


import argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case            =   logging.get_logger(__name__)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Loading config file..."""	)

			def flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		str=""       ,  SCREAMING_SNAKE_CASE_   :		Tuple="."	):
						UpperCamelCase         =					[]
						for k, v in d.items():
									UpperCamelCase         =					parent_key + sep + k if parent_key else k
									if isinstance(SCREAMING_SNAKE_CASE_       ,  collections.abc.MutableMapping	):
												items.extend(flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  sep=SCREAMING_SNAKE_CASE_	).items()	)
									else:
												items.append((new_key, v)	)
						return dict(SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					argparse.Namespace()
			with open(SCREAMING_SNAKE_CASE_       ,  """r"""	) as yaml_file:
						try:
									UpperCamelCase         =					yaml.load(SCREAMING_SNAKE_CASE_       ,  Loader=yaml.FullLoader	)

									UpperCamelCase         =					flatten_yaml_as_dict(SCREAMING_SNAKE_CASE_	)
									for k, v in flat_cfg.items():
												setattr(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
						except yaml.YAMLError as exc:
									logger.error("""Error while loading config file: {}. Error message: {}""".format(SCREAMING_SNAKE_CASE_       ,  str(SCREAMING_SNAKE_CASE_	)	)	)
			return config






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					MobileViTVaConfig()

			UpperCamelCase         =					False

			# dataset
			if task_name.startswith("""imagenet1k_"""	):
						UpperCamelCase         =					1_000
						if int(task_name.strip().split("""_"""	)[-1]	) == 384:
									UpperCamelCase         =					384
						else:
									UpperCamelCase         =					256
						UpperCamelCase         =					"""imagenet-1k-id2label.json"""
			elif task_name.startswith("""imagenet21k_to_1k_"""	):
						UpperCamelCase         =					21_000
						if int(task_name.strip().split("""_"""	)[-1]	) == 384:
									UpperCamelCase         =					384
						else:
									UpperCamelCase         =					256
						UpperCamelCase         =					"""imagenet-22k-id2label.json"""
			elif task_name.startswith("""ade20k_"""	):
						UpperCamelCase         =					151
						UpperCamelCase         =					512
						UpperCamelCase         =					"""ade20k-id2label.json"""
						UpperCamelCase         =					True
			elif task_name.startswith("""voc_"""	):
						UpperCamelCase         =					21
						UpperCamelCase         =					512
						UpperCamelCase         =					"""pascal-voc-id2label.json"""
						UpperCamelCase         =					True

			# orig_config
			UpperCamelCase         =					load_orig_config_file(SCREAMING_SNAKE_CASE_	)
			assert getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.name"""       ,  -1	) == "mobilevit_v2", "Invalid model"
			UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.mitv2.width_multiplier"""       ,  1.0	)
			assert (
			    getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.mitv2.attn_norm_layer"""       ,  -1	) == "layer_norm_2d"
			), "Norm layers other than layer_norm_2d is not supported"
			UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.classification.activation.name"""       ,  """swish"""	)
			# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

			if is_segmentation_model:
						UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.output_stride"""       ,  16	)
						if "_deeplabv3" in task_name:
									UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.deeplabv3.aspp_rates"""       ,  [12, 24, 36]	)
									UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.deeplabv3.aspp_out_channels"""       ,  512	)
									UpperCamelCase         =					getattr(SCREAMING_SNAKE_CASE_       ,  """model.segmentation.deeplabv3.aspp_dropout"""       ,  0.1	)

    # id2label
			UpperCamelCase         =					"""huggingface/label-files"""
			UpperCamelCase         =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  repo_type="""dataset"""	)       ,  """r"""	)	)
			UpperCamelCase         =					{int(SCREAMING_SNAKE_CASE_	): v for k, v in idalabel.items()}
			UpperCamelCase         =					idalabel
			UpperCamelCase         =					{v: k for k, v in idalabel.items()}

			return config






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[str]	):





			"""simple docstring"""




			UpperCamelCase         =					dct.pop(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					val






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Optional[int]=False	):





			"""simple docstring"""




			if base_model:
						UpperCamelCase         =					""""""
			else:
						UpperCamelCase         =					"""mobilevitv2."""

