


'''simple docstring'''





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__  			=				logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
UpperCAmelCase__  			=				[]
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 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: Union[str, Any],_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A=	state_dict.pop(_SCREAMING_SNAKE_CASE )
			__A=	val






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			__A=	OrderedDict()
			for key, value in state_dict.items():
						if "backbone.0.body" in key:
									__A=	key.replace('backbone.0.body','backbone.conv_encoder.model' )
									__A=	value
						else:
									__A=	value

			return new_state_dict






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: str ):






			"""simple docstring"""




			__A=	''

			# 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)
						__A=	state_dict.pop(f"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
						__A=	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
						__A=	in_proj_weight[:256, :]
						__A=	in_proj_bias[:256]
						__A=	in_proj_weight[256:512, :]
						__A=	in_proj_bias[256:512]
						__A=	in_proj_weight[-256:, :]
						__A=	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
						__A=	state_dict.pop(f"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
						__A=	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
						__A=	in_proj_weight[:256, :]
						__A=	in_proj_bias[:256]
						__A=	in_proj_weight[256:512, :]
						__A=	in_proj_bias[256:512]
						__A=	in_proj_weight[-256:, :]
						__A=	in_proj_bias[-256:]
						# read in weights + bias of input projection layer of cross-attention
						__A=	state_dict.pop(
						    f"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
						__A=	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
						__A=	in_proj_weight_cross_attn[:256, :]
						__A=	in_proj_bias_cross_attn[:256]
						__A=	in_proj_weight_cross_attn[256:512, :]
						__A=	in_proj_bias_cross_attn[256:512]
						__A=	in_proj_weight_cross_attn[-256:, :]
						__A=	in_proj_bias_cross_attn[-256:]






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Any,_SCREAMING_SNAKE_CASE					: Union[str, Any] ):






			"""simple docstring"""




			__A,	__A=	image.size
			__A=	max(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE )
			__A=	800 if 'detection' in checkpoint_url else 1000
			__A=	target_max_size / current_max_size
			__A=	image.resize((int(round(scale * width ) ), int(round(scale * height ) )) )

			return resized_image






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: List[Any] ):






			"""simple docstring"""




			__A=	F.to_tensor(_SCREAMING_SNAKE_CASE )
			__A=	F.normalize(_SCREAMING_SNAKE_CASE,mean=[0.4_85, 0.4_56, 0.4_06],std=[0.2_29, 0.2_24, 0.2_25] )
			return image






@torch.no_grad()
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: Dict,_SCREAMING_SNAKE_CASE					: int,_SCREAMING_SNAKE_CASE					: List[str] ):






			"""simple docstring"""




			logger.info('Converting model...' )

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

			__A=	DetrImageProcessor(
			    format='coco_detection',max_size=800 if 'detection' in checkpoint_url else 1000 )
			__A=	TableTransformerForObjectDetection(_SCREAMING_SNAKE_CASE )
			model.load_state_dict(_SCREAMING_SNAKE_CASE )
			model.eval()

			# verify our conversion
			__A=	'example_pdf.png' if 'detection' in checkpoint_url else 'example_table.png'
			__A=	hf_hub_download(repo_id='nielsr/example-pdf',repo_type='dataset',filename=_SCREAMING_SNAKE_CASE )
			__A=	Image.open(_SCREAMING_SNAKE_CASE ).convert('RGB' )
			__A=	normalize(resize(_SCREAMING_SNAKE_CASE,_SCREAMING_SNAKE_CASE ) ).unsqueeze(0 )

			__A=	model(_SCREAMING_SNAKE_CASE )

			if "detection" in checkpoint_url:
						__A=	(1, 15, 3)
						__A=	torch.tensor(
						    [[-6.78_97, -16.99_85, 6.79_37], [-8.01_86, -22.21_92, 6.96_77], [-7.31_17, -21.07_08, 7.40_55]] )
						__A=	torch.tensor([[0.48_67, 0.17_67, 0.67_32], [0.67_18, 0.44_79, 0.38_30], [0.47_16, 0.17_60, 0.63_64]] )

			else:
						__A=	(1, 125, 7)
						__A=	torch.tensor(
						    [[-18.14_30, -8.32_14, 4.82_74], [-18.46_85, -7.13_61, -4.26_67], [-26.36_93, -9.34_29, -4.99_62]] )
						__A=	torch.tensor([[0.49_83, 0.55_95, 0.94_40], [0.49_16, 0.63_15, 0.59_54], [0.61_08, 0.86_37, 0.11_35]] )

			assert outputs.logits.shape == expected_shape
			assert torch.allclose(outputs.logits[0, :3, :3],_SCREAMING_SNAKE_CASE,atol=1e-4 )
			assert torch.allclose(outputs.pred_boxes[0, :3, :3],_SCREAMING_SNAKE_CASE,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(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
						model.save_pretrained(_SCREAMING_SNAKE_CASE )
						image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )

