
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:					Dict     =logging.get_logger(__name__)





@dataclass
class 						__lowercase:


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							field(default_factory=__A   )
       UpperCamelCase_				 =							field(default_factory=__A   )



       def SCREAMING_SNAKE_CASE_				(     self					:			Dict  ,      _lowerCAmelCase					:			Dict  ,      _lowerCAmelCase					:			Tuple  ,      _lowerCAmelCase					:			Optional[int]       )					->   str:
         _lowerCAmelCase						=		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					:			List[Any]  ,      _lowerCAmelCase					:			int       )					->   Union[str, Any]:
         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 SCREAMING_SNAKE_CASE_				(     self					:			str       )					->   List[Any]:
         # check the len of the state_dict keys to see if we have learnable params
         return list(filter(lambda _lowerCAmelCase       : len(list(x.state_dict().keys()       )       ) > 0  ,      self.traced       )       )





@dataclass
class 						__lowercase:


       """simple docstring"""







       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							42
       UpperCamelCase_				 =							0
       UpperCamelCase_				 =							field(default_factory=__A   )
       UpperCamelCase_				 =							field(default_factory=__A   )



       def __call__(     self					:			int  ,      _lowerCAmelCase					:			Tuple       )					->   str:
         _lowerCAmelCase						=		Tracker(self.dest       )(__lowercase       ).parametrized
         _lowerCAmelCase						=		Tracker(self.src       )(__lowercase       ).parametrized

         _lowerCAmelCase						=		list(filter(lambda _lowerCAmelCase       : type(__lowercase       ) not in self.src_skip  ,      __lowercase       )       )
         _lowerCAmelCase						=		list(filter(lambda _lowerCAmelCase       : 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 	_a  (					__SCREAMING_SNAKE_CASE					:  List[Any]		,				__SCREAMING_SNAKE_CASE					:  List[str]    ):






  """simple docstring"""




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

      all_keys.append((from_key, to_key)    )
      _lowerCAmelCase						=		f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
      _lowerCAmelCase						=		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:
    _lowerCAmelCase						=		from_state_dict.pop(__SCREAMING_SNAKE_CASE    )

  our_model.load_state_dict(__SCREAMING_SNAKE_CASE    )
  return our_model



def 	_a  (					__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  str		,				__SCREAMING_SNAKE_CASE					:  Union[str, Any]		,				__SCREAMING_SNAKE_CASE					:  int		,				__SCREAMING_SNAKE_CASE					:  Optional[int] = True		,				):






  """simple docstring"""




  print(f'''Downloading weights for {name}...'''    )
  _lowerCAmelCase						=		cached_download(__SCREAMING_SNAKE_CASE    )
  print(f'''Converting {name}...'''    )
  _lowerCAmelCase						=		torch.load(__SCREAMING_SNAKE_CASE    )['state_dict']
  from_model.load_state_dict(__SCREAMING_SNAKE_CASE    )
  from_model.eval()
  with torch.no_grad():
    _lowerCAmelCase						=		VanForImageClassification(__SCREAMING_SNAKE_CASE    ).eval()
    _lowerCAmelCase						=		ModuleTransfer(src=__SCREAMING_SNAKE_CASE		,				dest=__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		torch.randn((1, 3, 224, 224)    )
    module_transfer(__SCREAMING_SNAKE_CASE    )
    _lowerCAmelCase						=		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.'    )

  _lowerCAmelCase						=		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
    _lowerCAmelCase						=		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 	_a  (					__SCREAMING_SNAKE_CASE					:  Tuple		,				__SCREAMING_SNAKE_CASE					:  Optional[int] = None		,				__SCREAMING_SNAKE_CASE					:  str = True    ):






  """simple docstring"""




  _lowerCAmelCase						=		'imagenet-1k-id2label.json'
  _lowerCAmelCase						=		1000

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

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

  _lowerCAmelCase						=		partial(__SCREAMING_SNAKE_CASE		,				num_labels=__SCREAMING_SNAKE_CASE		,				idalabel=__SCREAMING_SNAKE_CASE		,				labelaid=__SCREAMING_SNAKE_CASE    )

  _lowerCAmelCase						=		{
      '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]		,				),
  }

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

  _lowerCAmelCase						=		{
      '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:					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:					List[str]     =parser.parse_args()
       _UpperCamelCase:					Path     =args.pytorch_dump_folder_path
       pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
       _UpperCamelCase:					List[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)




'''simple docstring'''

from ...configuration_utils import PretrainedConfig
from ...utils import is_detectrona_available, logging


_UpperCAmelCase					:			Optional[int]				     =					logging.get_logger(__name__)

_UpperCAmelCase					:			str				     =					{
    """layoutlmv2-base-uncased""": """https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/config.json""",
    """layoutlmv2-large-uncased""": """https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/config.json""",
    # See all LayoutLMv2 models at https://huggingface.co/models?filter=layoutlmv2
}

# soft dependency
if is_detectrona_available():
	import detectrona


class    a__					(			__A   ):







				"""simple docstring"""



