



import argparse
from collections import OrderedDict
from pathlib import Path

import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision.transforms import functional as F

from transformers import DetrImageProcessor, TableTransformerConfig, TableTransformerForObjectDetection
from transformers.utils import logging


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

# here we list all keys to be renamed (original name on the left, our name on the right)
lowerCAmelCase__					  =  []
for i in range(6):
				# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
				rename_keys.append(
				    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
				)
				rename_keys.append(
				    (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
				)
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
				rename_keys.append(
				    (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
				)
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
				rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
				# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
				)
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
				)
				rename_keys.append(
				    (
				        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
				        f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
				    )
				)
				rename_keys.append(
				    (
				        f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
				        f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
				    )
				)
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
				)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
				)
				rename_keys.append(
				    (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
				)
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
				rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

# convolutional projection + query embeddings + layernorm of encoder + layernorm of decoder + class and bounding box heads
rename_keys.extend(
    [
        ('''input_proj.weight''', '''input_projection.weight'''),
        ('''input_proj.bias''', '''input_projection.bias'''),
        ('''query_embed.weight''', '''query_position_embeddings.weight'''),
        ('''transformer.encoder.norm.weight''', '''encoder.layernorm.weight'''),
        ('''transformer.encoder.norm.bias''', '''encoder.layernorm.bias'''),
        ('''transformer.decoder.norm.weight''', '''decoder.layernorm.weight'''),
        ('''transformer.decoder.norm.bias''', '''decoder.layernorm.bias'''),
        ('''class_embed.weight''', '''class_labels_classifier.weight'''),
        ('''class_embed.bias''', '''class_labels_classifier.bias'''),
        ('''bbox_embed.layers.0.weight''', '''bbox_predictor.layers.0.weight'''),
        ('''bbox_embed.layers.0.bias''', '''bbox_predictor.layers.0.bias'''),
        ('''bbox_embed.layers.1.weight''', '''bbox_predictor.layers.1.weight'''),
        ('''bbox_embed.layers.1.bias''', '''bbox_predictor.layers.1.bias'''),
        ('''bbox_embed.layers.2.weight''', '''bbox_predictor.layers.2.weight'''),
        ('''bbox_embed.layers.2.bias''', '''bbox_predictor.layers.2.bias'''),
    ]
)


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   List[str]													=					state_dict.pop(lowerCamelCase__						)
				lowercase__					:   Dict													=					val


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Any													=					OrderedDict()
				for key, value in state_dict.items():
								if "backbone.0.body" in key:
												lowercase__					:   Optional[int]													=					key.replace("backbone.0.body"					,			"backbone.conv_encoder.model"						)
												lowercase__					:   Optional[int]													=					value
								else:
												lowercase__					:   Dict													=					value

				return new_state_dict


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   int													=					""

				# first: transformer encoder
				for i in range(6						):
								# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
								lowercase__					:   Union[str, Any]													=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""						)
								lowercase__					:   Tuple													=					state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias"""						)
								# next, add query, keys and values (in that order) to the state dict
								lowercase__					:   int													=					in_proj_weight[:256, :]
								lowercase__					:   int													=					in_proj_bias[:256]
								lowercase__					:   Union[str, Any]													=					in_proj_weight[256:512, :]
								lowercase__					:   Union[str, Any]													=					in_proj_bias[256:512]
								lowercase__					:   Any													=					in_proj_weight[-256:, :]
								lowercase__					:   str													=					in_proj_bias[-256:]
				# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
				for i in range(6						):
								# read in weights + bias of input projection layer of self-attention
								lowercase__					:   Any													=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""						)
								lowercase__					:   Optional[Any]													=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias"""						)
								# next, add query, keys and values (in that order) to the state dict
								lowercase__					:   Tuple													=					in_proj_weight[:256, :]
								lowercase__					:   List[Any]													=					in_proj_bias[:256]
								lowercase__					:   Tuple													=					in_proj_weight[256:512, :]
								lowercase__					:   List[Any]													=					in_proj_bias[256:512]
								lowercase__					:   Dict													=					in_proj_weight[-256:, :]
								lowercase__					:   int													=					in_proj_bias[-256:]
								# read in weights + bias of input projection layer of cross-attention
								lowercase__					:   Optional[Any]													=					state_dict.pop(
								    F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""						)
								lowercase__					:   Any													=					state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias"""						)
								# next, add query, keys and values (in that order) of cross-attention to the state dict
								lowercase__					:   Any													=					in_proj_weight_cross_attn[:256, :]
								lowercase__					:   List[str]													=					in_proj_bias_cross_attn[:256]
								lowercase__					:   Any													=					in_proj_weight_cross_attn[256:512, :]
								lowercase__					:   str													=					in_proj_bias_cross_attn[256:512]
								lowercase__					:   Tuple													=					in_proj_weight_cross_attn[-256:, :]
								lowercase__					:   str													=					in_proj_bias_cross_attn[-256:]


