

import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
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.utils import logging


logging.set_verbosity_info()
_UpperCAmelCase						:  str									=			logging.get_logger()







@dataclass
class 					lowercase							:
				__SCREAMING_SNAKE_CASE   :					nn.Module
				__SCREAMING_SNAKE_CASE   :					List[nn.Module]			     =     field(default_factory=lowercase_     )
				__SCREAMING_SNAKE_CASE   :					list			     =     field(default_factory=lowercase_     )




				def        a				(   self ,  snake_case ,  snake_case ,  snake_case			):
											snake_case_     							=       len(list(m.modules()			)			) == 1 or isinstance(__lowerCAmelCase ,  nn.Convad			) or isinstance(__lowerCAmelCase ,  nn.BatchNormad			)
											if has_not_submodules:
																		self.traced.append(__lowerCAmelCase			)




				def __call__(   self ,  snake_case			):



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




				@property
				def        a				(   self			):
											return list(filter(lambda snake_case			: len(list(x.state_dict().keys()			)			) > 0 ,  self.traced			)			)







@dataclass
class 					lowercase							:
				__SCREAMING_SNAKE_CASE   :					nn.Module
				__SCREAMING_SNAKE_CASE   :					nn.Module
				__SCREAMING_SNAKE_CASE   :					int			     =     1
				__SCREAMING_SNAKE_CASE   :					List			     =     field(default_factory=lowercase_     )
				__SCREAMING_SNAKE_CASE   :					List			     =     field(default_factory=lowercase_     )
				__SCREAMING_SNAKE_CASE   :					bool			     =     True




				def __call__(   self ,  snake_case			):
											snake_case_     							=       Tracker(self.dest			)(__lowerCAmelCase			).parametrized
											snake_case_     							=       Tracker(self.src			)(__lowerCAmelCase			).parametrized

											snake_case_     							=       list(filter(lambda snake_case			: type(__lowerCAmelCase			) not in self.src_skip ,  __lowerCAmelCase			)			)
											snake_case_     							=       list(filter(lambda snake_case			: type(__lowerCAmelCase			) not in self.dest_skip ,  __lowerCAmelCase			)			)

											if len(__lowerCAmelCase			) != len(__lowerCAmelCase			) and self.raise_if_mismatch:
																		raise Exception(
																		    F'''Numbers of operations are different. Source module has {len(__lowerCAmelCase			)} operations while'''
																		    F''' destination module has {len(__lowerCAmelCase			)}.'''			)

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







class 					lowercase							(			nn.Module     ):




				def __init__(   self ,  snake_case			):
											super().__init__()

											snake_case_     							=       []
											# - 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}'''
																		snake_case_     							=       len(__lowerCAmelCase			) + 1
																		feature_blocks.append((F'''res{block_index}''', v)			)

											snake_case_     							=       nn.ModuleDict(__lowerCAmelCase			)




				def        a				(   self ,  snake_case			):
											return get_trunk_forward_outputs(
											    __lowerCAmelCase ,  out_feat_keys=__lowerCAmelCase ,  feature_blocks=self._feature_blocks ,  )







class 					lowercase							(			lowercase_     ):




				def        a				(   self ,  snake_case			):
											snake_case_     							=       x.split('-'			)
											return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]			)




				def __getitem__(   self ,  snake_case			):



											if x not in self:
																		snake_case_     							=       self.convert_name_to_timm(__lowerCAmelCase			)
																		snake_case_     							=       partial(lambda: (timm.create_model(__lowerCAmelCase ,  pretrained=__lowerCAmelCase			).eval(), None)			)

											else:
																		snake_case_     							=       super().__getitem__(__lowerCAmelCase			)

											return val







class 					lowercase							(			lowercase_     ):




				def __getitem__(   self ,  snake_case			):



											if "seer" in x and "in1k" not in x:
																		snake_case_     							=       RegNetModel
											else:
																		snake_case_     							=       RegNetForImageClassification
											return val





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ ):







							'''simple docstring'''






							for from_key, to_key in keys:
														snake_case_     							=       from_state_dict[from_key].clone()
														print(F'''Copied key={from_key} to={to_key}''' )
							return to_state_dict





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__  ,				UpperCamelCase__ = True  ,				):







