




"""simple docstring"""

import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
							import torch

							from ..models.auto.modeling_auto import (
							    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
							    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
							)

if is_tf_available() and is_tensorflow_probability_available():
							import tensorflow as tf
							import tensorflow_probability as tfp

							from ..models.auto.modeling_tf_auto import (
							    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
							    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
							)
class 			lowerCamelCase__    (  lowerCamelCase_			):


			def __call__(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					requires_backends(self				, "pandas"							)
					import pandas as pd

					if table is None:
							raise ValueError("Keyword argument `table` cannot be None."							)
					elif query is None:
							if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) and table.get("query"							) is not None and table.get("table"							) is not None:
									snake_case		:						Tuple								=				[table]
							elif isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) and len(SCREAMING_SNAKE_CASE							) > 0:
									if not all(isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) for d in table							):
											raise ValueError(
											    F'''Keyword argument `table` should be a list of dict, but is {(type(SCREAMING_SNAKE_CASE							) for d in table)}'''							)

									if table[0].get("query"							) is not None and table[0].get("table"							) is not None:
											snake_case		:						int								=				table
									else:
											raise ValueError(
											    "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
											    F''' and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys.'''							)
							elif Dataset is not None and isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							) or isinstance(SCREAMING_SNAKE_CASE				, types.GeneratorType							):
									return table
							else:
									raise ValueError(
									    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
									    F'''is {type(SCREAMING_SNAKE_CASE							)})'''							)
					else:
							snake_case		:						Dict								=				[{"table": table, "query": query}]

					for tqa_pipeline_input in tqa_pipeline_inputs:
							if not isinstance(tqa_pipeline_input["table"]				, pd.DataFrame							):
									if tqa_pipeline_input["table"] is None:
											raise ValueError("Table cannot be None."							)

									snake_case		:						Tuple								=				pd.DataFrame(tqa_pipeline_input["table"]							)

					return tqa_pipeline_inputs







@add_end_docstrings(lowerCamelCase_			)
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      List[Any]								=     """table,query"""


			def __init__(     self				, SCREAMING_SNAKE_CASE=TableQuestionAnsweringArgumentHandler()				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					super().__init__(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				args_parser

					self.check_model_type(
					    dict(
					        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
					        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)
					    if self.framework == "tf"
					    else dict(
					        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()							)							)

					snake_case		:						Tuple								=				bool(getattr(self.model.config				, "aggregation_labels"				, SCREAMING_SNAKE_CASE							)							) and bool(
					    getattr(self.model.config				, "num_aggregation_labels"				, SCREAMING_SNAKE_CASE							)							)
					snake_case		:						Tuple								=				"tapas" if hasattr(self.model.config				, "aggregation_labels"							) else None


			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.model(**SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if self.framework == "pt":
							snake_case		:						Optional[int]								=				[]
							snake_case		:						Union[str, Any]								=				[]
							snake_case		:						Tuple								=				None
							snake_case		:						int								=				inputs["input_ids"].shape[0]

							snake_case		:						int								=				inputs["input_ids"].to(self.device							)
							snake_case		:						str								=				inputs["attention_mask"].to(self.device							)
							snake_case		:						Optional[Any]								=				inputs["token_type_ids"].to(self.device							)
							snake_case		:						List[str]								=				None

							for index in range(SCREAMING_SNAKE_CASE							):
									# If sequences have already been processed, the token type IDs will be created according to the previous
									# answer.
									if prev_answers is not None:
											snake_case		:						Tuple								=				token_type_ids_example[:, 3]  # shape (seq_len,)
											snake_case		:						str								=				np.zeros_like(prev_labels_example.cpu().numpy()							)  # shape (seq_len,)

											snake_case		:						Dict								=				token_type_ids[index]  # shape (seq_len, 7)
											for i in range(model_labels.shape[0]							):
													snake_case		:						Tuple								=				token_type_ids_example[:, 0].tolist()[i]
													snake_case		:						List[Any]								=				token_type_ids_example[:, 1].tolist()[i] - 1
													snake_case		:						List[Any]								=				token_type_ids_example[:, 2].tolist()[i] - 1

													if row_id >= 0 and col_id >= 0 and segment_id == 1:
															snake_case		:						Optional[Any]								=				int(prev_answers[(col_id, row_id)]							)

											snake_case		:						Any								=				torch.from_numpy(SCREAMING_SNAKE_CASE							).type(torch.long							).to(self.device							)

