






"""simple docstring"""



import multiprocessing
import os
from typing import BinaryIO, Optional, Union

import fsspec

from .. import Dataset, Features, NamedSplit, config
from ..formatting import query_table
from ..packaged_modules.json.json import Json
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader




class 			lowerCAmelCase__			(    __magic_name__   ):
   def __init__(		self      :	List[Any]							,			snake_case__      :	NestedDataStructureLike[PathLike]							,			snake_case__      :	Optional[NamedSplit] = None							,			snake_case__      :	Optional[Features] = None							,			snake_case__      :	str = None							,			snake_case__      :	bool = False							,			snake_case__      :	bool = False							,			snake_case__      :	Optional[str] = None							,			snake_case__      :	Optional[int] = None							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(
            snake_case__							,			split=snake_case__							,			features=snake_case__							,			cache_dir=snake_case__							,			keep_in_memory=snake_case__							,			streaming=snake_case__							,			num_proc=snake_case__							,			**snake_case__							,			)
        UpperCAmelCase__						:    Dict							=							field
        UpperCAmelCase__						:    int							=							path_or_paths if isinstance(snake_case__							,			snake_case__							) else {self.split: path_or_paths}
        UpperCAmelCase__						:    Dict							=							Json(
            cache_dir=snake_case__							,			data_files=snake_case__							,			features=snake_case__							,			field=snake_case__							,			**snake_case__							,			)




   def       __a							(		self      :	Dict							):


        '''simple docstring'''
        # Build iterable dataset
        if self.streaming:
             UpperCAmelCase__						:    Optional[int]							=							self.builder.as_streaming_dataset(split=self.split							)
        # Build regular (map-style) dataset
        else:
             UpperCAmelCase__						:    Dict							=							None
             UpperCAmelCase__						:    Optional[int]							=							None
             UpperCAmelCase__						:    Optional[Any]							=							None
             UpperCAmelCase__						:    Tuple							=							None

             self.builder.download_and_prepare(
                 download_config=snake_case__							,			download_mode=snake_case__							,			verification_mode=snake_case__							,			base_path=snake_case__							,			num_proc=self.num_proc							,			)
             UpperCAmelCase__						:    Dict							=							self.builder.as_dataset(
                 split=self.split							,			verification_mode=snake_case__							,			in_memory=self.keep_in_memory							)
        return dataset




class 			lowerCAmelCase__			:
   def __init__(		self      :	Optional[Any]							,			snake_case__      :	Dataset							,			snake_case__      :	Union[PathLike, BinaryIO]							,			snake_case__      :	Optional[int] = None							,			snake_case__      :	Optional[int] = None							,			**snake_case__      :	Optional[Any]							,			):


        '''simple docstring'''
        if num_proc is not None and num_proc <= 0:
             raise ValueError(f'num_proc {num_proc} must be an integer > 0.'							)

        UpperCAmelCase__						:    Union[str, Any]							=							dataset
        UpperCAmelCase__						:    List[Any]							=							path_or_buf
        UpperCAmelCase__						:    List[str]							=							batch_size if batch_size else config.DEFAULT_MAX_BATCH_SIZE
        UpperCAmelCase__						:    Any							=							num_proc
        UpperCAmelCase__						:    Optional[int]							=							"utf-8"
        UpperCAmelCase__						:    str							=							to_json_kwargs
   def       __a							(		self      :	List[Any]							):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							self.to_json_kwargs.pop("path_or_buf"							,			snake_case__							)
        UpperCAmelCase__						:    List[Any]							=							self.to_json_kwargs.pop("orient"							,			"records"							)
        UpperCAmelCase__						:    int							=							self.to_json_kwargs.pop("lines"							,			True if orient == "records" else False							)
        UpperCAmelCase__						:    List[str]							=							self.to_json_kwargs.pop("index"							,			False if orient in ["split", "table"] else True							)
        UpperCAmelCase__						:    Dict							=							self.to_json_kwargs.pop("compression"							,			snake_case__							)

        if compression not in [None, "infer", "gzip", "bz2", "xz"]:
             raise NotImplementedError(f'`datasets` currently does not support {compression} compression'							)

        if isinstance(self.path_or_buf							,			(str, bytes, os.PathLike)							):
             with fsspec.open(self.path_or_buf							,			"wb"							,			compression=snake_case__							) as buffer:
                  UpperCAmelCase__						:    str							=							self._write(file_obj=snake_case__							,			orient=snake_case__							,			lines=snake_case__							,			index=snake_case__							,			**self.to_json_kwargs							)
        else:
             if compression:
                  raise NotImplementedError(
                      f'The compression parameter is not supported when writing to a buffer, but compression={compression}'
                      " was passed. Please provide a local path instead."							)
             UpperCAmelCase__						:    int							=							self._write(
                 file_obj=self.path_or_buf							,			orient=snake_case__							,			lines=snake_case__							,			index=snake_case__							,			**self.to_json_kwargs							)
        return written
   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	Tuple							):


