




import argparse
import json
from pathlib import Path

import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform

from transformers import (
    BitConfig,
    ViTHybridConfig,
    ViTHybridForImageClassification,
    ViTHybridImageProcessor,
    ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__A						:							List[str]							=  logging.get_logger(__name__)
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					[]

			# fmt: off
			# stem:
			rename_keys.append(("""cls_token""", """vit.embeddings.cls_token""")   )
			rename_keys.append(("""pos_embed""", """vit.embeddings.position_embeddings""")   )

			rename_keys.append(("""patch_embed.proj.weight""", """vit.embeddings.patch_embeddings.projection.weight""")   )
			rename_keys.append(("""patch_embed.proj.bias""", """vit.embeddings.patch_embeddings.projection.bias""")   )

			# backbone
			rename_keys.append(("""patch_embed.backbone.stem.conv.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight""")   )
			rename_keys.append(("""patch_embed.backbone.stem.norm.weight""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight""")   )
			rename_keys.append(("""patch_embed.backbone.stem.norm.bias""", """vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias""")   )

			for stage_idx in range(len(config.backbone_config.depths   )   ):
						for layer_idx in range(config.backbone_config.depths[stage_idx]   ):
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight""")   )
									rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias""")   )

						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight""")   )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight""")   )
						rename_keys.append((f"""patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias""", f"""vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias""")   )

			# transformer encoder
			for i in range(config.num_hidden_layers   ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append((f"""blocks.{i}.norm1.weight""", f"""vit.encoder.layer.{i}.layernorm_before.weight""")   )
						rename_keys.append((f"""blocks.{i}.norm1.bias""", f"""vit.encoder.layer.{i}.layernorm_before.bias""")   )
						rename_keys.append((f"""blocks.{i}.attn.proj.weight""", f"""vit.encoder.layer.{i}.attention.output.dense.weight""")   )
						rename_keys.append((f"""blocks.{i}.attn.proj.bias""", f"""vit.encoder.layer.{i}.attention.output.dense.bias""")   )
						rename_keys.append((f"""blocks.{i}.norm2.weight""", f"""vit.encoder.layer.{i}.layernorm_after.weight""")   )
						rename_keys.append((f"""blocks.{i}.norm2.bias""", f"""vit.encoder.layer.{i}.layernorm_after.bias""")   )
						rename_keys.append((f"""blocks.{i}.mlp.fc1.weight""", f"""vit.encoder.layer.{i}.intermediate.dense.weight""")   )
						rename_keys.append((f"""blocks.{i}.mlp.fc1.bias""", f"""vit.encoder.layer.{i}.intermediate.dense.bias""")   )
						rename_keys.append((f"""blocks.{i}.mlp.fc2.weight""", f"""vit.encoder.layer.{i}.output.dense.weight""")   )
						rename_keys.append((f"""blocks.{i}.mlp.fc2.bias""", f"""vit.encoder.layer.{i}.output.dense.bias""")   )

			if base_model:
						# layernorm + pooler
						rename_keys.extend(
						    [
						        ("""norm.weight""", """layernorm.weight"""),
						        ("""norm.bias""", """layernorm.bias"""),
						        ("""pre_logits.fc.weight""", """pooler.dense.weight"""),
						        ("""pre_logits.fc.bias""", """pooler.dense.bias"""),
						    ]   )

