
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowercase__		:str   =    "▁"

lowercase__		:List[str]   =    {"vocab_file": "spiece.model"}

lowercase__		:List[Any]   =    {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

lowercase__		:Optional[int]   =    {
    "google/pegasus-xsum": 512,
}


lowercase__		:str   =    logging.get_logger(__name__)







class 				lowercase		(					SCREAMING_SNAKE_CASE__		):
							lowercase_       :       Dict       					=VOCAB_FILES_NAMES

							lowercase_       :       Optional[Any]       					=VOCAB_FILES_NAMES
							lowercase_       :       List[str]       					=PRETRAINED_VOCAB_FILES_MAP
							lowercase_       :       List[Any]       					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_       :       List[str]       					=['''input_ids''', '''attention_mask''']




							def __init__(				self   ,A__   ,A__="<pad>"   ,A__="</s>"   ,A__="<unk>"   ,A__="<mask_2>"   ,A__="<mask_1>"   ,A__=None   ,A__=1_0_3   ,A__ = None   ,**A__   ,):
												lowercase							=						offset
												if additional_special_tokens is not None:
																	if not isinstance(A__   ,A__):
																						raise TypeError(
																						    f'additional_special_tokens should be of type {type(A__)}, but is'
																						    f' {type(A__)}')

																	lowercase							=						(
																	    ([mask_token_sent] + additional_special_tokens)
																	    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																	    else additional_special_tokens
																	)
																	# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																	additional_special_tokens_extended += [
																	    f'<unk_{i}>' for i in range(len(A__)   ,self.offset - 1)
																	]

																	if len(set(A__)) != len(A__):
																						raise ValueError(
																						    '''Please make sure that the provided additional_special_tokens do not contain an incorrectly'''
																						    f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.')
																	lowercase							=						additional_special_tokens_extended
												else:
																	lowercase							=						[mask_token_sent] if mask_token_sent is not None else []
																	additional_special_tokens += [f'<unk_{i}>' for i in range(2   ,self.offset)]

												lowercase							=						{} if sp_model_kwargs is None else sp_model_kwargs

												super().__init__(
												    eos_token=A__   ,unk_token=A__   ,mask_token=A__   ,pad_token=A__   ,mask_token_sent=A__   ,offset=A__   ,additional_special_tokens=A__   ,sp_model_kwargs=self.sp_model_kwargs   ,**A__   ,)
												lowercase							=						mask_token_sent
												lowercase							=						vocab_file
												lowercase							=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(A__)

												# add special tokens to encoder dict
												lowercase							=						{
												    0: self.pad_token,
												    1: self.eos_token,
												}

												if self.mask_token_sent is not None:
																	self.encoder.update(
																	    {
																	        2: self.mask_token_sent,
																	        3: self.mask_token,
																	    })

												if self.offset > 0:
																	# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
																	# mask_token_sent is already added to list -> so start at 1
																	self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1   ,self.offset - 1)})

												lowercase							=						{v: k for k, v in self.encoder.items()}




							@property
							def  A__      (				self):
												return len(self.sp_model) + self.offset




							def  A__      (				self):
												lowercase							=						{self.convert_ids_to_tokens(A__): i for i in range(self.vocab_size)}
												vocab.update(self.added_tokens_encoder)
												return vocab




							def __getstate__(				self):
												lowercase							=						self.__dict__.copy()
												lowercase							=						None
												return state




							def __setstate__(				self   ,A__):
												lowercase							=						d

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

												lowercase							=						spm.SentencePieceProcessor(**self.sp_model_kwargs)
												self.sp_model.Load(self.vocab_file)




							def  A__      (				self   ,A__):
												return self.sp_model.encode(A__   ,out_type=A__)




							def  A__      (				self   ,A__):
												if token in self.decoder:
																	return self.decoder[token]
												elif token in self.added_tokens_decoder:
																	return self.added_tokens_decoder[token]
												lowercase							=						self.sp_model.piece_to_id(A__)
												return sp_id + self.offset




							def  A__      (				self   ,A__):
												if index in self.encoder:
																	return self.encoder[index]
												elif index in self.added_tokens_encoder:
																	return self.added_tokens_encoder[index]
												else:
																	lowercase							=						self.sp_model.IdToPiece(index - self.offset)
												return token




							def  A__      (				self   ,A__):
												lowercase							=						[]
												lowercase							=						''''''
												for token in tokens:
																	# make sure that special tokens are not decoded using sentencepiece model
																	if token in self.all_special_tokens:
																						out_string += self.sp_model.decode(A__) + token
																						lowercase							=						[]
																	else:
																						current_sub_tokens.append(A__)
												out_string += self.sp_model.decode(A__)
												return out_string.strip()




