


import logging

# standard libraries
import sys

import numpy as np
import pytest  # type: ignore

# Custom/local libraries
from matrix import matrix_operation as matop

__lowerCamelCase       :       str				   =			[[12, 10], [3, 9]]
__lowerCamelCase       :       Any				   =			[[3, 4], [7, 4]]
__lowerCamelCase       :       List[str]				   =			[[3, 0, 2], [2, 0, -2], [0, 1, 1]]
__lowerCamelCase       :       Any				   =			[[3, 0, -2], [2, 0, 2], [0, 1, 1]]
__lowerCamelCase       :       Union[str, Any]				   =			[[3, 0, 2], [2, 0, -2], [0, 1, 1], [2, 0, -2]]
__lowerCamelCase       :       str				   =			[1]
__lowerCamelCase       :       str				   =			[2]

__lowerCamelCase       :       List[Any]				   =			logging.getLogger()
__lowerCamelCase       :       List[Any]				   =			logging.DEBUG
__lowerCamelCase       :       Optional[Any]				   =			logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2'''),	[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Any:
				if (np.array(snake_case_	)).shape < (2, 2) or (np.array(snake_case_	)).shape < (2, 2):
								with pytest.raises(snake_case_	):
												logger.info(f'\n\t{test_addition.__name__} returned integer'	)
												matop.add(snake_case_,	snake_case_	)
				elif (np.array(snake_case_	)).shape == (np.array(snake_case_	)).shape:
								logger.info(f'\n\t{test_addition.__name__} with same matrix dims'	)
								A__     :							List[str]   				=(np.array(snake_case_	) + np.array(snake_case_	)).tolist()
								A__     :							Optional[int]   				=matop.add(snake_case_,	snake_case_	)
								assert theo == act
				else:
								with pytest.raises(snake_case_	):
												logger.info(f'\n\t{test_addition.__name__} with different matrix dims'	)
												matop.add(snake_case_,	snake_case_	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2'''),	[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Union[str, Any]:
				if (np.array(snake_case_	)).shape < (2, 2) or (np.array(snake_case_	)).shape < (2, 2):
								with pytest.raises(snake_case_	):
												logger.info(f'\n\t{test_subtraction.__name__} returned integer'	)
												matop.subtract(snake_case_,	snake_case_	)
				elif (np.array(snake_case_	)).shape == (np.array(snake_case_	)).shape:
								logger.info(f'\n\t{test_subtraction.__name__} with same matrix dims'	)
								A__     :							Tuple   				=(np.array(snake_case_	) - np.array(snake_case_	)).tolist()
								A__     :							Optional[Any]   				=matop.subtract(snake_case_,	snake_case_	)
								assert theo == act
				else:
								with pytest.raises(snake_case_	):
												logger.info(f'\n\t{test_subtraction.__name__} with different matrix dims'	)
												assert matop.subtract(snake_case_,	snake_case_	)



@pytest.mark.mat_ops
@pytest.mark.parametrize(
    ('''mat1''', '''mat2'''),	[(mat_a, mat_b), (mat_c, mat_d), (mat_d, mat_e), (mat_f, mat_h)]	)
def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							Tuple:
				if (np.array(snake_case_	)).shape < (2, 2) or (np.array(snake_case_	)).shape < (2, 2):
								logger.info(f'\n\t{test_multiplication.__name__} returned integer'	)
								with pytest.raises(snake_case_	):
												matop.add(snake_case_,	snake_case_	)
				elif (np.array(snake_case_	)).shape == (np.array(snake_case_	)).shape:
								logger.info(f'\n\t{test_multiplication.__name__} meets dim requirements'	)
								A__     :							Optional[int]   				=(np.matmul(snake_case_,	snake_case_	)).tolist()
								A__     :							str   				=matop.multiply(snake_case_,	snake_case_	)
								assert theo == act
				else:
								with pytest.raises(snake_case_	):
												logger.info(
												    f'\n\t{test_multiplication.__name__} does not meet dim requirements'	)
												assert matop.subtract(snake_case_,	snake_case_	)



@pytest.mark.mat_ops
def   SCREAMING_SNAKE_CASE__						(							)			->							List[str]:
				A__     :							List[Any]   				=(3.5 * np.array(snake_case_	)).tolist()
				A__     :							Any   				=matop.scalar_multiply(snake_case_,	3.5	)
				assert theo == act



@pytest.mark.mat_ops
def   SCREAMING_SNAKE_CASE__						(							)			->							int:
				A__     :							Union[str, Any]   				=(np.identity(5	)).tolist()
				A__     :							Optional[int]   				=matop.identity(5	)
				assert theo == act




@pytest.mark.mat_ops
@pytest.mark.parametrize('''mat''',	[mat_a, mat_b, mat_c, mat_d, mat_e, mat_f]	)
def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Union[str, Any]:
				if (np.array(snake_case_	)).shape < (2, 2):
								with pytest.raises(snake_case_	):
												logger.info(f'\n\t{test_transpose.__name__} returned integer'	)
												matop.transpose(snake_case_	)
				else:
								A__     :							int   				=(np.transpose(snake_case_	)).tolist()
								A__     :							str   				=matop.transpose(snake_case_,	return_map=snake_case_	)
								assert theo == act







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()
__lowerCamelCase       :       Union[str, Any]				   =			logging.get_logger()



@dataclass
class 			a      :
		__lowercase													=		42
		__lowercase													=		field(default_factory=UpperCamelCase_   )
		__lowercase													=		field(default_factory=UpperCamelCase_   )
		__lowercase													=		field(default_factory=UpperCamelCase_   )






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase							)->			List[Any]:
						'''simple docstring'''


