






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






@dataclass
class 						snake_case  :
    __magic_name__         =     42
    __magic_name__         =     field(default_factory=__SCREAMING_SNAKE_CASE       )
    __magic_name__         =     field(default_factory=__SCREAMING_SNAKE_CASE       )





    def 				lowerCamelCase__      (							self	:					int				,		A	:					int				,		A	:					Tensor				,		A	:					Tensor			):


      '''simple docstring'''




      a     :			str				=   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	:					Optional[int]				,		A	:					Tensor			):


      '''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	:					int			):


      '''simple docstring'''




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






@dataclass
class 						snake_case  :
    __magic_name__         =     42
    __magic_name__         =     42
    __magic_name__         =     0
    __magic_name__         =     field(default_factory=__SCREAMING_SNAKE_CASE       )
    __magic_name__         =     field(default_factory=__SCREAMING_SNAKE_CASE       )





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


      '''simple docstring'''




      a     :			Dict				=   Tracker(self.dest			)(UpperCamelCase__			).parametrized
      a     :			List[str]				=   Tracker(self.src			)(UpperCamelCase__			).parametrized

      a     :			Optional[int]				=   list(filter(lambda A			: type(UpperCamelCase__			) not in self.src_skip				,		UpperCamelCase__			)			)
      a     :			List[Any]				=   list(filter(lambda A			: 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 			snake_case  (A_      :Dict	,			A_      :List[Any]      ):







  '''simple docstring'''

  a     :			Union[str, Any]				=   from_model.state_dict()
  a     :			Optional[int]				=   our_model.state_dict()
  a     :			int				=   our_model.config
  a     :			int				=   []
  for stage_idx in range(len(config.hidden_sizes      )      ):
    for block_id in range(config.depths[stage_idx]      ):
      a     :			List[Any]				=   f'''block{stage_idx + 1}.{block_id}.layer_scale_1'''
      a     :			List[Any]				=   f'''van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight'''

      all_keys.append((from_key, to_key)      )
      a     :			int				=   f'''block{stage_idx + 1}.{block_id}.layer_scale_2'''
      a     :			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:
    a     :			Dict				=   from_state_dict.pop(SCREAMING_SNAKE_CASE__      )

  our_model.load_state_dict(SCREAMING_SNAKE_CASE__      )
  return our_model




def 			snake_case  (A_      :int	,			A_      :int	,			A_      :str	,			A_      :List[str]	,			A_      :Union[str, Any]	,			A_      :Union[str, Any] = True	,			):







  '''simple docstring'''

  print(f'''Downloading weights for {name}...'''      )
  a     :			int				=   cached_download(SCREAMING_SNAKE_CASE__      )
  print(f'''Converting {name}...'''      )
  a     :			List[Any]				=   torch.load(SCREAMING_SNAKE_CASE__      )['''state_dict''']
  from_model.load_state_dict(SCREAMING_SNAKE_CASE__      )
  from_model.eval()
  with torch.no_grad():
    a     :			Optional[int]				=   VanForImageClassification(SCREAMING_SNAKE_CASE__      ).eval()
    a     :			Union[str, Any]				=   ModuleTransfer(src=SCREAMING_SNAKE_CASE__	,			dest=SCREAMING_SNAKE_CASE__      )
    a     :			Dict				=   torch.randn((1, 3, 2_2_4, 2_2_4)      )
    module_transfer(SCREAMING_SNAKE_CASE__      )
    a     :			List[str]				=   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.'      )

  a     :			Dict				=   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
    a     :			int				=   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 			snake_case  (A_      :List[Any]	,			A_      :int = None	,			A_      :Optional[Any] = True      ):







  '''simple docstring'''

  a     :			Tuple				=   '''imagenet-1k-id2label.json'''
  a     :			List[str]				=   1_0_0_0

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

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

  a     :			Tuple				=   partial(SCREAMING_SNAKE_CASE__	,			num_labels=SCREAMING_SNAKE_CASE__	,			idalabel=SCREAMING_SNAKE_CASE__	,			labelaid=SCREAMING_SNAKE_CASE__      )

  a     :			str				=   {
      '''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]	,			),
  }

  a     :			List[Any]				=   {
      '''van-tiny''': van_tiny,
      '''van-small''': van_small,
      '''van-base''': van_base,
      '''van-large''': van_large,
  }

  a     :			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(
        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				:    Union[str, 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				:    Any     =						parser.parse_args()
   _UpperCamelCase				:    Any     =						args.pytorch_dump_folder_path
   pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
   _UpperCamelCase				:    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'''
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 BlipImageProcessor






class      snake_case__    (	unittest.TestCase ):







		"""simple docstring"""

		def __init__(	self							:       List[str]   ,     UpperCamelCase__							:       Union[str, Any]   ,     UpperCamelCase__							:       Optional[Any]=7   ,     UpperCamelCase__							:       Union[str, Any]=3   ,     UpperCamelCase__							:       Tuple=18   ,     UpperCamelCase__							:       Union[str, Any]=30   ,     UpperCamelCase__							:       Dict=400   ,     UpperCamelCase__							:       Tuple=True   ,     UpperCamelCase__							:       Optional[Any]=None   ,     UpperCamelCase__							:       Any=True   ,     UpperCamelCase__							:       Dict=False   ,     UpperCamelCase__							:       Dict=[0.48_145_466, 0.4_578_275, 0.40_821_073]   ,     UpperCamelCase__							:       int=[0.26_862_954, 0.26_130_258, 0.27_577_711]   ,     UpperCamelCase__							:       str=True   ,     )		->       Any:






