
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


lowercase__		:Union[str, Any]			  =   get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
		lowercase__		:Dict			  =   json.load(f)







@require_torch
class     lowercase		( unittest.TestCase		):




							def 			A__				(    self       ,A__):
												return FSMTTokenizer.from_pretrained(A__)




							def 			A__				(    self       ,A__):
												lowercase						     =						FSMTForConditionalGeneration.from_pretrained(A__).to(A__)
												if torch_device == "cuda":
																	model.half()
												return model





							@parameterized.expand(
							    [
							        ['''en-ru''', 2_6.0],
							        ['''ru-en''', 2_2.0],
							        ['''en-de''', 2_2.0],
							        ['''de-en''', 2_9.0],
							    ])
							@slow
							def 			A__				(    self       ,A__       ,A__):
												# 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
												lowercase						     =						f'facebook/wmt19-{pair}'
												lowercase						     =						self.get_tokenizer(A__)
												lowercase						     =						self.get_model(A__)

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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowercase__		:Dict			  =   {
    "configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
    "tokenization_luke": ["LukeTokenizer"],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase__		:List[str]			  =   [
							    "LUKE_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "LukeForEntityClassification",
							    "LukeForEntityPairClassification",
							    "LukeForEntitySpanClassification",
							    "LukeForMultipleChoice",
							    "LukeForQuestionAnswering",
							    "LukeForSequenceClassification",
							    "LukeForTokenClassification",
							    "LukeForMaskedLM",
							    "LukeModel",
							    "LukePreTrainedModel",
							]


if TYPE_CHECKING:
							from .configuration_luke import LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP, LukeConfig
							from .tokenization_luke import LukeTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_luke import (
														    LUKE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    LukeForEntityClassification,
														    LukeForEntityPairClassification,
														    LukeForEntitySpanClassification,
														    LukeForMaskedLM,
														    LukeForMultipleChoice,
														    LukeForQuestionAnswering,
														    LukeForSequenceClassification,
														    LukeForTokenClassification,
														    LukeModel,
														    LukePreTrainedModel,
														)

else:
							import sys

							lowercase__		:Tuple			  =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




