




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


UpperCAmelCase_						=logging.get_logger(__name__)

UpperCAmelCase_						="""▁"""

UpperCAmelCase_						={"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCAmelCase_						={
    """vocab_file""": {
        """facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
    }
}

UpperCAmelCase_						={
    """facebook/xglm-564M""": 2048,
}


class 							__UpperCamelCase			( __UpperCAmelCase  ):





				'''simple docstring'''



				__a							: Optional[Any]										=VOCAB_FILES_NAMES
				__a							: Optional[Any]										=PRETRAINED_VOCAB_FILES_MAP
				__a							: Optional[Any]										=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
				__a							: str										=["""input_ids""", """attention_mask"""]



				def __init__( self       , UpperCAmelCase_       , UpperCAmelCase_="<s>"       , UpperCAmelCase_="</s>"       , UpperCAmelCase_="</s>"       , UpperCAmelCase_="<s>"       , UpperCAmelCase_="<unk>"       , UpperCAmelCase_="<pad>"       , UpperCAmelCase_ = None       , **UpperCAmelCase_       , ):
											lowerCAmelCase   =		{} if sp_model_kwargs is None else sp_model_kwargs

											# Compatibility with the original tokenizer
											lowerCAmelCase   =		7
											lowerCAmelCase   =		[F"""<madeupword{i}>""" for i in range(self.num_madeup_words						)]

											lowerCAmelCase   =		kwargs.get('''additional_special_tokens'''       , []						)
											kwargs["additional_special_tokens"] += [
											    word for word in madeup_words if word not in kwargs["additional_special_tokens"]
											]

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

											lowerCAmelCase   =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.Load(str(UpperCAmelCase_						)						)
											lowerCAmelCase   =		vocab_file

											# Original fairseq vocab and spm vocab must be "aligned":
											# Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
											# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
											# fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
											# spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

											# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
											lowerCAmelCase   =		1

											# Mimic fairseq token-to-id alignment for the first 4 token
											lowerCAmelCase   =		{'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}

											lowerCAmelCase   =		len(self.sp_model						)
											lowerCAmelCase   =		{F"""<madeupword{i}>""": sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words						)}
											self.fairseq_tokens_to_ids.update(UpperCAmelCase_						)

											lowerCAmelCase   =		{v: k for k, v in self.fairseq_tokens_to_ids.items()}



				def __getstate__( self						):
											lowerCAmelCase   =		self.__dict__.copy()
											lowerCAmelCase   =		None
											lowerCAmelCase   =		self.sp_model.serialized_model_proto()
											return state



				def __setstate__( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		d

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

											lowerCAmelCase   =		spm.SentencePieceProcessor(**self.sp_model_kwargs						)
											self.sp_model.LoadFromSerializedProto(self.sp_model_proto						)



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):

											if token_ids_a is None:
																		return [self.sep_token_id] + token_ids_a
											lowerCAmelCase   =		[self.sep_token_id]
											return sep + token_ids_a + sep + sep + token_ids_a



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None       , UpperCAmelCase_ = False						):

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

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



				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											lowerCAmelCase   =		[self.sep_token_id]

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



				@property
				def       __snake_case		( self						):
											return len(self.sp_model						) + self.fairseq_offset + self.num_madeup_words



				def       __snake_case		( self						):
											lowerCAmelCase   =		{self.convert_ids_to_tokens(UpperCAmelCase_						): i for i in range(self.vocab_size						)}
											vocab.update(self.added_tokens_encoder						)
											return vocab



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



				def       __snake_case		( self       , UpperCAmelCase_						):
											if token in self.fairseq_tokens_to_ids:
																		return self.fairseq_tokens_to_ids[token]
											lowerCAmelCase   =		self.sp_model.PieceToId(UpperCAmelCase_						)