						A__     :							Tuple   				=len(list(m.modules()							)							) == 1 or isinstance(__UpperCamelCase	,     nn.Convad							) or isinstance(__UpperCamelCase	,     nn.BatchNormad							)
						if has_not_submodules:
										self.traced.append(__UpperCamelCase							)
										A__     :							Union[str, Any]   				=m






		def __call__(  self	,     __UpperCamelCase							)->			Any:
						'''simple docstring'''


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






		@property
		def       lowerCAmelCase_  (  self							)->			str:
						'''simple docstring'''


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







class 			a      (      nn.Module   ):






		def __init__(  self	,     __UpperCamelCase							)->			Optional[Any]:
						'''simple docstring'''


						super().__init__()

						A__     :							List[Tuple[str, nn.Module]]   				=[]
						# - 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__     :							Any   				=len(__UpperCamelCase							) + 1
										feature_blocks.append((F'res{block_index}', v)							)

						A__     :							Tuple   				=nn.ModuleDict(__UpperCamelCase							)






		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			List[str]:
						'''simple docstring'''


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







class 			a      (      UpperCamelCase_   ):






		def       lowerCAmelCase_  (  self							)->			int:
						'''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   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Dict[str, str]:
				A__     :							Any   				=RegNetConfig(depths=[2, 7, 1_7, 1],	hidden_sizes=[8, 8, 8, 8],	groups_width=8	)
				if "in1k" in model_name:
								A__     :							Any   				=RegNetForImageClassification(snake_case_	)
				else:
								A__     :							Tuple   				=RegNetModel(snake_case_	)
				# create from model (with small weights)
				A__     :							List[str]   				=FakeRegNetVisslWrapper(
				    RegNet(FakeRegNetParams(depth=2_7,	group_width=1_0_1_0,	w_a=1_7_4_4,	w_a=6_2_0.8_3,	w_m=2.5_2	)	)	)

				with torch.no_grad():
								A__     :							str   				=from_model.eval()
								A__     :							int   				=our_model.eval()

								A__     :							Optional[int]   				=torch.randn((1, 3, 3_2, 3_2)	)
								# trace both
								A__     :							int   				=Tracker(snake_case_	)
								A__     :							Optional[int]   				=dest_tracker(snake_case_	).parametrized

								pprint(dest_tracker.nameamodule	)
								A__     :							Optional[int]   				=Tracker(snake_case_	)
								A__     :							Any   				=src_tracker(snake_case_	).parametrized

				# convert the keys -> module dict to keys -> params
				def to_params_dict(snake_case_	):
								A__     :							Optional[Any]   				=OrderedDict()
								for name, module in dict_with_modules.items():
												for param_name, param in module.state_dict().items():
																A__     :							Union[str, Any]   				=param
								return params_dict

				A__     :							Optional[int]   				={}

				A__     :							int   				=to_params_dict(snake_case_	)
				A__     :							int   				=to_params_dict(snake_case_	)

				for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items(),	dst_state_dict.items()	):
								A__     :							int   				=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__     :							str   				='''classifier.1.weight'''
								A__     :							Union[str, Any]   				='''classifier.1.bias'''

				return from_to_ours_keys



def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_ = None,	snake_case_ = True	)			->							int:
				A__     :							Tuple   				='''imagenet-1k-id2label.json'''
				A__     :							Any   				=1_0_0_0

				A__     :							int   				='''huggingface/label-files'''
				A__     :							Tuple   				=num_labels
				A__     :							Optional[int]   				=json.load(open(cached_download(hf_hub_url(snake_case_,	snake_case_,	repo_type='''dataset'''	)	),	'''r'''	)	)
				A__     :							str   				={int(snake_case_	): v for k, v in idalabel.items()}

				A__     :							int   				=idalabel
				A__     :							Tuple   				={v: k for k, v in idalabel.items()}

				A__     :							str   				=partial(snake_case_,	num_labels=snake_case_,	idalabel=snake_case_,	labelaid=snake_case_	)

				A__     :							Tuple   				={
				    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
				    # finetuned on imagenet
				    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
				        depths=[2, 7, 1_7, 1],	hidden_sizes=[2_0_2_0, 4_0_4_0, 1_1_1_1_0, 2_8_2_8_0],	groups_width=1_0_1_0	),
				}

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

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

				A__     :							Tuple   				=get_from_to_our_keys(snake_case_	)

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

								A__     :							List[str]   				={}

								A__     :							int   				=list(from_state_dict.keys()	)
								A__     :							Optional[Any]   				=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__     :							Tuple   				=re.sub(snake_case_,	'''''',	snake_case_	)
												# 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__     :							Any   				=from_to_ours_keys[src_key]
												# store the parameter with our key
												A__     :							str   				=from_state_dict[key]
												not_used_keys.remove(snake_case_	)
								# check that all keys have been updated
								assert len(snake_case_	) == 0, f'Some keys where not used {",".join(snake_case_	)}'

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

								# save our state dict to disk
								torch.save(snake_case_,	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__     :							Optional[int]   				=names_to_config[model_name]
								A__     :							Any   				=RegNetModel
								if "in1k" in model_name:
												A__     :							Optional[int]   				=RegNetForImageClassification
								A__     :							Optional[int]   				=our_model_func(snake_case_	)
								# 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__     :							Any   				=our_model.state_dict().keys()
								PreTrainedModel._load_pretrained_model_low_mem(
								    snake_case_,	snake_case_,	[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__     :							List[Any]   				=3_8_4
								# we can use the convnext one
								A__     :							List[Any]   				=AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''',	size=snake_case_	)
								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__":
						__lowerCamelCase       :       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.",
						)

						__lowerCamelCase       :       List[str]				   =			parser.parse_args()

						__lowerCamelCase       :       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)



