


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   :    Dict     =		logging.get_logger()







@dataclass
class        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					nn.Module
		UpperCamelCase     :					List[nn.Module]			       =		field(default_factory=a_  )
		UpperCamelCase     :					list			       =		field(default_factory=a_  )







		def 					__A   ( self		,      A		,      A		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  len(list(m.modules()	)	) == 1 or isinstance(A		,      nn.Convad	) or isinstance(A		,      nn.BatchNormad	)
					if has_not_submodules:
								self.traced.append(A	)







		def __call__( self		,      A	)  -> Optional[int]:







					'''simple docstring'''







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







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







					'''simple docstring'''







					return list(filter(lambda A	: len(list(x.state_dict().keys()	)	) > 0		,      self.traced	)	)







@dataclass
class        __lowercase       :



		"""simple docstring"""

		UpperCamelCase     :					nn.Module
		UpperCamelCase     :					nn.Module
		UpperCamelCase     :					int			       =		1
		UpperCamelCase     :					List			       =		field(default_factory=a_  )
		UpperCamelCase     :					List			       =		field(default_factory=a_  )
		UpperCamelCase     :					bool			       =		True







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







					'''simple docstring'''







					lowerCamelCase    		=  Tracker(self.dest	)(A	).parametrized
					lowerCamelCase    		=  Tracker(self.src	)(A	).parametrized

					lowerCamelCase    		=  list(filter(lambda A	: type(A	) not in self.src_skip		,      A	)	)
					lowerCamelCase    		=  list(filter(lambda A	: type(A	) not in self.dest_skip		,      A	)	)

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

					for dest_m, src_m in zip(A		,      A	):
								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  ):



		"""simple docstring"""

		def __init__( self		,      A	)  -> Tuple:







					'''simple docstring'''







					super().__init__()

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

					lowerCamelCase    		=  nn.ModuleDict(A	)







		def 					__A   ( self		,      A	)  -> List[str]:







					'''simple docstring'''







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







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def 					__A   ( self		,      A	)  -> str:







					'''simple docstring'''







					lowerCamelCase    		=  x.split("""-"""	)
					return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]	)







		def __getitem__( self		,      A	)  -> Callable[[], Tuple[nn.Module, Dict]]:







					'''simple docstring'''







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

					else:
								lowerCamelCase    		=  super().__getitem__(A	)

					return val







class        __lowercase       (							a_  ):



		"""simple docstring"""

		def __getitem__( self		,      A	)  -> Callable[[], nn.Module]:







					'''simple docstring'''







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






def 	__lowerCamelCase						(							lowerCamelCase__		:						List[Any]  ,							lowerCamelCase__		:						Optional[Any]  ,							lowerCamelCase__		:						List[Tuple[str, str]]      ):





			'''simple docstring'''




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






def 	__lowerCamelCase						(							lowerCamelCase__		:						str  ,							lowerCamelCase__		:						Callable[[], nn.Module]  ,							lowerCamelCase__		:						Callable[[], nn.Module]  ,							lowerCamelCase__		:						RegNetConfig  ,							lowerCamelCase__		:						Path  ,							lowerCamelCase__		:						bool = True  ,							):





			'''simple docstring'''




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

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

			lowerCamelCase    		=  our_model(lowerCamelCase__  ,							output_hidden_states=lowerCamelCase__      )
			lowerCamelCase    		=  (
			    our_outputs.logits if isinstance(lowerCamelCase__  ,							lowerCamelCase__      ) else our_outputs.last_hidden_state
			)

			lowerCamelCase    		=  from_model(lowerCamelCase__      )
			lowerCamelCase    		=  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:
						lowerCamelCase    		=  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__  ,							)

						lowerCamelCase    		=  224 if """seer""" not in name else 384
						# we can use the convnext one
						lowerCamelCase    		=  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						(							lowerCamelCase__		:						Path  ,							lowerCamelCase__		:						str = None  ,							lowerCamelCase__		:						bool = True      ):





			'''simple docstring'''




			lowerCamelCase    		=  """imagenet-1k-id2label.json"""
			lowerCamelCase    		=  1000
			lowerCamelCase    		=  (1, num_labels)

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

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

			lowerCamelCase    		=  partial(lowerCamelCase__  ,							num_labels=lowerCamelCase__  ,							idalabel=lowerCamelCase__  ,							labelaid=lowerCamelCase__      )

			lowerCamelCase    		=  {
			    """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      ),
			}

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

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

			# pretrained
			lowerCamelCase    		=  partial(
			    lowerCamelCase__  ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch"""  ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )  ,							)

			lowerCamelCase    		=  partial(
			    lowerCamelCase__  ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch"""  ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )  ,							)

			lowerCamelCase    		=  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()      )  ,							)

			lowerCamelCase    		=  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_2_0.8_3  ,							w_m=2.5_2      )      )      )  ,							)

