




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
  from .features import FeatureType


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   42
    UpperCAmelCase__													=   None
    # Automatically constructed
    UpperCAmelCase__													=   "dict"
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   field(default='''Translation'''	,      init=UpperCAmelCase__	,      repr=UpperCAmelCase__  )


    def __call__(    self   :			Optional[int])		->str:





           '''simple docstring'''

           return pa.struct({lang: pa.string() for lang in sorted(self.languages)})





    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any])		->Union["FeatureType", Dict[str, "FeatureType"]]:





           '''simple docstring'''

           from .features import Value

           return {k: Value('''string''') for k in sorted(self.languages)}


@dataclass
class        UpperCamelCase_					:

    '''simple docstring'''






    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   None
    # Automatically constructed
    UpperCAmelCase__													=   "dict"
    UpperCAmelCase__													=   None
    UpperCAmelCase__													=   field(default='''TranslationVariableLanguages'''	,      init=UpperCAmelCase__	,      repr=UpperCAmelCase__  )


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





           '''simple docstring'''

           A__   				=					sorted(set(self.languages)) if self.languages else None
           A__   				=					len(self.languages) if self.languages else None


    def __call__(    self   :			int)		->List[str]:





           '''simple docstring'''

           return pa.struct({'''language''': pa.list_(pa.string()), '''translation''': pa.list_(pa.string())})


    def  SCREAMING_SNAKE_CASE							(    self   :			List[Any]      ,				UpperCAmelCase__   :			Any)		->Optional[Any]:





           '''simple docstring'''

           A__   				=					set(self.languages)
           if self.languages and set(UpperCAmelCase__) - lang_set:
                  raise ValueError(
                      f"""Some languages in example ({", ".join(sorted(set(UpperCAmelCase__) - lang_set))}) are not in valid set ({", ".join(UpperCAmelCase__)}).""")

           # Convert dictionary into tuples, splitting out cases where there are
           # multiple translations for a single language.
           A__   				=					[]
           for lang, text in translation_dict.items():
                  if isinstance(UpperCAmelCase__      ,				UpperCAmelCase__):
                         translation_tuples.append((lang, text))
                  else:
                         translation_tuples.extend([(lang, el) for el in text])

        # Ensure translations are in ascending order by language code.
           A__				,							A__   				=					zip(*sorted(UpperCAmelCase__))

           return {"language": languages, "translation": translations}





    def  SCREAMING_SNAKE_CASE							(    self   :			Tuple)		->Union["FeatureType", Dict[str, "FeatureType"]]:





           '''simple docstring'''

           from .features import Sequence, Value

           return {
               "language": Sequence(Value('''string''')),
               "translation": Sequence(Value('''string''')),
           }








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  ):

    '''simple docstring'''






    @slow
    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any])		->Any:





           '''simple docstring'''

           A__   				=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''      ,				return_dict=UpperCAmelCase__).to(UpperCAmelCase__)
           A__   				=					AutoTokenizer.from_pretrained('''google/mt5-small''')

           A__   				=					tokenizer('''Hello there'''      ,				return_tensors='''pt''').input_ids
           A__   				=					tokenizer('''Hi I am'''      ,				return_tensors='''pt''').input_ids

           A__   				=					model(input_ids.to(UpperCAmelCase__)      ,				labels=labels.to(UpperCAmelCase__)).loss
           A__   				=					-(labels.shape[-1] * loss.item())

           A__   				=					-84.9127
           self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4)


