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()
__UpperCamelCase	: Optional[Any]    		=  logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCamelCase	: Any    		=  []
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    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							state_dict.pop(_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							val
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					List[str]           =							OrderedDict()
						for key, value in state_dict.items():
												if "backbone.0.body" in key:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							key.replace('''backbone.0.body'''	,						'''backbone.conv_encoder.model'''		)
																		SCREAMING_SNAKE_CASE							:					str           =							value
												else:
																		SCREAMING_SNAKE_CASE							:					str           =							value

						return new_state_dict
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							''''''

						# 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)
												SCREAMING_SNAKE_CASE							:					Dict           =							state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight"""		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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
												SCREAMING_SNAKE_CASE							:					Tuple           =							in_proj_weight[:256, :]
												SCREAMING_SNAKE_CASE							:					int           =							in_proj_bias[:256]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							in_proj_weight[256:512, :]
												SCREAMING_SNAKE_CASE							:					List[str]           =							in_proj_bias[256:512]
												SCREAMING_SNAKE_CASE							:					Tuple           =							in_proj_weight[-256:, :]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							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
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight"""		)
												SCREAMING_SNAKE_CASE							:					Dict           =							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
												SCREAMING_SNAKE_CASE							:					str           =							in_proj_weight[:256, :]
												SCREAMING_SNAKE_CASE							:					List[str]           =							in_proj_bias[:256]
												SCREAMING_SNAKE_CASE							:					Any           =							in_proj_weight[256:512, :]
												SCREAMING_SNAKE_CASE							:					Any           =							in_proj_bias[256:512]
												SCREAMING_SNAKE_CASE							:					List[Any]           =							in_proj_weight[-256:, :]
												SCREAMING_SNAKE_CASE							:					List[str]           =							in_proj_bias[-256:]
												# read in weights + bias of input projection layer of cross-attention
												SCREAMING_SNAKE_CASE							:					Any           =							state_dict.pop(
												    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight"""		)
												SCREAMING_SNAKE_CASE							:					List[str]           =							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
												SCREAMING_SNAKE_CASE							:					Tuple           =							in_proj_weight_cross_attn[:256, :]
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							in_proj_bias_cross_attn[:256]
												SCREAMING_SNAKE_CASE							:					Dict           =							in_proj_weight_cross_attn[256:512, :]
												SCREAMING_SNAKE_CASE							:					Tuple           =							in_proj_bias_cross_attn[256:512]
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							in_proj_weight_cross_attn[-256:, :]
												SCREAMING_SNAKE_CASE							:					Dict           =							in_proj_bias_cross_attn[-256:]
def    A			(					_lowercase	,						_lowercase		):
						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE							:					List[str]           =							image.size
						SCREAMING_SNAKE_CASE							:					Tuple           =							max(_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					Tuple           =							800 if '''detection''' in checkpoint_url else 1_000
						SCREAMING_SNAKE_CASE							:					str           =							target_max_size / current_max_size
						SCREAMING_SNAKE_CASE							:					List[str]           =							image.resize((int(round(scale * width		)		), int(round(scale * height		)		))		)

						return resized_image
def    A			(					_lowercase		):
						SCREAMING_SNAKE_CASE							:					Optional[Any]           =							F.to_tensor(_lowercase		)
						SCREAMING_SNAKE_CASE							:					str           =							F.normalize(_lowercase	,						mean=[0.485, 0.456, 0.406]	,						std=[0.229, 0.224, 0.225]		)
						return image
@torch.no_grad()
def    A			(					_lowercase	,						_lowercase	,						_lowercase		):
						logger.info('''Converting model...'''		)

