
"""simple docstring"""




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 			__magic_name__       (       tf.keras.layers.Layer ):
							def __init__(     self ,				A_ ,				A_ ,				A_ = None ,				A_ = None       )     ->	Optional[Any]:





									"""simple docstring"""




									super().__init__()
									_lowercase:			Any       =     pad_token_id
									_lowercase:			List[Any]       =     max_length
									_lowercase:			List[str]       =     vocab
									_lowercase:			str       =     merges
									_lowercase:			Optional[int]       =     BytePairTokenizer(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				sequence_length=__SCREAMING_SNAKE_CASE       )
							@classmethod
							def 							lowercase_							(     cls ,				A_ ,				*A_ ,				**A_       )     ->	Any:





									"""simple docstring"""




									_lowercase:			Tuple       =     [''' '''.join(__SCREAMING_SNAKE_CASE       ) for m in tokenizer.bpe_ranks.keys()]
									_lowercase:			Tuple       =     tokenizer.get_vocab()
									return cls(__SCREAMING_SNAKE_CASE ,				__SCREAMING_SNAKE_CASE ,				*__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE       )
							@classmethod
							def 							lowercase_							(     cls ,				A_ ,				*A_ ,				**A_       )     ->	Optional[Any]:





									"""simple docstring"""




									_lowercase:			int       =     GPTaTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE ,				*__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE       )
									return cls.from_tokenizer(__SCREAMING_SNAKE_CASE ,				*__SCREAMING_SNAKE_CASE ,				**__SCREAMING_SNAKE_CASE       )
							@classmethod
							def 							lowercase_							(     cls ,				A_       )     ->	Any:





									"""simple docstring"""




									return cls(**__SCREAMING_SNAKE_CASE       )
							def 							lowercase_							(     self       )     ->	str:





									"""simple docstring"""




									return {
									    "vocab": self.vocab,
									    "merges": self.merges,
									    "max_length": self.max_length,
									    "pad_token_id": self.pad_token_id,
									}



							def 							lowercase_							(     self ,				A_ ,				A_ = None       )     ->	Optional[Any]:





									"""simple docstring"""




									_lowercase:			int       =     self.tf_tokenizer(__SCREAMING_SNAKE_CASE       )
									_lowercase:			int       =     tf.ones_like(__SCREAMING_SNAKE_CASE       )

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

											if max_length is not None:
													_lowercase ,				_lowercase:			Optional[int]       =     pad_model_inputs(
													    __SCREAMING_SNAKE_CASE ,				max_seq_length=__SCREAMING_SNAKE_CASE ,				pad_value=self.pad_token_id       )

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


"""simple docstring"""




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


A__   :					List[Any]							=   datasets.utils.logging.get_logger(__name__)


@dataclass
class 			__magic_name__       (       datasets.BuilderConfig ):
       UpperCamelCase_   =		None
       UpperCamelCase_   =		"utf-8"
       UpperCamelCase_   =		None
       UpperCamelCase_   =		None
       UpperCamelCase_   =		True  # deprecated
       UpperCamelCase_   =		None  # deprecated
       UpperCamelCase_   =		10 << 20  # 10MB
       UpperCamelCase_   =		None


class 			__magic_name__       (       datasets.ArrowBasedBuilder ):
       UpperCamelCase_   =		JsonConfig
       def 							lowercase_							(     self       )     ->	str:





         """simple docstring"""




         if self.config.block_size is not None:
           logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''       )
           _lowercase:			List[str]       =     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 							lowercase_							(     self ,				A_       )     ->	Any:





         """simple docstring"""




         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}'''       )
         _lowercase:			int       =     dl_manager.download_and_extract(self.config.data_files       )
         if isinstance(A_ ,				(str, list, tuple)       ):
           _lowercase:			Tuple       =     data_files
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			Dict       =     [dl_manager.iter_files(A_       ) for file in files]
           return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,				gen_kwargs={'''files''': files}       )]
         _lowercase:			str       =     []
         for split_name, files in data_files.items():
           if isinstance(A_ ,				A_       ):
             _lowercase:			Optional[Any]       =     [files]
           _lowercase:			str       =     [dl_manager.iter_files(A_       ) for file in files]
           splits.append(datasets.SplitGenerator(name=A_ ,				gen_kwargs={'''files''': files}       )       )
         return splits
       def 							lowercase_							(     self ,				A_       )     ->	pa.Table:





         """simple docstring"""




         if self.config.features is not None:
           # adding missing columns
           for column_name in set(self.config.features       ) - set(pa_table.column_names       ):
             _lowercase:			Any       =     self.config.features.arrow_schema.field(A_       ).type
             _lowercase:			Optional[Any]       =     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
           _lowercase:			Optional[int]       =     table_cast(A_ ,				self.config.features.arrow_schema       )
         return pa_table



       def 							lowercase_							(     self ,				A_       )     ->	str:





         """simple docstring"""




         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:
               _lowercase:			Optional[int]       =     json.load(A_       )

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

             # We accept two format: a list of dicts or a dict of lists
             if isinstance(A_ ,				(list, tuple)       ):
               _lowercase:			Dict       =     set().union(*[row.keys() for row in dataset]       )
               _lowercase:			List[str]       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
             else:
               _lowercase:			Dict       =     dataset
             _lowercase:			Union[str, 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:
               _lowercase:			int       =     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
               _lowercase:			Optional[int]       =     max(self.config.chunksize // 32 ,				16 << 10       )
               _lowercase:			List[Any]       =     (
                   self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
               )
               while True:
                 _lowercase:			Union[str, Any]       =     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":
                   _lowercase:			Any       =     batch.decode(self.config.encoding ,				errors=A_       ).encode('''utf-8'''       )
                 try:
                   while True:
                     try:
                       _lowercase:			Optional[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:
                       _lowercase:			Optional[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:
                       _lowercase:			Optional[int]       =     set().union(*[row.keys() for row in dataset]       )
                       _lowercase:			Tuple       =     {col: [row.get(A_       ) for row in dataset] for col in keys}
                       _lowercase:			str       =     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
