
"""simple docstring"""




import os
from typing import BinaryIO, Optional, Union

import numpy as np
import pyarrow.parquet as pq

from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS_MODULES
from ..packaged_modules.parquet.parquet import Parquet
from ..utils import logging
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader



def     __magic_name__		(					UpperCamelCase     :		Features     )     ->						Optional[int]:
    a__		=			np.inf

    def set_batch_size(UpperCamelCase     :		FeatureType     ) -> None:
        nonlocal batch_size
        if isinstance(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			min(UpperCamelCase							,		config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS     )
        elif isinstance(UpperCamelCase							,		UpperCamelCase     ):
            a__		=			min(UpperCamelCase							,		config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS     )
        elif isinstance(UpperCamelCase							,		UpperCamelCase     ) and feature.dtype == "binary":
            a__		=			min(UpperCamelCase							,		config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS     )

    _visit(UpperCamelCase							,		UpperCamelCase     )

    return None if batch_size is np.inf else batch_size







class     lowercase(_lowercase       ):



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = None     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = False     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     str:







         """simple docstring"""



         super().__init__(
             __SCREAMING_SNAKE_CASE     ,				split=__SCREAMING_SNAKE_CASE     ,				features=__SCREAMING_SNAKE_CASE     ,				cache_dir=__SCREAMING_SNAKE_CASE     ,				keep_in_memory=__SCREAMING_SNAKE_CASE     ,				streaming=__SCREAMING_SNAKE_CASE     ,				num_proc=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)
         a__		=			path_or_paths if isinstance(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   ) else {self.split: path_or_paths}
         a__		=			_PACKAGED_DATASETS_MODULES['parquet'][1]
         a__		=			Parquet(
             cache_dir=__SCREAMING_SNAKE_CASE     ,				data_files=__SCREAMING_SNAKE_CASE     ,				features=__SCREAMING_SNAKE_CASE     ,				hash=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE     ,				)




     def 			lowercase__  (							self   )		->     Dict:







         """simple docstring"""



         if self.streaming:
             a__		=			self.builder.as_streaming_dataset(split=self.split   )
         # Build regular (map-style) dataset
         else:
             a__		=			None
             a__		=			None
             a__		=			None
             a__		=			None

             self.builder.download_and_prepare(
                 download_config=__SCREAMING_SNAKE_CASE     ,				download_mode=__SCREAMING_SNAKE_CASE     ,				verification_mode=__SCREAMING_SNAKE_CASE     ,				base_path=__SCREAMING_SNAKE_CASE     ,				num_proc=self.num_proc     ,				)
             a__		=			self.builder.as_dataset(
                 split=self.split     ,				verification_mode=__SCREAMING_SNAKE_CASE     ,				in_memory=self.keep_in_memory   )
         return dataset







class     lowercase:



     def __init__(							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE = None     ,				**__SCREAMING_SNAKE_CASE     ,				)		->     List[str]:







         """simple docstring"""



         a__		=			dataset
         a__		=			path_or_buf
         a__		=			batch_size or get_writer_batch_size(dataset.features   )
         a__		=			parquet_writer_kwargs



     def 			lowercase__  (							self   )		->     int:







         """simple docstring"""



         a__		=			self.batch_size if self.batch_size else config.DEFAULT_MAX_BATCH_SIZE

         if isinstance(self.path_or_buf     ,				(str, bytes, os.PathLike)   ):
             with open(self.path_or_buf     ,				'wb+'   ) as buffer:
                 a__		=			self._write(file_obj=__SCREAMING_SNAKE_CASE     ,				batch_size=__SCREAMING_SNAKE_CASE     ,				**self.parquet_writer_kwargs   )
         else:
             a__		=			self._write(file_obj=self.path_or_buf     ,				batch_size=__SCREAMING_SNAKE_CASE     ,				**self.parquet_writer_kwargs   )
         return written




     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			0
         a__		=			parquet_writer_kwargs.pop('path_or_buf'     ,				__SCREAMING_SNAKE_CASE   )
         a__		=			self.dataset.features.arrow_schema

         a__		=			pq.ParquetWriter(__SCREAMING_SNAKE_CASE     ,				schema=__SCREAMING_SNAKE_CASE     ,				**__SCREAMING_SNAKE_CASE   )

         for offset in logging.tqdm(
             range(0     ,				len(self.dataset   )     ,				__SCREAMING_SNAKE_CASE   )     ,				unit='ba'     ,				disable=not logging.is_progress_bar_enabled()     ,				desc='Creating parquet from Arrow format'     ,				):
             a__		=			query_table(
                 table=self.dataset._data     ,				key=slice(__SCREAMING_SNAKE_CASE     ,				offset + batch_size   )     ,				indices=self.dataset._indices if self.dataset._indices is not None else None     ,				)
             writer.write_table(__SCREAMING_SNAKE_CASE   )
             written += batch.nbytes
         writer.close()
         return written




"""simple docstring"""




import io
import json
import unittest

from parameterized import parameterized

from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu


a		:       Any     						=       get_tests_dir() + '/test_data/fsmt/fsmt_val_data.json'
with io.open(filename, 'r', encoding='utf-8') as f:
    a		:       Tuple     						=       json.load(f)







@require_torch
class     lowercase(unittest.TestCase       ):



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     Optional[Any]:







         """simple docstring"""



         return FSMTTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE   )



     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE   )		->     int:







         """simple docstring"""



         a__		=			FSMTForConditionalGeneration.from_pretrained(__SCREAMING_SNAKE_CASE   ).to(__SCREAMING_SNAKE_CASE   )
         if torch_device == "cuda":
             model.half()
         return model




     @parameterized.expand(
         [
             ['en-ru', 26.0],
             ['ru-en', 22.0],
             ['en-de', 22.0],
             ['de-en', 29.0],
         ]   )
     @slow
     def 			lowercase__  (							self     ,				__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )		->     Tuple:







         """simple docstring"""



         a__		=			f'facebook/wmt19-{pair}'
         a__		=			self.get_tokenizer(__SCREAMING_SNAKE_CASE   )
         a__		=			self.get_model(__SCREAMING_SNAKE_CASE   )

         a__		=			bleu_data[pair]['src']
         a__		=			bleu_data[pair]['tgt']

         a__		=			tokenizer(__SCREAMING_SNAKE_CASE     ,				return_tensors='pt'     ,				truncation=__SCREAMING_SNAKE_CASE     ,				padding='longest'   ).to(__SCREAMING_SNAKE_CASE   )
         a__		=			model.generate(
             input_ids=batch.input_ids     ,				num_beams=8     ,				)
         a__		=			tokenizer.batch_decode(
             __SCREAMING_SNAKE_CASE     ,				skip_special_tokens=__SCREAMING_SNAKE_CASE     ,				clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE   )
         a__		=			calculate_bleu(__SCREAMING_SNAKE_CASE     ,				__SCREAMING_SNAKE_CASE   )
         print(__SCREAMING_SNAKE_CASE   )
         self.assertGreaterEqual(scores['bleu']     ,				__SCREAMING_SNAKE_CASE   )


