




import argparse
import json
import os
import re
from collections import OrderedDict
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from pprint import pprint
from typing import Dict, List, Tuple

import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.modeling_utils import PreTrainedModel
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase      : List[Any]					      =   logging.get_logger()

@dataclass
class 	__lowerCAmelCase			:
							UpperCamelCase__        =						42
							UpperCamelCase__        =						field(default_factory=__magic_name__				)
							UpperCamelCase__        =						field(default_factory=__magic_name__				)
							UpperCamelCase__        =						field(default_factory=__magic_name__				)
							def    lowerCamelCase__							(		self		:Dict	,   __magic_name__		:Tuple	,   __magic_name__		:Tensor	,   __magic_name__		:Tensor	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							len(list(m.modules()       )       ) == 1 or isinstance(__magic_name__	,   nn.Convad       ) or isinstance(__magic_name__	,   nn.BatchNormad       )
								if has_not_submodules:
									self.traced.append(__magic_name__       )
									a		       =							m
							def __call__(		self		:Optional[Any]	,   __magic_name__		:Tensor       ):







								'''simple docstring'''







								for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook	,   name=__magic_name__       )       )       )
								self.module(__magic_name__       )
								[x.remove() for x in self.handles]
								return self





							@property
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()       )       ) > 0}







class 	__lowerCAmelCase			(			nn.Module				):
							def __init__(		self		:Dict	,   __magic_name__		:nn.Module       ):







								'''simple docstring'''







								super().__init__()

								a		       =							[]
								# - get the stem
								feature_blocks.append(("""conv1""", model.stem)       )
								# - get all the feature blocks
								for k, v in model.trunk_output.named_children():
									assert k.startswith("""block"""       ), F'Unexpected layer name {k}'
									a		       =							len(__magic_name__       ) + 1
									feature_blocks.append((F'res{block_index}', v)       )

								a		       =							nn.ModuleDict(__magic_name__       )





							def    lowerCamelCase__							(		self		:str	,   __magic_name__		:Tensor       ):







								'''simple docstring'''







								return get_trunk_forward_outputs(
								    __magic_name__	,   out_feat_keys=__magic_name__	,   feature_blocks=self._feature_blocks	,   )







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								return [(8, 2, 2, 8, 1.0), (8, 2, 7, 8, 1.0), (8, 2, 17, 8, 1.0), (8, 2, 1, 8, 1.0)]







def 	__A	(			__lowerCamelCase					)   -> Dict[str, str]:
	a		       =							RegNetConfig(depths=[2, 7, 17, 1]							,    hidden_sizes=[8, 8, 8, 8]							,    groups_width=8					)
	if "in1k" in model_name:
		a		       =							RegNetForImageClassification(__lowerCamelCase					)
	else:
		a		       =							RegNetModel(__lowerCamelCase					)
	# create from model (with small weights)
	a		       =							FakeRegNetVisslWrapper(
	    RegNet(FakeRegNetParams(depth=27							,    group_width=1010							,    w_a=1744							,    w_a=620.83							,    w_m=2.52					)					)					)

	with torch.no_grad():
		a		       =							from_model.eval()
		a		       =							our_model.eval()

		a		       =							torch.randn((1, 3, 32, 32)					)
		# trace both
		a		       =							Tracker(__lowerCamelCase					)
		a		       =							dest_tracker(__lowerCamelCase					).parametrized

		pprint(dest_tracker.nameamodule					)
		a		       =							Tracker(__lowerCamelCase					)
		a		       =							src_tracker(__lowerCamelCase					).parametrized

	# convert the keys -> module dict to keys -> params
	def to_params_dict(__lowerCamelCase					):
		a		       =							OrderedDict()
		for name, module in dict_with_modules.items():
			for param_name, param in module.state_dict().items():
				a		       =							param
		return params_dict

	a		       =							{}

	a		       =							to_params_dict(__lowerCamelCase					)
	a		       =							to_params_dict(__lowerCamelCase					)

	for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()							,    dst_state_dict.items()					):
		a		       =							dest_key
		logger.info(f'{src_key} -> {dest_key}'					)
	# if "in1k" was in the model_name it means it must have a classification head (was finetuned)
	if "in1k" in model_name:
		a		       =							"""classifier.1.weight"""
		a		       =							"""classifier.1.bias"""

	return from_to_ours_keys





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase = None							,    __lowerCamelCase = True					)   -> List[Any]:
	a		       =							"""imagenet-1k-id2label.json"""
	a		       =							1000

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

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

	a		       =							partial(__lowerCamelCase							,    num_labels=__lowerCamelCase							,    idalabel=__lowerCamelCase							,    labelaid=__lowerCamelCase					)

	a		       =							{
	    """regnet-y-10b-seer""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,    hidden_sizes=[2020, 4040, 1_1110, 2_8280]							,    groups_width=1010					),
	    # finetuned on imagenet
	    """regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
	        depths=[2, 7, 17, 1]							,    hidden_sizes=[2020, 4040, 1_1110, 2_8280]							,    groups_width=1010					),
	}