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


if __name__ == "__main__":
					UpperCAmelCase__  			=				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__  			=				parser.parse_args()
					convert_table_transformer_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)







'''simple docstring'''





import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging


if is_torch_available():
					import torch


if TYPE_CHECKING:
					from transformers.pipelines.conversational import Conversation


UpperCAmelCase__  			=				logging.get_logger(__name__)
UpperCAmelCase__  			=				{'''vocab_file''': '''spiece.model'''}

UpperCAmelCase__  			=				{
    '''vocab_file''': {
        '''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
        '''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
    }
}

UpperCAmelCase__  			=				{
    '''AI-Sweden/gpt-sw3-126m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-350m''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-1.6b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-6.7b''': 2_0_4_8,
    '''AI-Sweden/gpt-sw3-20b''': 2_0_4_8,
}
class 						a__					(					a_   ):





			'''simple docstring'''

			A						:				int					      =			VOCAB_FILES_NAMES
			A						:				Optional[int]					      =			PRETRAINED_VOCAB_FILES_MAP
			A						:				Tuple					      =			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			A						:				int					      =			['''input_ids''', '''attention_mask''']
			def __init__(  self							:			int			,    lowerCAmelCase_							:			List[str]			,    lowerCAmelCase_							:			Union[str, Any]=False			,    lowerCAmelCase_							:			List[str]=False			,    lowerCAmelCase_							:			List[Any]=False			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			Union[str, Any]=None			,    lowerCAmelCase_							:			List[Any]=None			,    lowerCAmelCase_							:			Optional[Dict[str, Any]] = None			,    **lowerCAmelCase_							:			Optional[Any]			,    )      ->		None:
						__A=	{} if sp_model_kwargs is None else sp_model_kwargs

						__A=	kwargs.get('name_or_path'	)
						if name_or_path is None:
									logger.warning(
									    'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
									    ' you are testing the model, this can safely be ignored'	)
									__A=	'None'

						# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
						__A=	'<|endoftext|>' if eos_token is None else eos_token
						__A=	'<unk>' if unk_token is None else unk_token
						if "gpt-sw3-7b" in name_or_path:
									__A=	unk_token if pad_token is None else pad_token
									__A=	eos_token if bos_token is None else bos_token
						else:
									__A=	'<pad>' if pad_token is None else pad_token
									__A=	'<s>' if bos_token is None else bos_token

						super().__init__(
						    do_lower_case=lowerCAmelCase_			,    remove_space=lowerCAmelCase_			,    keep_accents=lowerCAmelCase_			,    bos_token=lowerCAmelCase_			,    eos_token=lowerCAmelCase_			,    unk_token=lowerCAmelCase_			,    pad_token=lowerCAmelCase_			,    sp_model_kwargs=self.sp_model_kwargs			,    **lowerCAmelCase_			,    )

						__A=	do_lower_case
						__A=	remove_space
						__A=	keep_accents
						__A=	vocab_file

						__A=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(lowerCAmelCase_	)

						# Used for whitespace normalization in input texts
						# fmt : off
						__A=	{' ', ' ', ' ', ' ', ' ', '　', ' ', ' ', ' ', ' ', '￼', ''}
						# fmt : on

						# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
						__A=	re.compile(
						    F"""[{"".join(map(lowerCAmelCase_			,    list(range(0			,    9	)	) + list(range(11			,    32	)	) + list(range(127			,    160	)	) + [160, 173, 8_203]	)	)}]"""	)
			def __getstate__(  self							:			Optional[int]	)      ->		Tuple:
						__A=	self.__dict__.copy()
						__A=	None
						return state
			def __setstate__(  self							:			int			,    lowerCAmelCase_							:			int	)      ->		Tuple:
						__A=	d

