"""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


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






@dataclass
class    a__    (   datasets.BuilderConfig	):
     snake_case_							=      10000
     snake_case_							=      None
     snake_case_							=      None






class    a__    (   datasets.ArrowBasedBuilder	):
     snake_case_							=      ParquetConfig


     def 	snake_case__	(     self  ):
          '''simple docstring'''



          return datasets.DatasetInfo(features=self.config.features  )


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''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__				 =							dl_manager.download_and_extract(self.config.data_files  )
          if isinstance(_UpperCAmelCase,				(str, list, tuple)  ):
               lowercase__				 =							data_files
               if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ):
                    lowercase__				 =							[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowercase__				 =							[dl_manager.iter_files(_UpperCAmelCase  ) for file in files]
               return [datasets.SplitGenerator(name=datasets.Split.TRAIN,				gen_kwargs={"files": files}  )]
          lowercase__				 =							[]
          for split_name, files in data_files.items():
               if isinstance(_UpperCAmelCase,				_UpperCAmelCase  ):
                    lowercase__				 =							[files]
               # Use `dl_manager.iter_files` to skip hidden files in an extracted archive
               lowercase__				 =							[dl_manager.iter_files(_UpperCAmelCase  ) 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(_UpperCAmelCase  ):
                         with open(_UpperCAmelCase,				"rb"  ) as f:
                              lowercase__				 =							datasets.Features.from_arrow_schema(pq.read_schema(_UpperCAmelCase  )  )
                         break
               splits.append(datasets.SplitGenerator(name=_UpperCAmelCase,				gen_kwargs={"files": files}  )  )
          return splits


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



          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
               lowercase__				 =							table_cast(_UpperCAmelCase,				self.info.features.arrow_schema  )
          return pa_table


     def 	snake_case__	(     self,				_UpperCAmelCase  ):
          '''simple docstring'''



          lowercase__				 =							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(_UpperCAmelCase  )  ):
               with open(_UpperCAmelCase,				"rb"  ) as f:
                    lowercase__				 =							pq.ParquetFile(_UpperCAmelCase  )
                    try:
                         for batch_idx, record_batch in enumerate(
                             parquet_file.iter_batches(batch_size=self.config.batch_size,				columns=self.config.columns  )  ):
                              lowercase__				 =							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(_UpperCAmelCase  )
                    except ValueError as e:
                         logger.error(F'''Failed to read file \'{file}\' with error {type(_UpperCAmelCase  )}: {e}'''  )
                         raise





"""simple docstring"""


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


lowerCAmelCase_:					List[Any]     =					logging.get_logger(__name__)

lowerCAmelCase_:					int     =					{
    "microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
    "microsoft/markuplm-large": "https://huggingface.co/microsoft/markuplm-large/resolve/main/config.json",
}






class    a__    (   _a	):
   snake_case_							=      "markuplm"


   def __init__(     self,				_UpperCAmelCase=3_0522,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=512,				_UpperCAmelCase=2,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=0,				_UpperCAmelCase=0,				_UpperCAmelCase=2,				_UpperCAmelCase=256,				_UpperCAmelCase=1024,				_UpperCAmelCase=216,				_UpperCAmelCase=1001,				_UpperCAmelCase=32,				_UpperCAmelCase=50,				_UpperCAmelCase="absolute",				_UpperCAmelCase=True,				_UpperCAmelCase=None,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(
            pad_token_id=_UpperCAmelCase,				bos_token_id=_UpperCAmelCase,				eos_token_id=_UpperCAmelCase,				**_UpperCAmelCase,				)
        lowercase__				 =							vocab_size
        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							hidden_act
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							max_position_embeddings
        lowercase__				 =							type_vocab_size
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps
        lowercase__				 =							position_embedding_type
        lowercase__				 =							use_cache
        lowercase__				 =							classifier_dropout
        # additional properties
        lowercase__				 =							max_depth
        lowercase__				 =							max_xpath_tag_unit_embeddings
        lowercase__				 =							max_xpath_subs_unit_embeddings
        lowercase__				 =							tag_pad_id
        lowercase__				 =							subs_pad_id
        lowercase__				 =							xpath_unit_hidden_size