	# add seer weights logic
	def load_using_classy_vision(__lowerCamelCase					) -> Tuple[Dict, Dict]:
		a		       =							torch.hub.load_state_dict_from_url(__lowerCamelCase							,    model_dir=str(__lowerCamelCase					)							,    map_location="""cpu"""					)
		# check if we have a head, if yes add it
		a		       =							files["""classy_state_dict"""]["""base_model"""]["""model"""]
		return model_state_dict["trunk"], model_state_dict["heads"]

	a		       =							{
	    """regnet-y-10b-seer""": partial(
	        __lowerCamelCase							,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch"""							,    ),
	    """regnet-y-10b-seer-in1k""": partial(
	        __lowerCamelCase							,    """https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch"""							,    ),
	}

	a		       =							get_from_to_our_keys(__lowerCamelCase					)

	if not (save_directory / f'{model_name}.pth').exists():
		logger.info("""Loading original state_dict."""					)
		a       ,				a		       =							names_to_from_model[model_name]()
		a		       =							from_state_dict_trunk
		if "in1k" in model_name:
			# add the head
			a		       =							{**from_state_dict_trunk, **from_state_dict_head}
		logger.info("""Done!"""					)

		a		       =							{}

		a		       =							list(from_state_dict.keys()					)
		a		       =							R"""\.block.-part."""
		# this is "interesting", so the original checkpoints have `block[0,1]-part` in each key name, we remove it
		for key in from_state_dict.keys():
			# remove the weird "block[0,1]-part" from the key
			a		       =							re.sub(__lowerCamelCase							,    """"""							,    __lowerCamelCase					)
			# now src_key from the model checkpoints is the one we got from the original model after tracing, so use it to get the correct destination key
			a		       =							from_to_ours_keys[src_key]
			# store the parameter with our key
			a		       =							from_state_dict[key]
			not_used_keys.remove(__lowerCamelCase					)
		# check that all keys have been updated
		assert len(__lowerCamelCase					) == 0, f'Some keys where not used {",".join(__lowerCamelCase					)}'

		logger.info(f'The following keys were not used: {",".join(__lowerCamelCase					)}'					)

		# save our state dict to disk
		torch.save(__lowerCamelCase							,    save_directory / f'{model_name}.pth'					)

		del converted_state_dict
	else:
		logger.info("""The state_dict was already stored on disk."""					)
	if push_to_hub:
		logger.info(f'Token is {os.environ["HF_TOKEN"]}'					)
		logger.info("""Loading our model."""					)
		# create our model
		a		       =							names_to_config[model_name]
		a		       =							RegNetModel
		if "in1k" in model_name:
			a		       =							RegNetForImageClassification
		a		       =							our_model_func(__lowerCamelCase					)
		# place our model to the meta device (so remove all the weights)
		our_model.to(torch.device("""meta"""					)					)
		logger.info("""Loading state_dict in our model."""					)
		# load state dict
		a		       =							our_model.state_dict().keys()
		PreTrainedModel._load_pretrained_model_low_mem(
		    __lowerCamelCase							,    __lowerCamelCase							,    [save_directory / f'{model_name}.pth']					)
		logger.info("""Finally, pushing!"""					)
		# push it to hub
		our_model.push_to_hub(
		    repo_path_or_name=save_directory / model_name							,    commit_message="""Add model"""							,    output_dir=save_directory / model_name							,    )
		a		       =							384
		# we can use the convnext one
		a		       =							AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k"""							,    size=__lowerCamelCase					)
		image_processor.push_to_hub(
		    repo_path_or_name=save_directory / model_name							,    commit_message="""Add image processor"""							,    output_dir=save_directory / model_name							,    )


if __name__ == "__main__":
		__UpperCamelCase      : 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 regnet* architecture,"
		        " currently: regnetx-*, regnety-*. 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(
		    "--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      : Path					      =   args.pytorch_dump_folder_path
		pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
		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			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[str]	,   __magic_name__		:Dict	,   __magic_name__		:Union[str, Any]=False       ):







								'''simple docstring'''







								a       ,				a       ,				a		       =							(
								    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(__magic_name__       )	,   """wb"""       ).close()
								a		       =							pa.Table.from_pydict({"""filename""": [Path(__magic_name__       ).name] * 100}       )
								if take == -1:
									a		       =							len(__magic_name__       ) - skip
								if skip is not None and take is not None:
									a		       =							pa_table.slice(__magic_name__	,   __magic_name__       )
								return pa_table







class 	__lowerCAmelCase			(			__magic_name__				):
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							[train_info, test_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""test[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									self.assertEqual(dset["""filename"""][0]	,   F'{name}-test'       )
									self.assertEqual(dset.num_rows	,   33       )
									self.assertEqual(dset.num_columns	,   1       )

