



"""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()
snake_case_         = logging.get_logger(__name__)







@dataclass
class    A_    :



						"""simple docstring"""
						__UpperCamelCase	       =							42
						__UpperCamelCase	       =							field(default_factory=_a       )
						__UpperCamelCase	       =							field(default_factory=_a       )






						def 			UpperCAmelCase__  (      self				:str     ,						lowercase_				:Optional[int]     ,						lowercase_				:Tensor     ,						lowercase_				:Tensor      )       ->    List[str]:
												UpperCAmelCase    =	len(list(m.modules()      )      ) == 1 or isinstance(lowercase_     ,						nn.Convad      ) or isinstance(lowercase_     ,						nn.BatchNormad      )
												if has_not_submodules:
																		if not isinstance(lowercase_     ,						lowercase_      ):
																								self.traced.append(lowercase_      )






						def __call__(      self				:str     ,						lowercase_				:Tensor      )       ->    Optional[int]:
												for m in self.module.modules():
																		self.handles.append(m.register_forward_hook(self._forward_hook      )      )
												self.module(lowercase_      )
												[x.remove() for x in self.handles]
												return self






						@property
						def 			UpperCAmelCase__  (      self				:Tuple      )       ->    List[Any]:
												# check the len of the state_dict keys to see if we have learnable params
												return list(filter(lambda lowercase_      : len(list(x.state_dict().keys()      )      ) > 0     ,						self.traced      )      )







@dataclass
class    A_    :



						"""simple docstring"""
						__UpperCamelCase	       =							42
						__UpperCamelCase	       =							42
						__UpperCamelCase	       =							0
						__UpperCamelCase	       =							field(default_factory=_a       )
						__UpperCamelCase	       =							field(default_factory=_a       )






						def __call__(      self				:Optional[Any]     ,						lowercase_				:Tensor      )       ->    Optional[int]:
												UpperCAmelCase    =	Tracker(self.dest      )(lowercase_      ).parametrized
												UpperCAmelCase    =	Tracker(self.src      )(lowercase_      ).parametrized

												UpperCAmelCase    =	list(filter(lambda lowercase_      : type(lowercase_      ) not in self.src_skip     ,						lowercase_      )      )
												UpperCAmelCase    =	list(filter(lambda lowercase_      : type(lowercase_      ) not in self.dest_skip     ,						lowercase_      )      )

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

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


def 				_lowerCAmelCase   (     lowercase_	,			lowercase_		):
						UpperCAmelCase    =	from_model.state_dict()
						UpperCAmelCase    =	our_model.state_dict()
						UpperCAmelCase    =	our_model.config
						UpperCAmelCase    =	[]
						for stage_idx in range(len(config.hidden_sizes		)		):
												for block_id in range(config.depths[stage_idx]		):
																		UpperCAmelCase    =	F"""block{stage_idx + 1}.{block_id}.layer_scale_1"""
																		UpperCAmelCase    =	F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

																		all_keys.append((from_key, to_key)		)
																		UpperCAmelCase    =	F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
																		UpperCAmelCase    =	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:
												UpperCAmelCase    =	from_state_dict.pop(lowerCamelCase__		)

						our_model.load_state_dict(lowerCamelCase__		)
						return our_model

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_	,			lowercase_	,			lowercase_	,			lowercase_	,			lowercase_ = True	,			):
						print(F"""Downloading weights for {name}..."""		)
						UpperCAmelCase    =	cached_download(lowerCamelCase__		)
						print(F"""Converting {name}..."""		)
						UpperCAmelCase    =	torch.load(lowerCamelCase__		)['state_dict']
						from_model.load_state_dict(lowerCamelCase__		)
						from_model.eval()
						with torch.no_grad():
												UpperCAmelCase    =	VanForImageClassification(lowerCamelCase__		).eval()
												UpperCAmelCase    =	ModuleTransfer(src=lowerCamelCase__	,			dest=lowerCamelCase__		)
												UpperCAmelCase    =	torch.randn((1, 3, 224, 224)		)
												module_transfer(lowerCamelCase__		)
												UpperCAmelCase    =	copy_parameters(lowerCamelCase__	,			lowerCamelCase__		)

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

						UpperCAmelCase    =	name
						print(lowerCamelCase__		)

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

												# we can use the convnext one
												UpperCAmelCase    =	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=lowerCamelCase__	,			)

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

def 				_lowerCAmelCase   (     lowercase_	,			lowercase_ = None	,			lowercase_ = True		):
						UpperCAmelCase    =	'imagenet-1k-id2label.json'
						UpperCAmelCase    =	1000

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

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

						UpperCAmelCase    =	partial(lowerCamelCase__	,			num_labels=lowerCamelCase__	,			idalabel=lowerCamelCase__	,			labelaid=lowerCamelCase__		)

						UpperCAmelCase    =	{
						    '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]	,			),
						}

						UpperCAmelCase    =	{
						    'van-tiny': van_tiny,
						    'van-small': van_small,
						    'van-base': van_base,
						    'van-large': van_large,
						}

						UpperCAmelCase    =	{
						    '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(
												    lowerCamelCase__	,			names_to_config[model_name]	,			checkpoint=names_to_original_checkpoints[model_name]	,			from_model=names_to_original_models[model_name]()	,			save_directory=lowerCamelCase__	,			push_to_hub=lowerCamelCase__	,			)
						else:
												for model_name, config in names_to_config.items():
																		convert_weight_and_push(
																		    lowerCamelCase__	,			lowerCamelCase__	,			checkpoint=names_to_original_checkpoints[model_name]	,			from_model=names_to_original_models[model_name]()	,			save_directory=lowerCamelCase__	,			push_to_hub=lowerCamelCase__	,			)


if __name__ == "__main__":
				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 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.""",
				)

				snake_case_         = parser.parse_args()
				snake_case_         = args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				snake_case_         = 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 warnings

from .generation import FlaxGenerationMixin







class    A_    (  SCREAMING_SNAKE_CASE_       ):



						"""simple docstring"""
						# warning at import time
						warnings.warn(
						    """Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will """
						    """be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead."""			,     SCREAMING_SNAKE_CASE_			,     )





