



"""simple docstring"""

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()
A_	:						Any				=			logging.get_logger()
@dataclass
class 						lowerCAmelCase__	:


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		nn.Module
	_SCREAMING_SNAKE_CASE  :		List[nn.Module]     	=   field(default_factory=_lowerCamelCase   )
	_SCREAMING_SNAKE_CASE  :		list     	=   field(default_factory=_lowerCamelCase   )
	_SCREAMING_SNAKE_CASE  :		Dict[str, nn.Module]     	=   field(default_factory=_lowerCamelCase   )





	def  _lowerCAmelCase    (			self							:					Union[str, Any]    ,					_SCREAMING_SNAKE_CASE							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Tensor    ,					_SCREAMING_SNAKE_CASE							:					Tensor    ,					_SCREAMING_SNAKE_CASE							:					str						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    len(list(m.modules()						)						) == 1 or isinstance(_SCREAMING_SNAKE_CASE    ,					nn.Convad						) or isinstance(_SCREAMING_SNAKE_CASE    ,					nn.BatchNormad						)
					if has_not_submodules:
									self.traced.append(_SCREAMING_SNAKE_CASE						)
									SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    m





	def __call__(			self							:					str    ,					_SCREAMING_SNAKE_CASE							:					Tensor						)	->  Tuple:





					"""simple docstring"""


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







	@property
	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Optional[Any]:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  nn.Module   ):


	'''simple docstring'''







	def __init__(			self							:					int    ,					_SCREAMING_SNAKE_CASE							:					nn.Module						)	->  int:





					"""simple docstring"""


					super().__init__()

					SCREAMING_SNAKE_CASE	:       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}"""
									SCREAMING_SNAKE_CASE	:       Dict 							=    len(_SCREAMING_SNAKE_CASE						) + 1
									feature_blocks.append((f"""res{block_index}""", v)						)

					SCREAMING_SNAKE_CASE	:       Dict 							=    nn.ModuleDict(_SCREAMING_SNAKE_CASE						)







	def  _lowerCAmelCase    (			self							:					Any    ,					_SCREAMING_SNAKE_CASE							:					Tensor						)	->  Union[str, Any]:





					"""simple docstring"""


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




class 						lowerCAmelCase__	(  _lowerCamelCase   ):


	'''simple docstring'''







	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  List[str]:





					"""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    __snake_case       (  __A			:	str    )    ->					Dict[str, str]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    RegNetConfig(depths=[2, 7, 17, 1]       ,		hidden_sizes=[8, 8, 8, 8]       ,		groups_width=8    )
				if "in1k" in model_name:
								SCREAMING_SNAKE_CASE	:       Dict 							=    RegNetForImageClassification(__A    )
				else:
								SCREAMING_SNAKE_CASE	:       Optional[int] 							=    RegNetModel(__A    )
				# create from model (with small weights)
				SCREAMING_SNAKE_CASE	:       List[Any] 							=    FakeRegNetVisslWrapper(
				    RegNet(FakeRegNetParams(depth=27       ,		group_width=1010       ,		w_a=1744       ,		w_a=6_2_0.8_3       ,		w_m=2.5_2    )    )    )

				with torch.no_grad():
								SCREAMING_SNAKE_CASE	:       int 							=    from_model.eval()
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    our_model.eval()

								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    torch.randn((1, 3, 32, 32)    )
								# trace both
								SCREAMING_SNAKE_CASE	:       Dict 							=    Tracker(__A    )
								SCREAMING_SNAKE_CASE	:       str 							=    dest_tracker(__A    ).parametrized

								pprint(dest_tracker.nameamodule    )
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    Tracker(__A    )
								SCREAMING_SNAKE_CASE	:       Dict 							=    src_tracker(__A    ).parametrized