def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				lowercase__							,							lowercase__					:   Union[str, Any]													=					image.size
				lowercase__					:   Any													=					max(lowerCamelCase__					,			lowerCamelCase__						)
				lowercase__					:   Optional[Any]													=					800 if "detection" in checkpoint_url else 1_000
				lowercase__					:   int													=					target_max_size / current_max_size
				lowercase__					:   Optional[Any]													=					image.resize((int(round(scale * width						)						), int(round(scale * height						)						))						)

				return resized_image


def  __lowerCamelCase (       lowerCamelCase__						):

				"""simple docstring"""


				lowercase__					:   Tuple													=					F.to_tensor(lowerCamelCase__						)
				lowercase__					:   Any													=					F.normalize(lowerCamelCase__					,			mean=[0.485, 0.456, 0.406]					,			std=[0.229, 0.224, 0.225]						)
				return image


@torch.no_grad()
def  __lowerCamelCase (       lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						):

				"""simple docstring"""


				logger.info("Converting model..."						)

				# load original state dict
				lowercase__					:   List[Any]													=					torch.hub.load_state_dict_from_url(lowerCamelCase__					,			map_location="cpu"						)
				# rename keys
				for src, dest in rename_keys:
								rename_key(lowerCamelCase__					,			lowerCamelCase__					,			lowerCamelCase__						)
				lowercase__					:   Optional[Any]													=					rename_backbone_keys(lowerCamelCase__						)
				# query, key and value matrices need special treatment
				read_in_q_k_v(lowerCamelCase__						)
				# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
				lowercase__					:   str													=					"model."
				for key in state_dict.copy().keys():
								if not key.startswith("class_labels_classifier"						) and not key.startswith("bbox_predictor"						):
												lowercase__					:   List[str]													=					state_dict.pop(lowerCamelCase__						)
												lowercase__					:   Optional[Any]													=					val
    # create HuggingFace model and load state dict
				lowercase__					:   Optional[Any]													=					TableTransformerConfig(
				    backbone="resnet18"					,			mask_loss_coefficient=1					,			dice_loss_coefficient=1					,			ce_loss_coefficient=1					,			bbox_loss_coefficient=5					,			giou_loss_coefficient=2					,			eos_coefficient=0.4					,			class_cost=1					,			bbox_cost=5					,			giou_cost=2					,			)

				if "detection" in checkpoint_url:
								lowercase__					:   List[Any]													=					15
								lowercase__					:   Optional[Any]													=					2
								lowercase__					:   int													=					{0: "table", 1: "table rotated"}
								lowercase__					:   int													=					idalabel
								lowercase__					:   Dict													=					{v: k for k, v in idalabel.items()}
				else:
								lowercase__					:   Optional[Any]													=					125
								lowercase__					:   Optional[int]													=					6
								lowercase__					:   str													=					{
								    0: "table",
								    1: "table column",
								    2: "table row",
								    3: "table column header",
								    4: "table projected row header",
								    5: "table spanning cell",
								}
								lowercase__					:   Tuple													=					idalabel
								lowercase__					:   Union[str, Any]													=					{v: k for k, v in idalabel.items()}

				lowercase__					:   List[str]													=					DetrImageProcessor(
				    format="coco_detection"					,			max_size=800 if "detection" in checkpoint_url else 1_000						)
				lowercase__					:   Any													=					TableTransformerForObjectDetection(lowerCamelCase__						)
				model.load_state_dict(lowerCamelCase__						)
				model.eval()

