




"""simple docstring"""



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       UpperCAmelCase_    (		unittest.TestCase				):




    @patch("sys.argv"      ,					["fakeprogrampath", "env"] )
    def 				__magic_name__				(       self		:			Optional[Any] ) ->				List[Any]:




     '''simple docstring'''

     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		:			List[str] ) ->				int:




     '''simple docstring'''

     import transformers.commands.transformers_cli

     shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"      ,					ignore_errors=snake_case_ )  # 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		:			int ) ->				Optional[Any]:




     '''simple docstring'''

     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=snake_case_ )

     # 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		:			Union[str, Any] ) ->				Dict:




     '''simple docstring'''

     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=snake_case_ )

     # 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" ) )











"""simple docstring"""



def     _SCREAMING_SNAKE_CASE  (				lowercase_ = 10_00		)		->						int:
 A__			=							0
 A__			=							0
 while 1:
  num += 3
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
  num += 1
  if num >= n:
   break
  total += num
  num += 2
  if num >= n:
   break
  total += num
  num += 3
  if num >= n:
   break
  total += num
 return total


if __name__ == "__main__":
      print(f'{solution() = }')





