






from typing import TYPE_CHECKING

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


snake_case_   :	Dict							      ={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	str							      =[
					    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''FlaubertForMultipleChoice''',
					    '''FlaubertForQuestionAnswering''',
					    '''FlaubertForQuestionAnsweringSimple''',
					    '''FlaubertForSequenceClassification''',
					    '''FlaubertForTokenClassification''',
					    '''FlaubertModel''',
					    '''FlaubertWithLMHeadModel''',
					    '''FlaubertPreTrainedModel''',
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					snake_case_   :	Optional[int]							      =[
					    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFFlaubertForMultipleChoice''',
					    '''TFFlaubertForQuestionAnsweringSimple''',
					    '''TFFlaubertForSequenceClassification''',
					    '''TFFlaubertForTokenClassification''',
					    '''TFFlaubertModel''',
					    '''TFFlaubertPreTrainedModel''',
					    '''TFFlaubertWithLMHeadModel''',
					]


if TYPE_CHECKING:
					from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
					from .tokenization_flaubert import FlaubertTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flaubert import (
										    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FlaubertForMultipleChoice,
										    FlaubertForQuestionAnswering,
										    FlaubertForQuestionAnsweringSimple,
										    FlaubertForSequenceClassification,
										    FlaubertForTokenClassification,
										    FlaubertModel,
										    FlaubertPreTrainedModel,
										    FlaubertWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_flaubert import (
										    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFlaubertForMultipleChoice,
										    TFFlaubertForQuestionAnsweringSimple,
										    TFFlaubertForSequenceClassification,
										    TFFlaubertForTokenClassification,
										    TFFlaubertModel,
										    TFFlaubertPreTrainedModel,
										    TFFlaubertWithLMHeadModel,
										)

else:
					import sys

					snake_case_   :	str							      =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''simple docstring'''


import io
import json
import subprocess


lowerCamelCase__     						=  [
    ['en', 'ru'],
    ['ru', 'en'],
    ['en', 'de'],
    ['de', 'en'],
]

lowerCamelCase__     						=  8






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase ):
		_UpperCAmelCase		:							List[str]             =   {}
		for src, tgt in pairs:
				_UpperCAmelCase		:							Tuple             =   F"""{src}-{tgt}"""
				_UpperCAmelCase		:							Tuple             =   F"""sacrebleu -t wmt19 -l {pair} --echo src""".split()
				_UpperCAmelCase		:							List[Any]             =   subprocess.run(__lowerCAmelCase   ,			stdout=subprocess.PIPE ).stdout.decode("utf-8" ).splitlines()
				_UpperCAmelCase		:							Tuple             =   F"""sacrebleu -t wmt19 -l {pair} --echo ref""".split()
				_UpperCAmelCase		:							List[str]             =   subprocess.run(__lowerCAmelCase   ,			stdout=subprocess.PIPE ).stdout.decode("utf-8" ).splitlines()
				_UpperCAmelCase		:							Tuple             =   {"src": src_lines[:n_objs], "tgt": tgt_lines[:n_objs]}
		return text


lowerCamelCase__     						=  get_all_data(pairs, n_objs)
lowerCamelCase__     						=  './fsmt_val_data.json'
with io.open(filename, 'w', encoding='utf-8') as f:
						lowerCamelCase__     						=  json.dump(text, f, indent=2, ensure_ascii=False)