						# for backward compatibility
						if not hasattr(self			,    'sp_model_kwargs'	):
									__A=	{}

						__A=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
						self.sp_model.Load(self.vocab_file	)
			@property
			# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
			def 					lowerCAmelCase     (  self							:			Tuple	)      ->		int:
						return len(self.sp_model	)
			def 					lowerCAmelCase     (  self							:			int			,    lowerCAmelCase_							:			str	)      ->		str:
						__A=	self.non_printing_characters_re.sub(''			,    lowerCAmelCase_	)

						# Normalize whitespaces
						__A=	''.join([char if char not in self.whitespaces else ' ' for char in text]	)

						# NFC Unicode normalization
						__A=	unicodedata.normalize('NFC'			,    lowerCAmelCase_	)
						return text
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			str			,    **lowerCAmelCase_							:			Optional[Any]	)      ->		List[str]:
						__A=	self.preprocess_text(lowerCAmelCase_	)
						return self.sp_model.encode(lowerCAmelCase_			,    out_type=lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Any			,    lowerCAmelCase_							:			str	)      ->		int:
						return self.sp_model.PieceToId(lowerCAmelCase_	)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			int	)      ->		str:
						return self.sp_model.IdToPiece(lowerCAmelCase_	)
			@staticmethod
			def 					lowerCAmelCase     (  lowerCAmelCase_							:			str	)      ->		str:
						return out_string
			def 					lowerCAmelCase     (  self							:			Union[str, Any]			,    lowerCAmelCase_							:			List[str]	)      ->		str:
						__A=	[]
						__A=	''
						__A=	False
						for token in tokens:
									# make sure that special tokens are not decoded using sentencepiece model
									if token in self.all_special_tokens:
												# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
												if not prev_is_special:
															out_string += " "

												out_string += self.sp_model.decode(lowerCAmelCase_	) + token
												__A=	True
												__A=	[]
									else:
												current_sub_tokens.append(lowerCAmelCase_	)
												__A=	False
						out_string += self.sp_model.decode(lowerCAmelCase_	)

						return out_string
			def 					lowerCAmelCase     (  self							:			List[Any]	)      ->		Dict[str, int]:
						__A=	{self.convert_ids_to_tokens(lowerCAmelCase_	): i for i in range(self.vocab_size	)}
						vocab.update(self.added_tokens_encoder	)
						return vocab
			def 					lowerCAmelCase     (  self							:			Dict			,    lowerCAmelCase_							:			str			,    lowerCAmelCase_							:			Optional[str] = None	)      ->		Tuple[str]:
						if not os.path.isdir(lowerCAmelCase_	):
									logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""	)
									return
						__A=	os.path.join(
						    lowerCAmelCase_			,    (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']	)

						if os.path.abspath(self.vocab_file	) != os.path.abspath(lowerCAmelCase_	) and os.path.isfile(self.vocab_file	):
									copyfile(self.vocab_file			,    lowerCAmelCase_	)
						elif not os.path.isfile(self.vocab_file	):
									with open(lowerCAmelCase_			,    'wb'	) as fi:
												__A=	self.sp_model.serialized_model_proto()
												fi.write(lowerCAmelCase_	)

						return (out_vocab_file,)
			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			Union[str, List[str]]			,    lowerCAmelCase_							:			Union[str, bool] = False	)      ->		Union[List[int], List[List[int]], "torch.Tensor"]:

						if isinstance(lowerCAmelCase_			,    lowerCAmelCase_	):
									__A=	self.preprocess_text(lowerCAmelCase_	)
									__A=	self.sp_model.encode(lowerCAmelCase_	)
						else:
									__A=	[self.preprocess_text(lowerCAmelCase_	) for t in text]
									__A=	self.sp_model.encode(lowerCAmelCase_	)

						if return_tensors is True or return_tensors == "pt":
									__A=	torch.tensor(lowerCAmelCase_	)

						return token_ids
			def 					lowerCAmelCase     (  self							:			Tuple			,    lowerCAmelCase_							:			Union[int, List[int]]	)      ->		str:
						return self.sp_model.decode(lowerCAmelCase_	)

			def 					lowerCAmelCase     (  self							:			Optional[Any]			,    lowerCAmelCase_							:			"Conversation"	)      ->		List[int]:
						__A=	[F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
						__A=	(
						    F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(lowerCAmelCase_	) + F"""{self.bos_token}Bot:"""
						)
						return self.encode(text=lowerCAmelCase_	)