				"""simple docstring"""







				snake_case   :    Dict			       =		size if size is not None else {'''height''': 20, '''width''': 20}
				snake_case   :    Union[str, Any]			       =		parent
				snake_case   :    Optional[int]			       =		batch_size
				snake_case   :    Dict			       =		num_channels
				snake_case   :    List[Any]			       =		image_size
				snake_case   :    List[Any]			       =		min_resolution
				snake_case   :    Dict			       =		max_resolution
				snake_case   :    Dict			       =		do_resize
				snake_case   :    Optional[int]			       =		size
				snake_case   :    Dict			       =		do_normalize
				snake_case   :    Tuple			       =		image_mean
				snake_case   :    Optional[int]			       =		image_std
				snake_case   :    int			       =		do_pad
				snake_case   :    List[Any]			       =		do_convert_rgb



		def 	lowerCAmelCase							(	self							:       str	)		->       Optional[int]:






				"""simple docstring"""







				return {
				    "do_resize": self.do_resize,
				    "size": self.size,
				    "do_normalize": self.do_normalize,
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_convert_rgb": self.do_convert_rgb,
				    "do_pad": self.do_pad,
				}



		def 	lowerCAmelCase							(	self							:       str   ,     UpperCamelCase__							:       Optional[int]=False   ,     UpperCamelCase__							:       Tuple=False   ,     UpperCamelCase__							:       Union[str, Any]=False	)		->       List[str]:






				"""simple docstring"""







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

				if equal_resolution:
						snake_case   :    Any			       =		[]
						for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        255   ,     size=(self.num_channels, self.max_resolution, self.max_resolution)   ,     dtype=np.uinta	)	)
				else:
						snake_case   :    Union[str, Any]			       =		[]
						for i in range(self.batch_size	):
								snake_case       ,snake_case   :    List[Any]			       =		np.random.choice(np.arange(self.min_resolution   ,     self.max_resolution	)   ,     2	)
								image_inputs.append(np.random.randint(255   ,     size=(self.num_channels, width, height)   ,     dtype=np.uinta	)	)

				if not numpify and not torchify:
						# PIL expects the channel dimension as last dimension
						snake_case   :    Optional[int]			       =		[Image.fromarray(np.moveaxis(UpperCamelCase__   ,     0   ,     -1	)	) for x in image_inputs]

				if torchify:
						snake_case   :    Optional[Any]			       =		[torch.from_numpy(UpperCamelCase__	) for x in image_inputs]

				return image_inputs







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		BlipImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Dict	)		->       Optional[int]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		BlipImageProcessingTester(self	)



		@property
		def 	lowerCAmelCase							(	self							:       Any	)		->       Union[str, Any]:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Dict			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       Any	)		->       Dict:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Union[str, Any]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    Optional[int]			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    int			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )



		def 	lowerCAmelCase							(	self							:       List[str]	)		->       Tuple:






				"""simple docstring"""







				snake_case   :    Any			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				snake_case   :    List[str]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     numpify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     np.ndarray	)

				# Test not batched input
				snake_case   :    Dict			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Dict			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )



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






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				snake_case   :    List[Any]			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__   ,     torchify=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     torch.Tensor	)

				# Test not batched input
				snake_case   :    List[str]			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    str			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.image_processor_tester.num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )







@require_torch
@require_vision
class      snake_case__    (	__SCREAMING_SNAKE_CASE  , unittest.TestCase ):







		"""simple docstring"""

		lowerCamelCase							=		BlipImageProcessor if is_vision_available() else None



		def 	lowerCAmelCase							(	self							:       Tuple	)		->       Union[str, Any]:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		BlipImageProcessingTester(self   ,     num_channels=4	)
				snake_case   :    Optional[int]			       =		3



		@property
		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Tuple:






				"""simple docstring"""







				return self.image_processor_tester.prepare_image_processor_dict()



		def 	lowerCAmelCase							(	self							:       List[Any]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Union[str, Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_resize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''size'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_normalize'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_mean'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''image_std'''	)	)
				self.assertTrue(hasattr(UpperCamelCase__   ,     '''do_convert_rgb'''	)	)



		def 	lowerCAmelCase							(	self							:       Optional[Any]	)		->       List[str]:






				"""simple docstring"""







				pass



		def 	lowerCAmelCase							(	self							:       Optional[int]	)		->       Any:






				"""simple docstring"""







				snake_case   :    Optional[Any]			       =		self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				snake_case   :    Tuple			       =		self.image_processor_tester.prepare_inputs(equal_resolution=UpperCamelCase__	)
				for image in image_inputs:
						self.assertIsInstance(UpperCamelCase__   ,     Image.Image	)

				# Test not batched input
				snake_case   :    int			       =		image_processor(image_inputs[0]   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        1,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )

				# Test batched
				snake_case   :    Union[str, Any]			       =		image_processor(UpperCamelCase__   ,     return_tensors='''pt'''	).pixel_values
				self.assertEqual(
				    encoded_images.shape   ,     (
				        self.image_processor_tester.batch_size,
				        self.expected_encoded_image_num_channels,
				        self.image_processor_tester.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )   ,     )