				__UpperCamelCase     :		List[Any]						=						'layoutlmv2'




				def __init__(self							,      __lowercase=3_05_22							,      __lowercase=7_68							,      __lowercase=12							,      __lowercase=12							,      __lowercase=30_72							,      __lowercase="gelu"							,      __lowercase=0.1							,      __lowercase=0.1							,      __lowercase=5_12							,      __lowercase=2							,      __lowercase=0.0_2							,      __lowercase=1e-12							,      __lowercase=0							,      __lowercase=10_24							,      __lowercase=1_28							,      __lowercase=32							,      __lowercase=True							,      __lowercase=2_56							,      __lowercase=64							,      __lowercase=True							,      __lowercase=[7, 7, 2_56]							,      __lowercase=1_28							,      __lowercase=1_28							,      __lowercase=True							,      __lowercase=True							,      __lowercase=False							,      __lowercase=None							,      **__lowercase							,      ):
										super().__init__(
										    vocab_size=__lowercase							,      hidden_size=__lowercase							,      num_hidden_layers=__lowercase							,      num_attention_heads=__lowercase							,      intermediate_size=__lowercase							,      hidden_act=__lowercase							,      hidden_dropout_prob=__lowercase							,      attention_probs_dropout_prob=__lowercase							,      max_position_embeddings=__lowercase							,      type_vocab_size=__lowercase							,      initializer_range=__lowercase							,      layer_norm_eps=__lowercase							,      pad_token_id=__lowercase							,      **__lowercase							,      )
										__lowerCAmelCase											=					max_ad_position_embeddings
										__lowerCAmelCase											=					max_rel_pos
										__lowerCAmelCase											=					rel_pos_bins
										__lowerCAmelCase											=					fast_qkv
										__lowerCAmelCase											=					max_rel_ad_pos
										__lowerCAmelCase											=					rel_ad_pos_bins
										__lowerCAmelCase											=					convert_sync_batchnorm
										__lowerCAmelCase											=					image_feature_pool_shape
										__lowerCAmelCase											=					coordinate_size
										__lowerCAmelCase											=					shape_size
										__lowerCAmelCase											=					has_relative_attention_bias
										__lowerCAmelCase											=					has_spatial_attention_bias
										__lowerCAmelCase											=					has_visual_segment_embedding
										__lowerCAmelCase											=					(
										    detectrona_config_args if detectrona_config_args is not None else self.get_default_detectrona_config()
										)




				@classmethod
				def        _snake_case	(self		):
										return {
										    "MODEL.MASK_ON": True,
										    "MODEL.PIXEL_STD": [5_7.3_7_5, 5_7.1_2_0, 5_8.3_9_5],
										    "MODEL.BACKBONE.NAME": "build_resnet_fpn_backbone",
										    "MODEL.FPN.IN_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.ANCHOR_GENERATOR.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RPN.IN_FEATURES": ["p2", "p3", "p4", "p5", "p6"],
										    "MODEL.RPN.PRE_NMS_TOPK_TRAIN": 20_00,
										    "MODEL.RPN.PRE_NMS_TOPK_TEST": 10_00,
										    "MODEL.RPN.POST_NMS_TOPK_TRAIN": 10_00,
										    "MODEL.POST_NMS_TOPK_TEST": 10_00,
										    "MODEL.ROI_HEADS.NAME": "StandardROIHeads",
										    "MODEL.ROI_HEADS.NUM_CLASSES": 5,
										    "MODEL.ROI_HEADS.IN_FEATURES": ["p2", "p3", "p4", "p5"],
										    "MODEL.ROI_BOX_HEAD.NAME": "FastRCNNConvFCHead",
										    "MODEL.ROI_BOX_HEAD.NUM_FC": 2,
										    "MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION": 14,
										    "MODEL.ROI_MASK_HEAD.NAME": "MaskRCNNConvUpsampleHead",
										    "MODEL.ROI_MASK_HEAD.NUM_CONV": 4,
										    "MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION": 7,
										    "MODEL.RESNETS.DEPTH": 1_01,
										    "MODEL.RESNETS.SIZES": [[32], [64], [1_28], [2_56], [5_12]],
										    "MODEL.RESNETS.ASPECT_RATIOS": [[0.5, 1.0, 2.0]],
										    "MODEL.RESNETS.OUT_FEATURES": ["res2", "res3", "res4", "res5"],
										    "MODEL.RESNETS.NUM_GROUPS": 32,
										    "MODEL.RESNETS.WIDTH_PER_GROUP": 8,
										    "MODEL.RESNETS.STRIDE_IN_1X1": False,
										}




				def        _snake_case	(self		):
										__lowerCAmelCase											=					detectrona.config.get_cfg()
										for k, v in self.detectrona_config_args.items():
																__lowerCAmelCase											=					k.split('''.'''		)
																__lowerCAmelCase											=					detectrona_config
																for attribute in attributes[:-1]:
																						__lowerCAmelCase											=					getattr(__lowercase							,      __lowercase		)
																setattr(__lowercase							,      attributes[-1]							,      __lowercase		)

										return detectrona_config

