


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

@dataclass
class   A__ (   datasets.BuilderConfig     ):







  """simple docstring"""

  __magic_name__        =		None
  __magic_name__        =		'utf-8'
  __magic_name__        =		None
  __magic_name__        =		None
  __magic_name__        =		True  # deprecated
  __magic_name__        =		None  # deprecated
  __magic_name__        =		10 << 20  # 10MB
  __magic_name__        =		None






class   A__ (   datasets.ArrowBasedBuilder     ):







  """simple docstring"""

  __magic_name__        =		JsonConfig
  def 		a_      (	self   ):
       if self.config.block_size is not None:
            logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead'''   )
            snake_case					=    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      , __snake_case   ):
       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}'''   )
       snake_case					=    dl_manager.download_and_extract(self.config.data_files   )
       if isinstance(__snake_case      , (str, list, tuple)   ):
            snake_case					=    data_files
            if isinstance(__snake_case      , __snake_case   ):
                 snake_case					=    [files]
            snake_case					=    [dl_manager.iter_files(__snake_case   ) for file in files]
            return [datasets.SplitGenerator(name=datasets.Split.TRAIN      , gen_kwargs={'''files''': files}   )]
       snake_case					=    []
       for split_name, files in data_files.items():
            if isinstance(__snake_case      , __snake_case   ):
                 snake_case					=    [files]
            snake_case					=    [dl_manager.iter_files(__snake_case   ) for file in files]
            splits.append(datasets.SplitGenerator(name=__snake_case      , gen_kwargs={'''files''': files}   )   )
       return splits
  def 		a_      (	self      , __snake_case   ):
       if self.config.features is not None:
            # adding missing columns
            for column_name in set(self.config.features   ) - set(pa_table.column_names   ):
                 snake_case					=    self.config.features.arrow_schema.field(__snake_case   ).type
                 snake_case					=    pa_table.append_column(__snake_case      , pa.array([None] * len(__snake_case   )      , type=__snake_case   )   )
            # more expensive cast to support nested structures with keys in a different order
            # allows str <-> int/float or str to Audio for example
            snake_case					=    table_cast(__snake_case      , self.config.features.arrow_schema   )
       return pa_table




  def 		a_      (	self      , __snake_case   ):
       for file_idx, file in enumerate(itertools.chain.from_iterable(__snake_case   )   ):
            # 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(__snake_case      , encoding=self.config.encoding      , errors=self.config.encoding_errors   ) as f:
                      snake_case					=    json.load(__snake_case   )

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

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

            # If the file has one json object per line
            else:
                 with open(__snake_case      , '''rb'''   ) as f:
                      snake_case					=    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
                      snake_case					=    max(self.config.chunksize // 3_2      , 1_6 << 1_0   )
                      snake_case					=    (
                          self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
                      )
                      while True:
                           snake_case					=    f.read(self.config.chunksize   )
                           if not batch:
                                break
                           # Finish current line
                           try:
                                batch += f.readline()
                           except (AttributeError, io.UnsupportedOperation):
                                batch += readline(__snake_case   )
                           # PyArrow only accepts utf-8 encoded bytes
                           if self.config.encoding != "utf-8":
                                snake_case					=    batch.decode(self.config.encoding      , errors=__snake_case   ).encode('''utf-8'''   )
                           try:
                                while True:
                                     try:
                                          snake_case					=    paj.read_json(
                                              io.BytesIO(__snake_case   )      , read_options=paj.ReadOptions(block_size=__snake_case   )   )
                                          break
                                     except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
                                          if (
                                              isinstance(__snake_case      , pa.ArrowInvalid   )
                                              and "straddling" not in str(__snake_case   )
                                              or block_size > len(__snake_case   )
                                          ):
                                               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(__snake_case   )} 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(
                                         __snake_case      , encoding=self.config.encoding      , errors=self.config.encoding_errors   ) as f:
                                          snake_case					=    json.load(__snake_case   )
                                except json.JSONDecodeError:
                                     logger.error(F'''Failed to read file \'{file}\' with error {type(__snake_case   )}: {e}'''   )
                                     raise e
                                # If possible, parse the file as a list of json objects and exit the loop
                                if isinstance(__snake_case      , __snake_case   ):  # list is the only sequence type supported in JSON
                                     try:
                                          snake_case					=    set().union(*[row.keys() for row in dataset]   )
                                          snake_case					=    {col: [row.get(__snake_case   ) for row in dataset] for col in keys}
                                          snake_case					=    pa.Table.from_pydict(__snake_case   )
                                     except (pa.ArrowInvalid, AttributeError) as e:
                                          logger.error(F'''Failed to read file \'{file}\' with error {type(__snake_case   )}: {e}'''   )
                                          raise ValueError(F'''Not able to read records in the JSON file at {file}.'''   ) from None
                                     yield file_idx, self._cast_table(__snake_case   )
                                     break
                                else:
                                     logger.error(F'''Failed to read file \'{file}\' with error {type(__snake_case   )}: {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(__snake_case   )
                           batch_idx += 1








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


_SCREAMING_SNAKE_CASE      :			Dict      				=   logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE      :			Optional[Any]      				=   {
    "alibaba-damo/mgp-str-base": "https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json",
}

class   A__ (   snake_case__     ):







    """simple docstring"""

    __magic_name__        =		'mgp-str'
    def __init__(	self      , __snake_case=[3_2, 1_2_8]      , __snake_case=4      , __snake_case=3      , __snake_case=2_7      , __snake_case=3_8      , __snake_case=5_0_2_5_7      , __snake_case=3_0_5_2_2      , __snake_case=7_6_8      , __snake_case=1_2      , __snake_case=1_2      , __snake_case=4.0      , __snake_case=True      , __snake_case=False      , __snake_case=1E-5      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=0.0      , __snake_case=False      , __snake_case=0.02      , **__snake_case      , ):
         super().__init__(**__snake_case   )

         snake_case					=    image_size
         snake_case					=    patch_size
         snake_case					=    num_channels
         snake_case					=    max_token_length
         snake_case					=    num_character_labels
         snake_case					=    num_bpe_labels
         snake_case					=    num_wordpiece_labels
         snake_case					=    hidden_size
         snake_case					=    num_hidden_layers
         snake_case					=    num_attention_heads
         snake_case					=    mlp_ratio
         snake_case					=    distilled
         snake_case					=    layer_norm_eps
         snake_case					=    drop_rate
         snake_case					=    qkv_bias
         snake_case					=    attn_drop_rate
         snake_case					=    drop_path_rate
         snake_case					=    output_aa_attentions
         snake_case					=    initializer_range