							def  A__      (				self   ,A__=False):
												return 1




							def  A__      (				self   ,A__):
												lowercase							=						set(self.all_special_ids)  # call it once instead of inside list comp
												all_special_ids.remove(self.unk_token_id)  # <unk> is only sometimes special

												return [1 if x in all_special_ids else 0 for x in seq]




							def  A__      (				self   ,A__   ,A__ = None   ,A__ = False):
												if already_has_special_tokens:
																	return self._special_token_mask(A__)
												elif token_ids_a is None:
																	return self._special_token_mask(A__) + [1]
												else:
																	return self._special_token_mask(token_ids_a + token_ids_a) + [1]




							def  A__      (				self   ,A__   ,A__=None):
												if token_ids_a is None:
																	return token_ids_a + [self.eos_token_id]
												# We don't expect to process pairs, but leave the pair logic for API consistency
												return token_ids_a + token_ids_a + [self.eos_token_id]





							def  A__      (				self   ,A__   ,A__ = None):
												if not os.path.isdir(A__):
																	logger.error(f'Vocabulary path ({save_directory}) should be a directory')
																	return
												lowercase							=						os.path.join(
												    A__   ,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])

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

												return (out_vocab_file,)










# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
		import torch




class     snake_case__							(TensorFormatter[Mapping, """torch.Tensor""", Mapping]   ):
				"""simple docstring"""



				def __init__(      self				:     Tuple					,      __lowerCamelCase				:     Union[str, Any]=None					,      **__lowerCamelCase				:     Any							)					->     Optional[Any]:
							super().__init__(features=__lowerCamelCase							)
							a    =			torch_tensor_kwargs
							import torch  # noqa import torch at initialization





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Dict							)					->     Dict:
							import torch

							if isinstance(__lowerCamelCase					,      __lowerCamelCase							) and column:
										if all(
										    isinstance(__lowerCamelCase					,      torch.Tensor							) and x.shape == column[0].shape and x.dtype == column[0].dtype
										    for x in column							):
													return torch.stack(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     List[Any]							)					->     str:
							import torch

							if isinstance(__lowerCamelCase					,      (str, bytes, type(__lowerCamelCase							))							):
										return value
							elif isinstance(__lowerCamelCase					,      (np.character, np.ndarray)							) and np.issubdtype(value.dtype					,      np.character							):
										return value.tolist()

							a    =			{}

							if isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.integer							):
										a    =			{"dtype": torch.intaa}
							elif isinstance(__lowerCamelCase					,      (np.number, np.ndarray)							) and np.issubdtype(value.dtype					,      np.floating							):
										a    =			{"dtype": torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
										import PIL.Image

										if isinstance(__lowerCamelCase					,      PIL.Image.Image							):
													a    =			np.asarray(__lowerCamelCase							)
							return torch.tensor(__lowerCamelCase					,      **{**default_dtype, **self.torch_tensor_kwargs}							)





				def 							__UpperCAmelCase							(      self				:     List[str]					,      __lowerCamelCase				:     Tuple							)					->     List[str]:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(__lowerCamelCase					,      "__array__"							) and not isinstance(__lowerCamelCase					,      torch.Tensor							):
										a    =			data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(__lowerCamelCase					,      np.ndarray							):
										if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
													return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							elif isinstance(__lowerCamelCase					,      (list, tuple)							):
										return self._consolidate([self.recursive_tensorize(__lowerCamelCase							) for substruct in data_struct]							)
							return self._tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     dict							)					->     str:
							return map_nested(self._recursive_tensorize					,      __lowerCamelCase					,      map_list=__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_row(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_row(__lowerCamelCase							)
							return self.recursive_tensorize(__lowerCamelCase							)





				def 							__UpperCAmelCase							(      self				:     int					,      __lowerCamelCase				:     pa.Table							)					->     "torch.Tensor":
							a    =			self.numpy_arrow_extractor().extract_column(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_column(__lowerCamelCase					,      pa_table.column_names[0]							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							a    =			self._consolidate(__lowerCamelCase							)
							return column





				def 							__UpperCAmelCase							(      self				:     Optional[Any]					,      __lowerCamelCase				:     pa.Table							)					->     Mapping:
							a    =			self.numpy_arrow_extractor().extract_batch(__lowerCamelCase							)
							a    =			self.python_features_decoder.decode_batch(__lowerCamelCase							)
							a    =			self.recursive_tensorize(__lowerCamelCase							)
							for column_name in batch:
										a    =			self._consolidate(batch[column_name]							)
							return batch






