




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






@dataclass
class        __lowerCAmelCase					:
			snake_case_ :					nn.Module
			snake_case_ :					List[nn.Module]						=	field(default_factory=UpperCAmelCase__					)
			snake_case_ :					list						=	field(default_factory=UpperCAmelCase__					)







			def UpperCamelCase     (			self       :     Dict       ,       snake_case__       :     List[str]       ,       snake_case__       :     Optional[Any]       ,       snake_case__       :     str ):




									"""simple docstring"""


									_UpperCAmelCase						=	len(list(m.modules() ) ) == 1 or isinstance(__snake_case       ,       nn.Convad ) or isinstance(__snake_case       ,       nn.BatchNormad )
									if has_not_submodules:
															if not isinstance(__snake_case       ,       __snake_case ):
																					self.traced.append(__snake_case )







			def __call__(			self       :     str       ,       snake_case__       :     str ):




									"""simple docstring"""


									for m in self.module.modules():
															self.handles.append(m.register_forward_hook(self._forward_hook ) )
									self.module(__snake_case )
									[x.remove() for x in self.handles]
									return self







			@property
			def UpperCamelCase     (			self       :     Dict ):




									"""simple docstring"""


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






@dataclass
class        __lowerCAmelCase					:
			snake_case_ :					nn.Module
			snake_case_ :					nn.Module
			snake_case_ :					int						=	0
			snake_case_ :					List						=	field(default_factory=UpperCAmelCase__					)
			snake_case_ :					List						=	field(default_factory=UpperCAmelCase__					)







			def __call__(			self       :     str       ,       snake_case__       :     List[str] ):




									"""simple docstring"""


									_UpperCAmelCase						=	Tracker(self.dest )(__snake_case ).parametrized
									_UpperCAmelCase						=	Tracker(self.src )(__snake_case ).parametrized

									_UpperCAmelCase						=	list(filter(lambda snake_case__ : type(__snake_case ) not in self.src_skip       ,       __snake_case ) )
									_UpperCAmelCase						=	list(filter(lambda snake_case__ : type(__snake_case ) not in self.dest_skip       ,       __snake_case ) )

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

									for dest_m, src_m in zip(__snake_case       ,       __snake_case ):
															dest_m.load_state_dict(src_m.state_dict() )
															if self.verbose == 1:
																					print(F"""Transfered from={src_m} to={dest_m}""" )




def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_		):

						'''simple docstring'''


						_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(snake_case_		)

						our_model.load_state_dict(snake_case_		)
						return our_model


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_      ,       snake_case_ = True      ,       ):

						'''simple docstring'''


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

						_UpperCAmelCase						=	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
												_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=snake_case_      ,       )

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


def 				__SCREAMING_SNAKE_CASE    (     snake_case_      ,       snake_case_ = None      ,       snake_case_ = True		):

						'''simple docstring'''


						_UpperCAmelCase						=	"imagenet-1k-id2label.json"
						_UpperCAmelCase						=	1000

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

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

						_UpperCAmelCase						=	partial(snake_case_      ,       num_labels=snake_case_      ,       idalabel=snake_case_      ,       labelaid=snake_case_		)

						_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(
												    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__":
				lowercase_      :							Optional[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.',
				)

				lowercase_      :							int    							=					parser.parse_args()
				lowercase_      :							Path    							=					args.pytorch_dump_folder_path
				pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
				lowercase_      :							List[str]    							=					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 os
import tempfile
from pathlib import Path
from unittest import TestCase

import pyarrow as pa
import pytest

from datasets.arrow_dataset import Dataset
from datasets.arrow_reader import ArrowReader, BaseReader, FileInstructions, ReadInstruction, make_file_instructions
from datasets.info import DatasetInfo
from datasets.splits import NamedSplit, Split, SplitDict, SplitInfo

from .utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self	,				__snake_case	,				__snake_case=False							)						->     Tuple:






								"""simple docstring"""



