
from typing import Callable, Optional

from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream



class 		UpperCamelCase__  (	__lowercase   ):
   def __init__(self			: List[Any] ,    snake_case_			: Callable ,    snake_case_			: Optional[Features] = None ,    snake_case_			: str = None ,    snake_case_			: bool = False ,    snake_case_			: bool = False ,    snake_case_			: Optional[dict] = None ,    snake_case_			: Optional[int] = None ,    **snake_case_			: Dict ,    ):
    super().__init__(
        features=snake_case_ ,    cache_dir=snake_case_ ,    keep_in_memory=snake_case_ ,    streaming=snake_case_ ,    num_proc=snake_case_ ,    **snake_case_ ,    )
    __a			:  List[str]		    =  Generator(
        cache_dir=snake_case_ ,    features=snake_case_ ,    generator=snake_case_ ,    gen_kwargs=snake_case_ ,    **snake_case_ ,    )







   def 				lowerCAmelCase      (self			: Union[str, Any]       ):
    # Build iterable dataset
    if self.streaming:
     __a			:  Union[str, Any]		    =  self.builder.as_streaming_dataset(split='''train'''       )
    # Build regular (map-style) dataset
    else:
     __a			:  Optional[Any]		    =  None
     __a			:  Optional[Any]		    =  None
     __a			:  List[Any]		    =  None
     __a			:  Dict		    =  None

     self.builder.download_and_prepare(
         download_config=snake_case_ ,    download_mode=snake_case_ ,    verification_mode=snake_case_ ,    base_path=snake_case_ ,    num_proc=self.num_proc ,    )
     __a			:  Union[str, Any]		    =  self.builder.as_dataset(
         split='''train''' ,    verification_mode=snake_case_ ,    in_memory=self.keep_in_memory       )
    return dataset


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
      from transformers import AutoModelForSeqaSeqLM, AutoTokenizer



@require_torch
@require_sentencepiece
@require_tokenizers
class 		UpperCamelCase__  (	unittest.TestCase   ):
   @slow
   def 				lowerCAmelCase      (self			: Tuple       ):
    __a			:  List[str]		    =  AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' ,    return_dict=snake_case_       ).to(snake_case_       )
    __a			:  List[Any]		    =  AutoTokenizer.from_pretrained('''google/mt5-small'''       )

    __a			:  Optional[int]		    =  tokenizer('''Hello there''' ,    return_tensors='''pt'''       ).input_ids
    __a			:  Dict		    =  tokenizer('''Hi I am''' ,    return_tensors='''pt'''       ).input_ids

    __a			:  Optional[Any]		    =  model(input_ids.to(snake_case_       ) ,    labels=labels.to(snake_case_       )       ).loss
    __a			:  Tuple		    =  -(labels.shape[-1] * loss.item())

    __a			:  Dict		    =  -84.9127
    self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 1E-4       )