						# if just the base model, we should remove "vit" from all keys that start with "vit"
						SCREAMING_SNAKE_CASE           =					[(pair[0], pair[1][4:]) if pair[1].startswith("""vit"""   ) else pair for pair in rename_keys]
			else:
						# layernorm + classification head
						rename_keys.extend(
						    [
						        ("""norm.weight""", """vit.layernorm.weight"""),
						        ("""norm.bias""", """vit.layernorm.bias"""),
						        ("""head.weight""", """classifier.weight"""),
						        ("""head.bias""", """classifier.bias"""),
						    ]   )
			# fmt: on

			return rename_keys
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			for i in range(config.num_hidden_layers   ):
						if base_model:
									SCREAMING_SNAKE_CASE           =					""""""
						else:
									SCREAMING_SNAKE_CASE           =					"""vit."""
						# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""blocks.{i}.attn.qkv.weight"""   )
						SCREAMING_SNAKE_CASE           =					state_dict.pop(f"""blocks.{i}.attn.qkv.bias"""   )
						# next, add query, keys and values (in that order) to the state dict
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    : config.hidden_size, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_bias[: config.hidden_size]
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    config.hidden_size : config.hidden_size * 2, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_bias[
						    config.hidden_size : config.hidden_size * 2
						]
						SCREAMING_SNAKE_CASE           =					in_proj_weight[
						    -config.hidden_size :, :
						]
						SCREAMING_SNAKE_CASE           =					in_proj_bias[-config.hidden_size :]
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					["""head.weight""", """head.bias"""]
			for k in ignore_keys:
						state_dict.pop(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					dct.pop(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					val
def 					lowerCamelCase_    (    ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					"""http://images.cocodataset.org/val2017/000000039769.jpg"""
			SCREAMING_SNAKE_CASE           =					Image.open(requests.get(SCREAMING_SNAKE_CASE					,	stream=SCREAMING_SNAKE_CASE   ).raw   )
			return im
@torch.no_grad()
def 					lowerCamelCase_    (    SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE=False   ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE           =					BitConfig(
			    global_padding="""same"""					,	layer_type="""bottleneck"""					,	depths=(3, 4, 9)					,	out_features=["""stage3"""]					,	embedding_dynamic_padding=SCREAMING_SNAKE_CASE					,	)
			SCREAMING_SNAKE_CASE           =					ViTHybridConfig(backbone_config=SCREAMING_SNAKE_CASE					,	image_size=3_84					,	num_labels=10_00   )
			SCREAMING_SNAKE_CASE           =					False

			# load original model from timm
			SCREAMING_SNAKE_CASE           =					timm.create_model(SCREAMING_SNAKE_CASE					,	pretrained=SCREAMING_SNAKE_CASE   )
			timm_model.eval()

			# load state_dict of original model, remove and rename some keys
			SCREAMING_SNAKE_CASE           =					timm_model.state_dict()
			if base_model:
						remove_classification_head_(SCREAMING_SNAKE_CASE   )
			SCREAMING_SNAKE_CASE           =					create_rename_keys(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			for src, dest in rename_keys:
						rename_key(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )
			read_in_q_k_v(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			SCREAMING_SNAKE_CASE           =					"""huggingface/label-files"""
			SCREAMING_SNAKE_CASE           =					"""imagenet-1k-id2label.json"""
			SCREAMING_SNAKE_CASE           =					json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE					,	repo_type="""dataset"""   )					,	"""r"""   )   )
			SCREAMING_SNAKE_CASE           =					{int(SCREAMING_SNAKE_CASE   ): v for k, v in idalabel.items()}
			SCREAMING_SNAKE_CASE           =					idalabel
			SCREAMING_SNAKE_CASE           =					{v: k for k, v in idalabel.items()}

			# load HuggingFace model
			if vit_name[-5:] == "in21k":
						SCREAMING_SNAKE_CASE           =					ViTHybridModel(SCREAMING_SNAKE_CASE   ).eval()
			else:
						SCREAMING_SNAKE_CASE           =					ViTHybridForImageClassification(SCREAMING_SNAKE_CASE   ).eval()
			model.load_state_dict(SCREAMING_SNAKE_CASE   )

			# create image processor
			SCREAMING_SNAKE_CASE           =					create_transform(**resolve_data_config({}					,	model=SCREAMING_SNAKE_CASE   )   )
			SCREAMING_SNAKE_CASE           =					transform.transforms