								UpperCAmelCase						,   UpperCAmelCase						,   UpperCAmelCase:					Dict 						=     (
								    filename_skip_take["filename"],
								    filename_skip_take["skip"] if "skip" in filename_skip_take else None,
								    filename_skip_take["take"] if "take" in filename_skip_take else None,
								)
								open(os.path.join(__snake_case							)	,				"wb"							).close()
								UpperCAmelCase:					Any 						=     pa.Table.from_pydict({"filename": [Path(__snake_case							).name] * 1_0_0}							)
								if take == -1:
									UpperCAmelCase:					List[Any] 						=     len(__snake_case							) - skip
								if skip is not None and take is not None:
									UpperCAmelCase:					Optional[int] 						=     pa_table.slice(__snake_case	,				__snake_case							)
								return pa_table





class 		__lowerCamelCase     (   lowercase		):



							def     A__ (     self							)						->     int:






								"""simple docstring"""



								UpperCAmelCase:					Tuple 						=     "my_name"
								UpperCAmelCase:					str 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					Optional[int] 						=     [train_info, test_info]
								UpperCAmelCase:					Tuple 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[int] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					int 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					int 						=     "test[:33%]"
									UpperCAmelCase:					Dict 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-test'							)
									self.assertEqual(dset.num_rows	,				3_3							)
									self.assertEqual(dset.num_columns	,				1							)

									UpperCAmelCase:					int 						=     ["train", "test[:33%]"]
									UpperCAmelCase:					str 						=     [Split.TRAIN, ReadInstruction.from_spec("test[:33%]"							)]
									for instructions in [instructionsa, instructionsa]:
										UpperCAmelCase:					List[str] 						=     [reader.read(__snake_case	,				__snake_case	,				__snake_case							) for instr in instructions]
										UpperCAmelCase						,   UpperCAmelCase:					List[Any] 						=     (Dataset(**__snake_case							) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["filename"][0]	,				F'{name}-train'							)
										self.assertEqual(train_dset.num_rows	,				1_0_0							)
										self.assertEqual(train_dset.num_columns	,				1							)
										self.assertIsInstance(train_dset.split	,				__snake_case							)
										self.assertEqual(str(train_dset.split							)	,				"train"							)
										self.assertEqual(test_dset["filename"][0]	,				F'{name}-test'							)
										self.assertEqual(test_dset.num_rows	,				3_3							)
										self.assertEqual(test_dset.num_columns	,				1							)
										self.assertIsInstance(test_dset.split	,				__snake_case							)
										self.assertEqual(str(test_dset.split							)	,				"test[:33%]"							)
										del train_dset, test_dset



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






								"""simple docstring"""



								UpperCAmelCase:					str 						=     "my_name"
								UpperCAmelCase:					List[Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0_0	,				shard_lengths=[1_0_0] * 1_0							)
								UpperCAmelCase:					List[Any] 						=     [train_info]
								UpperCAmelCase:					Dict 						=     SplitDict()
								split_dict.add(__snake_case							)
								UpperCAmelCase:					int 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					Optional[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					Optional[Any] 						=     "train[:33%]"
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read(__snake_case	,				__snake_case	,				__snake_case							)							)
									self.assertEqual(dset["filename"][0]	,				F'{name}-train-00000-of-00010'							)
									self.assertEqual(dset["filename"][-1]	,				F'{name}-train-00003-of-00010'							)
									self.assertEqual(dset.num_rows	,				3_3_0							)
									self.assertEqual(dset.num_columns	,				1							)




							def     A__ (     self							)						->     Any:






								"""simple docstring"""



								UpperCAmelCase:					Union[str, Any] 						=     SplitInfo(name="train"	,				num_examples=1_0_0							)
								UpperCAmelCase:					int 						=     SplitInfo(name="test"	,				num_examples=1_0_0							)
								UpperCAmelCase:					List[Any] 						=     SplitDict()
								split_dict.add(__snake_case							)
								split_dict.add(__snake_case							)
								UpperCAmelCase:					Optional[Any] 						=     DatasetInfo(splits=__snake_case							)

								with tempfile.TemporaryDirectory() as tmp_dir:
									UpperCAmelCase:					List[Any] 						=     ReaderTest(__snake_case	,				__snake_case							)

