
from typing import TYPE_CHECKING

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


lowercase__				:				str						=      {
    "configuration_led": ["LED_PRETRAINED_CONFIG_ARCHIVE_MAP", "LEDConfig"],
    "tokenization_led": ["LEDTokenizer"],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__				:				str						=      ["LEDTokenizerFast"]

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


try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				lowercase__				:				Dict						=      ["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

				lowercase__				:				int						=      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












"""simple docstring"""



import json
import logging
import os
import subprocess
from argparse import ArgumentParser


a  :						Dict					       =							logging.getLogger(__name__)




def       snake_case__		(   )    ->Any:
 UpperCAmelCase__ 						=				ArgumentParser()
 UpperCAmelCase__						,	UpperCAmelCase__ 						=				parser.parse_known_args()
 for arg in unknown:
  if arg.startswith(("""-""", """--""")			):
   parser.add_argument(arg.split("""="""			)[0]			)

 return parser.parse_args()




def       snake_case__		(   )    ->List[Any]:
 UpperCAmelCase__ 						=				parse_args()
 UpperCAmelCase__ 						=				8_8_8_8
 UpperCAmelCase__ 						=				int(os.environ["""SM_NUM_GPUS"""]			)
 UpperCAmelCase__ 						=				json.loads(os.environ["""SM_HOSTS"""]			)
 UpperCAmelCase__ 						=				len(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				os.environ["""SM_CURRENT_HOST"""]
 UpperCAmelCase__ 						=				hosts.index(_SCREAMING_SNAKE_CASE			)
 UpperCAmelCase__ 						=				"""INFO"""

 if num_nodes > 1:
  UpperCAmelCase__ 						=				F'''python -m torch.distributed.launch \
                --nnodes={num_nodes}  \
                --node_rank={rank}  \
                --nproc_per_node={num_gpus}  \
                --master_addr={hosts[0]}  \
                --master_port={port} \
                ./run_glue.py \
                {''.join(['nested fstring' for parameter,value in args.__dict__.items()]			)}'''
 else:
  UpperCAmelCase__ 						=				F'''python -m torch.distributed.launch \
            --nproc_per_node={num_gpus}  \
            ./run_glue.py \
            {''.join(['nested fstring' for parameter,value in args.__dict__.items()]			)}'''
 try:
  subprocess.run(_SCREAMING_SNAKE_CASE       ,	shell=_SCREAMING_SNAKE_CASE			)
 except Exception as e:
  logger.info(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
 main()



