



"""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


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 SCREAMING_SNAKE_CASE__					(		unittest.TestCase      ):



      def     lowerCAmelCase__(self						,     _lowercase							):





             '''simple docstring'''

             return FSMTTokenizer.from_pretrained(_lowercase							)



      def     lowerCAmelCase__(self						,     _lowercase							):





             '''simple docstring'''

             __a		:		int									=       FSMTForConditionalGeneration.from_pretrained(_lowercase							).to(_lowercase							)
             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						,     _lowercase						,     _lowercase							):





             '''simple docstring'''

             __a		:		List[Any]									=       F'''facebook/wmt19-{pair}'''
             __a		:		Optional[Any]									=       self.get_tokenizer(_lowercase							)
             __a		:		int									=       self.get_model(_lowercase							)

             __a		:		Optional[int]									=       bleu_data[pair]["""src"""]
             __a		:		Optional[Any]									=       bleu_data[pair]["""tgt"""]

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






"""simple docstring"""

from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tensorflow_text_available, is_torch_available


lowercase__			   =  {
    "configuration_ernie": ["ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP", "ErnieConfig", "ErnieOnnxConfig"],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	lowercase__			   =  [
	    "ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "ErnieForCausalLM",
	    "ErnieForMaskedLM",
	    "ErnieForMultipleChoice",
	    "ErnieForNextSentencePrediction",
	    "ErnieForPreTraining",
	    "ErnieForQuestionAnswering",
	    "ErnieForSequenceClassification",
	    "ErnieForTokenClassification",
	    "ErnieModel",
	    "ErniePreTrainedModel",
	]

if TYPE_CHECKING:
	from .configuration_ernie import ERNIE_PRETRAINED_CONFIG_ARCHIVE_MAP, ErnieConfig, ErnieOnnxConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_ernie import (
		    ERNIE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    ErnieForCausalLM,
		    ErnieForMaskedLM,
		    ErnieForMultipleChoice,
		    ErnieForNextSentencePrediction,
		    ErnieForPreTraining,
		    ErnieForQuestionAnswering,
		    ErnieForSequenceClassification,
		    ErnieForTokenClassification,
		    ErnieModel,
		    ErniePreTrainedModel,
		)

else:
	import sys

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