			UpperCamelCase         =					[]
			for k in state_dict.keys():
						if k[:8] == "encoder.":
									UpperCamelCase         =					k[8:]
						else:
									UpperCamelCase         =					k

						if ".block." in k:
									UpperCamelCase         =					k_new.replace(""".block."""       ,  """."""	)
						if ".conv." in k:
									UpperCamelCase         =					k_new.replace(""".conv."""       ,  """.convolution."""	)
						if ".norm." in k:
									UpperCamelCase         =					k_new.replace(""".norm."""       ,  """.normalization."""	)

						if "conv_1." in k:
									UpperCamelCase         =					k_new.replace("""conv_1."""       ,  f'{model_prefix}conv_stem.'	)
						for i in [1, 2]:
									if f'layer_{i}.' in k:
												UpperCamelCase         =					k_new.replace(f'layer_{i}.'       ,  f'{model_prefix}encoder.layer.{i-1}.layer.'	)
						if ".exp_1x1." in k:
									UpperCamelCase         =					k_new.replace(""".exp_1x1."""       ,  """.expand_1x1."""	)
						if ".red_1x1." in k:
									UpperCamelCase         =					k_new.replace(""".red_1x1."""       ,  """.reduce_1x1."""	)

						for i in [3, 4, 5]:
									if f'layer_{i}.0.' in k:
												UpperCamelCase         =					k_new.replace(f'layer_{i}.0.'       ,  f'{model_prefix}encoder.layer.{i-1}.downsampling_layer.'	)
									if f'layer_{i}.1.local_rep.0.' in k:
												UpperCamelCase         =					k_new.replace(f'layer_{i}.1.local_rep.0.'       ,  f'{model_prefix}encoder.layer.{i-1}.conv_kxk.'	)
									if f'layer_{i}.1.local_rep.1.' in k:
												UpperCamelCase         =					k_new.replace(f'layer_{i}.1.local_rep.1.'       ,  f'{model_prefix}encoder.layer.{i-1}.conv_1x1.'	)

						for i in [3, 4, 5]:
									if i == 3:
												UpperCamelCase         =					[0, 1]
									elif i == 4:
												UpperCamelCase         =					[0, 1, 2, 3]
									elif i == 5:
												UpperCamelCase         =					[0, 1, 2]

									for j in j_in:
												if f'layer_{i}.1.global_rep.{j}.' in k:
															UpperCamelCase         =					k_new.replace(
															    f'layer_{i}.1.global_rep.{j}.'       ,  f'{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'	)
									if f'layer_{i}.1.global_rep.{j+1}.' in k:
												UpperCamelCase         =					k_new.replace(
												    f'layer_{i}.1.global_rep.{j+1}.'       ,  f'{model_prefix}encoder.layer.{i-1}.layernorm.'	)

									if f'layer_{i}.1.conv_proj.' in k:
												UpperCamelCase         =					k_new.replace(f'layer_{i}.1.conv_proj.'       ,  f'{model_prefix}encoder.layer.{i-1}.conv_projection.'	)

						if "pre_norm_attn.0." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_attn.0."""       ,  """layernorm_before."""	)
						if "pre_norm_attn.1." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_attn.1."""       ,  """attention."""	)
						if "pre_norm_ffn.0." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_ffn.0."""       ,  """layernorm_after."""	)
						if "pre_norm_ffn.1." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_ffn.1."""       ,  """ffn.conv1."""	)
						if "pre_norm_ffn.3." in k:
									UpperCamelCase         =					k_new.replace("""pre_norm_ffn.3."""       ,  """ffn.conv2."""	)

