





from typing import List, Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A				:							Optional[Any]							= logging.get_logger(__name__)

_A				:							str							= {
    """allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/config.json""",
    # See all LED models at https://huggingface.co/models?filter=led
}



class 				__snake_case  (     __SCREAMING_SNAKE_CASE						):


    '''simple docstring'''







    lowerCamelCase__						:		List[Any]							=  """led"""
    lowerCamelCase__						:		Union[str, Any]							=  {
        """num_attention_heads""": """encoder_attention_heads""",
        """hidden_size""": """d_model""",
        """attention_probs_dropout_prob""": """attention_dropout""",
        """initializer_range""": """init_std""",
    }



    def __init__(					self      ,							A_=5_02_65      ,							A_=1_63_84      ,							A_=10_24      ,							A_=12      ,							A_=40_96      ,							A_=16      ,							A_=12      ,							A_=40_96      ,							A_=16      ,							A_=0.0      ,							A_=0.0      ,							A_=True      ,							A_=True      ,							A_="gelu"      ,							A_=10_24      ,							A_=0.1      ,							A_=0.0      ,							A_=0.0      ,							A_=0.02      ,							A_=2      ,							A_=0.0      ,							A_=1      ,							A_=0      ,							A_=2      ,							A_ = 5_12      ,							**A_      ,							):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__       							= vocab_size
        SCREAMING_SNAKE_CASE__       							= max_encoder_position_embeddings
        SCREAMING_SNAKE_CASE__       							= max_decoder_position_embeddings
        SCREAMING_SNAKE_CASE__       							= d_model
        SCREAMING_SNAKE_CASE__       							= encoder_ffn_dim
        SCREAMING_SNAKE_CASE__       							= encoder_layers
        SCREAMING_SNAKE_CASE__       							= encoder_attention_heads
        SCREAMING_SNAKE_CASE__       							= decoder_ffn_dim
        SCREAMING_SNAKE_CASE__       							= decoder_layers
        SCREAMING_SNAKE_CASE__       							= decoder_attention_heads
        SCREAMING_SNAKE_CASE__       							= dropout
        SCREAMING_SNAKE_CASE__       							= attention_dropout
        SCREAMING_SNAKE_CASE__       							= activation_dropout
        SCREAMING_SNAKE_CASE__       							= activation_function
        SCREAMING_SNAKE_CASE__       							= init_std
        SCREAMING_SNAKE_CASE__       							= encoder_layerdrop
        SCREAMING_SNAKE_CASE__       							= decoder_layerdrop
        SCREAMING_SNAKE_CASE__       							= classifier_dropout
        SCREAMING_SNAKE_CASE__       							= use_cache
        SCREAMING_SNAKE_CASE__       							= encoder_layers
        SCREAMING_SNAKE_CASE__       							= attention_window

        super().__init__(
            pad_token_id=A_      ,							bos_token_id=A_      ,							eos_token_id=A_      ,							is_encoder_decoder=A_      ,							decoder_start_token_id=A_      ,							**A_      ,							)







import io
import json
import subprocess


_A				:							str							= [
    ["""en""", """ru"""],
    ["""ru""", """en"""],
    ["""en""", """de"""],
    ["""de""", """en"""],
]

_A				:							int							= 8


def        __snake_case       (						lowerCAmelCase_   ,       lowerCAmelCase_  )							->	Optional[Any]:
    SCREAMING_SNAKE_CASE__       							= {}
    for src, tgt in pairs:
        SCREAMING_SNAKE_CASE__       							= f'''{src}-{tgt}'''
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo src'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= f'''sacrebleu -t wmt19 -l {pair} --echo ref'''.split()
        SCREAMING_SNAKE_CASE__       							= subprocess.run(lowerCAmelCase_   ,       stdout=subprocess.PIPE  ).stdout.decode('''utf-8'''  ).splitlines()
        SCREAMING_SNAKE_CASE__       							= {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
    return text


_A				:							Optional[Any]							= get_all_data(pairs, n_objs)
_A				:							Optional[Any]							= """./fsmt_val_data.json"""
with io.open(filename, """w""", encoding="""utf-8""") as f:
  _A				:							int							= json.dump(text, f, indent=2, ensure_ascii=False)