									snake_case		:						Any								=				input_ids[index]
									snake_case		:						Tuple								=				attention_mask[index]  # shape (seq_len,)
									snake_case		:						List[Any]								=				token_type_ids[index]  # shape (seq_len, 7)
									snake_case		:						List[str]								=				self.model(
									    input_ids=input_ids_example.unsqueeze(0							)				, attention_mask=attention_mask_example.unsqueeze(0							)				, token_type_ids=token_type_ids_example.unsqueeze(0							)				, )
									snake_case		:						Tuple								=				outputs.logits

									if self.aggregate:
											all_aggregations.append(outputs.logits_aggregation							)

									all_logits.append(SCREAMING_SNAKE_CASE							)

									snake_case		:						Optional[Any]								=				torch.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE							)
									snake_case		:						Union[str, Any]								=				dist_per_token.probs * attention_mask_example.type(torch.floataa							).to(
									    dist_per_token.probs.device							)

									snake_case		:						int								=				collections.defaultdict(SCREAMING_SNAKE_CASE							)
									for i, p in enumerate(probabilities.squeeze().tolist()							):
											snake_case		:						str								=				token_type_ids_example[:, 0].tolist()[i]
											snake_case		:						Union[str, Any]								=				token_type_ids_example[:, 1].tolist()[i] - 1
											snake_case		:						List[Any]								=				token_type_ids_example[:, 2].tolist()[i] - 1
											if col >= 0 and row >= 0 and segment_id == 1:
													coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE							)

									snake_case		:						List[str]								=				{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}

							snake_case		:						Optional[int]								=				torch.cat(tuple(SCREAMING_SNAKE_CASE							)				, 0							)

							return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(SCREAMING_SNAKE_CASE							)				, 0							))
					else:
							snake_case		:						int								=				[]
							snake_case		:						Dict								=				[]
							snake_case		:						Dict								=				None
							snake_case		:						List[Any]								=				inputs["input_ids"].shape[0]

							snake_case		:						Tuple								=				inputs["input_ids"]
							snake_case		:						Optional[int]								=				inputs["attention_mask"]
							snake_case		:						Dict								=				inputs["token_type_ids"].numpy()
							snake_case		:						Dict								=				None

							for index in range(SCREAMING_SNAKE_CASE							):
									# If sequences have already been processed, the token type IDs will be created according to the previous
									# answer.
									if prev_answers is not None:
											snake_case		:						Optional[int]								=				token_type_ids_example[:, 3]  # shape (seq_len,)
											snake_case		:						Optional[int]								=				np.zeros_like(SCREAMING_SNAKE_CASE				, dtype=np.intaa							)  # shape (seq_len,)

											snake_case		:						str								=				token_type_ids[index]  # shape (seq_len, 7)
											for i in range(model_labels.shape[0]							):
													snake_case		:						int								=				token_type_ids_example[:, 0].tolist()[i]
													snake_case		:						Any								=				token_type_ids_example[:, 1].tolist()[i] - 1
													snake_case		:						Optional[Any]								=				token_type_ids_example[:, 2].tolist()[i] - 1

													if row_id >= 0 and col_id >= 0 and segment_id == 1:
															snake_case		:						Dict								=				int(prev_answers[(col_id, row_id)]							)

											snake_case		:						Tuple								=				model_labels

									snake_case		:						List[Any]								=				input_ids[index]
									snake_case		:						List[str]								=				attention_mask[index]  # shape (seq_len,)
									snake_case		:						Union[str, Any]								=				token_type_ids[index]  # shape (seq_len, 7)
									snake_case		:						List[Any]								=				self.model(
									    input_ids=np.expand_dims(SCREAMING_SNAKE_CASE				, axis=0							)				, attention_mask=np.expand_dims(SCREAMING_SNAKE_CASE				, axis=0							)				, token_type_ids=np.expand_dims(SCREAMING_SNAKE_CASE				, axis=0							)				, )
									snake_case		:						str								=				outputs.logits

									if self.aggregate:
											all_aggregations.append(outputs.logits_aggregation							)

									all_logits.append(SCREAMING_SNAKE_CASE							)

									snake_case		:						int								=				tfp.distributions.Bernoulli(logits=SCREAMING_SNAKE_CASE							)
									snake_case		:						int								=				dist_per_token.probs_parameter() * tf.cast(SCREAMING_SNAKE_CASE				, tf.floataa							)

