



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


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


@require_torch
class 				_a						(       unittest.TestCase			):





							'''simple docstring'''

							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											return FSMTTokenizer.from_pretrained(__UpperCAmelCase				)



							def 		_A	(							self ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Tuple      						= FSMTForConditionalGeneration.from_pretrained(__UpperCAmelCase				).to(__UpperCAmelCase				)
											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 ,					__UpperCAmelCase ,					__UpperCAmelCase				):







											"""simple docstring"""



											a__	:							Optional[int]      						= f'facebook/wmt19-{pair}'
											a__	:							int      						= self.get_tokenizer(__UpperCAmelCase				)
											a__	:							int      						= self.get_model(__UpperCAmelCase				)

											a__	:							Dict      						= bleu_data[pair]["src"]
											a__	:							Any      						= bleu_data[pair]["tgt"]

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







from typing import TYPE_CHECKING

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


lowerCamelCase		       =      {
    """configuration_trocr""": ["""TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TrOCRConfig"""],
    """processing_trocr""": ["""TrOCRProcessor"""],
}


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """TROCR_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """TrOCRForCausalLM""",
						    """TrOCRPreTrainedModel""",
						]


if TYPE_CHECKING:
						from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
						from .processing_trocr import TrOCRProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
						import sys

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


