




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


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







@dataclass
class 		lowercase							(     datasets.BuilderConfig	):
     _a											=		None
     _a											=		"utf-8"
     _a											=		None
     _a											=		None
     _a											=		True  # deprecated
     _a											=		None  # deprecated
     _a											=		1_0 << 2_0  # 10MB
     _a											=		None







class 		lowercase							(     datasets.ArrowBasedBuilder	):
     _a											=		JsonConfig

     def       a__			(						self       )		->							Any:
           if self.config.block_size is not None:
                 logger.warning("""The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead"""       )
                 _A							:  Tuple    =		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       a__			(						self	,  _a       )		->							List[str]:
           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}'''       )
           _A							:  Optional[Any]    =		dl_manager.download_and_extract(self.config.data_files       )
           if isinstance(_a	,  (str, list, tuple)       ):
                 _A							:  Union[str, Any]    =		data_files
                 if isinstance(_a	,  _a       ):
                       _A							:  Optional[Any]    =		[files]
                 _A							:  Dict    =		[dl_manager.iter_files(_a       ) for file in files]
                 return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,  gen_kwargs={"""files""": files}       )]
           _A							:  Union[str, Any]    =		[]
           for split_name, files in data_files.items():
                 if isinstance(_a	,  _a       ):
                       _A							:  Dict    =		[files]
                 _A							:  int    =		[dl_manager.iter_files(_a       ) for file in files]
                 splits.append(datasets.SplitGenerator(name=_a	,  gen_kwargs={"""files""": files}       )       )
           return splits

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



     def       a__			(						self	,  _a       )		->							Dict:
           for file_idx, file in enumerate(itertools.chain.from_iterable(_a       )       ):
                 # 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(_a	,  encoding=self.config.encoding	,  errors=self.config.encoding_errors       ) as f:
                             _A							:  List[str]    =		json.load(_a       )

                       # We keep only the field we are interested in
                       _A							:  Optional[Any]    =		dataset[self.config.field]

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

                 # If the file has one json object per line
                 else:
                       with open(_a	,  """rb"""       ) as f:
                             _A							:  Dict    =		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
                             _A							:  Dict    =		max(self.config.chunksize // 32	,  16 << 10       )
                             _A							:  int    =		(
                                 self.config.encoding_errors if self.config.encoding_errors is not None else """strict"""
                             )
                             while True:
                                   _A							:  Tuple    =		f.read(self.config.chunksize       )
                                   if not batch:
                                         break
                                   # Finish current line
                                   try:
                                         batch += f.readline()
                                   except (AttributeError, io.UnsupportedOperation):
                                         batch += readline(_a       )
                                   # PyArrow only accepts utf-8 encoded bytes
                                   if self.config.encoding != "utf-8":
                                         _A							:  List[Any]    =		batch.decode(self.config.encoding	,  errors=_a       ).encode("""utf-8"""       )
                                   try:
                                         while True:
                                               try:
                                                     _A							:  int    =		paj.read_json(
                                                         io.BytesIO(_a       )	,  read_options=paj.ReadOptions(block_size=_a       )       )
                                                     break
                                               except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                                     if (
                                                         isinstance(_a	,  pa.ArrowInvalid       )
                                                         and "straddling" not in str(_a       )
                                                         or block_size > len(_a       )
                                                     ):
                                                           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(_a       )} 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(
                                                   _a	,  encoding=self.config.encoding	,  errors=self.config.encoding_errors       ) as f:
                                                     _A							:  Union[str, Any]    =		json.load(_a       )
                                         except json.JSONDecodeError:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a       )}: {e}'''       )
                                               raise e
                                         # If possible, parse the file as a list of json objects and exit the loop
                                         if isinstance(_a	,  _a       ):  # list is the only sequence type supported in JSON
                                               try:
                                                     _A							:  int    =		set().union(*[row.keys() for row in dataset]       )
                                                     _A							:  Dict    =		{col: [row.get(_a       ) for row in dataset] for col in keys}
                                                     _A							:  Union[str, Any]    =		pa.Table.from_pydict(_a       )
                                               except (pa.ArrowInvalid, AttributeError) as e:
                                                     logger.error(F'''Failed to read file \'{file}\' with error {type(_a       )}: {e}'''       )
                                                     raise ValueError(F'''Not able to read records in the JSON file at {file}.'''       ) from None
                                               yield file_idx, self._cast_table(_a       )
                                               break
                                         else:
                                               logger.error(F'''Failed to read file \'{file}\' with error {type(_a       )}: {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(_a       )
                                   batch_idx += 1







import os
from typing import Dict, List, Union

import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs

from .tokenization_gpta import GPTaTokenizer







class 		lowercase							(     tf.keras.layers.Layer	):

     def __init__(						self	,  _a	,  _a	,  _a = None	,  _a = None       )		->							Any:
           super().__init__()
           _A							:  Dict    =		pad_token_id
           _A							:  List[Any]    =		max_length
           _A							:  Optional[int]    =		vocab
           _A							:  Optional[int]    =		merges
           _A							:  Optional[int]    =		BytePairTokenizer(_a	,  _a	,  sequence_length=_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							str:
           _A							:  Any    =		[""" """.join(_a       ) for m in tokenizer.bpe_ranks.keys()]
           _A							:  str    =		tokenizer.get_vocab()
           return cls(_a	,  _a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a	,  *_a	,  **_a       )		->							List[Any]:
           _A							:  Union[str, Any]    =		GPTaTokenizer.from_pretrained(_a	,  *_a	,  **_a       )
           return cls.from_tokenizer(_a	,  *_a	,  **_a       )

     @classmethod
     def       a__			(						cls	,  _a       )		->							Union[str, Any]:
           return cls(**_a       )

     def       a__			(						self       )		->							Union[str, Any]:
           return {
               "vocab": self.vocab,
               "merges": self.merges,
               "max_length": self.max_length,
               "pad_token_id": self.pad_token_id,
           }



     def       a__			(						self	,  _a	,  _a = None       )		->							int:
           _A							:  Optional[int]    =		self.tf_tokenizer(_a       )
           _A							:  Tuple    =		tf.ones_like(_a       )

           if self.pad_token_id is not None:
                 # pad the tokens up to max length
                 _A							:  Dict    =		max_length if max_length is not None else self.max_length

                 if max_length is not None:
                       _A    ,     _A							:  Dict    =		pad_model_inputs(
                           _a	,  max_seq_length=_a	,  pad_value=self.pad_token_id       )

           return {"attention_mask": attention_mask, "input_ids": input_ids}

