



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


UpperCamelCase_									=   logging.get_logger(__name__)







class       _snake_case				(     __snake_case ):






   '''simple docstring'''






   A__      : Optional[Any]       =						["pixel_values"]


   def __init__(    self:     Optional[Any]  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Dict[str, int]] = None  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Union[int, float] = 1 / 255  ,lowerCamelCase_:     bool = True  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,**lowerCamelCase_:     Dict  ,)	->      None:
       super().__init__(**lowerCamelCase_							)
       UpperCAmelCase_     :       Optional[Any]  		=					size if size is not None else {"""height""": 224, """width""": 224}
       UpperCAmelCase_     :       str  		=					get_size_dict(lowerCamelCase_							)
       UpperCAmelCase_     :       Tuple  		=					do_resize
       UpperCAmelCase_     :       Optional[Any]  		=					do_rescale
       UpperCAmelCase_     :       Optional[Any]  		=					do_normalize
       UpperCAmelCase_     :       int  		=					size
       UpperCAmelCase_     :       List[Any]  		=					resample
       UpperCAmelCase_     :       Optional[Any]  		=					rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
       UpperCAmelCase_     :       Any  		=					image_std if image_std is not None else IMAGENET_STANDARD_STD


   def       A__    (    self:     Union[str, Any]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Dict[str, int]  ,lowerCamelCase_:     PILImageResampling = PILImageResampling.BILINEAR  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     Optional[int]  ,)	->      np.ndarray:
       UpperCAmelCase_     :       Dict  		=					get_size_dict(lowerCamelCase_							)
       if "height" not in size or "width" not in size:
           raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''							)
       return resize(
           lowerCamelCase_  ,size=(size["""height"""], size["""width"""])  ,resample=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     List[str]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     float  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     List[Any]							)	->      np.ndarray:
       return rescale(lowerCamelCase_  ,scale=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)


   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     np.ndarray  ,lowerCamelCase_:     Union[float, List[float]]  ,lowerCamelCase_:     Union[float, List[float]]  ,lowerCamelCase_:     Optional[Union[str, ChannelDimension]] = None  ,**lowerCamelCase_:     List[Any]  ,)	->      np.ndarray:
       return normalize(lowerCamelCase_  ,mean=lowerCamelCase_  ,std=lowerCamelCase_  ,data_format=lowerCamelCase_  ,**lowerCamelCase_							)






   def       A__    (    self:     Optional[int]  ,lowerCamelCase_:     ImageInput  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Dict[str, int] = None  ,lowerCamelCase_:     PILImageResampling = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[float] = None  ,lowerCamelCase_:     Optional[bool] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[float, List[float]]] = None  ,lowerCamelCase_:     Optional[Union[str, TensorType]] = None  ,lowerCamelCase_:     Union[str, ChannelDimension] = ChannelDimension.FIRST  ,**lowerCamelCase_:     int  ,)	->      List[Any]:
       UpperCAmelCase_     :       str  		=					do_resize if do_resize is not None else self.do_resize
       UpperCAmelCase_     :       Dict  		=					do_rescale if do_rescale is not None else self.do_rescale
       UpperCAmelCase_     :       Dict  		=					do_normalize if do_normalize is not None else self.do_normalize
       UpperCAmelCase_     :       int  		=					resample if resample is not None else self.resample
       UpperCAmelCase_     :       str  		=					rescale_factor if rescale_factor is not None else self.rescale_factor
       UpperCAmelCase_     :       Dict  		=					image_mean if image_mean is not None else self.image_mean
       UpperCAmelCase_     :       int  		=					image_std if image_std is not None else self.image_std

       UpperCAmelCase_     :       Tuple  		=					size if size is not None else self.size
       UpperCAmelCase_     :       Union[str, Any]  		=					get_size_dict(lowerCamelCase_							)

       UpperCAmelCase_     :       Optional[int]  		=					make_list_of_images(lowerCamelCase_							)

       if not valid_images(lowerCamelCase_							):
           raise ValueError(
               """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
               """torch.Tensor, tf.Tensor or jax.ndarray."""							)

       if do_resize and size is None:
           raise ValueError("""Size must be specified if do_resize is True."""							)

       if do_rescale and rescale_factor is None:
           raise ValueError("""Rescale factor must be specified if do_rescale is True."""							)

       # All transformations expect numpy arrays.
       UpperCAmelCase_     :       Optional[Any]  		=					[to_numpy_array(lowerCamelCase_							) for image in images]

       if do_resize:
           UpperCAmelCase_     :       int  		=					[self.resize(image=lowerCamelCase_  ,size=lowerCamelCase_  ,resample=lowerCamelCase_							) for image in images]

       if do_rescale:
           UpperCAmelCase_     :       Optional[Any]  		=					[self.rescale(image=lowerCamelCase_  ,scale=lowerCamelCase_							) for image in images]

       if do_normalize:
           UpperCAmelCase_     :       str  		=					[self.normalize(image=lowerCamelCase_  ,mean=lowerCamelCase_  ,std=lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       Any  		=					[to_channel_dimension_format(lowerCamelCase_  ,lowerCamelCase_							) for image in images]

