'''simple docstring'''


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


snake_case__	:     int      =    {
    '''configuration_led''': ['''LED_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LEDConfig'''],
    '''tokenization_led''': ['''LEDTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     Optional[int]      =    ['''LEDTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     List[str]      =    [
     '''LED_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''LEDForConditionalGeneration''',
     '''LEDForQuestionAnswering''',
     '''LEDForSequenceClassification''',
     '''LEDModel''',
     '''LEDPreTrainedModel''',
 ]


try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__	:     Optional[Any]      =    ['''TFLEDForConditionalGeneration''', '''TFLEDModel''', '''TFLEDPreTrainedModel''']


if TYPE_CHECKING:
 from .configuration_led import LED_PRETRAINED_CONFIG_ARCHIVE_MAP, LEDConfig
 from .tokenization_led import LEDTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_led_fast import LEDTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_led import (
      LED_PRETRAINED_MODEL_ARCHIVE_LIST,
      LEDForConditionalGeneration,
      LEDForQuestionAnswering,
      LEDForSequenceClassification,
      LEDModel,
      LEDPreTrainedModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel, TFLEDPreTrainedModel

else:
 import sys

 snake_case__	:     Dict      =    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)







'''simple docstring'''


import io
import json
import subprocess


snake_case__	:     Any      =    [
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

snake_case__	:     Tuple      =    8


def    _lowerCamelCase       ( lowerCamelCase_   :				Tuple      ,	lowerCamelCase_   :				Union[str, Any]  ):



     """simple docstring"""





     UpperCAmelCase_				:  List[Any]  = {}
     for src, tgt in pairs:
          UpperCAmelCase_				:  str  = F'''{src}-{tgt}'''
          UpperCAmelCase_				:  List[str]  = F'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
          UpperCAmelCase_				:  Any  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  Any  = F'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
          UpperCAmelCase_				:  List[str]  = subprocess.run(lowerCamelCase_      ,	stdout=subprocess.PIPE  ).stdout.decode('utf-8'  ).splitlines()
          UpperCAmelCase_				:  str  = {'src': src_lines[:n_objs], 'tgt': tgt_lines[:n_objs]}
     return text


snake_case__	:     Union[str, Any]      =    get_all_data(pairs, n_objs)
snake_case__	:     Dict      =    '''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
 snake_case__	:     List[str]      =    json.dump(text, f, indent=2, ensure_ascii=False)






