
'''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()
__SCREAMING_SNAKE_CASE		:  List[Any]        =      logging.get_logger(__name__)
@dataclass
class  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							42
	lowercase__	    =							field(default_factory=__UpperCamelCase   )
	lowercase__	    =							field(default_factory=__UpperCamelCase   )
	def __lowerCamelCase    (				self  ,    __UpperCamelCase  ,    __UpperCamelCase  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     List[Any]								=	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  SCREAMING_SNAKE_CASE__       :
	lowercase__	    =							42
	lowercase__	    =							42
	lowercase__	    =							0
	lowercase__	    =							field(default_factory=__UpperCamelCase   )
	lowercase__	    =							field(default_factory=__UpperCamelCase   )
	def __call__(				self  ,    __UpperCamelCase			):



					'''simple docstring'''






					__a       :     str								=	Tracker(self.dest			)(__UpperCamelCase			).parametrized
					__a       :     int								=	Tracker(self.src			)(__UpperCamelCase			).parametrized

					__a       :     Union[str, Any]								=	list(filter(lambda __UpperCamelCase			: type(__UpperCamelCase			) not in self.src_skip  ,    __UpperCamelCase			)			)
					__a       :     Union[str, Any]								=	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 				_snake_case (		lowercase			,    lowercase						)       ->       nn.Module:
				# nn.Parameter cannot be tracked by the Tracker, thus we need to manually convert them
				__a       :     Any								=	from_model.state_dict()
				__a       :     Optional[int]								=	our_model.state_dict()
				__a       :     Optional[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       :     Optional[int]								=	F"""van.encoder.stages.{stage_idx}.layers.{block_id}.attention_scaling.weight"""

												all_keys.append((from_key, to_key)						)
												__a       :     Optional[int]								=	F"""block{stage_idx + 1}.{block_id}.layer_scale_2"""
												__a       :     int								=	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       :     str								=	from_state_dict.pop(lowercase						)

				our_model.load_state_dict(lowercase						)
				return our_model

def 				_snake_case (		lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase			,    lowercase = True			,    )       ->       int:
				print(F"""Downloading weights for {name}..."""						)
				__a       :     List[Any]								=	cached_download(lowercase						)
				print(F"""Converting {name}..."""						)
				__a       :     Optional[Any]								=	torch.load(lowercase						)["""state_dict"""]
				from_model.load_state_dict(lowercase						)
				from_model.eval()
				with torch.no_grad():
								__a       :     Dict								=	VanForImageClassification(lowercase						).eval()
								__a       :     Union[str, Any]								=	ModuleTransfer(src=lowercase			,    dest=lowercase						)
								__a       :     Optional[Any]								=	torch.randn((1, 3, 2_2_4, 2_2_4)						)
								module_transfer(lowercase						)
								__a       :     Union[str, Any]								=	copy_parameters(lowercase			,    lowercase						)

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

				__a       :     Any								=	name
				print(lowercase						)

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

								# we can use the convnext one
								__a       :     Optional[Any]								=	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=lowercase			,    )

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

def 				_snake_case (		lowercase			,    lowercase = None			,    lowercase = True						)       ->       Optional[Any]:
				__a       :     Union[str, Any]								=	"""imagenet-1k-id2label.json"""
				__a       :     Union[str, Any]								=	1_0_0_0

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

				__a       :     Dict								=	idalabel
				__a       :     Optional[int]								=	{v: k for k, v in idalabel.items()}

				__a       :     Dict								=	partial(lowercase			,    num_labels=lowercase			,    idalabel=lowercase			,    labelaid=lowercase						)

				__a       :     Union[str, Any]								=	{
				    """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[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(
								    lowercase			,    names_to_config[model_name]			,    checkpoint=names_to_original_checkpoints[model_name]			,    from_model=names_to_original_models[model_name]()			,    save_directory=lowercase			,    push_to_hub=lowercase			,    )
				else:
								for model_name, config in names_to_config.items():
												convert_weight_and_push(
												    lowercase			,    lowercase			,    checkpoint=names_to_original_checkpoints[model_name]			,    from_model=names_to_original_models[model_name]()			,    save_directory=lowercase			,    push_to_hub=lowercase			,    )


if __name__ == "__main__":
					__SCREAMING_SNAKE_CASE		:  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.',
					)

					__SCREAMING_SNAKE_CASE		:  int        =      parser.parse_args()
					__SCREAMING_SNAKE_CASE		:  Path        =      args.pytorch_dump_folder_path
					pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
					__SCREAMING_SNAKE_CASE		:  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)

'''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 TvltImageProcessor

def 				_snake_case (		lowercase			,    lowercase=1_0			,    lowercase=1_0			,    lowercase=False			,    lowercase=False						)       ->       Any:
				__a       :     Union[str, Any]								=	[]
				for i in range(image_processor_tester.num_frames						):
								video.append(np.random.randint(2_5_5			,    size=(image_processor_tester.num_channels, width, height)			,    dtype=np.uinta						)						)

				if not numpify and not torchify:
								# PIL expects the channel dimension as last dimension
								__a       :     Optional[Any]								=	[Image.fromarray(np.moveaxis(lowercase			,    0			,    -1						)						) for frame in video]

				if torchify:
								__a       :     List[str]								=	[torch.from_numpy(lowercase						) for frame in video]

				return video

def 				_snake_case (		lowercase			,    lowercase=False			,    lowercase=False			,    lowercase=False						)       ->       Optional[Any]:
				assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