											# Need to return unknown token if the SP model returned 0
											return spm_id + self.fairseq_offset if spm_id else self.unk_token_id



				def       __snake_case		( self       , UpperCAmelCase_						):
											if index in self.fairseq_ids_to_tokens:
																		return self.fairseq_ids_to_tokens[index]
											return self.sp_model.IdToPiece(index - self.fairseq_offset						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											lowerCAmelCase   =		''''''.join(UpperCAmelCase_						).replace(UpperCAmelCase_       , ''' '''						).strip()
											return out_string






				def       __snake_case		( self       , UpperCAmelCase_       , UpperCAmelCase_ = None						):
											if not os.path.isdir(UpperCAmelCase_						):
																		logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""						)
																		return
											lowerCAmelCase   =		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_						) and os.path.isfile(self.vocab_file						):
																		copyfile(self.vocab_file       , UpperCAmelCase_						)
											elif not os.path.isfile(self.vocab_file						):
																		with open(UpperCAmelCase_       , '''wb'''						) as fi:
																									lowerCAmelCase   =		self.sp_model.serialized_model_proto()
																									fi.write(UpperCAmelCase_						)

											return (out_vocab_file,)









import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional

import pyarrow as pa
import pyarrow.json as paj

import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline


UpperCAmelCase_						=datasets.utils.logging.get_logger(__name__)


@dataclass
class 							__UpperCamelCase			( datasets.BuilderConfig  ):





				'''simple docstring'''



				__a							: Optional[datasets.Features]										=None
				__a							: str										="utf-8"
				__a							: Optional[str]										=None
				__a							: Optional[str]										=None
				__a							: bool										=True  # deprecated
				__a							: Optional[int]										=None  # deprecated
				__a							: int										=1_0 << 2_0  # 10MB
				__a							: Optional[bool]										=None




class 							__UpperCamelCase			( datasets.ArrowBasedBuilder  ):





				'''simple docstring'''



				__a							: str										=JsonConfig



				def       __snake_case		( self						):
											if self.config.block_size is not None:
																		logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''						)
																		lowerCAmelCase   =		self.config.block_size
											if self.config.use_threads is not True:
																		logger.warning(
																		    '''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.'''						)
											if self.config.newlines_in_values is not None:
																		raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported'''						)
											return datasets.DatasetInfo(features=self.config.features						)



				def       __snake_case		( self       , UpperCAmelCase_						):
											if not self.config.data_files:
																		raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}"""						)
											lowerCAmelCase   =		dl_manager.download_and_extract(self.config.data_files						)
											if isinstance(UpperCAmelCase_       , (str, list, tuple)						):
																		lowerCAmelCase   =		data_files
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[files]
																		lowerCAmelCase   =		[dl_manager.iter_files(UpperCAmelCase_						) for file in files]
																		return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs={'''files''': files}						)]
											lowerCAmelCase   =		[]
											for split_name, files in data_files.items():
																		if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):
																									lowerCAmelCase   =		[files]
																		lowerCAmelCase   =		[dl_manager.iter_files(UpperCAmelCase_						) for file in files]
																		splits.append(datasets.SplitGenerator(name=UpperCAmelCase_       , gen_kwargs={'''files''': files}						)						)
											return splits



				def       __snake_case		( self       , UpperCAmelCase_						):
											if self.config.features is not None:
																		# adding missing columns
																		for column_name in set(self.config.features						) - set(pa_table.column_names						):
																									lowerCAmelCase   =		self.config.features.arrow_schema.field(UpperCAmelCase_						).type
																									lowerCAmelCase   =		pa_table.append_column(UpperCAmelCase_       , pa.array([None] * len(UpperCAmelCase_						)       , type=UpperCAmelCase_						)						)
																		# more expensive cast to support nested structures with keys in a different order
																		# allows str <-> int/float or str to Audio for example
																		lowerCAmelCase   =		table_cast(UpperCAmelCase_       , self.config.features.arrow_schema						)
											return pa_table






				def       __snake_case		( self       , UpperCAmelCase_						):
											for file_idx, file in enumerate(itertools.chain.from_iterable(UpperCAmelCase_						)						):
																		# If the file is one json object and if we need to look at the list of items in one specific field
																		if self.config.field is not None:
																									with open(UpperCAmelCase_       , encoding=self.config.encoding       , errors=self.config.encoding_errors						) as f:
																																lowerCAmelCase   =		json.load(UpperCAmelCase_						)

