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 					_UpperCamelCase (      unittest.TestCase	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							@patch('sys.argv'		,				['fakeprogrampath', 'env']		)
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		):
  													# 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 		_UpperCAmelCase	(		self  :				int		):
  													import transformers.commands.transformers_cli
  
  													shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'		,				ignore_errors=SCREAMING_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 		_UpperCAmelCase	(		self  :				str		):
  													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=SCREAMING_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 		_UpperCAmelCase	(		self  :				Dict		):
  													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=SCREAMING_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'		)		)
  
  
  
style_context: 
  
  
  from math import pi
  
  
  def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
  						return degree / (180 / pi)
  
  
  if __name__ == "__main__":
  		from doctest import testmod
  
  		testmod()
  
  
  