							'''simple docstring'''






							print(F'''Converting {name}...''' )
							with torch.no_grad():
														snake_case_							,       snake_case_     							=       from_model_func()
														snake_case_     							=       our_model_func(lowerCAmelCase__ ).eval()
														snake_case_     							=       ModuleTransfer(src=lowerCAmelCase__  ,				dest=lowerCAmelCase__  ,				raise_if_mismatch=lowerCAmelCase__ )
														snake_case_     							=       torch.randn((1, 3, 224, 224) )
														module_transfer(lowerCAmelCase__ )

							if from_state_dict is not None:
														snake_case_     							=       []
														# for seer - in1k finetuned we have to manually copy the head
														if "seer" in name and "in1k" in name:
																					snake_case_     							=       [('0.clf.0.weight', 'classifier.1.weight'), ('0.clf.0.bias', 'classifier.1.bias')]
														snake_case_     							=       manually_copy_vissl_head(lowerCAmelCase__  ,				our_model.state_dict()  ,				lowerCAmelCase__ )
														our_model.load_state_dict(lowerCAmelCase__ )

							snake_case_     							=       our_model(lowerCAmelCase__  ,				output_hidden_states=lowerCAmelCase__ )
							snake_case_     							=       (
							    our_outputs.logits if isinstance(lowerCAmelCase__  ,				lowerCAmelCase__ ) else our_outputs.last_hidden_state
							)

							snake_case_     							=       from_model(lowerCAmelCase__ )
							snake_case_     							=       from_output[-1] if type(lowerCAmelCase__ ) is list else from_output

							# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
							if "seer" in name and "in1k" in name:
														snake_case_     							=       our_outputs.hidden_states[-1]

							assert torch.allclose(lowerCAmelCase__  ,				lowerCAmelCase__ ), "The model logits don't match the original one."

							if push_to_hub:
														our_model.push_to_hub(
														    repo_path_or_name=save_directory / name  ,				commit_message='Add model'  ,				use_temp_dir=lowerCAmelCase__  ,				)

														snake_case_     							=       224 if 'seer' not in name else 384
														# we can use the convnext one
														snake_case_     							=       AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k'  ,				size=lowerCAmelCase__ )
														image_processor.push_to_hub(
														    repo_path_or_name=save_directory / name  ,				commit_message='Add image processor'  ,				use_temp_dir=lowerCAmelCase__  ,				)

														print(F'''Pushed {name}''' )





def    __lowerCamelCase      (					UpperCamelCase__  ,				UpperCamelCase__ = None  ,				UpperCamelCase__ = True ):







							'''simple docstring'''






							snake_case_     							=       'imagenet-1k-id2label.json'
							snake_case_     							=       1000
							snake_case_     							=       (1, num_labels)

