






"""simple docstring"""



import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
						from .tokenization_xlm_roberta import XLMRobertaTokenizer
else:
						__A		:	Any      =		None


__A		:	Any      =		logging.get_logger(__name__)

__A		:	Tuple      =		{'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}

__A		:	str      =		{
    '''vocab_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/sentencepiece.bpe.model''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/sentencepiece.bpe.model'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/sentencepiece.bpe.model'''
        ),
    },
    '''tokenizer_file''': {
        '''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/tokenizer.json''',
        '''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/tokenizer.json''',
        '''xlm-roberta-large-finetuned-conll02-dutch''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll02-spanish''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-english''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/tokenizer.json'''
        ),
        '''xlm-roberta-large-finetuned-conll03-german''': (
            '''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/tokenizer.json'''
        ),
    },
}

__A		:	int      =		{
    '''xlm-roberta-base''': 512,
    '''xlm-roberta-large''': 512,
    '''xlm-roberta-large-finetuned-conll02-dutch''': 512,
    '''xlm-roberta-large-finetuned-conll02-spanish''': 512,
    '''xlm-roberta-large-finetuned-conll03-english''': 512,
    '''xlm-roberta-large-finetuned-conll03-german''': 512,
}





class  _UpperCAmelCase	(			UpperCamelCase_							):
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       VOCAB_FILES_NAMES
					SCREAMING_SNAKE_CASE_		:    Optional[Any]							   =       PRETRAINED_VOCAB_FILES_MAP
					SCREAMING_SNAKE_CASE_		:    List[Any]							   =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					SCREAMING_SNAKE_CASE_		:    Union[str, Any]							   =       ['input_ids', 'attention_mask']
					SCREAMING_SNAKE_CASE_		:    Optional[int]							   =       XLMRobertaTokenizer




					def __init__(    self					:						Dict   ,						A					:						Optional[Any]=None   ,						A					:						Union[str, Any]=None   ,						A					:						List[Any]="<s>"   ,						A					:						Union[str, Any]="</s>"   ,						A					:						List[str]="</s>"   ,						A					:						str="<s>"   ,						A					:						Union[str, Any]="<unk>"   ,						A					:						str="<pad>"   ,						A					:						List[str]="<mask>"   ,						**A					:						Tuple   ,						)  ->  str:
							lowercase_			:			Dict								=       AddedToken(lowercase_   ,						lstrip=lowercase_   ,						rstrip=lowercase_       ) if isinstance(lowercase_   ,						lowercase_       ) else mask_token

							super().__init__(
							    lowercase_   ,						tokenizer_file=lowercase_   ,						bos_token=lowercase_   ,						eos_token=lowercase_   ,						sep_token=lowercase_   ,						cls_token=lowercase_   ,						unk_token=lowercase_   ,						pad_token=lowercase_   ,						mask_token=lowercase_   ,						**lowercase_   ,						)

							lowercase_			:			int								=       vocab_file
							lowercase_			:			Any								=       False if not self.vocab_file else True




					def   A  (    self					:						Optional[int]   ,						A					:						List[int]   ,						A					:						Optional[List[int]] = None       )  ->  List[int]:




							if token_ids_a is None:
									return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
							lowercase_			:			str								=       [self.cls_token_id]
							lowercase_			:			Any								=       [self.sep_token_id]
							return cls + token_ids_a + sep + sep + token_ids_a + sep




					def   A  (    self					:						str   ,						A					:						List[int]   ,						A					:						Optional[List[int]] = None       )  ->  List[int]:
							lowercase_			:			int								=       [self.sep_token_id]
							lowercase_			:			Union[str, Any]								=       [self.cls_token_id]

							if token_ids_a is None:
									return len(cls + token_ids_a + sep       ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]






					def   A  (    self					:						Optional[int]   ,						A					:						str   ,						A					:						Optional[str] = None       )  ->  Tuple[str]:




							if not self.can_save_slow_tokenizer:
									raise ValueError(
									    '''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
									    '''tokenizer.'''       )

							if not os.path.isdir(lowercase_       ):
									logger.error(F'''Vocabulary path ({save_directory}) should be a directory.'''       )
									return
							lowercase_			:			Optional[Any]								=       os.path.join(
							    lowercase_   ,						(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )

							if os.path.abspath(self.vocab_file       ) != os.path.abspath(lowercase_       ):
									copyfile(self.vocab_file   ,						lowercase_       )

							return (out_vocab_file,)











"""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		:	List[str]      =		logging.get_logger()





@dataclass
class  _UpperCAmelCase	:
					SCREAMING_SNAKE_CASE_		:    nn.Module
					SCREAMING_SNAKE_CASE_		:    List[nn.Module]							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    list							   =       field(default_factory=_A							)
					SCREAMING_SNAKE_CASE_		:    Dict[str, nn.Module]							   =       field(default_factory=_A							)




					def   A  (    self					:						Tuple   ,						A					:						List[str]   ,						A					:						Tensor   ,						A					:						Tensor   ,						A					:						str       )  ->  Optional[int]:
							lowercase_			:			Optional[int]								=       len(list(m.modules()       )       ) == 1 or isinstance(A   ,						nn.Convad       ) or isinstance(A   ,						nn.BatchNormad       )
							if has_not_submodules:
									self.traced.append(A       )
									lowercase_			:			Tuple								=       m




					def __call__(    self					:						Optional[int]   ,						A					:						Tensor       )  ->  Tuple:
							for name, m in self.module.named_modules():
									self.handles.append(m.register_forward_hook(partial(self._forward_hook   ,						name=A       )       )       )
							self.module(A       )
							[x.remove() for x in self.handles]
							return self






