

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_								=     get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
							lowercase_								=     json.load(f)
@require_torch
class 				_snake_case			(					unittest.TestCase):




				def 							A__      (						self					:						Optional[int],					__lowercase					:						List[str]						):
							return FSMTTokenizer.from_pretrained(_A						)




				def 							A__      (						self					:						str,					__lowercase					:						str						):
							lowercase__					  =				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 							A__      (						self					:						Union[str, Any],					__lowercase					:						Any,					__lowercase					:						str						):
							# 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_								=     {
    """configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowercase_								=     [
							    """TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TimesformerModel""",
							    """TimesformerForVideoClassification""",
							    """TimesformerPreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_timesformer import TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, TimesformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_timesformer import (
														    TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TimesformerForVideoClassification,
														    TimesformerModel,
														    TimesformerPreTrainedModel,
														)

else:
							import sys

							lowercase_								=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