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

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

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

							snake_case_     							=       {
							    'regnet-x-002': ImageNetPreTrainedConfig(
							        depths=[1, 1, 4, 7]  ,				hidden_sizes=[24, 56, 152, 368]  ,				groups_width=8  ,				layer_type='x' ),
							    'regnet-x-004': ImageNetPreTrainedConfig(
							        depths=[1, 2, 7, 12]  ,				hidden_sizes=[32, 64, 160, 384]  ,				groups_width=16  ,				layer_type='x' ),
							    'regnet-x-006': ImageNetPreTrainedConfig(
							        depths=[1, 3, 5, 7]  ,				hidden_sizes=[48, 96, 240, 528]  ,				groups_width=24  ,				layer_type='x' ),
							    'regnet-x-008': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 5]  ,				hidden_sizes=[64, 128, 288, 672]  ,				groups_width=16  ,				layer_type='x' ),
							    'regnet-x-016': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 2]  ,				hidden_sizes=[72, 168, 408, 912]  ,				groups_width=24  ,				layer_type='x' ),
							    'regnet-x-032': ImageNetPreTrainedConfig(
							        depths=[2, 6, 15, 2]  ,				hidden_sizes=[96, 192, 432, 1008]  ,				groups_width=48  ,				layer_type='x' ),
							    'regnet-x-040': ImageNetPreTrainedConfig(
							        depths=[2, 5, 14, 2]  ,				hidden_sizes=[80, 240, 560, 1360]  ,				groups_width=40  ,				layer_type='x' ),
							    'regnet-x-064': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]  ,				hidden_sizes=[168, 392, 784, 1624]  ,				groups_width=56  ,				layer_type='x' ),
							    'regnet-x-080': ImageNetPreTrainedConfig(
							        depths=[2, 5, 15, 1]  ,				hidden_sizes=[80, 240, 720, 1920]  ,				groups_width=120  ,				layer_type='x' ),
							    'regnet-x-120': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]  ,				hidden_sizes=[224, 448, 896, 2240]  ,				groups_width=112  ,				layer_type='x' ),
							    'regnet-x-160': ImageNetPreTrainedConfig(
							        depths=[2, 6, 13, 1]  ,				hidden_sizes=[256, 512, 896, 2048]  ,				groups_width=128  ,				layer_type='x' ),
							    'regnet-x-320': ImageNetPreTrainedConfig(
							        depths=[2, 7, 13, 1]  ,				hidden_sizes=[336, 672, 1344, 2520]  ,				groups_width=168  ,				layer_type='x' ),
							    # y variant
							    'regnet-y-002': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]  ,				hidden_sizes=[24, 56, 152, 368]  ,				groups_width=8 ),
							    'regnet-y-004': ImageNetPreTrainedConfig(
							        depths=[1, 3, 6, 6]  ,				hidden_sizes=[48, 104, 208, 440]  ,				groups_width=8 ),
							    'regnet-y-006': ImageNetPreTrainedConfig(
							        depths=[1, 3, 7, 4]  ,				hidden_sizes=[48, 112, 256, 608]  ,				groups_width=16 ),
							    'regnet-y-008': ImageNetPreTrainedConfig(
							        depths=[1, 3, 8, 2]  ,				hidden_sizes=[64, 128, 320, 768]  ,				groups_width=16 ),
							    'regnet-y-016': ImageNetPreTrainedConfig(
							        depths=[2, 6, 17, 2]  ,				hidden_sizes=[48, 120, 336, 888]  ,				groups_width=24 ),
							    'regnet-y-032': ImageNetPreTrainedConfig(
							        depths=[2, 5, 13, 1]  ,				hidden_sizes=[72, 216, 576, 1512]  ,				groups_width=24 ),
							    'regnet-y-040': ImageNetPreTrainedConfig(
							        depths=[2, 6, 12, 2]  ,				hidden_sizes=[128, 192, 512, 1088]  ,				groups_width=64 ),
							    'regnet-y-064': ImageNetPreTrainedConfig(
							        depths=[2, 7, 14, 2]  ,				hidden_sizes=[144, 288, 576, 1296]  ,				groups_width=72 ),
							    'regnet-y-080': ImageNetPreTrainedConfig(
							        depths=[2, 4, 10, 1]  ,				hidden_sizes=[168, 448, 896, 2016]  ,				groups_width=56 ),
							    'regnet-y-120': ImageNetPreTrainedConfig(
							        depths=[2, 5, 11, 1]  ,				hidden_sizes=[224, 448, 896, 2240]  ,				groups_width=112 ),
							    'regnet-y-160': ImageNetPreTrainedConfig(
							        depths=[2, 4, 11, 1]  ,				hidden_sizes=[224, 448, 1232, 3024]  ,				groups_width=112 ),
							    'regnet-y-320': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]  ,				hidden_sizes=[232, 696, 1392, 3712]  ,				groups_width=232 ),
							    # models created by SEER -> https://arxiv.org/abs/2202.08360
							    'regnet-y-320-seer': RegNetConfig(depths=[2, 5, 12, 1]  ,				hidden_sizes=[232, 696, 1392, 3712]  ,				groups_width=232 ),
							    'regnet-y-640-seer': RegNetConfig(depths=[2, 5, 12, 1]  ,				hidden_sizes=[328, 984, 1968, 4920]  ,				groups_width=328 ),
							    'regnet-y-1280-seer': RegNetConfig(
							        depths=[2, 7, 17, 1]  ,				hidden_sizes=[528, 1056, 2904, 7392]  ,				groups_width=264 ),
							    'regnet-y-2560-seer': RegNetConfig(
							        depths=[3, 7, 16, 1]  ,				hidden_sizes=[640, 1696, 2544, 5088]  ,				groups_width=640 ),
							    'regnet-y-10b-seer': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]  ,				hidden_sizes=[2020, 4040, 11110, 28280]  ,				groups_width=1010 ),
							    # finetuned on imagenet
							    'regnet-y-320-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]  ,				hidden_sizes=[232, 696, 1392, 3712]  ,				groups_width=232 ),
							    'regnet-y-640-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 5, 12, 1]  ,				hidden_sizes=[328, 984, 1968, 4920]  ,				groups_width=328 ),
							    'regnet-y-1280-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]  ,				hidden_sizes=[528, 1056, 2904, 7392]  ,				groups_width=264 ),
							    'regnet-y-2560-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[3, 7, 16, 1]  ,				hidden_sizes=[640, 1696, 2544, 5088]  ,				groups_width=640 ),
							    'regnet-y-10b-seer-in1k': ImageNetPreTrainedConfig(
							        depths=[2, 7, 17, 1]  ,				hidden_sizes=[2020, 4040, 11110, 28280]  ,				groups_width=1010 ),
							}