						# load original state dict
						SCREAMING_SNAKE_CASE							:					List[Any]           =							torch.hub.load_state_dict_from_url(_lowercase	,						map_location='''cpu'''		)
						# rename keys
						for src, dest in rename_keys:
												rename_key(_lowercase	,						_lowercase	,						_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							rename_backbone_keys(_lowercase		)
						# query, key and value matrices need special treatment
						read_in_q_k_v(_lowercase		)
						# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
						SCREAMING_SNAKE_CASE							:					Any           =							'''model.'''
						for key in state_dict.copy().keys():
												if not key.startswith('''class_labels_classifier'''		) and not key.startswith('''bbox_predictor'''		):
																		SCREAMING_SNAKE_CASE							:					int           =							state_dict.pop(_lowercase		)
																		SCREAMING_SNAKE_CASE							:					int           =							val
    # create HuggingFace model and load state dict
						SCREAMING_SNAKE_CASE							:					Optional[int]           =							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:
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							15
												SCREAMING_SNAKE_CASE							:					int           =							2
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							{0: '''table''', 1: '''table rotated'''}
												SCREAMING_SNAKE_CASE							:					Dict           =							idalabel
												SCREAMING_SNAKE_CASE							:					int           =							{v: k for k, v in idalabel.items()}
						else:
												SCREAMING_SNAKE_CASE							:					List[str]           =							125
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							6
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							{
												    0: '''table''',
												    1: '''table column''',
												    2: '''table row''',
												    3: '''table column header''',
												    4: '''table projected row header''',
												    5: '''table spanning cell''',
												}
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							idalabel
												SCREAMING_SNAKE_CASE							:					List[Any]           =							{v: k for k, v in idalabel.items()}

						SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							DetrImageProcessor(
						    format='''coco_detection'''	,						max_size=800 if '''detection''' in checkpoint_url else 1_000		)
						SCREAMING_SNAKE_CASE							:					int           =							TableTransformerForObjectDetection(_lowercase		)
						model.load_state_dict(_lowercase		)
						model.eval()

						# verify our conversion
						SCREAMING_SNAKE_CASE							:					List[Any]           =							'''example_pdf.png''' if '''detection''' in checkpoint_url else '''example_table.png'''
						SCREAMING_SNAKE_CASE							:					int           =							hf_hub_download(repo_id='''nielsr/example-pdf'''	,						repo_type='''dataset'''	,						filename=_lowercase		)
						SCREAMING_SNAKE_CASE							:					List[Any]           =							Image.open(_lowercase		).convert('''RGB'''		)
						SCREAMING_SNAKE_CASE							:					List[str]           =							normalize(resize(_lowercase	,						_lowercase		)		).unsqueeze(0		)

						SCREAMING_SNAKE_CASE							:					List[Any]           =							model(_lowercase		)

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

						else:
												SCREAMING_SNAKE_CASE							:					int           =							(1, 125, 7)
												SCREAMING_SNAKE_CASE							:					Tuple           =							torch.tensor(
												    [[-18.1430, -8.3214, 4.8274], [-18.4685, -7.1361, -4.2667], [-26.3693, -9.3429, -4.9962]]		)
												SCREAMING_SNAKE_CASE							:					Optional[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]	,						_lowercase	,						atol=1e-4		)
						assert torch.allclose(outputs.pred_boxes[0, :3, :3]	,						_lowercase	,						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(_lowercase		).mkdir(exist_ok=_lowercase		)
												model.save_pretrained(_lowercase		)
												image_processor.save_pretrained(_lowercase		)

						if push_to_hub:
												# Push model to HF hub
												logger.info('''Pushing model to the hub...'''		)
												SCREAMING_SNAKE_CASE							:					Dict           =							(
												    '''microsoft/table-transformer-detection'''
												    if '''detection''' in checkpoint_url
												    else '''microsoft/table-transformer-structure-recognition'''
												)
												model.push_to_hub(_lowercase		)
												image_processor.push_to_hub(_lowercase		)


if __name__ == "__main__":
					__UpperCamelCase	: str    		=  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.'
					)
					__UpperCamelCase	: Optional[int]    		=  parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)






import os
import re
import warnings
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_ta import TaTokenizer
else:
					__UpperCamelCase	: Dict    		=  None


__UpperCamelCase	: Tuple    		=  logging.get_logger(__name__)

__UpperCamelCase	: Optional[int]    		=  {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}

__UpperCamelCase	: Optional[int]    		=  {
    'vocab_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
    },
    'tokenizer_file': {
        't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
        't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
        't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
        't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
        't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
    },
}


# TODO(PVP) - this should be removed in Transformers v5
__UpperCamelCase	: Union[str, Any]    		=  {
    't5-small': 512,
    't5-base': 512,
    't5-large': 512,
    't5-3b': 512,
    't5-11b': 512,
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	VOCAB_FILES_NAMES
						UpperCamelCase_  =	PRETRAINED_VOCAB_FILES_MAP
						UpperCamelCase_  =	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCamelCase_  =	["""input_ids""", """attention_mask"""]
						UpperCamelCase_  =	TaTokenizer