				__a       :     str								=	[]
				for i in range(image_processor_tester.batch_size						):
								if equal_resolution:
												__a       :     int								=	image_processor_tester.max_resolution
								else:
												__a							,	__a       :     Any								=	np.random.choice(
												    np.arange(image_processor_tester.min_resolution			,    image_processor_tester.max_resolution						)			,    2						)
												__a       :     Any								=	prepare_video(
												    image_processor_tester=lowercase			,    width=lowercase			,    height=lowercase			,    numpify=lowercase			,    torchify=lowercase			,    )
								video_inputs.append(lowercase						)

				return video_inputs
class  SCREAMING_SNAKE_CASE__       (  unittest.TestCase   ):
	def __init__(				self  ,    __UpperCamelCase  ,    __UpperCamelCase=7  ,    __UpperCamelCase=3  ,    __UpperCamelCase=4  ,    __UpperCamelCase=18  ,    __UpperCamelCase=30  ,    __UpperCamelCase=400  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    __UpperCamelCase=True  ,    __UpperCamelCase=[0.5, 0.5, 0.5]  ,    __UpperCamelCase=[0.5, 0.5, 0.5]  ,    __UpperCamelCase=True  ,    __UpperCamelCase=None  ,    ):



					'''simple docstring'''






					__a       :     int								=	size if size is not None else {"""shortest_edge""": 18}
					__a       :     Optional[int]								=	crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

					__a       :     Optional[Any]								=	parent
					__a       :     Dict								=	batch_size
					__a       :     Optional[Any]								=	num_channels
					__a       :     Dict								=	num_frames
					__a       :     Dict								=	image_size
					__a       :     int								=	min_resolution
					__a       :     Tuple								=	max_resolution
					__a       :     Optional[Any]								=	do_resize
					__a       :     str								=	size
					__a       :     int								=	do_normalize
					__a       :     Any								=	image_mean
					__a       :     Optional[Any]								=	image_std
					__a       :     List[Any]								=	do_center_crop
					__a       :     List[Any]								=	crop_size

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					return {
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "do_normalize": self.do_normalize,
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_center_crop": self.do_center_crop,
					    "crop_size": self.crop_size,
					}







@require_torch
@require_vision
class  SCREAMING_SNAKE_CASE__       (  __UpperCamelCase			,					unittest.TestCase   ):
	lowercase__	    =							TvltImageProcessor if is_vision_available() else None
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[int]								=	TvltImageProcessorTester(self			)
	@property
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					return self.image_processor_tester.prepare_image_processor_dict()
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[Any]								=	self.image_processing_class(**self.image_processor_dict			)
					self.assertTrue(hasattr(__UpperCamelCase  ,    """image_mean"""			)			)
					self.assertTrue(hasattr(__UpperCamelCase  ,    """image_std"""			)			)
					self.assertTrue(hasattr(__UpperCamelCase  ,    """do_normalize"""			)			)
					self.assertTrue(hasattr(__UpperCamelCase  ,    """do_resize"""			)			)
					self.assertTrue(hasattr(__UpperCamelCase  ,    """do_center_crop"""			)			)
					self.assertTrue(hasattr(__UpperCamelCase  ,    """size"""			)			)
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Union[str, Any]								=	self.image_processing_class(**self.image_processor_dict			)
					# create random PIL videos
					__a       :     List[str]								=	prepare_video_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase			)
					for video in video_inputs:
									self.assertIsInstance(__UpperCamelCase  ,    __UpperCamelCase			)
									self.assertIsInstance(video[0]  ,    Image.Image			)

					# Test not batched input
					__a       :     int								=	image_processor(video_inputs[0]  ,    return_tensors="""pt"""			).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,    (
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )  ,    )

					# Test batched
					__a       :     int								=	image_processor(__UpperCamelCase  ,    return_tensors="""pt"""			).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,    (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )  ,    )
	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     List[str]								=	self.image_processing_class(**self.image_processor_dict			)
					# create random numpy tensors
					__a       :     Union[str, Any]								=	prepare_video_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase  ,    numpify=__UpperCamelCase			)
					for video in video_inputs:
									self.assertIsInstance(__UpperCamelCase  ,    __UpperCamelCase			)
									self.assertIsInstance(video[0]  ,    np.ndarray			)

					# Test not batched input
					__a       :     Optional[int]								=	image_processor(video_inputs[0]  ,    return_tensors="""pt"""			).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,    (
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )  ,    )

					# Test batched
					__a       :     Dict								=	image_processor(__UpperCamelCase  ,    return_tensors="""pt"""			).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,    (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )  ,    )

	def __lowerCamelCase    (				self			):



					'''simple docstring'''






					__a       :     Optional[Any]								=	self.image_processing_class(**self.image_processor_dict			)
					# create random PyTorch tensors
					__a       :     Optional[int]								=	prepare_video_inputs(self.image_processor_tester  ,    equal_resolution=__UpperCamelCase  ,    torchify=__UpperCamelCase			)
					for video in video_inputs:
									self.assertIsInstance(__UpperCamelCase  ,    __UpperCamelCase			)
									self.assertIsInstance(video[0]  ,    torch.Tensor			)

					# Test not batched input
					__a       :     Optional[int]								=	image_processor(video_inputs[0]  ,    return_tensors="""pt"""			).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,    (
					        1,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )  ,    )

					# Test batched
					__a       :     int								=	image_processor(__UpperCamelCase  ,    return_tensors="""pt"""			).pixel_values
					self.assertEqual(
					    encoded_videos.shape  ,    (
					        self.image_processor_tester.batch_size,
					        self.image_processor_tester.num_frames,
					        self.image_processor_tester.num_channels,
					        self.image_processor_tester.crop_size["""height"""],
					        self.image_processor_tester.crop_size["""width"""],
					    )  ,    )