'''simple docstring'''

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

from transformers.testing_utils import TestCasePlus, get_gpu_count, slow


a        =						[
    os.path.join(os.path.dirname(__file__), dirname)
    for dirname in [
        "text-classification",
        "language-modeling",
        "summarization",
        "token-classification",
        "question-answering",
    ]
]
sys.path.extend(SRC_DIRS)


if SRC_DIRS is not None:
    import run_clm_flax
    import run_flax_glue
    import run_flax_ner
    import run_mlm_flax
    import run_qa
    import run_summarization_flax
    import run_ta_mlm_flax


logging.basicConfig(level=logging.DEBUG)

a        =						logging.getLogger()

def 						__magic_name__       (							)						->					Union[str, Any]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       argparse.ArgumentParser()
   parser.add_argument("""-f"""      )
   __SCREAMING_SNAKE_CASE      							=       parser.parse_args()
   return args.f

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase="eval"      )						->					Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       os.path.join(lowerCAmelCase__							,	f"""{split}_results.json"""      )
   if os.path.exists(lowerCAmelCase__      ):
      with open(lowerCAmelCase__							,	"""r"""      ) as f:
         return json.load(lowerCAmelCase__      )
   raise ValueError(f"""can't find {path}"""      )


a        =						logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class __a (   _snake_case			):





   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_glue.py
            --model_name_or_path distilbert-base-uncased
            --output_dir {tmp_dir}
            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv
            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --learning_rate=1e-4
            --eval_steps=2
            --warmup_steps=2
            --seed=42
            --max_seq_length=128
            """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_flax_glue.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase    )
         self.assertGreaterEqual(result["""eval_accuracy"""]				,0.75    )





   @slow
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_clm_flax.py
            --model_name_or_path distilgpt2
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --block_size 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_clm_flax.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase    )
         self.assertLess(result["""eval_perplexity"""]				,100    )





   @slow
   def      UpperCAmelCase__     (					self				:			Optional[Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_summarization.py
            --model_name_or_path t5-small
            --train_file tests/fixtures/tests_samples/xsum/sample.json
            --validation_file tests/fixtures/tests_samples/xsum/sample.json
            --test_file tests/fixtures/tests_samples/xsum/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=8
            --do_train
            --do_eval
            --do_predict
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
            --predict_with_generate
        """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_summarization_flax.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase				,split="""test"""    )
         self.assertGreaterEqual(result["""test_rouge1"""]				,10    )
         self.assertGreaterEqual(result["""test_rouge2"""]				,2    )
         self.assertGreaterEqual(result["""test_rougeL"""]				,7    )
         self.assertGreaterEqual(result["""test_rougeLsum"""]				,7    )





   @slow
   def      UpperCAmelCase__     (					self				:			Union[str, Any]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_mlm.py
            --model_name_or_path distilroberta-base
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --logging_steps 2 --eval_steps 2
            --do_train
            --do_eval
            --num_train_epochs=1
        """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_mlm_flax.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase    )
         self.assertLess(result["""eval_perplexity"""]				,42    )





   @slow
   def      UpperCAmelCase__     (					self				:			List[str]    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_t5_mlm_flax.py
            --model_name_or_path t5-small
            --train_file ./tests/fixtures/sample_text.txt
            --validation_file ./tests/fixtures/sample_text.txt
            --do_train
            --do_eval
            --max_seq_length 128
            --per_device_train_batch_size 4
            --per_device_eval_batch_size 4
            --num_train_epochs 2
            --logging_steps 2 --eval_steps 2
            --output_dir {tmp_dir}
            --overwrite_output_dir
            """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_ta_mlm_flax.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase    )
         self.assertGreaterEqual(result["""eval_accuracy"""]				,0.42    )





   @slow
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       7 if get_gpu_count() > 1 else 2

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_flax_ner.py
            --model_name_or_path bert-base-uncased
            --train_file tests/fixtures/tests_samples/conll/sample.json
            --validation_file tests/fixtures/tests_samples/conll/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --do_train
            --do_eval
            --warmup_steps=2
            --learning_rate=2e-4
            --logging_steps 2 --eval_steps 2
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=2
            --num_train_epochs={epochs}
            --seed 7
        """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_flax_ner.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase    )
         self.assertGreaterEqual(result["""eval_accuracy"""]				,0.75    )
         self.assertGreaterEqual(result["""eval_f1"""]				,0.3    )





   @slow
   def      UpperCAmelCase__     (					self				:			Tuple    ):

      '''simple docstring'''

      __SCREAMING_SNAKE_CASE      							=       self.get_auto_remove_tmp_dir()
      __SCREAMING_SNAKE_CASE      							=       f"""
            run_qa.py
            --model_name_or_path bert-base-uncased
            --version_2_with_negative
            --train_file tests/fixtures/tests_samples/SQUAD/sample.json
            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json
            --output_dir {tmp_dir}
            --overwrite_output_dir
            --num_train_epochs=3
            --warmup_steps=2
            --do_train
            --do_eval
            --logging_steps 2 --eval_steps 2
            --learning_rate=2e-4
            --per_device_train_batch_size=2
            --per_device_eval_batch_size=1
        """.split()

      with patch.object(_UpperCAmelCase				,"""argv"""				,_UpperCAmelCase    ):
         run_qa.main()
         __SCREAMING_SNAKE_CASE      							=       get_results(_UpperCAmelCase    )
         self.assertGreaterEqual(result["""eval_f1"""]				,30    )
         self.assertGreaterEqual(result["""eval_exact"""]				,30    )












'''simple docstring'''

import requests
from bsa import BeautifulSoup

def 						__magic_name__       (							__UpperCAmelCase							,	__UpperCAmelCase      )						->					str:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE      							=       BeautifulSoup(requests.get(__UpperCAmelCase							,	params=__UpperCAmelCase      ).content							,	"""html.parser"""      )
   __SCREAMING_SNAKE_CASE      							=       soup.find("""div"""							,	attrs={"""class""": """gs_ri"""}      )
   __SCREAMING_SNAKE_CASE      							=       div.find("""div"""							,	attrs={"""class""": """gs_fl"""}      ).find_all("""a"""      )
   return anchors[2].get_text()


if __name__ == "__main__":
    a        =						{
        "title": (
            "Precisely geometry controlled microsupercapacitors for ultrahigh areal "
            "capacitance, volumetric capacitance, and energy density"
        ),
        "journal": "Chem. Mater.",
        "volume": 30,
        "pages": "3979-3990",
        "year": 2018,
        "hl": "en",
    }
    print(get_citation("https://scholar.google.com/scholar_lookup", params=params))





