code: 
  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    _snake_case      (  unittest.TestCase       ):
  
  
  
  
  
  
  
  			@patch("sys.argv"		,     ["fakeprogrampath", "env"]	)
  			def     SCREAMING_SNAKE_CASE					(       self	):
  				# 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     SCREAMING_SNAKE_CASE					(       self	):
  				import transformers.commands.transformers_cli
  
  				shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"		,     ignore_errors=_a	)  # 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     SCREAMING_SNAKE_CASE					(       self	):
  				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=_a	)
  
  				# 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     SCREAMING_SNAKE_CASE					(       self	):
  				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=_a	)
  
  				# 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"	)	)
  
style_context: 
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if not isinstance(_snake_case   ,     _snake_case     ) or number < 0:
  		raise ValueError("Input must be a non-negative integer"     )
  
  	__magic_name__	:	Dict			     =  number & ~(number - 1)
  	__magic_name__	:	Optional[Any]			     =  0
  	while intermediate:
  		intermediate >>= 1
  		index += 1
  	return index - 1
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod(verbose=True)
  