




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


@require_torch
class _lowerCamelCase    (					unittest.TestCase						):






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		List[str]   )			->						Union[str, Any]:

         """simple docstring"""
         return FSMTTokenizer.from_pretrained(UpperCamelCase   )






   def 	_lowerCAmelCase   (		self       :		Optional[int]   ,     UpperCamelCase       :		List[Any]   )			->						Optional[int]:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[Any]   =	FSMTForConditionalGeneration.from_pretrained(UpperCamelCase   ).to(UpperCamelCase   )
         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 	_lowerCAmelCase   (		self       :		int   ,     UpperCamelCase       :		Union[str, Any]   ,     UpperCamelCase       :		Tuple   )			->						Dict:

         """simple docstring"""
         lowerCAmelCase__      :			Optional[int]   =	f"""facebook/wmt19-{pair}"""
         lowerCAmelCase__      :			List[str]   =	self.get_tokenizer(UpperCamelCase   )
         lowerCAmelCase__      :			Tuple   =	self.get_model(UpperCamelCase   )

         lowerCAmelCase__      :			Union[str, Any]   =	bleu_data[pair]["""src"""]
         lowerCAmelCase__      :			List[str]   =	bleu_data[pair]["""tgt"""]

         lowerCAmelCase__      :			Any   =	tokenizer(UpperCamelCase   ,     return_tensors="""pt"""   ,     truncation=UpperCamelCase   ,     padding="""longest"""   ).to(UpperCamelCase   )
         lowerCAmelCase__      :			List[str]   =	model.generate(
             input_ids=batch.input_ids   ,     num_beams=8   ,     )
         lowerCAmelCase__      :			Optional[int]   =	tokenizer.batch_decode(
             UpperCamelCase   ,     skip_special_tokens=UpperCamelCase   ,     clean_up_tokenization_spaces=UpperCamelCase   )
         lowerCAmelCase__      :			Optional[Any]   =	calculate_bleu(UpperCamelCase   ,     UpperCamelCase   )
         print(UpperCamelCase   )
         self.assertGreaterEqual(scores["""bleu"""]   ,     UpperCamelCase   )











"""simple docstring"""


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate


@dataclass(frozen=a_						)
class _lowerCamelCase    (					a_						):
   _lowerCamelCase    :str     				=     field(default="audio-classification"       ,	metadata={"include_in_asdict_even_if_is_default": True}						)
   _lowerCamelCase    :ClassVar[Features]     				=     Features({"audio": Audio()}						)
   _lowerCamelCase    :ClassVar[Features]     				=     Features({"labels": ClassLabel}						)
   _lowerCamelCase    :str     				=     "audio"
   _lowerCamelCase    :str     				=     "labels"






   def 	_lowerCAmelCase   (		self       :		str   ,     UpperCamelCase       :		Optional[int]   )			->						Union[str, Any]:

         """simple docstring"""
         if self.label_column not in features:
               raise ValueError(f"""Column {self.label_column} is not present in features."""   )
         if not isinstance(features[self.label_column]   ,     UpperCamelCase   ):
               raise ValueError(f"""Column {self.label_column} is not a ClassLabel."""   )
         lowerCAmelCase__      :			str   =	copy.deepcopy(self   )
         lowerCAmelCase__      :			Optional[int]   =	self.label_schema.copy()
         lowerCAmelCase__      :			List[Any]   =	features[self.label_column]
         lowerCAmelCase__      :			Optional[int]   =	label_schema
         return task_template






   @property
   def 	_lowerCAmelCase   (		self       :		List[Any]   )			->						Dict[str, str]:

         """simple docstring"""
         return {
             self.audio_column: "audio",
             self.label_column: "labels",
         }