			SCREAMING_SNAKE_CASE           =					{
			    """bilinear""": PILImageResampling.BILINEAR,
			    """bicubic""": PILImageResampling.BICUBIC,
			    """nearest""": PILImageResampling.NEAREST,
			}

			SCREAMING_SNAKE_CASE           =					ViTHybridImageProcessor(
			    do_resize=SCREAMING_SNAKE_CASE					,	size={"""shortest_edge""": timm_transforms[0].size}					,	resample=pillow_resamplings[timm_transforms[0].interpolation.value]					,	do_center_crop=SCREAMING_SNAKE_CASE					,	crop_size={"""height""": timm_transforms[1].size[0], """width""": timm_transforms[1].size[1]}					,	do_normalize=SCREAMING_SNAKE_CASE					,	image_mean=timm_transforms[-1].mean.tolist()					,	image_std=timm_transforms[-1].std.tolist()					,	)

			SCREAMING_SNAKE_CASE           =					prepare_img()
			SCREAMING_SNAKE_CASE           =					transform(SCREAMING_SNAKE_CASE   ).unsqueeze(0   )
			SCREAMING_SNAKE_CASE           =					processor(SCREAMING_SNAKE_CASE					,	return_tensors="""pt"""   ).pixel_values

			# verify pixel values
			assert torch.allclose(SCREAMING_SNAKE_CASE					,	SCREAMING_SNAKE_CASE   )

			# verify logits
			with torch.no_grad():
						SCREAMING_SNAKE_CASE           =					model(SCREAMING_SNAKE_CASE   )
						SCREAMING_SNAKE_CASE           =					outputs.logits

			print("""Predicted class:"""					,	logits.argmax(-1   ).item()   )
			if base_model:
						SCREAMING_SNAKE_CASE           =					timm_model.forward_features(SCREAMING_SNAKE_CASE   )
						assert timm_pooled_output.shape == outputs.pooler_output.shape
						assert torch.allclose(SCREAMING_SNAKE_CASE					,	outputs.pooler_output					,	atol=1E-3   )
			else:
						SCREAMING_SNAKE_CASE           =					timm_model(SCREAMING_SNAKE_CASE   )
						assert timm_logits.shape == outputs.logits.shape
						assert torch.allclose(SCREAMING_SNAKE_CASE					,	outputs.logits					,	atol=1E-3   )
			print("""Looks ok!"""   )

			if pytorch_dump_folder_path is not None:
						Path(SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=SCREAMING_SNAKE_CASE   )
						print(f"""Saving model {vit_name} to {pytorch_dump_folder_path}"""   )
						model.save_pretrained(SCREAMING_SNAKE_CASE   )
						print(f"""Saving processor to {pytorch_dump_folder_path}"""   )
						processor.save_pretrained(SCREAMING_SNAKE_CASE   )

			if push_to_hub:
						print(f"""Pushing model and processor to the hub {vit_name}"""   )
						model.push_to_hub(f"""ybelkada/{vit_name}"""   )
						processor.push_to_hub(f"""ybelkada/{vit_name}"""   )


if __name__ == "__main__":
		__A						:							Optional[Any]							=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--vit_name""",
		    default="""vit_base_r50_s16_384""",
		    type=str,
		    help="""Name of the hybrid ViT timm model you'd like to convert.""",
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
		)

		__A						:							List[Any]							=  parser.parse_args()
		convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)












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:
		__A						:							Tuple							=  None


__A						:							Any							=  logging.get_logger(__name__)


__A						:							Union[str, Any]							=  {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}

__A						:							List[Any]							=  {
    """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""",
    },
}

__A						:							int							=  {
    """facebook/mbart-large-en-ro""": 1_0_2_4,
    """facebook/mbart-large-cc25""": 1_0_2_4,
}

# fmt: off
__A						:							List[Any]							=  ["""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_      (   A       ):




