

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






@dataclass
class 						__magic_name__					(							datasets.BuilderConfig				):
     UpperCamelCase_	:int										=      1_0_0_0_0
     UpperCamelCase_	:Optional[List[str]]										=      None
     UpperCamelCase_	:Optional[datasets.Features]										=      None






class 						__magic_name__					(							datasets.ArrowBasedBuilder				):
     UpperCamelCase_	:str										=      ParquetConfig






     def 	UpperCAmelCase_				(				self	)->     Dict:
         return datasets.DatasetInfo(features=self.config.features	)






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






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     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
             UpperCamelCase_			  =						table_cast(_lowercase		,						self.info.features.arrow_schema	)
         return pa_table






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     List[Any]:
         UpperCamelCase_			  =						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(_lowercase	)	):
             with open(_lowercase		,						"rb"	) as f:
                 UpperCamelCase_			  =						pq.ParquetFile(_lowercase	)
                 try:
                     for batch_idx, record_batch in enumerate(
                         parquet_file.iter_batches(batch_size=self.config.batch_size		,						columns=self.config.columns	)	):
                         UpperCamelCase_			  =						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(_lowercase	)
                 except ValueError as e:
                     logger.error(F"Failed to read file '{file}' with error {type(_lowercase	)}: {e}"	)
                     raise



import os
import unittest

from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class 						__magic_name__					(							snake_case			,      unittest.TestCase				):
     UpperCamelCase_	:List[Any]										=      LayoutLMTokenizer
     UpperCamelCase_	:Dict										=      LayoutLMTokenizerFast
     UpperCamelCase_	:List[str]										=      True
     UpperCamelCase_	:Dict										=      True






     def 	UpperCAmelCase_				(				self	)->     str:
         super().setUp()

         UpperCamelCase_			  =						[
             "[UNK]",
             "[CLS]",
             "[SEP]",
             "want",
             "##want",
             "##ed",
             "wa",
             "un",
             "runn",
             "##ing",
             ",",
             "low",
             "lowest",
         ]
         UpperCamelCase_			  =						os.path.join(self.tmpdirname		,						VOCAB_FILES_NAMES["vocab_file"]	)
         with open(self.vocab_file		,						"w"		,						encoding="utf-8"	) as vocab_writer:
             vocab_writer.write("".join([x + "\n" for x in vocab_tokens]	)	)






     def 	UpperCAmelCase_				(				self		,						**_lowercase	)->     List[str]:
         return LayoutLMTokenizer.from_pretrained(self.tmpdirname		,						**_lowercase	)






     def 	UpperCAmelCase_				(				self		,						_lowercase	)->     Union[str, Any]:
         UpperCamelCase_			  =						"UNwant\u00E9d,running"
         UpperCamelCase_			  =						"unwanted, running"
         return input_text, output_text






     def 	UpperCAmelCase_				(				self	)->     Tuple:
         UpperCamelCase_			  =						self.tokenizer_class(self.vocab_file	)

         UpperCamelCase_			  =						tokenizer.tokenize("UNwant\u00E9d,running"	)
         self.assertListEqual(_lowercase		,						["un", "##want", "##ed", ",", "runn", "##ing"]	)
         self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase	)		,						[7, 4, 5, 10, 8, 9]	)






     def 	UpperCAmelCase_				(				self	)->     Dict:
         pass
