import argparse
import logging
import sys
from unittest.mock import patch

import run_glue_deebert

from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase			:							Dict     =logging.getLogger()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Union[str, Any] 						=						argparse.ArgumentParser()
    parser.add_argument("""-f"""	)
    _lowerCamelCase     :		int 						=						parser.parse_args()
    return args.f




class 	__snake_case	( __lowerCAmelCase		):





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->None:


          """simple docstring"""


          _lowerCamelCase     :		str 						=						logging.StreamHandler(sys.stdout)
          logger.addHandler(_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				List[str])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Dict 						=						get_gpu_count()

          if n_gpu > 1:
              pass
              # XXX: doesn't quite work with n_gpu > 1 https://github.com/huggingface/transformers/issues/10560
              # script = f"{self.examples_dir_str}/research_projects/deebert/run_glue_deebert.py"
              # distributed_args = f"-m torch.distributed.launch --nproc_per_node={n_gpu} {script}".split()
              # cmd = [sys.executable] + distributed_args + args
              # execute_subprocess_async(cmd, env=self.get_env())
              # XXX: test the results - need to save them first into .json file
          else:
              args.insert(0    ,  """run_glue_deebert.py""")
              with patch.object(_UpperCamelCase    ,  """argv"""    ,  _UpperCamelCase):
                  _lowerCamelCase     :		Tuple 						=						run_glue_deebert.main()
                  for value in result.values():
                      self.assertGreaterEqual(_UpperCamelCase    ,  0.6_6_6)




      @slow
      @require_torch_non_multi_gpu
      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Any 						=						"""
            --model_type roberta
            --model_name_or_path roberta-base
            --task_name MRPC
            --do_train
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --max_seq_length 128
            --per_gpu_eval_batch_size=1
            --per_gpu_train_batch_size=8
            --learning_rate 2e-4
            --num_train_epochs 3
            --overwrite_output_dir
            --seed 42
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --save_steps 0
            --overwrite_cache
            --eval_after_first_stage
            """.split()
          self.run_and_check(_UpperCamelCase)

          _lowerCamelCase     :		int 						=						"""
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --eval_each_highway
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
          self.run_and_check(_UpperCamelCase)

          _lowerCamelCase     :		str 						=						"""
            --model_type roberta
            --model_name_or_path ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --task_name MRPC
            --do_eval
            --do_lower_case
            --data_dir ./tests/fixtures/tests_samples/MRPC/
            --output_dir ./examples/deebert/saved_models/roberta-base/MRPC/two_stage
            --plot_data_dir ./examples/deebert/results/
            --max_seq_length 128
            --early_exit_entropy 0.1
            --eval_highway
            --overwrite_cache
            --per_gpu_eval_batch_size=1
            """.split()
          self.run_and_check(_UpperCamelCase)





from math import log

from scipy.constants import Boltzmann, physical_constants

lowerCAmelCase			:							List[Any]     =300  # TEMPERATURE (unit = K)







def 						A__  (					__A   , __A   , __A   , ):







    '''simple docstring'''

    if donor_conc <= 0:
        raise ValueError("""Donor concentration should be positive"""	)
    elif acceptor_conc <= 0:
        raise ValueError("""Acceptor concentration should be positive"""	)
    elif intrinsic_conc <= 0:
        raise ValueError("""Intrinsic concentration should be positive"""	)
    elif donor_conc <= intrinsic_conc:
        raise ValueError(
            """Donor concentration should be greater than intrinsic concentration"""	)
    elif acceptor_conc <= intrinsic_conc:
        raise ValueError(
            """Acceptor concentration should be greater than intrinsic concentration"""	)
    else:
        return (
            Boltzmann
            * T
            * log((donor_conc * acceptor_conc) / intrinsic_conc**2	)
            / physical_constants["electron volt"][0]
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()