						if "classifier.1." in k:
									UpperCamelCase         =					k_new.replace("""classifier.1."""       ,  """classifier."""	)

						if "seg_head." in k:
									UpperCamelCase         =					k_new.replace("""seg_head."""       ,  """segmentation_head."""	)
						if ".aspp_layer." in k:
									UpperCamelCase         =					k_new.replace(""".aspp_layer."""       ,  """."""	)
						if ".aspp_pool." in k:
									UpperCamelCase         =					k_new.replace(""".aspp_pool."""       ,  """."""	)

						rename_keys.append((k, k_new)	)
			return rename_keys






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




			UpperCamelCase         =					[]
			for k in state_dict.keys():
						if k.startswith("""seg_head.aux_head."""	):
									keys_to_ignore.append(SCREAMING_SNAKE_CASE_	)
			for k in keys_to_ignore:
						state_dict.pop(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)






def 							_lowercase    (		):





			"""simple docstring"""




			UpperCamelCase         =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
			UpperCamelCase         =					Image.open(requests.get(SCREAMING_SNAKE_CASE_       ,  stream=SCREAMING_SNAKE_CASE_	).raw	)
			return im






@torch.no_grad()
def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		Tuple       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					get_mobilevitva_config(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load original state_dict
			UpperCamelCase         =					torch.load(SCREAMING_SNAKE_CASE_       ,  map_location="""cpu"""	)

			# load huggingface model
			if task_name.startswith("""ade20k_"""	) or task_name.startswith("""voc_"""	):
						UpperCamelCase         =					MobileViTVaForSemanticSegmentation(SCREAMING_SNAKE_CASE_	).eval()
						UpperCamelCase         =					False
			else:
						UpperCamelCase         =					MobileViTVaForImageClassification(SCREAMING_SNAKE_CASE_	).eval()
						UpperCamelCase         =					False

			# remove and rename some keys of load the original model
			UpperCamelCase         =					checkpoint
			remove_unused_keys(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					create_rename_keys(SCREAMING_SNAKE_CASE_       ,  base_model=SCREAMING_SNAKE_CASE_	)
			for rename_key_src, rename_key_dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			# load modified state_dict
			model.load_state_dict(SCREAMING_SNAKE_CASE_	)

			# Check outputs on an image, prepared by MobileViTImageProcessor
			UpperCamelCase         =					MobileViTImageProcessor(crop_size=config.image_size       ,  size=config.image_size + 32	)
			UpperCamelCase         =					image_processor(images=prepare_img()       ,  return_tensors="""pt"""	)
			UpperCamelCase         =					model(**SCREAMING_SNAKE_CASE_	)

			# verify classification model
			if task_name.startswith("""imagenet"""	):
						UpperCamelCase         =					outputs.logits
						UpperCamelCase         =					logits.argmax(-1	).item()
						print("""Predicted class:"""       ,  model.config.idalabel[predicted_class_idx]	)
						if task_name.startswith("""imagenet1k_256"""	) and config.width_multiplier == 1.0:
									# expected_logits for base variant
									UpperCamelCase         =					torch.tensor([-1.6336e00, -7.3204e-02, -5.1883e-01]	)
									assert torch.allclose(logits[0, :3]       ,  SCREAMING_SNAKE_CASE_       ,  atol=1e-4	)

			Path(SCREAMING_SNAKE_CASE_	).mkdir(exist_ok=SCREAMING_SNAKE_CASE_	)
			print(f'Saving model {task_name} to {pytorch_dump_folder_path}'	)
			model.save_pretrained(SCREAMING_SNAKE_CASE_	)
			print(f'Saving image processor to {pytorch_dump_folder_path}'	)
			image_processor.save_pretrained(SCREAMING_SNAKE_CASE_	)