									snake_case		:						Any								=				collections.defaultdict(SCREAMING_SNAKE_CASE							)
									snake_case		:						List[str]								=				token_type_ids_example
									for i, p in enumerate(tf.squeeze(SCREAMING_SNAKE_CASE							).numpy().tolist()							):
											snake_case		:						List[Any]								=				token_type_ids_example[:, 0].tolist()[i]
											snake_case		:						Optional[Any]								=				token_type_ids_example[:, 1].tolist()[i] - 1
											snake_case		:						List[str]								=				token_type_ids_example[:, 2].tolist()[i] - 1
											if col >= 0 and row >= 0 and segment_id == 1:
													coords_to_probs[(col, row)].append(SCREAMING_SNAKE_CASE							)

									snake_case		:						Optional[Any]								=				{key: np.array(coords_to_probs[key]							).mean() > 0.5 for key in coords_to_probs}

							snake_case		:						str								=				tf.concat(tuple(SCREAMING_SNAKE_CASE							)				, 0							)

							return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(SCREAMING_SNAKE_CASE							)				, 0							))


			def __call__(     self				, *SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				self._args_parser(*SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)

					snake_case		:						Any								=				super().__call__(SCREAMING_SNAKE_CASE				, **SCREAMING_SNAKE_CASE							)
					if len(SCREAMING_SNAKE_CASE							) == 1:
							return results[0]
					return results


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=None				, **SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Optional[Any]								=				{}
					if padding is not None:
							snake_case		:						List[Any]								=				padding
					if truncation is not None:
							snake_case		:						int								=				truncation

					snake_case		:						List[str]								=				{}
					if sequential is not None:
							snake_case		:						int								=				sequential
					return preprocess_params, forward_params, {}


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=None				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=None							):



					"""simple docstring"""







					if truncation is None:
							if self.type == "tapas":
									snake_case		:						List[Any]								=				"drop_rows_to_fit"
							else:
									snake_case		:						Union[str, Any]								=				"do_not_truncate"

					snake_case		:						Dict								=				pipeline_input["table"], pipeline_input["query"]
					if table.empty:
							raise ValueError("table is empty"							)
					if query is None or query == "":
							raise ValueError("query is empty"							)
					snake_case		:						Union[str, Any]								=				self.tokenizer(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, return_tensors=self.framework				, truncation=SCREAMING_SNAKE_CASE				, padding=SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[int]								=				table
					return inputs


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=False							):



					"""simple docstring"""







					snake_case		:						Dict								=				model_inputs.pop("table"							)

					if self.type == "tapas":
							if sequential:
									snake_case		:						Dict								=				self.sequential_inference(**SCREAMING_SNAKE_CASE							)
							else:
									snake_case		:						Tuple								=				self.batch_inference(**SCREAMING_SNAKE_CASE							)
					else:
							snake_case		:						int								=				self.model.generate(**SCREAMING_SNAKE_CASE							)
					snake_case		:						int								=				{"model_inputs": model_inputs, "table": table, "outputs": outputs}
					return model_outputs



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						int								=				model_outputs["model_inputs"]
					snake_case		:						Tuple								=				model_outputs["table"]
					snake_case		:						Union[str, Any]								=				model_outputs["outputs"]
					if self.type == "tapas":
							if self.aggregate:
									snake_case		:						Union[str, Any]								=				outputs[:2]
									snake_case		:						Any								=				self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
									snake_case		:						List[str]								=				predictions
									snake_case		:						List[str]								=				{i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(SCREAMING_SNAKE_CASE							)}

									snake_case		:						Optional[int]								=				self.model.config.no_aggregation_label_index
									snake_case		:						int								=				{
									    i: aggregators[i] + " > " for i, pred in enumerate(SCREAMING_SNAKE_CASE							) if pred != no_agg_label_index
									}
							else:
									snake_case		:						Any								=				outputs[0]
									snake_case		:						int								=				self.tokenizer.convert_logits_to_predictions(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
									snake_case		:						Any								=				predictions[0]
									snake_case		:						Any								=				{}
									snake_case		:						Optional[int]								=				{}
							snake_case		:						Union[str, Any]								=				[]
							for index, coordinates in enumerate(SCREAMING_SNAKE_CASE							):
									snake_case		:						List[str]								=				[table.iat[coordinate] for coordinate in coordinates]
									snake_case		:						Union[str, Any]								=				aggregators.get(SCREAMING_SNAKE_CASE				, ""							)
									snake_case		:						str								=				aggregators_prefix.get(SCREAMING_SNAKE_CASE				, ""							)
									snake_case		:						Union[str, Any]								=				{
									    "answer": aggregator_prefix + ", ".join(SCREAMING_SNAKE_CASE							),
									    "coordinates": coordinates,
									    "cells": [table.iat[coordinate] for coordinate in coordinates],
									}
									if aggregator:
											snake_case		:						Optional[int]								=				aggregator

