
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							=       logging.get_logger(__name__)

@dataclass
class 		lowercase_							:
 A__    :					nn.Module
 A__    :					List[nn.Module]    =     field(default_factory=__SCREAMING_SNAKE_CASE					)
 A__    :					list    =     field(default_factory=__SCREAMING_SNAKE_CASE					)

 def lowerCamelCase_					(   self  ,						__UpperCamelCase  ,						__UpperCamelCase  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							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  ,						__UpperCamelCase  ):




        """simple docstring"""


        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 lowerCamelCase_					(   self  ):




        """simple docstring"""


        return list(filter(lambda __UpperCamelCase  : len(list(x.state_dict().keys()  )  ) > 0  ,						self.traced  )  )



@dataclass
class 		lowercase_							:
 A__    :					nn.Module
 A__    :					nn.Module
 A__    :					int    =     0
 A__    :					List    =     field(default_factory=__SCREAMING_SNAKE_CASE					)
 A__    :					List    =     field(default_factory=__SCREAMING_SNAKE_CASE					)

 def __call__(   self  ,						__UpperCamelCase  ):




        """simple docstring"""


        UpperCamelCase_											=							Tracker(self.dest  )(__UpperCamelCase  ).parametrized
        UpperCamelCase_											=							Tracker(self.src  )(__UpperCamelCase  ).parametrized

        UpperCamelCase_											=							list(filter(lambda __UpperCamelCase  : type(__UpperCamelCase  ) not in self.src_skip  ,						__UpperCamelCase  )  )
        UpperCamelCase_											=							list(filter(lambda __UpperCamelCase  : 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 					lowerCamelCase__					(    a__	:	nn.Module						,   a__	:	nn.Module  )		->						nn.Module:
       # nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
       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(a__  )

       our_model.load_state_dict(a__  )
       return our_model




def 					lowerCamelCase__					(    a__	:	str						,   a__	:	VanConfig						,   a__	:	str						,   a__	:	nn.Module						,   a__	:	Path						,   a__	:	bool = True						,   )		->						Any:
       print(f'''Downloading weights for {name}...'''  )
       UpperCamelCase_											=							cached_download(a__  )
       print(f'''Converting {name}...'''  )
       UpperCamelCase_											=							torch.load(a__  )["""state_dict"""]
       from_model.load_state_dict(a__  )
       from_model.eval()
       with torch.no_grad():
              UpperCamelCase_											=							VanForImageClassification(a__  ).eval()
              UpperCamelCase_											=							ModuleTransfer(src=a__						,   dest=a__  )
              UpperCamelCase_											=							torch.randn((1, 3, 224, 224)  )
              module_transfer(a__  )
              UpperCamelCase_											=							copy_parameters(a__						,   a__  )

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

       UpperCamelCase_											=							name
       print(a__  )

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

              # 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=a__						,   )

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




def 					lowerCamelCase__					(    a__	:	Path						,   a__	:	str = None						,   a__	:	bool = True  )		->						Tuple:
       UpperCamelCase_											=							"""imagenet-1k-id2label.json"""
       UpperCamelCase_											=							1000

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

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

       UpperCamelCase_											=							partial(a__						,   num_labels=a__						,   idalabel=a__						,   labelaid=a__  )

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


if __name__ == "__main__":
      _A							=       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							=       parser.parse_args()
      _A							=       args.pytorch_dump_folder_path
      pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
      _A							=       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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A							=       logging.get_logger(__name__)

_A							=       {
    '''facebook/opt-125m''': '''https://huggingface.co/facebook/opt-125m/blob/main/config.json''',
    '''facebook/opt-350m''': '''https://huggingface.co/facebook/opt-350m/blob/main/config.json''',
    '''facebook/opt-1.3b''': '''https://huggingface.co/facebook/opt-1.3b/blob/main/config.json''',
    '''facebook/opt-2.7b''': '''https://huggingface.co/facebook/opt-2.7b/blob/main/config.json''',
    '''facebook/opt-6.7b''': '''https://huggingface.co/facebook/opt-6.7b/blob/main/config.json''',
    '''facebook/opt-13b''': '''https://huggingface.co/facebook/opt-13b/blob/main/config.json''',
}

class 		lowercase_							(							__SCREAMING_SNAKE_CASE					):
 A__    :					Dict    =     """opt"""
 A__    :					int    =     ["""past_key_values"""]

 def __init__(   self  ,						__UpperCamelCase=5_0_2_7_2  ,						__UpperCamelCase=7_6_8  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase=3_0_7_2  ,						__UpperCamelCase=2_0_4_8  ,						__UpperCamelCase=True  ,						__UpperCamelCase=False  ,						__UpperCamelCase=None  ,						__UpperCamelCase=0.1  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=1_2  ,						__UpperCamelCase="relu"  ,						__UpperCamelCase=0.0  ,						__UpperCamelCase=0.02  ,						__UpperCamelCase=True  ,						__UpperCamelCase=1  ,						__UpperCamelCase=2  ,						__UpperCamelCase=2  ,						__UpperCamelCase=True  ,						__UpperCamelCase=True  ,						**__UpperCamelCase  ,						):




        """simple docstring"""


        super().__init__(
            pad_token_id=__UpperCamelCase  ,						bos_token_id=__UpperCamelCase  ,						eos_token_id=__UpperCamelCase  ,						**__UpperCamelCase  ,						)
        UpperCamelCase_											=							vocab_size
        UpperCamelCase_											=							max_position_embeddings
        UpperCamelCase_											=							num_attention_heads
        UpperCamelCase_											=							word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
        UpperCamelCase_											=							ffn_dim
        UpperCamelCase_											=							hidden_size
        UpperCamelCase_											=							num_hidden_layers
        UpperCamelCase_											=							dropout
        UpperCamelCase_											=							attention_dropout
        UpperCamelCase_											=							activation_function
        UpperCamelCase_											=							init_std
        UpperCamelCase_											=							layerdrop
        UpperCamelCase_											=							use_cache
        UpperCamelCase_											=							do_layer_norm_before
        # We keep these variables at `True` for backward compatibility.
        UpperCamelCase_											=							enable_bias
        UpperCamelCase_											=							layer_norm_elementwise_affine

        # Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
        # with checkpoints that have been fine-tuned before transformers v4.20.1
        # see https://github.com/facebookresearch/metaseq/pull/164
        UpperCamelCase_											=							_remove_final_layer_norm



