





'''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


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

@dataclass
class    lowerCAmelCase_		(		datasets.BuilderConfig		):
	__lowerCamelCase						:							Optional[datasets.Features]         =					None
	__lowerCamelCase						:							str         =					"utf-8"
	__lowerCamelCase						:							Optional[str]         =					None
	__lowerCamelCase						:							Optional[str]         =					None
	__lowerCamelCase						:							bool         =					True  # deprecated
	__lowerCamelCase						:							Optional[int]         =					None  # deprecated
	__lowerCamelCase						:							int         =					10 << 20  # 10MB
	__lowerCamelCase						:							Optional[bool]         =					None







class    lowerCAmelCase_		(		datasets.ArrowBasedBuilder		):
	__lowerCamelCase						:							Optional[int]         =					JsonConfig




	def 							_snake_case   (	self		)    ->							Union[str, Any]:
					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			,     _lowerCAmelCase		)    ->							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}'''		)
					_lowerCAmelCase				=      dl_manager.download_and_extract(self.config.data_files		)
					if isinstance(_lowerCAmelCase			,     (str, list, tuple)		):
									_lowerCAmelCase				=      data_files
									if isinstance(_lowerCAmelCase			,     _lowerCAmelCase		):
													_lowerCAmelCase				=      [files]
									_lowerCAmelCase				=      [dl_manager.iter_files(_lowerCAmelCase		) 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(_lowerCAmelCase			,     _lowerCAmelCase		):
													_lowerCAmelCase				=      [files]
									_lowerCAmelCase				=      [dl_manager.iter_files(_lowerCAmelCase		) for file in files]
									splits.append(datasets.SplitGenerator(name=_lowerCAmelCase			,     gen_kwargs={"files": files}		)		)
					return splits




	def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							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		):
													_lowerCAmelCase				=      self.config.features.arrow_schema.field(_lowerCAmelCase		).type
													_lowerCAmelCase				=      pa_table.append_column(_lowerCAmelCase			,     pa.array([None] * len(_lowerCAmelCase		)			,     type=_lowerCAmelCase		)		)
									# 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(_lowerCAmelCase			,     self.config.features.arrow_schema		)
					return pa_table





	def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[Any]:
					for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase		)		):
									# 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(_lowerCAmelCase			,     encoding=self.config.encoding			,     errors=self.config.encoding_errors		) as f:
																	_lowerCAmelCase				=      json.load(_lowerCAmelCase		)

													# 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(_lowerCAmelCase			,     (list, tuple)		):
																	_lowerCAmelCase				=      set().union(*[row.keys() for row in dataset]		)
																	_lowerCAmelCase				=      {col: [row.get(_lowerCAmelCase		) for row in dataset] for col in keys}
													else:
																	_lowerCAmelCase				=      dataset
													_lowerCAmelCase				=      pa.Table.from_pydict(_lowerCAmelCase		)
													yield file_idx, self._cast_table(_lowerCAmelCase		)

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









'''simple docstring'''


import jax.numpy as jnp

from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig


_SCREAMING_SNAKE_CASE	 =							logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE	 =							"T5Config"


def 	__a(SCREAMING_SNAKE_CASE_  :					jnp.array		,       SCREAMING_SNAKE_CASE_  :					int		,       SCREAMING_SNAKE_CASE_  :					int    ):



    '''simple docstring'''





    _lowerCAmelCase				=      jnp.zeros_like(SCREAMING_SNAKE_CASE_    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 1:].set(input_ids[:, :-1]    )
    _lowerCAmelCase				=      shifted_input_ids.at[:, 0].set(SCREAMING_SNAKE_CASE_    )

    _lowerCAmelCase				=      jnp.where(shifted_input_ids == -100		,       SCREAMING_SNAKE_CASE_		,       SCREAMING_SNAKE_CASE_    )
    return shifted_input_ids

class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Any         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							List[str]         =					"mt5"
 __lowerCamelCase						:							Dict         =					MTaConfig







class    lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase						:							Optional[Any]         =					"mt5"
 __lowerCamelCase						:							str         =					MTaConfig