									answers.append(SCREAMING_SNAKE_CASE							)
							if len(SCREAMING_SNAKE_CASE							) == 0:
									raise PipelineException("Empty answer"							)
					else:
							snake_case		:						Dict								=				[{"answer": answer} for answer in self.tokenizer.batch_decode(SCREAMING_SNAKE_CASE				, skip_special_tokens=SCREAMING_SNAKE_CASE							)]

					return answers if len(SCREAMING_SNAKE_CASE							) > 1 else answers[0]







"""simple docstring"""

import os
import re
import unicodedata
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


__A 		=			logging.get_logger(__name__)
__A 		=			{"vocab_file": "spiece.model"}

__A 		=			{
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

__A 		=			{
    "google/fnet-base": 512,
    "google/fnet-large": 512,
}

__A 		=			"▁"
class 			lowerCamelCase__    (  lowerCamelCase_			):
			a__ :      Dict								=     VOCAB_FILES_NAMES
			a__ :      Tuple								=     PRETRAINED_VOCAB_FILES_MAP
			a__ :      int								=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			a__ :      List[Any]								=     ["""input_ids""", """token_type_ids"""]


			def __init__(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE=False				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE=True				, SCREAMING_SNAKE_CASE="<unk>"				, SCREAMING_SNAKE_CASE="[SEP]"				, SCREAMING_SNAKE_CASE="<pad>"				, SCREAMING_SNAKE_CASE="[CLS]"				, SCREAMING_SNAKE_CASE="[MASK]"				, SCREAMING_SNAKE_CASE = None				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					snake_case		:						Dict								=				(
					    AddedToken(SCREAMING_SNAKE_CASE				, lstrip=SCREAMING_SNAKE_CASE				, rstrip=SCREAMING_SNAKE_CASE				, normalized=SCREAMING_SNAKE_CASE							)
					    if isinstance(SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE							)
					    else mask_token
					)

					snake_case		:						Optional[int]								=				{} if sp_model_kwargs is None else sp_model_kwargs

					super().__init__(
					    do_lower_case=SCREAMING_SNAKE_CASE				, remove_space=SCREAMING_SNAKE_CASE				, keep_accents=SCREAMING_SNAKE_CASE				, unk_token=SCREAMING_SNAKE_CASE				, sep_token=SCREAMING_SNAKE_CASE				, pad_token=SCREAMING_SNAKE_CASE				, cls_token=SCREAMING_SNAKE_CASE				, mask_token=SCREAMING_SNAKE_CASE				, sp_model_kwargs=self.sp_model_kwargs				, **SCREAMING_SNAKE_CASE				, )

					snake_case		:						List[str]								=				do_lower_case
					snake_case		:						Any								=				remove_space
					snake_case		:						Union[str, Any]								=				keep_accents
					snake_case		:						Optional[int]								=				vocab_file

					snake_case		:						Dict								=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
					self.sp_model.Load(SCREAMING_SNAKE_CASE							)


			@property
			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					return len(self.sp_model							)


			def 							lowerCamelCase_				(     self							):



					"""simple docstring"""







					snake_case		:						Optional[int]								=				{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 __getstate__(     self							):



					"""simple docstring"""







					snake_case		:						Tuple								=				self.__dict__.copy()
					snake_case		:						List[Any]								=				None
					return state


			def __setstate__(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Union[str, Any]								=				d

					# for backward compatibility
					if not hasattr(self				, "sp_model_kwargs"							):
							snake_case		:						List[Any]								=				{}

					snake_case		:						Tuple								=				spm.SentencePieceProcessor(**self.sp_model_kwargs							)
					self.sp_model.Load(self.vocab_file							)