							snake_case_     							=       NameToOurModelFuncMap()
							snake_case_     							=       NameToFromModelFuncMap()
							# add seer weights logic

							def load_using_classy_vision(UpperCamelCase__  ,				UpperCamelCase__ ) -> Tuple[nn.Module, Dict]:
														snake_case_     							=       torch.hub.load_state_dict_from_url(lowerCAmelCase__  ,				model_dir=str(lowerCAmelCase__ )  ,				map_location='cpu' )
														snake_case_     							=       model_func()
														# check if we have a head, if yes add it
														snake_case_     							=       files['classy_state_dict']['base_model']['model']
														snake_case_     							=       model_state_dict['trunk']
														model.load_state_dict(lowerCAmelCase__ )
														return model.eval(), model_state_dict["heads"]

							# pretrained
							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'  ,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )  ,				)

							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'  ,				lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27  ,				group_width=1010  ,				w_a=1744  ,				w_a=6_20.83  ,				w_m=2.52 ) ) )  ,				)

							# IN1K finetuned
							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'  ,				lambda: FakeRegNetVisslWrapper(RegNetYaagf() )  ,				)

							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'  ,				lambda: FakeRegNetVisslWrapper(RegNetYaaagf() )  ,				)

							snake_case_     							=       partial(
							    lowerCAmelCase__  ,				'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'  ,				lambda: FakeRegNetVisslWrapper(
							        RegNet(RegNetParams(depth=27  ,				group_width=1010  ,				w_a=1744  ,				w_a=6_20.83  ,				w_m=2.52 ) ) )  ,				)

							if model_name:
														convert_weight_and_push(
														    lowerCAmelCase__  ,				names_to_from_model_map[model_name]  ,				names_to_ours_model_map[model_name]  ,				names_to_config[model_name]  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				)
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(
																					    lowerCAmelCase__  ,				names_to_from_model_map[model_name]  ,				names_to_ours_model_map[model_name]  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				lowerCAmelCase__  ,				)
							return config, expected_shape


if __name__ == "__main__":
					_UpperCAmelCase						:  Union[str, 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						:  Optional[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)





"""simple docstring"""






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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


__lowerCAmelCase       :			List[str]   	=logging.get_logger(__name__)

__lowerCAmelCase       :			str   	={"""vocab_file""": """sentencepiece.bpe.model"""}

__lowerCAmelCase       :			Dict   	={
    """vocab_file""": {
        """moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
        """moussaKam/barthez-orangesum-title""": (
            """https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
        ),
    },
}

__lowerCAmelCase       :			Tuple   	={
    """moussaKam/mbarthez""": 1_0_2_4,
    """moussaKam/barthez""": 1_0_2_4,
    """moussaKam/barthez-orangesum-title""": 1_0_2_4,
}

__lowerCAmelCase       :			Optional[int]   	="""▁"""





class 					_A		(			lowerCAmelCase      ):
      snake_case__     :  Tuple				    =      VOCAB_FILES_NAMES
      snake_case__     :  Optional[Any]				    =      PRETRAINED_VOCAB_FILES_MAP
      snake_case__     :  Dict				    =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      snake_case__     :  Optional[Any]				    =      ['input_ids', 'attention_mask']

