






'''simple docstring'''






from typing import TYPE_CHECKING

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


__A     						= {
    '''configuration_esm''': ['''ESM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''EsmConfig'''],
    '''tokenization_esm''': ['''EsmTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''EsmForMaskedLM''',
          '''EsmForSequenceClassification''',
          '''EsmForTokenClassification''',
          '''EsmModel''',
          '''EsmPreTrainedModel''',
      ]
      __A     						= ['''EsmForProteinFolding''', '''EsmFoldPreTrainedModel''']

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''TFEsmForMaskedLM''',
          '''TFEsmForSequenceClassification''',
          '''TFEsmForTokenClassification''',
          '''TFEsmModel''',
          '''TFEsmPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
      from .tokenization_esm import EsmTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_esm import (
                ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                EsmForMaskedLM,
                EsmForSequenceClassification,
                EsmForTokenClassification,
                EsmModel,
                EsmPreTrainedModel,
            )
            from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_esm import (
                TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFEsmForMaskedLM,
                TFEsmForSequenceClassification,
                TFEsmForTokenClassification,
                TFEsmModel,
                TFEsmPreTrainedModel,
            )


else:
      import sys

      __A     						= _LazyModule(__name__, globals()['''__file__'''], _import_structure)



'''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)