				# verify our conversion
				lowercase__					:   Any													=					"example_pdf.png" if "detection" in checkpoint_url else "example_table.png"
				lowercase__					:   Dict													=					hf_hub_download(repo_id="nielsr/example-pdf"					,			repo_type="dataset"					,			filename=lowerCamelCase__						)
				lowercase__					:   Tuple													=					Image.open(lowerCamelCase__						).convert("RGB"						)
				lowercase__					:   int													=					normalize(resize(lowerCamelCase__					,			lowerCamelCase__						)						).unsqueeze(0						)

				lowercase__					:   Dict													=					model(lowerCamelCase__						)

				if "detection" in checkpoint_url:
								lowercase__					:   Tuple													=					(1, 15, 3)
								lowercase__					:   Optional[Any]													=					torch.tensor(
								    [[-6.7897, -16.9985, 6.7937], [-8.0186, -22.2192, 6.9677], [-7.3117, -21.0708, 7.4055]]						)
								lowercase__					:   Union[str, Any]													=					torch.tensor([[0.4867, 0.1767, 0.6732], [0.6718, 0.4479, 0.3830], [0.4716, 0.1760, 0.6364]]						)

				else:
								lowercase__					:   List[str]													=					(1, 125, 7)
								lowercase__					:   int													=					torch.tensor(
								    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]						)
								lowercase__					:   Union[str, Any]													=					torch.tensor([[0.4983, 0.5595, 0.9440], [0.4916, 0.6315, 0.5954], [0.6108, 0.8637, 0.1135]]						)

				assert outputs.logits.shape == expected_shape
				assert torch.allclose(outputs.logits[0, :3, :3]					,			lowerCamelCase__					,			atol=1e-4						)
				assert torch.allclose(outputs.pred_boxes[0, :3, :3]					,			lowerCamelCase__					,			atol=1e-4						)
				print("Looks ok!"						)

				if pytorch_dump_folder_path is not None:
								# Save model and image processor
								logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}..."""						)
								Path(lowerCamelCase__						).mkdir(exist_ok=lowerCamelCase__						)
								model.save_pretrained(lowerCamelCase__						)
								image_processor.save_pretrained(lowerCamelCase__						)

				if push_to_hub:
								# Push model to HF hub
								logger.info("Pushing model to the hub..."						)
								lowercase__					:   Any													=					(
								    "microsoft/table-transformer-detection"
								    if "detection" in checkpoint_url
								    else "microsoft/table-transformer-structure-recognition"
								)
								model.push_to_hub(lowerCamelCase__						)
								image_processor.push_to_hub(lowerCamelCase__						)


if __name__ == "__main__":
				lowerCAmelCase__					  =  argparse.ArgumentParser()

				parser.add_argument(
				    '''--checkpoint_url''',
				    default='''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
				    type=str,
				    choices=[
				        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_detection_detr_r18.pth''',
				        '''https://pubtables1m.blob.core.windows.net/model/pubtables1m_structure_detr_r18.pth''',
				    ],
				    help='''URL of the Table Transformer checkpoint you\'d like to convert.''',
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)
				lowerCAmelCase__					  =  parser.parse_args()
				convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)









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__					  =  logging.get_logger(__name__)

lowerCAmelCase__					  =  '''▁'''

lowerCAmelCase__					  =  {'''vocab_file''': '''sentencepiece.bpe.model''', '''monolingual_vocab_file''': '''dict.txt'''}

lowerCAmelCase__					  =  {
    '''vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model''',
    },
    '''monolingual_vocab_file''': {
        '''vinai/bartpho-syllable''': '''https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt''',
    },
}

lowerCAmelCase__					  =  {'''vinai/bartpho-syllable''': 1_0_2_4}

class 				snake_case__(_UpperCamelCase      ):

					"""simple docstring"""






					lowercase_								=		VOCAB_FILES_NAMES
					lowercase_								=		PRETRAINED_VOCAB_FILES_MAP
					lowercase_								=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
					lowercase_								=		["""input_ids""", """attention_mask"""]







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

									lowercase__					:   int													=					{} if sp_model_kwargs is None else sp_model_kwargs

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

									lowercase__					:   Dict													=					vocab_file
									lowercase__					:   Union[str, Any]													=					monolingual_vocab_file
									lowercase__					:   Union[str, Any]													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.Load(str(SCREAMING_SNAKE_CASE					)					)

