

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


UpperCAmelCase__ :     List[Any]				    =    [
    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)

UpperCAmelCase__ :     Optional[Any]				    =    logging.getLogger()






def 					__lowercase      (     )     ->						Dict:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   argparse.ArgumentParser()
      parser.add_argument("""-f"""			)
      SCREAMING_SNAKE_CASE :      List[Any]       =   parser.parse_args()
      return args.f






def 					__lowercase      (     _A						,    _A="eval"			)     ->						List[str]:
      SCREAMING_SNAKE_CASE :      int       =   os.path.join(_A						,    F"{split}_results.json"			)
      if os.path.exists(_A			):
            with open(_A						,    """r"""			) as f:
                  return json.load(_A			)
      raise ValueError(F"can't find {path}"			)


UpperCAmelCase__ :     Tuple				    =    logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)







class        a__       (   UpperCamelCase_       ):







  """simple docstring"""




  def  _lowercase      (			self    : Optional[Any]		)					->Union[str, Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   f"\n            run_glue.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --eval_steps=2\n            --warmup_steps=2\n            --seed=42\n            --max_seq_length=128\n            ".split()

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_flax_glue.main()
              SCREAMING_SNAKE_CASE :      List[Any]       =   get_results(__a		)
              self.assertGreaterEqual(result["""eval_accuracy"""]				,				0.75		)


  @slow
  def  _lowercase      (			self    : Any		)					->List[str]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE :      Any       =   f"\n            run_clm_flax.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --block_size 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_clm_flax.main()
              SCREAMING_SNAKE_CASE :      Any       =   get_results(__a		)
              self.assertLess(result["""eval_perplexity"""]				,				1_0_0		)


  @slow
  def  _lowercase      (			self    : str		)					->int:
        """simple docstring"""







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

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_summarization_flax.main()
              SCREAMING_SNAKE_CASE :      Dict       =   get_results(__a				,				split="""test"""		)
              self.assertGreaterEqual(result["""test_rouge1"""]				,				1_0		)
              self.assertGreaterEqual(result["""test_rouge2"""]				,				2		)
              self.assertGreaterEqual(result["""test_rougeL"""]				,				7		)
              self.assertGreaterEqual(result["""test_rougeLsum"""]				,				7		)


  @slow
  def  _lowercase      (			self    : Optional[int]		)					->str:
        """simple docstring"""







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

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_mlm_flax.main()
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   get_results(__a		)
              self.assertLess(result["""eval_perplexity"""]				,				4_2		)


  @slow
  def  _lowercase      (			self    : List[Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   self.get_auto_remove_tmp_dir()
        SCREAMING_SNAKE_CASE :      List[Any]       =   f"\n            run_t5_mlm_flax.py\n            --model_name_or_path t5-small\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --do_train\n            --do_eval\n            --max_seq_length 128\n            --per_device_train_batch_size 4\n            --per_device_eval_batch_size 4\n            --num_train_epochs 2\n            --logging_steps 2 --eval_steps 2\n            --output_dir {tmp_dir}\n            --overwrite_output_dir\n            ".split()

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_ta_mlm_flax.main()
              SCREAMING_SNAKE_CASE :      List[Any]       =   get_results(__a		)
              self.assertGreaterEqual(result["""eval_accuracy"""]				,				0.42		)


  @slow
  def  _lowercase      (			self    : Optional[Any]		)					->Optional[Any]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Tuple       =   7 if get_gpu_count() > 1 else 2

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

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_flax_ner.main()
              SCREAMING_SNAKE_CASE :      List[Any]       =   get_results(__a		)
              self.assertGreaterEqual(result["""eval_accuracy"""]				,				0.75		)
              self.assertGreaterEqual(result["""eval_f1"""]				,				0.3		)




  @slow
  def  _lowercase      (			self    : Any		)					->Union[str, Any]:
        """simple docstring"""







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

        with patch.object(__a				,				"""argv"""				,				__a		):
              run_qa.main()
              SCREAMING_SNAKE_CASE :      Tuple       =   get_results(__a		)
              self.assertGreaterEqual(result["""eval_f1"""]				,				3_0		)
              self.assertGreaterEqual(result["""eval_exact"""]				,				3_0		)







from math import ceil






def 					__lowercase      (     _A = 1001			)     ->						int:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   1

      for i in range(1						,    int(ceil(n / 2.0			)			)			):
            SCREAMING_SNAKE_CASE :      Dict       =   2 * i + 1
            SCREAMING_SNAKE_CASE :      Optional[int]       =   2 * i
            SCREAMING_SNAKE_CASE :      str       =   total + 4 * odd**2 - 6 * even

      return total


if __name__ == "__main__":
  import sys

  if len(sys.argv) == 1:
    print(solution())
  else:
    try:
      UpperCAmelCase__ :     str				    =    int(sys.argv[1])
      print(solution(n))
    except ValueError:
      print("""Invalid entry - please enter a number""")