			# IN1K finetuned
			lowerCamelCase    		=  partial(
			    lowerCamelCase__  ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )  ,							)

			lowerCamelCase    		=  partial(
			    lowerCamelCase__  ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()      )  ,							)

			lowerCamelCase    		=  partial(
			    lowerCamelCase__  ,							"""https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch"""  ,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()      )  ,							)

			lowerCamelCase    		=  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_2_0.8_3  ,							w_m=2.5_2      )      )      )  ,							)

			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   :    List[str]     =		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   :    int     =		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
from shutil import copyfile
from typing import List, Optional, Tuple

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


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCAmelCase   :    Union[str, Any]     =		None


UpperCAmelCase   :    Any     =		logging.get_logger(__name__)


UpperCAmelCase   :    Optional[Any]     =		"▁"

UpperCAmelCase   :    str     =		{"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}

UpperCAmelCase   :    Dict     =		{
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"},
    "tokenizer_file": {
        "google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json"
    },
}

UpperCAmelCase   :    List[Any]     =		{
    "google/pegasus-xsum": 5_12,
}







class        __lowercase       (							a_  ):



		"""simple docstring"""

		UpperCamelCase     :					str			       =		VOCAB_FILES_NAMES
		UpperCamelCase     :					int			       =		PRETRAINED_VOCAB_FILES_MAP
		UpperCamelCase     :					List[Any]			       =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
		UpperCamelCase     :					Any			       =		PegasusTokenizer
		UpperCamelCase     :					Optional[Any]			       =		["input_ids", "attention_mask"]







		def __init__( self		,      A=None		,      A=None		,      A="<pad>"		,      A="</s>"		,      A="<unk>"		,      A="<mask_2>"		,      A="<mask_1>"		,      A=None		,      A=1_03		,      **A		,      )  -> Any:







					'''simple docstring'''







					lowerCamelCase    		=  offset

					if additional_special_tokens is not None:
								if not isinstance(A		,      A	):
											raise TypeError(
											    F'additional_special_tokens should be of type {type(A	)}, but is'
											    F' {type(A	)}'	)

								lowerCamelCase    		=  (
								    ([mask_token_sent] + additional_special_tokens)
								    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
								    else additional_special_tokens
								)
								# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
								additional_special_tokens_extended += [
								    F'<unk_{i}>' for i in range(len(A	)		,      self.offset - 1	)
								]

								if len(set(A	)	) != len(A	):
											raise ValueError(
											    """Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
											    F' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'	)
								lowerCamelCase    		=  additional_special_tokens_extended
					else:
								lowerCamelCase    		=  [mask_token_sent] if mask_token_sent is not None else []
								additional_special_tokens += [F'<unk_{i}>' for i in range(2		,      self.offset	)]

					super().__init__(
					    A		,      tokenizer_file=A		,      pad_token=A		,      eos_token=A		,      unk_token=A		,      mask_token=A		,      mask_token_sent=A		,      offset=A		,      additional_special_tokens=A		,      **A		,      )
					lowerCamelCase    		=  vocab_file
					lowerCamelCase    		=  False if not self.vocab_file else True







		def 					__A   ( self		,      A	)  -> Dict:







					'''simple docstring'''







					lowerCamelCase    		=  set(self.all_special_ids	)  # call it once instead of inside list comp
					all_special_ids.remove(self.unk_token_id	)  # <unk> is only sometimes special

					if all_special_ids != set(range(len(self.additional_special_tokens	) + 3	)	):
								raise ValueError(
								    """There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
								    F' {len(self.additional_special_tokens	)} additional_special_tokens, but got {all_special_ids}'	)

					return [1 if x in all_special_ids else 0 for x in seq]







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







					'''simple docstring'''







					if already_has_special_tokens:
								return self._special_token_mask(A	)
					elif token_ids_a is None:
								return self._special_token_mask(A	) + [1]
					else:
								return self._special_token_mask(token_ids_a + token_ids_a	) + [1]







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







					'''simple docstring'''







					if token_ids_a is None:
								return token_ids_a + [self.eos_token_id]
					# We don't expect to process pairs, but leave the pair logic for API consistency
					return token_ids_a + token_ids_a + [self.eos_token_id]







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







					'''simple docstring'''







					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(A	):
								logger.error(F'Vocabulary path ({save_directory}) should be a directory'	)
								return
					lowerCamelCase    		=  os.path.join(
					    A		,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]	)

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

					return (out_vocab_file,)

