






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_		:						Any             =    json.load(f)

@require_torch
class 		_snake_case  (  unittest.TestCase					):






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       str:
						return FSMTTokenizer.from_pretrained(a)






			def    SCREAMING_SNAKE_CASE__    (       self		,					a)      ->       Any:
						SCREAMING_SNAKE_CASE						= FSMTForConditionalGeneration.from_pretrained(a).to(a)
						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    SCREAMING_SNAKE_CASE__    (       self		,					a		,					a)      ->       List[Any]:
						# note: this test is not testing the best performance since it only evals a small batch
						# but it should be enough to detect a regression in the output quality
						SCREAMING_SNAKE_CASE						= f'''facebook/wmt19-{pair}'''
						SCREAMING_SNAKE_CASE						= self.get_tokenizer(a)
						SCREAMING_SNAKE_CASE						= self.get_model(a)

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

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












from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_torch_available,
)


a_		:						int             =    {
    'configuration_speecht5': [
        'SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP',
        'SpeechT5Config',
        'SpeechT5HifiGanConfig',
    ],
    'feature_extraction_speecht5': ['SpeechT5FeatureExtractor'],
    'processing_speecht5': ['SpeechT5Processor'],
}

try:
				if not is_sentencepiece_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[Any]             =    ['SpeechT5Tokenizer']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				a_		:						Optional[int]             =    [
				    'SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'SpeechT5ForSpeechToText',
				    'SpeechT5ForSpeechToSpeech',
				    'SpeechT5ForTextToSpeech',
				    'SpeechT5Model',
				    'SpeechT5PreTrainedModel',
				    'SpeechT5HifiGan',
				]

if TYPE_CHECKING:
				from .configuration_speechta import (
				    SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    SPEECHT5_PRETRAINED_HIFIGAN_CONFIG_ARCHIVE_MAP,
				    SpeechTaConfig,
				    SpeechTaHifiGanConfig,
				)
				from .feature_extraction_speechta import SpeechTaFeatureExtractor
				from .processing_speechta import SpeechTaProcessor

				try:
								if not is_sentencepiece_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_speechta import SpeechTaTokenizer

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_speechta import (
								    SPEECHT5_PRETRAINED_MODEL_ARCHIVE_LIST,
								    SpeechTaForSpeechToSpeech,
								    SpeechTaForSpeechToText,
								    SpeechTaForTextToSpeech,
								    SpeechTaHifiGan,
								    SpeechTaModel,
								    SpeechTaPreTrainedModel,
								)

else:
				import sys

				a_		:						Dict             =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