																									# We keep only the field we are interested in
																									lowerCAmelCase   =		dataset[self.config.field]

																									# We accept two format: a list of dicts or a dict of lists
																									if isinstance(UpperCAmelCase_       , (list, tuple)						):
																																lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																									else:
																																lowerCAmelCase   =		dataset
																									lowerCAmelCase   =		pa.Table.from_pydict(UpperCAmelCase_						)
																									yield file_idx, self._cast_table(UpperCAmelCase_						)

																		# If the file has one json object per line
																		else:
																									with open(UpperCAmelCase_       , '''rb'''						) as f:
																																lowerCAmelCase   =		0
																																# Use block_size equal to the chunk size divided by 32 to leverage multithreading
																																# Set a default minimum value of 16kB if the chunk size is really small
																																lowerCAmelCase   =		max(self.config.chunksize // 32       , 16 << 10						)
																																lowerCAmelCase   =		(
																																    self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
																																)
																																while True:
																																							lowerCAmelCase   =		f.read(self.config.chunksize						)
																																							if not batch:
																																														break
																																							# Finish current line
																																							try:
																																														batch += f.readline()
																																							except (AttributeError, io.UnsupportedOperation):
																																														batch += readline(UpperCAmelCase_						)
																																							# PyArrow only accepts utf-8 encoded bytes
																																							if self.config.encoding != "utf-8":
																																														lowerCAmelCase   =		batch.decode(self.config.encoding       , errors=UpperCAmelCase_						).encode('''utf-8'''						)
																																							try:
																																														while True:
																																																					try:
																																																												lowerCAmelCase   =		paj.read_json(
																																																												    io.BytesIO(UpperCAmelCase_						)       , read_options=paj.ReadOptions(block_size=UpperCAmelCase_						)						)
																																																												break
																																																					except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
																																																												if (
																																																												    isinstance(UpperCAmelCase_       , pa.ArrowInvalid						)
																																																												    and "straddling" not in str(UpperCAmelCase_						)
																																																												    or block_size > len(UpperCAmelCase_						)
																																																												):
																																																																			raise
																																																												else:
																																																																			# Increase the block size in case it was too small.
																																																																			# The block size will be reset for the next file.
																																																																			logger.debug(
																																																																			    F"""Batch of {len(UpperCAmelCase_						)} bytes couldn't be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}."""						)
																																																																			block_size *= 2
																																							except pa.ArrowInvalid as e:
																																														try:
																																																					with open(
																																																					    UpperCAmelCase_       , encoding=self.config.encoding       , errors=self.config.encoding_errors						) as f:
																																																												lowerCAmelCase   =		json.load(UpperCAmelCase_						)
																																														except json.JSONDecodeError:
																																																					logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																					raise e
																																														# If possible, parse the file as a list of json objects and exit the loop
																																														if isinstance(UpperCAmelCase_       , UpperCAmelCase_						):  # list is the only sequence type supported in JSON
																																																					try:
																																																												lowerCAmelCase   =		set().union(*[row.keys() for row in dataset]						)
																																																												lowerCAmelCase   =		{col: [row.get(UpperCAmelCase_						) for row in dataset] for col in keys}
																																																												lowerCAmelCase   =		pa.Table.from_pydict(UpperCAmelCase_						)
																																																					except (pa.ArrowInvalid, AttributeError) as e:
																																																												logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																												raise ValueError(F"""Not able to read records in the JSON file at {file}."""						) from None
																																																					yield file_idx, self._cast_table(UpperCAmelCase_						)
																																																					break
																																														else:
																																																					logger.error(F"""Failed to read file '{file}' with error {type(UpperCAmelCase_						)}: {e}"""						)
																																																					raise ValueError(
																																																					    F"""Not able to read records in the JSON file at {file}. """
																																																					    F"""You should probably indicate the field of the JSON file containing your records. """
																																																					    F"""This JSON file contain the following fields: {str(list(dataset.keys()						)						)}. """
																																																					    F"""Select the correct one and provide it as `field='XXX'` to the dataset loading method. """						) from None
                        # Uncomment for debugging (will print the Arrow table size and elements)
                        # logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
                        # logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																																							yield (file_idx, batch_idx), self._cast_table(UpperCAmelCase_						)
																																							batch_idx += 1