									UpperCAmelCase:					List[str] 						=     [
									    {"filename": os.path.join(__snake_case	,				"train"							)},
									    {"filename": os.path.join(__snake_case	,				"test"							), "skip": 1_0, "take": 1_0},
									]
									UpperCAmelCase:					Tuple 						=     Dataset(**reader.read_files(__snake_case	,				original_instructions="train+test[10:20]"							)							)
									self.assertEqual(dset.num_rows	,				1_1_0							)
									self.assertEqual(dset.num_columns	,				1							)
									del dset



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		str				,					snake_case_							:		str				,					snake_case_							:		Any				):



	'''simple docstring'''



	UpperCAmelCase:					Union[str, Any] 						=     arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					int 						=     ArrowReader.read_table(snake_case_				,					in_memory=snake_case_				)
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



@pytest.mark.parametrize("in_memory"				,					[False, True]				)
def      __UpperCAmelCase    (  snake_case_							:		List[str]				,					snake_case_							:		Optional[int]				,					snake_case_							:		int				):



	'''simple docstring'''



	UpperCAmelCase:					List[Any] 						=     arrow_file
	UpperCAmelCase:					Tuple 						=     ArrowReader(""				,					snake_case_				)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		UpperCAmelCase:					Any 						=     reader.read_files([{"filename": filename}]				,					in_memory=snake_case_				)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	UpperCAmelCase:					Any 						=     dataset_kwargs["arrow_table"]
	assert table.shape == dataset.data.shape
	assert set(table.column_names				) == set(dataset.data.column_names				)
	assert dict(table.to_pydict()				) == dict(dataset.data.to_pydict()				)  # to_pydict returns OrderedDict



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	assert ReadInstruction("train"				,					to=1_0				,					unit="abs"				).to_spec() == "train[:10]"
	assert ReadInstruction("train"				,					from_=-8_0				,					to=1_0				,					unit="%"				).to_spec() == "train[-80%:10%]"

	UpperCAmelCase:					str 						=     "train+test"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test

	UpperCAmelCase:					List[Any] 						=     "train[2:10]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_abs

	UpperCAmelCase:					Tuple 						=     "train[15%:-20%]"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct

	UpperCAmelCase:					Dict 						=     "train[:10%](closest)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == "train[:10%]"

	UpperCAmelCase:					List[str] 						=     "train[:10%](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_pct_rounding

	UpperCAmelCase:					Any 						=     "train[:10%](pct1_dropremainder)+test[-10%:](pct1_dropremainder)"
	assert ReadInstruction.from_spec(snake_case_				).to_spec() == spec_train_test_pct_rounding



def      __UpperCAmelCase    (  ):



	'''simple docstring'''



	UpperCAmelCase:					Dict 						=     "dummy"
	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					List[str] 						=     "train[:33%]"
	UpperCAmelCase:					Optional[int] 						=     "arrow"
	UpperCAmelCase:					List[Any] 						=     "prefix"

	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train.arrow'				), "skip": 0, "take": 3_3}
	]

	UpperCAmelCase:					int 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				,					shard_lengths=[1_0] * 1_0				)]
	UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
	assert isinstance(snake_case_				,					snake_case_				)
	assert file_instructions.num_examples == 3_3
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00000-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00001-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00002-of-00010.arrow'				), "skip": 0, "take": -1},
	    {"filename": os.path.join(snake_case_				,					F'{name}-train-00003-of-00010.arrow'				), "skip": 0, "take": 3},
	]



@pytest.mark.parametrize("name, expected_exception"				,					[(None, TypeError), ("", ValueError)]				)
def      __UpperCAmelCase    (  snake_case_							:		Union[str, Any]				,					snake_case_							:		Dict				):



	'''simple docstring'''



	UpperCAmelCase:					Optional[int] 						=     [SplitInfo(name="train"				,					num_examples=1_0_0				)]
	UpperCAmelCase:					Union[str, Any] 						=     "train"
	UpperCAmelCase:					List[str] 						=     "arrow"
	UpperCAmelCase:					Any 						=     "prefix_path"
	with pytest.raises(snake_case_				):
		UpperCAmelCase:					List[Any] 						=     make_file_instructions(snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				,					snake_case_				)