									# Load the reduced vocab

									# Keep order of special tokens for backward compatibility
									lowercase__					:   Any													=					{}
									lowercase__					:   int													=					0
									for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
													if str(SCREAMING_SNAKE_CASE					) not in self.fairseq_tokens_to_ids:
																	lowercase__					:   Dict													=					cnt
																	cnt += 1
									with open(SCREAMING_SNAKE_CASE				,       "r"				,       encoding="utf-8"					) as f:
													for line in f.readlines():
																	lowercase__					:   int													=					line.strip().split()[0]
																	lowercase__					:   List[str]													=					len(self.fairseq_tokens_to_ids					)
									if str(SCREAMING_SNAKE_CASE					) not in self.fairseq_tokens_to_ids:
													lowercase__					:   Optional[Any]													=					len(self.fairseq_tokens_to_ids					)

									lowercase__					:   List[str]													=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}







					def __getstate__(							self :     int					):
									lowercase__					:   Dict													=					self.__dict__.copy()
									lowercase__					:   Union[str, Any]													=					None
									lowercase__					:   Dict													=					self.sp_model.serialized_model_proto()
									return state







					def __setstate__(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     Optional[Any]					):
									lowercase__					:   Union[str, Any]													=					d

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

									lowercase__					:   List[str]													=					spm.SentencePieceProcessor(**self.sp_model_kwargs					)
									self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None					):

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







					def 			snake_case					(							self :     Dict				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None				,       SCREAMING_SNAKE_CASE :     bool = False					):

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

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







					def 			snake_case					(							self :     str				,       SCREAMING_SNAKE_CASE :     List[int]				,       SCREAMING_SNAKE_CASE :     Optional[List[int]] = None					):
									lowercase__					:   Tuple													=					[self.sep_token_id]
									lowercase__					:   str													=					[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 			snake_case					(							self :     Optional[int]					):
									return len(self.fairseq_ids_to_tokens					)







					def 			snake_case					(							self :     List[Any]					):
									lowercase__					:   Any													=					{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE					): i for i in range(self.vocab_size					)}
									vocab.update(self.added_tokens_encoder					)
									return vocab







					def 			snake_case					(							self :     Union[str, Any]				,       SCREAMING_SNAKE_CASE :     str					):
									return self.sp_model.encode(SCREAMING_SNAKE_CASE				,       out_type=SCREAMING_SNAKE_CASE					)







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     int					):
									if token in self.fairseq_tokens_to_ids:
													return self.fairseq_tokens_to_ids[token]
									else:
													return self.unk_token_id







					def 			snake_case					(							self :     List[str]				,       SCREAMING_SNAKE_CASE :     Any					):
									return self.fairseq_ids_to_tokens[index]







					def 			snake_case					(							self :     List[Any]				,       SCREAMING_SNAKE_CASE :     Union[str, Any]					):
									lowercase__					:   str													=					"".join(SCREAMING_SNAKE_CASE					).replace(SCREAMING_SNAKE_CASE				,       " "					).strip()
									return out_string







					def 			snake_case					(							self :     Optional[int]				,       SCREAMING_SNAKE_CASE :     str				,       SCREAMING_SNAKE_CASE :     Optional[str] = None					):
									if not os.path.isdir(SCREAMING_SNAKE_CASE					):
													logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""					)
													return
									lowercase__					:   str													=					os.path.join(
									    SCREAMING_SNAKE_CASE				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]					)
									lowercase__					:   List[str]													=					os.path.join(
									    SCREAMING_SNAKE_CASE				,       (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["monolingual_vocab_file"]				,       )

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

									if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
									    SCREAMING_SNAKE_CASE					) and os.path.isfile(self.monolingual_vocab_file					):
													copyfile(self.monolingual_vocab_file				,       SCREAMING_SNAKE_CASE					)
									elif not os.path.isfile(self.monolingual_vocab_file					):
													with open(SCREAMING_SNAKE_CASE				,       "w"				,       encoding="utf-8"					) as fp:
																	for token in self.fairseq_tokens_to_ids:
																					if token not in self.all_special_tokens:
																									fp.write(f"""{str(SCREAMING_SNAKE_CASE					)} \n"""					)

									return out_vocab_file, out_monolingual_vocab_file




