





'''simple docstring'''


import itertools
from dataclasses import dataclass
from typing import List, Optional

import pyarrow as pa
import pyarrow.parquet as pq

import datasets
from datasets.table import table_cast


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

@dataclass
class    lowerCAmelCase_		(		datasets.BuilderConfig		):
 __lowerCamelCase						:							int         =					10_000
 __lowerCamelCase						:							Optional[List[str]]         =					None
 __lowerCamelCase						:							Optional[datasets.Features]         =					None







class    lowerCAmelCase_		(		datasets.ArrowBasedBuilder		):
 __lowerCamelCase						:							str         =					ParquetConfig




 def 							_snake_case   (	self		)    ->							Optional[Any]:
     return datasets.DatasetInfo(features=self.config.features		)




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							int:
     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]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _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]
         # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
         _lowerCAmelCase				=      [dl_manager.iter_files(_lowerCAmelCase		) for file in files]
         # Infer features is they are stoed in the arrow schema
         if self.info.features is None:
             for file in itertools.chain.from_iterable(_lowerCAmelCase		):
                 with open(_lowerCAmelCase			,     "rb"		) as f:
                     _lowerCAmelCase				=      datasets.Features.from_arrow_schema(pq.read_schema(_lowerCAmelCase		)		)
                 break
         splits.append(datasets.SplitGenerator(name=_lowerCAmelCase			,     gen_kwargs={"files": files}		)		)
     return splits




 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							pa.Table:
     if self.info.features is not None:
         # more expensive cast to support nested features with keys in a different order
         # allows str <-> int/float or str to Audio for example
         _lowerCAmelCase				=      table_cast(_lowerCAmelCase			,     self.info.features.arrow_schema		)
     return pa_table





 def 							_snake_case   (	self			,     _lowerCAmelCase		)    ->							List[str]:
     _lowerCAmelCase				=      self.info.features.arrow_schema if self.info.features is not None else None
     if self.info.features is not None and self.config.columns is not None:
         if sorted(field.name for field in schema		) != sorted(self.config.columns		):
             raise ValueError(
                 f'''Tried to load parquet data with columns \'{self.config.columns}\' with mismatching features \'{self.info.features}\''''		)
     for file_idx, file in enumerate(itertools.chain.from_iterable(_lowerCAmelCase		)		):
         with open(_lowerCAmelCase			,     "rb"		) as f:
             _lowerCAmelCase				=      pq.ParquetFile(_lowerCAmelCase		)
             try:
                 for batch_idx, record_batch in enumerate(
                     parquet_file.iter_batches(batch_size=self.config.batch_size			,     columns=self.config.columns		)		):
                     _lowerCAmelCase				=      pa.Table.from_batches([record_batch]		)
                     # 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 f'''{file_idx}_{batch_idx}''', self._cast_table(_lowerCAmelCase		)
             except ValueError as e:
                 logger.error(f'''Failed to read file \'{file}\' with error {type(_lowerCAmelCase		)}: {e}'''		)
                 raise









'''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


