




import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch
class 							__lowercase   (			unittest.TestCase    ):



     @patch('''sys.argv'''      ,		['''fakeprogrampath''', '''env''']       )
     def 				__magic_name__		(  self   :							int       ):
       # test transformers-cli env
       import transformers.commands.transformers_cli

       with CaptureStd() as cs:
         transformers.commands.transformers_cli.main()
       self.assertIn('''Python version'''      ,		cs.out       )
       self.assertIn('''Platform'''      ,		cs.out       )
       self.assertIn('''Using distributed or parallel set-up in script?'''      ,		cs.out       )



     @is_pt_tf_cross_test
     @patch(
         '''sys.argv'''      ,		['''fakeprogrampath''', '''pt-to-tf''', '''--model-name''', '''hf-internal-testing/tiny-random-gptj''', '''--no-pr''']       )
     def 				__magic_name__		(  self   :							str       ):
       import transformers.commands.transformers_cli

       shutil.rmtree('''/tmp/hf-internal-testing/tiny-random-gptj'''      ,		ignore_errors=lowercase__       )  # cleans potential past runs
       transformers.commands.transformers_cli.main()

       # The original repo has no TF weights -- if they exist, they were created by the CLI
       self.assertTrue(os.path.exists('''/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5'''       )       )



     @require_torch
     @patch('''sys.argv'''      ,		['''fakeprogrampath''', '''download''', '''hf-internal-testing/tiny-random-gptj''', '''--cache-dir''', '''/tmp''']       )
     def 				__magic_name__		(  self   :							Optional[Any]       ):
       import transformers.commands.transformers_cli

       # # remove any previously downloaded model to start clean
       shutil.rmtree('''/tmp/models--hf-internal-testing--tiny-random-gptj'''      ,		ignore_errors=lowercase__       )

       # run the command
       transformers.commands.transformers_cli.main()

       # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/blobs'''       )       )
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/refs'''       )       )
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots'''       )       )



     @require_torch
     @patch(
         '''sys.argv'''      ,		[
             '''fakeprogrampath''',
             '''download''',
             '''hf-internal-testing/test_dynamic_model_with_tokenizer''',
             '''--trust-remote-code''',
             '''--cache-dir''',
             '''/tmp''',
         ]      ,		)
     def 				__magic_name__		(  self   :							Tuple       ):
       import transformers.commands.transformers_cli

       # # remove any previously downloaded model to start clean
       shutil.rmtree('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer'''      ,		ignore_errors=lowercase__       )

       # run the command
       transformers.commands.transformers_cli.main()

       # check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs'''       )       )
       self.assertTrue(os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs'''       )       )
       self.assertTrue(
           os.path.exists('''/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots'''       )       )






import math






def 				UpperCAmelCase__  (							_A			):






  """simple docstring"""


  a_		    =  math.log(2      ,      10			) * number
  a_		    =  round((log_number - int(_A			))      ,      15			)

  return difference






def 				UpperCAmelCase__  (							_A = 678_910			):






  """simple docstring"""


  a_		    =  90
  a_		    =  0

  a_		    =  math.log(1.23      ,      10			)
  a_		    =  math.log(1.24      ,      10			)
  a_		    =  0

  while position < number:
    a_		    =  log_difference(_A			)

    if difference >= upper_limit:
      power_iterator += 93

    elif difference < lower_limit:
      power_iterator += 196

    else:
      a_		    =  power_iterator
      power_iterator += 196
      position += 1

  return previous_power


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  print(F"""{solution() = }""")