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

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    {}

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    to_params_dict(__A    )
				SCREAMING_SNAKE_CASE	:       str 							=    to_params_dict(__A    )

				for (src_key, src_param), (dest_key, dest_param) in zip(src_state_dict.items()       ,		dst_state_dict.items()    ):
								SCREAMING_SNAKE_CASE	:       List[str] 							=    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:
								SCREAMING_SNAKE_CASE	:       Tuple 							=    'classifier.1.weight'
								SCREAMING_SNAKE_CASE	:       int 							=    'classifier.1.bias'

				return from_to_ours_keys
def    __snake_case       (  __A			:	Path       ,		__A			:	str = None       ,		__A			:	bool = True    )    ->					Optional[Any]:






				'''simple docstring'''
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    'imagenet-1k-id2label.json'
				SCREAMING_SNAKE_CASE	:       Dict 							=    1000

				SCREAMING_SNAKE_CASE	:       Any 							=    'huggingface/label-files'
				SCREAMING_SNAKE_CASE	:       List[str] 							=    num_labels
				SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    json.load(open(cached_download(hf_hub_url(__A       ,		__A       ,		repo_type='dataset'    )    )       ,		'r'    )    )
				SCREAMING_SNAKE_CASE	:       Dict 							=    {int(__A    ): v for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE	:       List[str] 							=    idalabel
				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    {v: k for k, v in idalabel.items()}

				SCREAMING_SNAKE_CASE	:       List[str] 							=    partial(__A       ,		num_labels=__A       ,		idalabel=__A       ,		labelaid=__A    )

				SCREAMING_SNAKE_CASE	:       List[Any] 							=    {
				    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]       ,		hidden_sizes=[2020, 4040, 11110, 28280]       ,		groups_width=1010    ),
				    # finetuned on imagenet
				    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
				        depths=[2, 7, 17, 1]       ,		hidden_sizes=[2020, 4040, 11110, 28280]       ,		groups_width=1010    ),
				}

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

				SCREAMING_SNAKE_CASE	:       Optional[int] 							=    {
				    'regnet-y-10b-seer': partial(
				        __A       ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'       ,		),
				    'regnet-y-10b-seer-in1k': partial(
				        __A       ,		'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'       ,		),
				}

				SCREAMING_SNAKE_CASE	:       Dict 							=    get_from_to_our_keys(__A    )

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

								SCREAMING_SNAKE_CASE	:       int 							=    {}

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

								logger.info(F"""The following keys were not used: {",".join(__A    )}"""    )

								# save our state dict to disk
								torch.save(__A       ,		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
								SCREAMING_SNAKE_CASE	:       str 							=    names_to_config[model_name]
								SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    RegNetModel
								if "in1k" in model_name:
												SCREAMING_SNAKE_CASE	:       int 							=    RegNetForImageClassification
								SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    our_model_func(__A    )
								# 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
								SCREAMING_SNAKE_CASE	:       Dict 							=    our_model.state_dict().keys()
								PreTrainedModel._load_pretrained_model_low_mem(
								    __A       ,		__A       ,		[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       ,		)
								SCREAMING_SNAKE_CASE	:       Dict 							=    384
								# we can use the convnext one
								SCREAMING_SNAKE_CASE	:       int 							=    AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'       ,		size=__A    )
								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__":
							A_	:						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.',
							)

							A_	:						Any				=			parser.parse_args()

							A_	:						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)










"""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 ConvNextImageProcessor
class 						lowerCAmelCase__	(  unittest.TestCase   ):