if __name__ == "__main__":
			__snake_case            =   argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    "--task",
			    default="imagenet1k_256",
			    type=str,
			    help=(
			        "Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
			        "\n                Classification (ImageNet-1k)\n                    - MobileViTV2 (256x256) : imagenet1k_256\n                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n                      imagenet21k_to_1k_256\n                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n                      ImageNet-1k 384x384) : imagenet21k_to_1k_384\n                Segmentation\n                    - ADE20K Dataset : ade20k_deeplabv3\n                    - Pascal VOC 2012 Dataset: voc_deeplabv3\n            "
			    ),
			    choices=[
			        "imagenet1k_256",
			        "imagenet1k_384",
			        "imagenet21k_to_1k_256",
			        "imagenet21k_to_1k_384",
			        "ade20k_deeplabv3",
			        "voc_deeplabv3",
			    ],
			)

			parser.add_argument(
			    "--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
			)
			parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
			parser.add_argument(
			    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
			)

			__snake_case            =   parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)








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

from tokenizers import processors

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


if is_sentencepiece_available():
			from .tokenization_mbart import MBartTokenizer
else:
			__snake_case            =   None


__snake_case            =   logging.get_logger(__name__)


__snake_case            =   {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}

__snake_case            =   {
    "vocab_file": {
        "facebook/mbart-large-en-ro": (
            "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"
        ),
        "facebook/mbart-large-cc25": (
            "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"
        ),
    },
    "tokenizer_file": {
        "facebook/mbart-large-en-ro": "https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/tokenizer.json",
        "facebook/mbart-large-cc25": "https://huggingface.co/facebook/mbart-large-cc25/resolve/main/tokenizer.json",
    },
}

__snake_case            =   {
    "facebook/mbart-large-en-ro": 1_024,
    "facebook/mbart-large-cc25": 1_024,
}

# fmt: off
__snake_case            =   ["ar_AR", "cs_CZ", "de_DE", "en_XX", "es_XX", "et_EE", "fi_FI", "fr_XX", "gu_IN", "hi_IN", "it_IT", "ja_XX", "kk_KZ", "ko_KR", "lt_LT", "lv_LV", "my_MM", "ne_NP", "nl_XX", "ro_RO", "ru_RU", "si_LK", "tr_TR", "vi_VN", "zh_CN"]




class    UpperCAmelCase					(       __snake_case    ):
			lowercase						= VOCAB_FILES_NAMES
			lowercase						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase						= PRETRAINED_VOCAB_FILES_MAP
			lowercase						= ["""input_ids""", """attention_mask"""]
			lowercase						= MBartTokenizer

			lowercase						= []
			lowercase						= []




			def __init__(						self							:		Optional[int]       ,       __magic_name__							:		str=None       ,       __magic_name__							:		Optional[int]=None       ,       __magic_name__							:		Dict="<s>"       ,       __magic_name__							:		Union[str, Any]="</s>"       ,       __magic_name__							:		List[str]="</s>"       ,       __magic_name__							:		Optional[int]="<s>"       ,       __magic_name__							:		int="<unk>"       ,       __magic_name__							:		str="<pad>"       ,       __magic_name__							:		List[str]="<mask>"       ,       __magic_name__							:		Tuple=None       ,       __magic_name__							:		Dict=None       ,       __magic_name__							:		Dict=None       ,       **__magic_name__							:		Tuple       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					AddedToken(__magic_name__       ,       lstrip=__magic_name__       ,       rstrip=__magic_name__						) if isinstance(__magic_name__       ,       __magic_name__						) else mask_token

						super().__init__(
						    vocab_file=__magic_name__       ,       tokenizer_file=__magic_name__       ,       bos_token=__magic_name__       ,       eos_token=__magic_name__       ,       sep_token=__magic_name__       ,       cls_token=__magic_name__       ,       unk_token=__magic_name__       ,       pad_token=__magic_name__       ,       mask_token=__magic_name__       ,       src_lang=__magic_name__       ,       tgt_lang=__magic_name__       ,       additional_special_tokens=__magic_name__       ,       **__magic_name__       ,       )