						UpperCamelCase_  =	[]


						def __init__( self			:       str					,	UpperCamelCase__			:       Optional[int]=None					,	UpperCamelCase__			:       Dict=None					,	UpperCamelCase__			:       str="</s>"					,	UpperCamelCase__			:       str="<unk>"					,	UpperCamelCase__			:       Optional[int]="<pad>"					,	UpperCamelCase__			:       Optional[Any]=100					,	UpperCamelCase__			:       List[Any]=None					,	**UpperCamelCase__			:       str					,	):





												'''simple docstring'''





												if extra_ids > 0 and additional_special_tokens is None:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							[f"""<extra_id_{i}>""" for i in range(UpperCamelCase__		)]
												elif extra_ids > 0 and additional_special_tokens is not None:
																		# Check that we have the right number of extra special tokens
																		SCREAMING_SNAKE_CASE							:					int           =							len(set(filter(lambda UpperCamelCase__		: bool('''extra_id_''' in str(UpperCamelCase__		)		)					,	UpperCamelCase__		)		)		)
																		if extra_tokens != extra_ids:
																								raise ValueError(
																								    f"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
																								    ''' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'''
																								    ''' tokens'''		)

												super().__init__(
												    UpperCamelCase__					,	tokenizer_file=UpperCamelCase__					,	eos_token=UpperCamelCase__					,	unk_token=UpperCamelCase__					,	pad_token=UpperCamelCase__					,	extra_ids=UpperCamelCase__					,	additional_special_tokens=UpperCamelCase__					,	**UpperCamelCase__					,	)

												SCREAMING_SNAKE_CASE							:					str           =							vocab_file
												SCREAMING_SNAKE_CASE							:					int           =							False if not self.vocab_file else True
												SCREAMING_SNAKE_CASE							:					str           =							extra_ids


						@staticmethod
						def  __A ( UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[int]					,	UpperCamelCase__			:       Optional[Any]		):





												'''simple docstring'''





												if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
																		SCREAMING_SNAKE_CASE							:					List[str]           =							TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
																		if init_max_model_length is not None and init_max_model_length != max_model_length:
																								return init_max_model_length
																		elif init_max_model_length is None:
																								warnings.warn(
																								    '''This tokenizer was incorrectly instantiated with a model max length of'''
																								    f""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
																								    ''' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'''
																								    ''' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'''
																								    f""" {pretrained_model_name_or_path} automatically truncating your input to"""
																								    f""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
																								    f""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
																								    ''' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'''
																								    ''' instantiate this tokenizer with `model_max_length` set to your preferred value.'''					,	UpperCamelCase__					,	)

												return max_model_length


						def  __A ( self			:       Tuple					,	UpperCamelCase__			:       str					,	UpperCamelCase__			:       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(UpperCamelCase__		):
																		logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""		)
																		return
												SCREAMING_SNAKE_CASE							:					Any           =							os.path.join(
												    UpperCamelCase__					,	(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']		)

												if os.path.abspath(self.vocab_file		) != os.path.abspath(UpperCamelCase__		):
																		copyfile(self.vocab_file					,	UpperCamelCase__		)
																		logger.info(f"""Copy vocab file to {out_vocab_file}"""		)

												return (out_vocab_file,)


						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Optional[int]           =							token_ids_a + [self.eos_token_id]
												if token_ids_a is None:
																		return self.prefix_tokens + token_ids_a
												else:
																		SCREAMING_SNAKE_CASE							:					Tuple           =							token_ids_a + [self.eos_token_id]
																		return self.prefix_tokens + token_ids_a + token_ids_a


						def  __A ( self			:       Any					,	UpperCamelCase__			:       List[int]					,	UpperCamelCase__			:       Optional[List[int]] = None		):





												'''simple docstring'''





												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[self.eos_token_id]

												if token_ids_a is None:
																		return len(token_ids_a + eos		) * [0]
												return len(token_ids_a + eos + token_ids_a + eos		) * [0]


						def  __A ( self			:       Dict		):





												'''simple docstring'''





												return list(
												    set(filter(lambda UpperCamelCase__		: bool(re.search(r'''<extra_id_\d+>'''					,	UpperCamelCase__		)		) is not None					,	self.additional_special_tokens		)		)		)



						def  __A ( self			:       List[Any]		):





												'''simple docstring'''





												return [self.convert_tokens_to_ids(UpperCamelCase__		) for token in self.get_sentinel_tokens()]





