




"""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()
a								=							logging.get_logger(__name__)






@dataclass
class  SCREAMING_SNAKE_CASE__   :
						_a        =  42
						_a        =  field(default_factory=_a   )
						_a        =  field(default_factory=_a   )


						def 					__lowercase	(							self			:     Tuple  ,  lowerCAmelCase			:     Dict  ,  lowerCAmelCase			:     Tensor  ,  lowerCAmelCase			:     Tensor   ):
										lowerCAmelCase		=			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			:     Optional[int]  ,  lowerCAmelCase			:     Tensor   ):
										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 					__lowercase	(							self			:     Tuple   ):
										# 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  SCREAMING_SNAKE_CASE__   :
						_a        =  42
						_a        =  42
						_a        =  0
						_a        =  field(default_factory=_a   )
						_a        =  field(default_factory=_a   )


						def __call__(							self			:     Dict  ,  lowerCAmelCase			:     Tensor   ):
										lowerCAmelCase		=			Tracker(self.dest   )(lowerCAmelCase   ).parametrized
										lowerCAmelCase		=			Tracker(self.src   )(lowerCAmelCase   ).parametrized

										lowerCAmelCase		=			list(filter(lambda lowerCAmelCase   : type(lowerCAmelCase   ) not in self.src_skip  ,  lowerCAmelCase   )   )
										lowerCAmelCase		=			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 							lowercase   (snake_case__				:	nn.Module ,    snake_case__				:	nn.Module	)       ->					nn.Module:


				'''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(snake_case__	)

				our_model.load_state_dict(snake_case__	)
				return our_model





def 							lowercase   (snake_case__				:	str ,    snake_case__				:	VanConfig ,    snake_case__				:	str ,    snake_case__				:	nn.Module ,    snake_case__				:	Path ,    snake_case__				:	bool = True ,    )       ->					str:


				'''simple docstring'''






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

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

				lowerCAmelCase		=			name
				print(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=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=snake_case__ ,    )

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





def 							lowercase   (snake_case__				:	Path ,    snake_case__				:	str = None ,    snake_case__				:	bool = True	)       ->					Dict:


				'''simple docstring'''






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

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

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

				lowerCAmelCase		=			partial(snake_case__ ,    num_labels=snake_case__ ,    idalabel=snake_case__ ,    labelaid=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(
								    snake_case__ ,    names_to_config[model_name] ,    checkpoint=names_to_original_checkpoints[model_name] ,    from_model=names_to_original_models[model_name]() ,    save_directory=snake_case__ ,    push_to_hub=snake_case__ ,    )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    snake_case__ ,    snake_case__ ,    checkpoint=names_to_original_checkpoints[model_name] ,    from_model=names_to_original_models[model_name]() ,    save_directory=snake_case__ ,    push_to_hub=snake_case__ ,    )


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)











"""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, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import OwlViTImageProcessor






class  SCREAMING_SNAKE_CASE__   (   unittest.TestCase   ):


						def __init__(							self			:     int  ,  lowerCAmelCase			:     Optional[int]  ,  lowerCAmelCase			:     Optional[Any]=7  ,  lowerCAmelCase			:     str=3  ,  lowerCAmelCase			:     str=18  ,  lowerCAmelCase			:     Tuple=30  ,  lowerCAmelCase			:     Union[str, Any]=400  ,  lowerCAmelCase			:     List[str]=True  ,  lowerCAmelCase			:     List[str]=None  ,  lowerCAmelCase			:     int=True  ,  lowerCAmelCase			:     int=None  ,  lowerCAmelCase			:     Optional[int]=True  ,  lowerCAmelCase			:     Dict=[0.4814_5466, 0.457_8275, 0.4082_1073]  ,  lowerCAmelCase			:     str=[0.2686_2954, 0.2613_0258, 0.2757_7711]  ,  lowerCAmelCase			:     Any=True  ,  ):
										lowerCAmelCase		=			parent
										lowerCAmelCase		=			batch_size
										lowerCAmelCase		=			num_channels
										lowerCAmelCase		=			image_size
										lowerCAmelCase		=			min_resolution
										lowerCAmelCase		=			max_resolution
										lowerCAmelCase		=			do_resize
										lowerCAmelCase		=			size if size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_center_crop
										lowerCAmelCase		=			crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
										lowerCAmelCase		=			do_normalize
										lowerCAmelCase		=			image_mean
										lowerCAmelCase		=			image_std
										lowerCAmelCase		=			do_convert_rgb




						def 					__lowercase	(							self			:     Tuple   ):
										return {
										    "do_resize": self.do_resize,
										    "size": self.size,
										    "do_center_crop": self.do_center_crop,
										    "crop_size": self.crop_size,
										    "do_normalize": self.do_normalize,
										    "image_mean": self.image_mean,
										    "image_std": self.image_std,
										    "do_convert_rgb": self.do_convert_rgb,
										}






@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__   (   _a  ,			unittest.TestCase   ):
						_a        =  OwlViTImageProcessor if is_vision_available() else None


						def 					__lowercase	(							self			:     Union[str, Any]   ):
										lowerCAmelCase		=			OwlViTImageProcessingTester(self   )


						@property
						def 					__lowercase	(							self			:     List[Any]   ):
										return self.image_processor_tester.prepare_image_processor_dict()


						def 					__lowercase	(							self			:     List[str]   ):
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_resize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """size"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """center_crop"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_normalize"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_mean"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """image_std"""   )   )
										self.assertTrue(hasattr(lowerCAmelCase  ,  """do_convert_rgb"""   )   )


						def 					__lowercase	(							self			:     List[Any]   ):
										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict   )
										self.assertEqual(image_processor.size  ,  {"""height""": 18, """width""": 18}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 18, """width""": 18}   )

										lowerCAmelCase		=			self.image_processing_class.from_dict(self.image_processor_dict  ,  size=42  ,  crop_size=84   )
										self.assertEqual(image_processor.size  ,  {"""height""": 42, """width""": 42}   )
										self.assertEqual(image_processor.crop_size  ,  {"""height""": 84, """width""": 84}   )


						def 					__lowercase	(							self			:     str   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PIL images
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase   )

										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  Image.Image   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )


						def 					__lowercase	(							self			:     Tuple   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random numpy tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  numpify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  np.ndarray   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )




						def 					__lowercase	(							self			:     Optional[int]   ):
										# Initialize image_processing
										lowerCAmelCase		=			self.image_processing_class(**self.image_processor_dict   )
										# create random PyTorch tensors
										lowerCAmelCase		=			prepare_image_inputs(self.image_processor_tester  ,  equal_resolution=lowerCAmelCase  ,  torchify=lowerCAmelCase   )
										for image in image_inputs:
														self.assertIsInstance(lowerCAmelCase  ,  torch.Tensor   )

										# Test not batched input
										lowerCAmelCase		=			image_processing(image_inputs[0]  ,  return_tensors="""pt"""   ).pixel_values
										self.assertEqual(
										    encoded_images.shape  ,  (
										        1,
										        self.image_processor_tester.num_channels,
										        self.image_processor_tester.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )

										# Test batched
										lowerCAmelCase		=			image_processing(lowerCAmelCase  ,  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.crop_size["""height"""],
										        self.image_processor_tester.crop_size["""width"""],
										    )  ,  )