        '''simple docstring'''
        UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							args

        UpperCAmelCase__						:    Dict							=							query_table(
            table=self.dataset.data							,			key=slice(snake_case__							,			offset + self.batch_size							)							,			indices=self.dataset._indices							,			)
        UpperCAmelCase__						:    Union[str, Any]							=							batch.to_pandas().to_json(
            path_or_buf=snake_case__							,			orient=snake_case__							,			lines=snake_case__							,			index=snake_case__							,			**snake_case__							)
        if not json_str.endswith("\n"							):
             json_str += "\n"
        return json_str.encode(self.encoding							)




   def       __a							(		self      :	Union[str, Any]							,			snake_case__      :	BinaryIO							,			snake_case__      :	int							,			snake_case__      :	Optional[Any]							,			snake_case__      :	Any							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        UpperCAmelCase__						:    str							=							0

        if self.num_proc is None or self.num_proc == 1:
             for offset in logging.tqdm(
                 range(0							,			len(self.dataset							)							,			self.batch_size							)							,			unit="ba"							,			disable=not logging.is_progress_bar_enabled()							,			desc="Creating json from Arrow format"							,			):
                  UpperCAmelCase__						:    List[str]							=							self._batch_json((offset, orient, lines, index, to_json_kwargs)							)
                  written += file_obj.write(snake_case__							)
        else:
             UpperCAmelCase__							,				UpperCAmelCase__						:    Dict							=							len(self.dataset							), self.batch_size
             with multiprocessing.Pool(self.num_proc							) as pool:
                  for json_str in logging.tqdm(
                      pool.imap(
                          self._batch_json							,			[(offset, orient, lines, index, to_json_kwargs) for offset in range(0							,			snake_case__							,			snake_case__							)]							,			)							,			total=(num_rows // batch_size) + 1 if num_rows % batch_size else num_rows // batch_size							,			unit="ba"							,			disable=not logging.is_progress_bar_enabled()							,			desc="Creating json from Arrow format"							,			):
                       written += file_obj.write(snake_case__							)

        return written










"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase	:	Optional[int]				=      logging.get_logger(__name__)

_lowerCAmelCase	:	Tuple				=      {
    """uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}




class 			lowerCAmelCase__			(    __magic_name__   ):
   SCREAMING_SNAKE_CASE_    ='''mra'''
   def __init__(		self      :	Any							,			snake_case__      :	List[str]=5_0_2_6_5							,			snake_case__      :	Any=7_6_8							,			snake_case__      :	Union[str, Any]=1_2							,			snake_case__      :	Optional[Any]=1_2							,			snake_case__      :	Tuple=3_0_7_2							,			snake_case__      :	str="gelu"							,			snake_case__      :	Any=0.1							,			snake_case__      :	Tuple=0.1							,			snake_case__      :	Tuple=5_1_2							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	List[Any]=0.02							,			snake_case__      :	str=1e-5							,			snake_case__      :	List[Any]="absolute"							,			snake_case__      :	str=4							,			snake_case__      :	List[str]="full"							,			snake_case__      :	Tuple=0							,			snake_case__      :	Any=0							,			snake_case__      :	Union[str, Any]=1							,			snake_case__      :	int=0							,			snake_case__      :	int=2							,			**snake_case__      :	List[Any]							,			):


        '''simple docstring'''
        super().__init__(pad_token_id=snake_case__							,			bos_token_id=snake_case__							,			eos_token_id=snake_case__							,			**snake_case__							)

        UpperCAmelCase__						:    List[Any]							=							vocab_size
        UpperCAmelCase__						:    str							=							max_position_embeddings
        UpperCAmelCase__						:    Any							=							hidden_size
        UpperCAmelCase__						:    Union[str, Any]							=							num_hidden_layers
        UpperCAmelCase__						:    str							=							num_attention_heads
        UpperCAmelCase__						:    int							=							intermediate_size
        UpperCAmelCase__						:    int							=							hidden_act
        UpperCAmelCase__						:    List[str]							=							hidden_dropout_prob
        UpperCAmelCase__						:    List[str]							=							attention_probs_dropout_prob
        UpperCAmelCase__						:    Any							=							initializer_range
        UpperCAmelCase__						:    Any							=							type_vocab_size
        UpperCAmelCase__						:    Dict							=							layer_norm_eps
        UpperCAmelCase__						:    Tuple							=							position_embedding_type
        UpperCAmelCase__						:    List[str]							=							block_per_row
        UpperCAmelCase__						:    Optional[Any]							=							approx_mode
        UpperCAmelCase__						:    Any							=							initial_prior_first_n_blocks
        UpperCAmelCase__						:    List[Any]							=							initial_prior_diagonal_n_blocks