       UpperCAmelCase_     :       List[str]  		=					{"""pixel_values""": images}
       return BatchFeature(data=lowerCamelCase_  ,tensor_type=lowerCamelCase_							)





'''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()
a  :     Any       =       logging.get_logger(__name__)

@dataclass
class      UpperCamelCase_					:
	lowercase		=					42
	lowercase		=					field(default_factory=__magic_name__     )
	lowercase		=					field(default_factory=__magic_name__     )





	def 					_lowercase(    self						,						A						,						A						,						A       )     ->     Optional[Any]:
								UpperCAmelCase	:						Tuple					=						len(list(m.modules()       )       ) == 1 or isinstance(A						,						nn.Convad       ) or isinstance(A						,						nn.BatchNormad       )
								if has_not_submodules:
															if not isinstance(A						,						A       ):
																						self.traced.append(A       )





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






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




@dataclass
class      UpperCamelCase_					:
	lowercase		=					42
	lowercase		=					42
	lowercase		=					0
	lowercase		=					field(default_factory=__magic_name__     )
	lowercase		=					field(default_factory=__magic_name__     )





	def __call__(    self						,						A       )     ->     str:
								UpperCAmelCase	:						Dict					=						Tracker(self.dest       )(A       ).parametrized
								UpperCAmelCase	:						Union[str, Any]					=						Tracker(self.src       )(A       ).parametrized

								UpperCAmelCase	:						Optional[int]					=						list(filter(lambda A       : type(A       ) not in self.src_skip						,						A       )       )
								UpperCAmelCase	:						Optional[int]					=						list(filter(lambda A       : type(A       ) not in self.dest_skip						,						A       )       )

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

								for dest_m, src_m in zip(A						,						A       ):
															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						) ->     nn.Module:
							# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
							UpperCAmelCase	:						Optional[Any]					=						from_model.state_dict()
							UpperCAmelCase	:						int					=						our_model.state_dict()
							UpperCAmelCase	:						Optional[int]					=						our_model.config
							UpperCAmelCase	:						str					=						[]
							for stage_idx in range(len(config.hidden_sizes						)						):
														for block_id in range(config.depths[stage_idx]						):
																					UpperCAmelCase	:						List[Any]					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
																					UpperCAmelCase	:						Any					=						F'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

																					all_keys.append((from_key, to_key)						)
																					UpperCAmelCase	:						Optional[int]					=						F'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
																					UpperCAmelCase	:						str					=						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	:						List[Any]					=						from_state_dict.pop(_lowercase						)

							our_model.load_state_dict(_lowercase						)
							return our_model



def  __lowerCamelCase    (   _lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase      ,							_lowercase = True      ,							) ->     List[Any]:
							print(F'''Downloading weights for {name}...'''						)
							UpperCAmelCase	:						List[str]					=						cached_download(_lowercase						)
							print(F'''Converting {name}...'''						)
							UpperCAmelCase	:						int					=						torch.load(_lowercase						)["""state_dict"""]
							from_model.load_state_dict(_lowercase						)
							from_model.eval()
							with torch.no_grad():
														UpperCAmelCase	:						Tuple					=						VanForImageClassification(_lowercase						).eval()
														UpperCAmelCase	:						int					=						ModuleTransfer(src=_lowercase      ,							dest=_lowercase						)
														UpperCAmelCase	:						Any					=						torch.randn((1, 3, 2_2_4, 2_2_4)						)
														module_transfer(_lowercase						)
														UpperCAmelCase	:						Tuple					=						copy_parameters(_lowercase      ,							_lowercase						)

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

							UpperCAmelCase	:						List[str]					=						name
							print(_lowercase						)

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

														# we can use the convnext one
														UpperCAmelCase	:						Dict					=						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=_lowercase      ,							)

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



def  __lowerCamelCase    (   _lowercase      ,							_lowercase = None      ,							_lowercase = True						) ->     List[Any]:
							UpperCAmelCase	:						int					=						"""imagenet-1k-id2label.json"""
							UpperCAmelCase	:						List[Any]					=						1_0_0_0

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

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

							UpperCAmelCase	:						Any					=						partial(_lowercase      ,							num_labels=_lowercase      ,							idalabel=_lowercase      ,							labelaid=_lowercase						)

							UpperCAmelCase	:						Optional[Any]					=						{
							    """van-tiny""": ImageNetPreTrainedConfig(
							        hidden_sizes=[3_2, 6_4, 1_6_0, 2_5_6]      ,							depths=[3, 3, 5, 2]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-small""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[2, 2, 4, 2]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-base""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[3, 3, 1_2, 3]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							    """van-large""": ImageNetPreTrainedConfig(
							        hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]      ,							depths=[3, 5, 2_7, 3]      ,							mlp_ratios=[8, 8, 4, 4]      ,							),
							}

							UpperCAmelCase	:						Optional[Any]					=						{
							    """van-tiny""": van_tiny,
							    """van-small""": van_small,
							    """van-base""": van_base,
							    """van-large""": van_large,
							}

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


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

							a  :     List[str]       =       parser.parse_args()
							a  :     Path       =       args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							a  :     Union[str, Any]       =       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)

