




'''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_						:			Tuple  	=						logging.get_logger(__name__)

@dataclass
class    _a				:
     SCREAMING_SNAKE_CASE_							:    nn.Module
     SCREAMING_SNAKE_CASE_							:    List[nn.Module]    				=						field(default_factory=__lowerCAmelCase							)
     SCREAMING_SNAKE_CASE_							:    list    				=						field(default_factory=__lowerCAmelCase							)






     def 				_lowercase	( self					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE					,_SCREAMING_SNAKE_CASE    )							->			Any:
          _snake_case							=      len(list(m.modules()    )    ) == 1 or isinstance(_UpperCamelCase					,nn.Convad    ) or isinstance(_UpperCamelCase					,nn.BatchNormad    )
          if has_not_submodules:
               if not isinstance(_UpperCamelCase					,_UpperCamelCase    ):
                    self.traced.append(_UpperCamelCase    )






     def __call__( self					,_SCREAMING_SNAKE_CASE    )							->			List[Any]:

          for m in self.module.modules():
               self.handles.append(m.register_forward_hook(self._forward_hook    )    )
          self.module(_UpperCamelCase    )
          [x.remove() for x in self.handles]
          return self






     @property
     def 				_lowercase	( self    )							->			str:
          return list(filter(lambda _SCREAMING_SNAKE_CASE    : len(list(x.state_dict().keys()    )    ) > 0					,self.traced    )    )







@dataclass
class    _a				:
     SCREAMING_SNAKE_CASE_							:    nn.Module
     SCREAMING_SNAKE_CASE_							:    nn.Module
     SCREAMING_SNAKE_CASE_							:    int    				=						0
     SCREAMING_SNAKE_CASE_							:    List    				=						field(default_factory=__lowerCAmelCase							)
     SCREAMING_SNAKE_CASE_							:    List    				=						field(default_factory=__lowerCAmelCase							)






     def __call__( self					,_SCREAMING_SNAKE_CASE    )							->			Optional[Any]:
          _snake_case							=      Tracker(self.dest    )(_UpperCamelCase    ).parametrized
          _snake_case							=      Tracker(self.src    )(_UpperCamelCase    ).parametrized

          _snake_case							=      list(filter(lambda _SCREAMING_SNAKE_CASE    : type(_UpperCamelCase    ) not in self.src_skip					,_UpperCamelCase    )    )
          _snake_case							=      list(filter(lambda _SCREAMING_SNAKE_CASE    : type(_UpperCamelCase    ) not in self.dest_skip					,_UpperCamelCase    )    )

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

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







def 	__a	(   _UpperCamelCase:    nn.Module					,						_UpperCamelCase:    nn.Module    )						-> Any:

     """simple docstring"""




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

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

     our_model.load_state_dict(UpperCAmelCase__    )
     return our_model





def 	__a	(   _UpperCamelCase:    str					,						_UpperCamelCase:    VanConfig					,						_UpperCamelCase:    str					,						_UpperCamelCase:    nn.Module					,						_UpperCamelCase:    Path					,						_UpperCamelCase:    bool = True					,						)						-> Any:

     """simple docstring"""




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

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

     _snake_case							=      name
     print(UpperCAmelCase__    )

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

          # we can use the convnext one
          _snake_case							=      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=UpperCAmelCase__					,						)

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





def 	__a	(   _UpperCamelCase:    Path					,						_UpperCamelCase:    str = None					,						_UpperCamelCase:    bool = True    )						-> Union[str, Any]:

     """simple docstring"""




     _snake_case							=      "imagenet-1k-id2label.json"
     _snake_case							=      1_000

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

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

     _snake_case							=      partial(UpperCAmelCase__					,						num_labels=UpperCAmelCase__					,						idalabel=UpperCAmelCase__					,						labelaid=UpperCAmelCase__    )

     _snake_case							=      {
         "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]					,						),
     }

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

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


if __name__ == "__main__":
   UpperCamelCase_						:			Optional[Any]  	=						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[Any]  	=						parser.parse_args()
   UpperCamelCase_						:			Path  	=						args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   UpperCamelCase_						:			Dict  	=						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)



import json
import os
from dataclasses import dataclass

import numpy as np
import pyarrow as pa

import datasets
from utils import get_duration


_lowerCamelCase    :      int	 =						10_00_00_00_00_00
_lowerCamelCase    :      Union[str, Any]	 =						1_00_00

_lowerCamelCase ,  _lowerCamelCase    :      List[str]	 =						os.path.split(__file__)
_lowerCamelCase    :      Any	 =						os.path.join(RESULTS_BASEPATH, '''results''', RESULTS_FILENAME.replace('''.py''', '''.json'''))





def 		__lowerCamelCase							(UpperCAmelCase__   :							int					,       UpperCAmelCase__   :							int     ):
					SCREAMING_SNAKE_CASE				=				pa.Table.from_pydict({"col": [0] * chunk_size}     )
					SCREAMING_SNAKE_CASE				=				pa.concat_tables([table] * (num_examples // chunk_size)     )
					return datasets.Dataset(UpperCAmelCase__					,       fingerprint="table_100B"     )


@dataclass
class   lowercase			:
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int
							lowercase__  :				int




							def 		__snake_case(  self      :    Tuple					)    -> Dict:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE				=				np.random.default_rng(self.seed					)
												SCREAMING_SNAKE_CASE				=				rng.integers(low=self.low		,							high=self.high		,							size=self.size					).tolist()




							def __iter__(  self      :    Union[str, Any]					)    -> Dict:



												'''simple docstring'''

												return iter(self._sampled_values					)





							def __len__(  self      :    Any					)    -> int:



												'''simple docstring'''

												return self.size





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[0]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[-1]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[range(len(UpperCAmelCase__     ) // 2					,       len(UpperCAmelCase__     ) // 2 + 1_0_2_4     )]





@get_duration
def 		__lowerCamelCase							(UpperCAmelCase__   :							datasets.Dataset     ):
					SCREAMING_SNAKE_CASE				=				dataset[RandIter(0					,       len(UpperCAmelCase__     )					,       1_0_2_4					,       seed=4_2     )]





def 		__lowerCamelCase							():
					SCREAMING_SNAKE_CASE				=				{"num examples": SPEED_TEST_N_EXAMPLES}
					SCREAMING_SNAKE_CASE				=				(get_first_row, get_last_row, get_batch_of_aaaa_rows, get_batch_of_aaaa_random_rows)
					print("generating dataset"     )
					SCREAMING_SNAKE_CASE				=				generate_100B_dataset(num_examples=UpperCAmelCase__					,       chunk_size=UpperCAmelCase__     )
					print("Functions"     )
					for func in functions:
										print(func.__name__     )
										SCREAMING_SNAKE_CASE				=				func(UpperCAmelCase__     )

					with open(UpperCAmelCase__					,       "wb"     ) as f:
										f.write(json.dumps(UpperCAmelCase__     ).encode("utf-8"     )     )


if __name__ == "__main__":  # useful to run the profiler
							benchmark_table_100B()




