exception:
												pass

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   sorted(content["""tokenizer_classes"""]  )
								_SCREAMING_SNAKE_CASE				: Union[str, Any]  	=   sorted(content["""processor_classes"""]  )
								_SCREAMING_SNAKE_CASE				: List[Any]  	=   sorted(content["""model_classes"""]  )

								_SCREAMING_SNAKE_CASE				: List[Any]  	=   content
								with open(os.path.join(SCREAMING_SNAKE_CASE__							,   """hub_tiny_model_summary.json"""  )							,   """w"""  ) as fp:
												json.dump(SCREAMING_SNAKE_CASE__							,   SCREAMING_SNAKE_CASE__							,   ensure_ascii=SCREAMING_SNAKE_CASE__							,   indent=4  )


if __name__ == "__main__":
				UpperCAmelCase_	:     Any							     =	argparse.ArgumentParser()
				parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
				UpperCAmelCase_	:     Optional[int]							     =	parser.parse_args()

				# This has to be `spawn` to avoid hanging forever!
				multiprocessing.set_start_method('spawn')

				UpperCAmelCase_	:     Any							     =	'tiny_models'
				UpperCAmelCase_	:     Tuple							     =	True
				UpperCAmelCase_	:     Tuple							     =	None
				UpperCAmelCase_	:     Optional[int]							     =	get_tiny_model_names_from_repo()
				UpperCAmelCase_	:     Dict							     =	True
				UpperCAmelCase_	:     str							     =	True
				UpperCAmelCase_	:     Optional[int]							     =	'hf-internal-testing'

				create_tiny_models(
				    output_path,
				    all,
				    model_types,
				    models_to_skip,
				    no_check,
				    upload,
				    organization,
				    token=os.environ.get('TOKEN', None),
				    num_workers=args.num_workers,
				)




