
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__	:  Optional[Any]      =   logging.get_logger(__name__)






@dataclass
class UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					List[nn.Module]				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					list				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							Any    ,    lowerCAmelCase_  :							Tensor    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						Optional[Any]	    = len(list(m.modules()  )  ) == 1 or isinstance(lowerCAmelCase_    ,    nn.Convad  ) or isinstance(lowerCAmelCase_    ,    nn.BatchNormad  )
      if has_not_submodules:
         if not isinstance(lowerCAmelCase_    ,    lowerCAmelCase_  ):
            self.traced.append(lowerCAmelCase_  )


   def __call__(      self  :							str    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      for m in self.module.modules():
         self.handles.append(m.register_forward_hook(self._forward_hook  )  )
      self.module(lowerCAmelCase_  )
      [x.remove() for x in self.handles]
      return self


   @property
   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # 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 UpperCAmelCase     :





   '''simple docstring'''
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					nn.Module
   __UpperCamelCase	:					int				       =					0
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )
   __UpperCamelCase	:					List				       =					field(default_factory=SCREAMING_SNAKE_CASE__     )


   def __call__(      self  :							Any    ,    lowerCAmelCase_  :							Tensor  ):

      """simple docstring"""
      _A:						int	    = Tracker(self.dest  )(lowerCAmelCase_  ).parametrized
      _A:						Union[str, Any]	    = Tracker(self.src  )(lowerCAmelCase_  ).parametrized

      _A:						Any	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.src_skip    ,    lowerCAmelCase_  )  )
      _A:						int	    = list(filter(lambda lowerCAmelCase_  : type(lowerCAmelCase_  ) not in self.dest_skip    ,    lowerCAmelCase_  )  )

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

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

         all_keys.append((from_key, to_key)   )
         _A:						str	    = f"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
         _A:						Union[str, Any]	    = 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:
      _A:						Union[str, Any]	    = from_state_dict.pop(a   )

   our_model.load_state_dict(a   )
   return our_model






def 						lowerCamelCase__							(      a	,	a	,	a	,	a	,	a	,	a = True	,	)   ->     Dict:
   print(f"""Downloading weights for {name}..."""   )
   _A:						Dict	    = cached_download(a   )
   print(f"""Converting {name}..."""   )
   _A:						int	    = torch.load(a   )['''state_dict''']
   from_model.load_state_dict(a   )
   from_model.eval()
   with torch.no_grad():
      _A:						Any	    = VanForImageClassification(a   ).eval()
      _A:						Tuple	    = ModuleTransfer(src=a	,	dest=a   )
      _A:						List[str]	    = torch.randn((1, 3, 2_24, 2_24)   )
      module_transfer(a   )
      _A:						Tuple	    = 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.'''   )

   _A:						str	    = 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
      _A:						Tuple	    = 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	,	a = None	,	a = True   )   ->     int:
   _A:						str	    = '''imagenet-1k-id2label.json'''
   _A:						Optional[int]	    = 10_00

   _A:						str	    = '''huggingface/label-files'''
   _A:						List[str]	    = num_labels
   _A:						Union[str, Any]	    = json.load(open(hf_hub_download(a	,	a	,	repo_type='''dataset'''   )	,	'''r'''   )   )
   _A:						List[str]	    = {int(a   ): v for k, v in idalabel.items()}

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

   _A:						Optional[int]	    = partial(a	,	num_labels=a	,	idalabel=a	,	labelaid=a   )

   _A:						Optional[int]	    = {
       '''van-tiny''': ImageNetPreTrainedConfig(
           hidden_sizes=[32, 64, 1_60, 2_56]	,	depths=[3, 3, 5, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-small''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[2, 2, 4, 2]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-base''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 3, 12, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
       '''van-large''': ImageNetPreTrainedConfig(
           hidden_sizes=[64, 1_28, 3_20, 5_12]	,	depths=[3, 5, 27, 3]	,	mlp_ratios=[8, 8, 4, 4]	,	),
   }

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

   _A:						Union[str, Any]	    = {
       '''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__":
   UpperCAmelCase__	:  Tuple      =   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__	:  Dict      =   parser.parse_args()
   UpperCAmelCase__	:  Path      =   args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   UpperCAmelCase__	:  Optional[int]      =   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 unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import SwinaSRImageProcessor
   from transformers.image_transforms import get_image_size






class UpperCAmelCase     (		unittest.TestCase     ):





   '''simple docstring'''


   def __init__(      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[int]    ,    lowerCAmelCase_  :							Optional[int]=7    ,    lowerCAmelCase_  :							Dict=3    ,    lowerCAmelCase_  :							Optional[Any]=1_8    ,    lowerCAmelCase_  :							Union[str, Any]=3_0    ,    lowerCAmelCase_  :							int=4_0_0    ,    lowerCAmelCase_  :							int=True    ,    lowerCAmelCase_  :							Dict=1 / 2_5_5    ,    lowerCAmelCase_  :							Union[str, Any]=True    ,    lowerCAmelCase_  :							str=8    ,    ):