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					if self.remove_space:
							snake_case		:						int								=				" ".join(inputs.strip().split()							)
					else:
							snake_case		:						Union[str, Any]								=				inputs
					snake_case		:						Union[str, Any]								=				outputs.replace("``"				, "\""							).replace("''"				, "\""							)

					if not self.keep_accents:
							snake_case		:						List[Any]								=				unicodedata.normalize("NFKD"				, SCREAMING_SNAKE_CASE							)
							snake_case		:						int								=				"".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE							)]							)
					if self.do_lower_case:
							snake_case		:						Union[str, Any]								=				outputs.lower()

					return outputs


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						List[str]								=				self.preprocess_text(SCREAMING_SNAKE_CASE							)
					snake_case		:						List[str]								=				self.sp_model.encode(SCREAMING_SNAKE_CASE				, out_type=SCREAMING_SNAKE_CASE							)
					snake_case		:						Optional[Any]								=				[]
					for piece in pieces:
							if len(SCREAMING_SNAKE_CASE							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
									snake_case		:						Union[str, Any]								=				self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE				, ""							)							)
									if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
											if len(cur_pieces[0]							) == 1:
													snake_case		:						List[Any]								=				cur_pieces[1:]
											else:
													snake_case		:						Dict								=				cur_pieces[0][1:]
									cur_pieces.append(piece[-1]							)
									new_pieces.extend(SCREAMING_SNAKE_CASE							)
							else:
									new_pieces.append(SCREAMING_SNAKE_CASE							)

					return new_pieces


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE							)


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE							):



					"""simple docstring"""







					snake_case		:						Dict								=				[]
					snake_case		:						Any								=				""
					snake_case		:						Dict								=				False
					for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
									if not prev_is_special:
											out_string += " "
									out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE							) + token
									snake_case		:						Optional[int]								=				True
									snake_case		:						Union[str, Any]								=				[]
							else:
									current_sub_tokens.append(SCREAMING_SNAKE_CASE							)
									snake_case		:						Tuple								=				False
					out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE							)
					return out_string.strip()


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = False				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = True				, **SCREAMING_SNAKE_CASE				, ):



					"""simple docstring"""







					snake_case		:						int								=				kwargs.pop("use_source_tokenizer"				, SCREAMING_SNAKE_CASE							)

					snake_case		:						Tuple								=				self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE				, skip_special_tokens=SCREAMING_SNAKE_CASE							)

					# To avoid mixing byte-level and unicode for byte-level BPT
					# we need to build string separately for added tokens and byte-level tokens
					# cf. https://github.com/huggingface/transformers/issues/1133
					snake_case		:						Any								=				[]
					snake_case		:						Union[str, Any]								=				[]
					for token in filtered_tokens:
							if skip_special_tokens and token in self.all_special_ids:
									continue
							if token in self.added_tokens_encoder:
									if current_sub_text:
											sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE							)							)
											snake_case		:						Optional[Any]								=				[]
									sub_texts.append(SCREAMING_SNAKE_CASE							)
							else:
									current_sub_text.append(SCREAMING_SNAKE_CASE							)
					if current_sub_text:
							sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE							)							)

					# Mimic the behavior of the Rust tokenizer:
					# No space after <unk>
					if spaces_between_special_tokens:
							snake_case		:						Any								=				re.sub(r"(<unk>) "				, r"\1"				, " ".join(SCREAMING_SNAKE_CASE							)							)
					else:
							snake_case		:						Optional[int]								=				"".join(SCREAMING_SNAKE_CASE							)

					snake_case		:						Optional[int]								=				(
					    clean_up_tokenization_spaces
					    if clean_up_tokenization_spaces is not None
					    else self.clean_up_tokenization_spaces
					)
					if clean_up_tokenization_spaces:
							snake_case		:						List[Any]								=				self.clean_up_tokenization(SCREAMING_SNAKE_CASE							)
							return clean_text
					else:
							return text


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







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


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None				, SCREAMING_SNAKE_CASE = False							):



					"""simple docstring"""







					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 not None:
							return [1] + ([0] * len(SCREAMING_SNAKE_CASE							)) + [1] + ([0] * len(SCREAMING_SNAKE_CASE							)) + [1]
					return [1] + ([0] * len(SCREAMING_SNAKE_CASE							)) + [1]


			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					snake_case		:						Dict								=				[self.sep_token_id]
					snake_case		:						Optional[int]								=				[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							) * [0] + len(token_ids_a + sep							) * [1]



			def 							lowerCamelCase_				(     self				, SCREAMING_SNAKE_CASE				, SCREAMING_SNAKE_CASE = None							):



					"""simple docstring"""







					if not os.path.isdir(SCREAMING_SNAKE_CASE							):
							logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
							return
					snake_case		:						Tuple								=				os.path.join(
					    SCREAMING_SNAKE_CASE				, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["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:
									snake_case		:						List[str]								=				self.sp_model.serialized_model_proto()
									fi.write(SCREAMING_SNAKE_CASE							)

					return (out_vocab_file,)

