


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'		)		)






from math import pi


def 							SCREAMING_SNAKE_CASE					(  _UpperCAmelCase				)    ->  float:
						return degree / (180 / pi)


if __name__ == "__main__":
		from doctest import testmod

		testmod()