							'''simple docstring'''



							a__						  =     VOCAB_FILES_NAMES
							a__						  =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							a__						  =     PRETRAINED_VOCAB_FILES_MAP
							a__						  =     ['''input_ids''', '''attention_mask''']
							a__						  =     MBartTokenizer

							a__						  =     []
							a__						  =     []





							def __init__(					self       :	str       ,     a       :	List[Any]=None       ,     a       :	Optional[int]=None       ,     a       :	str="<s>"       ,     a       :	Tuple="</s>"       ,     a       :	Optional[int]="</s>"       ,     a       :	int="<s>"       ,     a       :	List[str]="<unk>"       ,     a       :	Tuple="<pad>"       ,     a       :	Dict="<mask>"       ,     a       :	Optional[Any]=None       ,     a       :	List[Any]=None       ,     a       :	Any=None       ,     **a       :	Optional[Any]       ,     )			->					Any:
										# Mask token behave like a normal word, i.e. include the space before it
										SCREAMING_SNAKE_CASE           =					AddedToken(a       ,     lstrip=a       ,     rstrip=a			) if isinstance(a       ,     a			) else mask_token

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

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

										SCREAMING_SNAKE_CASE           =					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}			)
										SCREAMING_SNAKE_CASE           =					{
										    lang_code: self.convert_tokens_to_ids(a			) for lang_code in FAIRSEQ_LANGUAGE_CODES
										}

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





							@property
							def    _UpperCAmelCase     (					self       :	int			)			->					str:
										return self._src_lang





							@src_lang.setter
							def    _UpperCAmelCase     (					self       :	List[Any]       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					new_src_lang
										self.set_src_lang_special_tokens(self._src_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										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    _UpperCAmelCase     (					self       :	Tuple       ,     a       :	List[int]       ,     a       :	Optional[List[int]] = None			)			->					List[int]:
										SCREAMING_SNAKE_CASE           =					[self.sep_token_id]
										SCREAMING_SNAKE_CASE           =					[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    _UpperCAmelCase     (					self       :	Optional[Any]       ,     a       :	Optional[int]       ,     a       :	str       ,     a       :	Optional[str]       ,     a       :	Optional[str]       ,     **a       :	Optional[Any]			)			->					Optional[Any]:
										if src_lang is None or tgt_lang is None:
													raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model"""			)
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					self(a       ,     add_special_tokens=a       ,     return_tensors=a       ,     **a			)
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					tgt_lang_id
										return inputs





							def    _UpperCAmelCase     (					self       :	str       ,     a       :	List[str]       ,     a       :	str = "en_XX"       ,     a       :	Optional[List[str]] = None       ,     a       :	str = "ro_RO"       ,     **a       :	List[str]       ,     )			->					BatchEncoding:
										SCREAMING_SNAKE_CASE           =					src_lang
										SCREAMING_SNAKE_CASE           =					tgt_lang
										return super().prepare_seqaseq_batch(a       ,     a       ,     **a			)





							def    _UpperCAmelCase     (					self       :	Optional[Any]			)			->					str:
										return self.set_src_lang_special_tokens(self.src_lang			)





							def    _UpperCAmelCase     (					self       :	str			)			->					List[str]:
										return self.set_tgt_lang_special_tokens(self.tgt_lang			)





							def    _UpperCAmelCase     (					self       :	Dict       ,     a       :	List[str]			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

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

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Dict       ,     a       :	str			)			->					None:
										SCREAMING_SNAKE_CASE           =					self.convert_tokens_to_ids(a			)
										SCREAMING_SNAKE_CASE           =					[]
										SCREAMING_SNAKE_CASE           =					[self.eos_token_id, self.cur_lang_code]

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

										SCREAMING_SNAKE_CASE           =					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    _UpperCAmelCase     (					self       :	Dict       ,     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(a			):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory."""			)
													return
										SCREAMING_SNAKE_CASE           =					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,)






