


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






@require_torch
class  lowerCamelCase_(     unittest.TestCase ):




				def   a__(	self    :				Dict   ,					lowerCAmelCase    :				List[Any]   )->  Optional[Any]:

										"""simple docstring"""




										return FSMTTokenizer.from_pretrained(UpperCamelCase_   )




				def   a__(	self    :				Optional[int]   ,					lowerCAmelCase    :				int   )->  List[Any]:

										"""simple docstring"""




										UpperCAmelCase         =			FSMTForConditionalGeneration.from_pretrained(UpperCamelCase_   ).to(UpperCamelCase_   )
										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    :				int   ,					lowerCAmelCase    :				Any   ,					lowerCAmelCase    :				Union[str, Any]   )->  Tuple:

										"""simple docstring"""




										UpperCAmelCase         =			F"""facebook/wmt19-{pair}"""
										UpperCAmelCase         =			self.get_tokenizer(UpperCamelCase_   )
										UpperCAmelCase         =			self.get_model(UpperCamelCase_   )

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

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









'''simple docstring'''



# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase    :    Any			     =  {
    """configuration_cpmant""": ["""CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CpmAntConfig"""],
    """tokenization_cpmant""": ["""CpmAntTokenizer"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase    :    int			     =  [
	    """CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """CpmAntForCausalLM""",
	    """CpmAntModel""",
	    """CpmAntPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
	from .tokenization_cpmant import CpmAntTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_cpmant import (
		    CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    CpmAntForCausalLM,
		    CpmAntModel,
		    CpmAntPreTrainedModel,
		)


else:
	import sys

	_lowercase    :    Dict			     =  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