      """simple docstring"""
      _A:						Optional[Any]	    = parent
      _A:						int	    = batch_size
      _A:						List[str]	    = num_channels
      _A:						Union[str, Any]	    = image_size
      _A:						Optional[int]	    = min_resolution
      _A:						int	    = max_resolution
      _A:						int	    = do_rescale
      _A:						Optional[Any]	    = rescale_factor
      _A:						List[Any]	    = do_pad
      _A:						Optional[int]	    = pad_size


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      return {
          "do_rescale": self.do_rescale,
          "rescale_factor": self.rescale_factor,
          "do_pad": self.do_pad,
          "pad_size": self.pad_size,
      }


   def 				__magic_name__       (      self  :							Union[str, Any]    ,    lowerCAmelCase_  :							Optional[Any]=False    ,    lowerCAmelCase_  :							Tuple=False    ,    lowerCAmelCase_  :							Dict=False  ):

      """simple docstring"""
      assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

      if equal_resolution:
         _A:						Dict	    = []
         for i in range(self.batch_size  ):
            image_inputs.append(
                np.random.randint(
                    2_5_5    ,    size=(self.num_channels, self.max_resolution, self.max_resolution)    ,    dtype=np.uinta  )  )
      else:
         _A:						List[str]	    = []
         for i in range(self.batch_size  ):
            _A    ,						_A:						Optional[Any]	    = np.random.choice(np.arange(self.min_resolution    ,    self.max_resolution  )    ,    2  )
            image_inputs.append(np.random.randint(2_5_5    ,    size=(self.num_channels, width, height)    ,    dtype=np.uinta  )  )

      if not numpify and not torchify:
         # PIL expects the channel dimension as last dimension
         _A:						Union[str, Any]	    = [Image.fromarray(np.moveaxis(lowerCAmelCase_    ,    0    ,    -1  )  ) for x in image_inputs]

      if torchify:
         _A:						List[Any]	    = [torch.from_numpy(lowerCAmelCase_  ) for x in image_inputs]

      return image_inputs






@require_torch
@require_vision
class UpperCAmelCase     (		SCREAMING_SNAKE_CASE__							,							unittest.TestCase     ):





   '''simple docstring'''
   __UpperCamelCase	:					int				       =					SwinaSRImageProcessor if is_vision_available() else None


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      _A:						int	    = SwinaSRImageProcessingTester(self  )


   @property
   def 				__magic_name__       (      self  :							Optional[Any]  ):

      """simple docstring"""
      return self.image_processor_tester.prepare_image_processor_dict()


   def 				__magic_name__       (      self  :							Union[str, Any]  ):

      """simple docstring"""
      _A:						Any	    = self.image_processing_class(**self.image_processor_dict  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_rescale'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''rescale_factor'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''do_pad'''  )  )
      self.assertTrue(hasattr(lowerCAmelCase_    ,    '''pad_size'''  )  )


   def 				__magic_name__       (      self  :							List[Any]  ):

      """simple docstring"""
      pass


   def 				__magic_name__       (      self  :							Optional[int]    ,    lowerCAmelCase_  :							Dict  ):

      """simple docstring"""
      _A    ,						_A:						Any	    = get_image_size(lowerCAmelCase_  )
      _A:						Union[str, Any]	    = self.image_processor_tester.pad_size

      _A:						Optional[Any]	    = (old_height // size + 1) * size - old_height
      _A:						int	    = (old_width // size + 1) * size - old_width
      return old_height + pad_height, old_width + pad_width


   def 				__magic_name__       (      self  :							Dict  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[int]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PIL images
      _A:						str	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    Image.Image  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						str	    = self.calculate_expected_size(np.array(image_inputs[0]  )  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							Tuple  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Union[str, Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random numpy tensors
      _A:						List[str]	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    numpify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    np.ndarray  )

      # Test not batched input
      _A:						int	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Union[str, Any]	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )


   def 				__magic_name__       (      self  :							int  ):

      """simple docstring"""
      # Initialize image_processor
      _A:						Optional[Any]	    = self.image_processing_class(**self.image_processor_dict  )
      # create random PyTorch tensors
      _A:						Tuple	    = self.image_processor_tester.prepare_inputs(equal_resolution=lowerCAmelCase_    ,    torchify=lowerCAmelCase_  )
      for image in image_inputs:
         self.assertIsInstance(lowerCAmelCase_    ,    torch.Tensor  )

      # Test not batched input
      _A:						List[str]	    = image_processor(image_inputs[0]    ,    return_tensors='''pt'''  ).pixel_values
      _A    ,						_A:						Dict	    = self.calculate_expected_size(image_inputs[0]  )
      self.assertEqual(
          encoded_images.shape    ,    (
              1,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,    )