					@property
					def   A  (    self					:						int       )  ->  str:
							# check the len of the state_dict keys to see if we have learnable params
							return {k: v for k, v in self.nameamodule.items() if len(list(v.state_dict().keys()       )       ) > 0}





class  _UpperCAmelCase	(			nn.Module							):




					def __init__(    self					:						Optional[int]   ,						A					:						nn.Module       )  ->  Dict:
							super().__init__()

							lowercase_			:			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}'''
									lowercase_			:			Dict								=       len(A       ) + 1
									feature_blocks.append((F'''res{block_index}''', v)       )

							lowercase_			:			int								=       nn.ModuleDict(A       )






					def   A  (    self					:						Optional[int]   ,						A					:						Tensor       )  ->  List[Any]:
							return get_trunk_forward_outputs(
							    A   ,						out_feat_keys=A   ,						feature_blocks=self._feature_blocks   ,						)





class  _UpperCAmelCase	(			_A							):




					def   A  (    self					:						Tuple       )  ->  Tuple:
							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 						lowercase   (    __snake_case    :			str     ):
		lowercase_			:			Dict								=       RegNetConfig(depths=[2, 7, 1_7, 1]		, hidden_sizes=[8, 8, 8, 8]		, groups_width=8     )
		if "in1k" in model_name:
				lowercase_			:			List[str]								=       RegNetForImageClassification(__snake_case     )
		else:
				lowercase_			:			Union[str, Any]								=       RegNetModel(__snake_case     )
		# create from model (with small weights)
		lowercase_			:			Dict								=       FakeRegNetVisslWrapper(
		    RegNet(FakeRegNetParams(depth=2_7		, group_width=1_0_1_0		, w_a=1_7_4_4		, w_a=620.83		, w_m=2.52     )     )     )

		with torch.no_grad():
				lowercase_			:			Tuple								=       from_model.eval()
				lowercase_			:			int								=       our_model.eval()

				lowercase_			:			List[Any]								=       torch.randn((1, 3, 3_2, 3_2)     )
				# trace both
				lowercase_			:			Optional[int]								=       Tracker(__snake_case     )
				lowercase_			:			List[Any]								=       dest_tracker(__snake_case     ).parametrized

				pprint(dest_tracker.nameamodule     )
				lowercase_			:			str								=       Tracker(__snake_case     )
				lowercase_			:			str								=       src_tracker(__snake_case     ).parametrized

		# convert the keys -> module dict to keys -> params
		def to_params_dict(__snake_case    :			Dict     ):
				lowercase_			:			Optional[int]								=       OrderedDict()
				for name, module in dict_with_modules.items():
						for param_name, param in module.state_dict().items():
								lowercase_			:			int								=       param
				return params_dict

		lowercase_			:			Tuple								=       {}

		lowercase_			:			Optional[Any]								=       to_params_dict(__snake_case     )
		lowercase_			:			Dict								=       to_params_dict(__snake_case     )

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

		return from_to_ours_keys



def 						lowercase   (    __snake_case    :			Path		, __snake_case    :			str = None		, __snake_case    :			bool = True     ):
		lowercase_			:			Optional[Any]								=       '''imagenet-1k-id2label.json'''
		lowercase_			:			str								=       1_0_0_0

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

		lowercase_			:			Union[str, Any]								=       idalabel
		lowercase_			:			Tuple								=       {v: k for k, v in idalabel.items()}

		lowercase_			:			Optional[Any]								=       partial(__snake_case		, num_labels=__snake_case		, idalabel=__snake_case		, labelaid=__snake_case     )

		lowercase_			:			int								=       {
		    '''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    :			str     ) -> Tuple[Dict, Dict]:
				lowercase_			:			str								=       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
				lowercase_			:			Any								=       files['''classy_state_dict''']['''base_model''']['''model''']
				return model_state_dict["trunk"], model_state_dict["heads"]

		lowercase_			:			int								=       {
		    '''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'''		, ),
		}

		lowercase_			:			List[Any]								=       get_from_to_our_keys(__snake_case     )

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

				lowercase_			:			List[Any]								=       {}

				lowercase_			:			int								=       list(from_state_dict.keys()     )
				lowercase_			:			int								=       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
						lowercase_			:			List[str]								=       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
						lowercase_			:			Dict								=       from_to_ours_keys[src_key]
						# store the parameter with our key
						lowercase_			:			int								=       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
				lowercase_			:			List[Any]								=       names_to_config[model_name]
				lowercase_			:			Union[str, Any]								=       RegNetModel
				if "in1k" in model_name:
						lowercase_			:			Optional[int]								=       RegNetForImageClassification
				lowercase_			:			Union[str, Any]								=       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
				lowercase_			:			Tuple								=       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		, )
				lowercase_			:			Union[str, Any]								=       3_8_4
				# we can use the convnext one
				lowercase_			:			int								=       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__":
						__A		:	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 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		:	Tuple      =		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)



