
from typing import Optional

import pyspark

from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader






class   UpperCAmelCase		(						SCREAMING_SNAKE_CASE__ ):



   def __init__(self		:					List[str]    ,						snake_case__		:					str    ,						snake_case__		:					List[str] = None    ,						snake_case__		:					Union[str, Any] = None    ,						snake_case__		:					Optional[Any] = True    ,						snake_case__		:					Optional[int] = None    ,						snake_case__		:					int = False    ,						snake_case__		:					Tuple = None    ,						snake_case__		:					Any = True    ,						snake_case__		:					List[str] = "arrow"    ,						**snake_case__		:					Optional[int]    ,						)		->	Optional[int]:


          '''simple docstring'''


          super().__init__(
              split=lowerCamelCase__    ,						features=lowerCamelCase__    ,						cache_dir=lowerCamelCase__    ,						keep_in_memory=lowerCamelCase__    ,						streaming=lowerCamelCase__    ,						**lowerCamelCase__    ,						)
          snake_case       :   Optional[int]							=      load_from_cache_file
          snake_case       :   Union[str, Any]							=      file_format
          snake_case       :   Any							=      Spark(
              df=lowerCamelCase__    ,						features=lowerCamelCase__    ,						cache_dir=lowerCamelCase__    ,						working_dir=lowerCamelCase__    ,						**lowerCamelCase__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	Union[str, Any]:


          '''simple docstring'''


          if self.streaming:
                 return self.builder.as_streaming_dataset(split=self.split )
          snake_case       :   int							=      None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
          self.builder.download_and_prepare(
              download_mode=lowerCamelCase__    ,						file_format=self._file_format    ,						)
          return self.builder.as_dataset(split=self.split )










from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
     import tensorflow as tf

     from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM






@require_tf
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE__     (		unittest.TestCase    ):




    '''simple docstring'''






    @slow
    def 			_lowerCAmelCase					(       self		):
        A :       Any          =   TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""		)
        A :       Dict          =   AutoTokenizer.from_pretrained("""google/mt5-small"""		)

        A :       Tuple          =   tokenizer("""Hello there""",		return_tensors="""tf"""		).input_ids
        A :       Optional[int]          =   tokenizer("""Hi I am""",		return_tensors="""tf"""		).input_ids

        A :       List[Any]          =   model(lowerCamelCase__,		labels=lowerCamelCase__		).loss
        A :       str          =   -tf.math.reduce_mean(lowerCamelCase__		).numpy()

        A :       Tuple          =   -21.22_8168
        self.assertTrue(abs(mtf_score - EXPECTED_SCORE		) < 2e-4		)