	'''simple docstring'''







	def __init__(			self							:					Tuple    ,					_SCREAMING_SNAKE_CASE							:					List[str]    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=7    ,					_SCREAMING_SNAKE_CASE							:					Optional[int]=3    ,					_SCREAMING_SNAKE_CASE							:					List[str]=18    ,					_SCREAMING_SNAKE_CASE							:					str=30    ,					_SCREAMING_SNAKE_CASE							:					str=400    ,					_SCREAMING_SNAKE_CASE							:					Any=True    ,					_SCREAMING_SNAKE_CASE							:					int=None    ,					_SCREAMING_SNAKE_CASE							:					Dict=0.8_7_5    ,					_SCREAMING_SNAKE_CASE							:					List[str]=True    ,					_SCREAMING_SNAKE_CASE							:					List[Any]=[0.5, 0.5, 0.5]    ,					_SCREAMING_SNAKE_CASE							:					str=[0.5, 0.5, 0.5]    ,					)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       int 							=    size if size is not None else {'shortest_edge': 20}
					SCREAMING_SNAKE_CASE	:       List[Any] 							=    parent
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    batch_size
					SCREAMING_SNAKE_CASE	:       Any 							=    num_channels
					SCREAMING_SNAKE_CASE	:       Any 							=    image_size
					SCREAMING_SNAKE_CASE	:       str 							=    min_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    max_resolution
					SCREAMING_SNAKE_CASE	:       Dict 							=    do_resize
					SCREAMING_SNAKE_CASE	:       List[str] 							=    size
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    crop_pct
					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    do_normalize
					SCREAMING_SNAKE_CASE	:       Dict 							=    image_mean
					SCREAMING_SNAKE_CASE	:       str 							=    image_std







	def  _lowerCAmelCase    (			self							:					Dict						)	->  Any:





					"""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,
					    "crop_pct": self.crop_pct,
					}




@require_torch
@require_vision
class 						lowerCAmelCase__	(  _lowerCamelCase       ,       unittest.TestCase   ):


	'''simple docstring'''







	_SCREAMING_SNAKE_CASE  :		Optional[Any]     	=   ConvNextImageProcessor if is_vision_available() else None





	def  _lowerCAmelCase    (			self							:					Any						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    ConvNextImageProcessingTester(self						)





	@property
	def  _lowerCAmelCase    (			self							:					Optional[int]						)	->  Dict:





					"""simple docstring"""


					return self.image_processor_tester.prepare_image_processor_dict()





	def  _lowerCAmelCase    (			self							:					str						)	->  int:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Any 							=    self.image_processing_class(**self.image_processor_dict						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_resize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'size'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'crop_pct'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'do_normalize'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_mean'						)						)
					self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE    ,					'image_std'						)						)





	def  _lowerCAmelCase    (			self							:					Union[str, Any]						)	->  Union[str, Any]:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[Any] 							=    self.image_processing_class.from_dict(self.image_processor_dict						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 20}						)

					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class.from_dict(self.image_processor_dict    ,					size=42						)
					self.assertEqual(image_processor.size    ,					{'shortest_edge': 42}						)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					pass





	def  _lowerCAmelCase    (			self							:					Tuple						)	->  Any:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       List[str] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PIL images
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					Image.Image						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       str 							=    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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       str 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





	def  _lowerCAmelCase    (			self							:					int						)	->  str:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random numpy tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					numpify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					np.ndarray						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       int 							=    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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Optional[int] 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)







	def  _lowerCAmelCase    (			self							:					Optional[Any]						)	->  Dict:





					"""simple docstring"""


					SCREAMING_SNAKE_CASE	:       str 							=    self.image_processing_class(**self.image_processor_dict						)
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE	:       Tuple 							=    prepare_image_inputs(self.image_processor_tester    ,					equal_resolution=_SCREAMING_SNAKE_CASE    ,					torchify=_SCREAMING_SNAKE_CASE						)
					for image in image_inputs:
									self.assertIsInstance(_SCREAMING_SNAKE_CASE    ,					torch.Tensor						)

					# Test not batched input
					SCREAMING_SNAKE_CASE	:       Union[str, Any] 							=    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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)

					# Test batched
					SCREAMING_SNAKE_CASE	:       Any 							=    image_processing(_SCREAMING_SNAKE_CASE    ,					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.size['shortest_edge'],
					        self.image_processor_tester.size['shortest_edge'],
					    )    ,					)