						UpperCamelCase         =					vocab_file
						UpperCamelCase         =					False if not self.vocab_file else True

						UpperCamelCase         =					FAIRSEQ_LANGUAGE_CODES.copy()

						if additional_special_tokens is not None:
									# Only add those special tokens if they are not already there.
									_additional_special_tokens.extend(
									    [t for t in additional_special_tokens if t not in _additional_special_tokens]						)

						self.add_special_tokens({"""additional_special_tokens""": _additional_special_tokens}						)
						UpperCamelCase         =					{
						    lang_code: self.convert_tokens_to_ids(__magic_name__						) for lang_code in FAIRSEQ_LANGUAGE_CODES
						}

						UpperCamelCase         =					src_lang if src_lang is not None else """en_XX"""
						UpperCamelCase         =					self.convert_tokens_to_ids(self._src_lang						)
						UpperCamelCase         =					tgt_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			@property
			def   lowerCamelCase_					(						self							:		int						):







						"""simple docstring"""







						return self._src_lang




			@src_lang.setter
			def   lowerCamelCase_					(						self							:		List[str]       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					new_src_lang
						self.set_src_lang_special_tokens(self._src_lang						)




			def   lowerCamelCase_					(						self							:		Optional[Any]       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







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




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[int]       ,       __magic_name__							:		Optional[List[int]] = None						):







						"""simple docstring"""







						UpperCamelCase         =					[self.sep_token_id]
						UpperCamelCase         =					[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   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		List[Any]       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str]       ,       __magic_name__							:		Optional[str]       ,       **__magic_name__							:		Tuple						):







						"""simple docstring"""







						if src_lang is None or tgt_lang is None:
									raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""						)
						UpperCamelCase         =					src_lang
						UpperCamelCase         =					self(__magic_name__       ,       add_special_tokens=__magic_name__       ,       return_tensors=__magic_name__       ,       **__magic_name__						)
						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					tgt_lang_id
						return inputs




			def   lowerCamelCase_					(						self							:		Dict       ,       __magic_name__							:		List[str]       ,       __magic_name__							:		str = "en_XX"       ,       __magic_name__							:		Optional[List[str]] = None       ,       __magic_name__							:		str = "ro_RO"       ,       **__magic_name__							:		int       ,       ):







						"""simple docstring"""







						UpperCamelCase         =					src_lang
						UpperCamelCase         =					tgt_lang
						return super().prepare_seqaseq_batch(__magic_name__       ,       __magic_name__       ,       **__magic_name__						)




			def   lowerCamelCase_					(						self							:		Dict						):







						"""simple docstring"""







						return self.set_src_lang_special_tokens(self.src_lang						)




			def   lowerCamelCase_					(						self							:		Any						):







						"""simple docstring"""







						return self.set_tgt_lang_special_tokens(self.tgt_lang						)




			def   lowerCamelCase_					(						self							:		int       ,       __magic_name__							:		Union[str, Any]						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,       self.prefix_tokens + self.suffix_tokens						)						)       ,       )




			def   lowerCamelCase_					(						self							:		Tuple       ,       __magic_name__							:		str						):







						"""simple docstring"""







						UpperCamelCase         =					self.convert_tokens_to_ids(__magic_name__						)
						UpperCamelCase         =					[]
						UpperCamelCase         =					[self.eos_token_id, self.cur_lang_code]

						UpperCamelCase         =					self.convert_ids_to_tokens(self.prefix_tokens						)
						UpperCamelCase         =					self.convert_ids_to_tokens(self.suffix_tokens						)

						UpperCamelCase         =					processors.TemplateProcessing(
						    single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str       ,       pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str       ,       special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str       ,       self.prefix_tokens + self.suffix_tokens						)						)       ,       )





			def   lowerCamelCase_					(						self							:		Any       ,       __magic_name__							:		str       ,       __magic_name__							:		Optional[str] = None						):







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

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

						return (out_vocab_file,)