									a		       =							["""train""", """test[:33%]"""]
									a		       =							[Split.TRAIN, ReadInstruction.from_spec("""test[:33%]"""       )]
									for instructions in [instructionsa, instructionsa]:
										a		       =							[reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       ) for instr in instructions]
										a       ,				a		       =							(Dataset(**__magic_name__       ) for dataset_kwargs in datasets_kwargs)
										self.assertEqual(train_dset["""filename"""][0]	,   F'{name}-train'       )
										self.assertEqual(train_dset.num_rows	,   100       )
										self.assertEqual(train_dset.num_columns	,   1       )
										self.assertIsInstance(train_dset.split	,   __magic_name__       )
										self.assertEqual(str(train_dset.split       )	,   """train"""       )
										self.assertEqual(test_dset["""filename"""][0]	,   F'{name}-test'       )
										self.assertEqual(test_dset.num_rows	,   33       )
										self.assertEqual(test_dset.num_columns	,   1       )
										self.assertIsInstance(test_dset.split	,   __magic_name__       )
										self.assertEqual(str(test_dset.split       )	,   """test[:33%]"""       )
										del train_dset, test_dset
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							"""my_name"""
								a		       =							SplitInfo(name="""train"""	,   num_examples=1000	,   shard_lengths=[100] * 10       )
								a		       =							[train_info]
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							"""train[:33%]"""
									a		       =							Dataset(**reader.read(__magic_name__	,   __magic_name__	,   __magic_name__       )       )
									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	,   330       )
									self.assertEqual(dset.num_columns	,   1       )





							def    lowerCamelCase__							(		self		:List[str]       ):







								'''simple docstring'''







								a		       =							SplitInfo(name="""train"""	,   num_examples=100       )
								a		       =							SplitInfo(name="""test"""	,   num_examples=100       )
								a		       =							SplitDict()
								split_dict.add(__magic_name__       )
								split_dict.add(__magic_name__       )
								a		       =							DatasetInfo(splits=__magic_name__       )

								with tempfile.TemporaryDirectory() as tmp_dir:
									a		       =							ReaderTest(__magic_name__	,   __magic_name__       )

									a		       =							[
									    {"""filename""": os.path.join(__magic_name__	,   """train"""       )},
									    {"""filename""": os.path.join(__magic_name__	,   """test"""       ), """skip""": 10, """take""": 10},
									]
									a		       =							Dataset(**reader.read_files(__magic_name__	,   original_instructions="""train+test[10:20]"""       )       )
									self.assertEqual(dset.num_rows	,   110       )
									self.assertEqual(dset.num_columns	,   1       )
									del dset







@pytest.mark.parametrize("""in_memory"""							,    [False, True]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> Optional[int]:
	a		       =							arrow_file
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							ArrowReader.read_table(__lowerCamelCase							,    in_memory=__lowerCamelCase					)
	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 	__A	(			__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)   -> str:
	a		       =							arrow_file
	a		       =							ArrowReader(""""""							,    __lowerCamelCase					)
	with assert_arrow_memory_increases() if in_memory else assert_arrow_memory_doesnt_increase():
		a		       =							reader.read_files([{"""filename""": filename}]							,    in_memory=__lowerCamelCase					)
	assert dataset_kwargs.keys() == {"arrow_table", "info", "split"}
	a		       =							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 	__A	(			)   -> int:
	assert ReadInstruction("""train"""							,    to=10							,    unit="""abs"""					).to_spec() == "train[:10]"
	assert ReadInstruction("""train"""							,    from_=-80							,    to=10							,    unit="""%"""					).to_spec() == "train[-80%:10%]"

	a		       =							"""train+test"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_test

	a		       =							"""train[2:10]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_abs

	a		       =							"""train[15%:-20%]"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct

	a		       =							"""train[:10%](closest)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == "train[:10%]"

	a		       =							"""train[:10%](pct1_dropremainder)"""
	assert ReadInstruction.from_spec(__lowerCamelCase					).to_spec() == spec_train_pct_rounding

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





def 	__A	(			)   -> Any:
	a		       =							"""dummy"""
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train[:33%]"""
	a		       =							"""arrow"""
	a		       =							"""prefix"""

	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train.arrow'					), "skip": 0, "take": 33}
	]

	a		       =							[SplitInfo(name="""train"""							,    num_examples=100							,    shard_lengths=[10] * 10					)]
	a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)
	assert isinstance(__lowerCamelCase							,    __lowerCamelCase					)
	assert file_instructions.num_examples == 33
	assert file_instructions.file_instructions == [
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00000-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00001-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00002-of-00010.arrow'					), "skip": 0, "take": -1},
	    {"filename": os.path.join(__lowerCamelCase							,    f'{name}-train-00003-of-00010.arrow'					), "skip": 0, "take": 3},
	]





@pytest.mark.parametrize("""name, expected_exception"""							,    [(None, TypeError), ("""""", ValueError)]					)
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> int:
	a		       =							[SplitInfo(name="""train"""							,    num_examples=100					)]
	a		       =							"""train"""
	a		       =							"""arrow"""
	a		       =							"""prefix_path"""
	with pytest.raises(__lowerCamelCase					):
		a		       =							make_file_instructions(__lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase							,    __lowerCamelCase					)