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="</s>"						,			__lowerCAmelCase="<s>"						,			__lowerCAmelCase="<unk>"						,			__lowerCAmelCase="<pad>"						,			__lowerCAmelCase="<mask>"						,			__lowerCAmelCase = None						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            lowercase								=      AddedToken(__lowerCAmelCase						,			lstrip=__lowerCAmelCase						,			rstrip=__lowerCAmelCase		) if isinstance(__lowerCAmelCase						,			__lowerCAmelCase		) else mask_token

            lowercase								=      {} if sp_model_kwargs is None else sp_model_kwargs

            super().__init__(
                bos_token=__lowerCAmelCase						,			eos_token=__lowerCAmelCase						,			unk_token=__lowerCAmelCase						,			sep_token=__lowerCAmelCase						,			cls_token=__lowerCAmelCase						,			pad_token=__lowerCAmelCase						,			mask_token=__lowerCAmelCase						,			sp_model_kwargs=self.sp_model_kwargs						,			**__lowerCAmelCase						,			)

            lowercase								=      vocab_file
            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(str(__lowerCAmelCase		)		)

            lowercase								=      {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

            lowercase								=      len(self.sp_model		) - 1
            lowercase								=      {v: k for k, v in self.fairseq_tokens_to_ids.items()}

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



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

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = False		):







            """simple docstring"""



            if already_has_special_tokens:
                  return super().get_special_tokens_mask(
                      token_ids_a=__lowerCAmelCase						,			token_ids_a=__lowerCAmelCase						,			already_has_special_tokens=__lowerCAmelCase		)

            if token_ids_a is None:
                  return [1] + ([0] * len(__lowerCAmelCase		)) + [1]
            return [1] + ([0] * len(__lowerCAmelCase		)) + [1, 1] + ([0] * len(__lowerCAmelCase		)) + [1]

      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



            lowercase								=      [self.sep_token_id]
            lowercase								=      [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]

      @property
      def 							A__			(		self		):







            """simple docstring"""



            return len(self.sp_model		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      {self.convert_ids_to_tokens(__lowerCAmelCase		): i for i in range(self.vocab_size		)}
            vocab.update(self.added_tokens_encoder		)
            return vocab

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            return self.sp_model.encode(__lowerCAmelCase						,			out_type=__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if token in self.fairseq_tokens_to_ids:
                  return self.fairseq_tokens_to_ids[token]
            lowercase								=      self.sp_model.PieceToId(__lowerCAmelCase		)

            return spm_id if spm_id else self.unk_token_id

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if index in self.fairseq_ids_to_tokens:
                  return self.fairseq_ids_to_tokens[index]
            return self.sp_model.IdToPiece(__lowerCAmelCase		)

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      []
            lowercase								=      """"""
            lowercase								=      False
            for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                        if not prev_is_special:
                              out_string += " "
                        out_string += self.sp_model.decode(__lowerCAmelCase		) + token
                        lowercase								=      True
                        lowercase								=      []
                  else:
                        current_sub_tokens.append(__lowerCAmelCase		)
                        lowercase								=      False
            out_string += self.sp_model.decode(__lowerCAmelCase		)
            return out_string.strip()

      def __getstate__(		self		):







            """simple docstring"""



            lowercase								=      self.__dict__.copy()
            lowercase								=      None
            return state

      def __setstate__(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      d

            # for backward compatibility
            if not hasattr(self						,			"""sp_model_kwargs"""		):
                  lowercase								=      {}

            lowercase								=      spm.SentencePieceProcessor(**self.sp_model_kwargs		)
            self.sp_model.Load(self.vocab_file		)







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None		):







            """simple docstring"""



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

            if os.path.abspath(self.vocab_file		) != os.path.abspath(__lowerCAmelCase		) and os.path.isfile(self.vocab_file		):
                  copyfile(self.vocab_file						,			__lowerCAmelCase		)
            elif not os.path.isfile(self.vocab_file		):
                  with open(__lowerCAmelCase						,			"""wb"""		) as fi:
                        lowercase								=      self.sp_model.serialized_model_proto()
                        fi.write(__lowerCAmelCase		)

            return (out_vocab_file,)



