






'''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    _snake_case   (				unittest.TestCase					):

					"""simple docstring"""





					@patch('sys.argv'   , ['fakeprogrampath', 'env']     )
					def  __SCREAMING_SNAKE_CASE (     self     )      ->	Tuple:
										# 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     )      ->	List[str]:
										import transformers.commands.transformers_cli

										shutil.rmtree('/tmp/hf-internal-testing/tiny-random-gptj'   , ignore_errors=UpperCAmelCase__     )  # 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     )      ->	List[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=UpperCAmelCase__     )

										# 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     )      ->	int:
										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=UpperCAmelCase__     )

										# 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'''
class    _snake_case   :

					"""simple docstring"""





					def __init__(     self   , UpperCAmelCase__     )      ->	None:
										a_									=   data
										a_									=   42
										a_									=   42



def 							a	(   _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_									=   0
					a_									=   x

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase )				->				None:






					"""simple docstring"""







					a_							,					a_									=   find_set(_UpperCAmelCase ), find_set(_UpperCAmelCase )
					if x == y:
										return

					elif x.rank > y.rank:
										a_									=   x
					else:
										a_									=   y
										if x.rank == y.rank:
															y.rank += 1

def 							a	(   _UpperCAmelCase )				->				Node:






					"""simple docstring"""







					if x != x.parent:
										a_									=   find_set(x.parent )
					return x.parent

def 							a	(   _UpperCAmelCase )				->				set:






					"""simple docstring"""







					a_									=   ({0, 1, 2}, {3, 4, 5})
					for s in sets:
										if node.data in s:
															return s
					a_									=   F'''{node.data} is not in {sets}'''
					raise ValueError(_UpperCAmelCase )

def 							a	(   )				->				None:






					"""simple docstring"""







					a_									=   [Node(_UpperCAmelCase ) for i in range(6 )]
					for v in vertex:
										make_set(_UpperCAmelCase )

					union_set(vertex[0]					,  vertex[1] )
					union_set(vertex[1]					,  vertex[2] )
					union_set(vertex[3]					,  vertex[4] )
					union_set(vertex[3]					,  vertex[5] )

					for nodea in vertex:
										for nodea in vertex:
															if find_python_set(_UpperCAmelCase ).isdisjoint(find_python_set(_UpperCAmelCase ) ):
																				assert find_set(_UpperCAmelCase ) != find_set(_UpperCAmelCase )
															else:
																				assert find_set(_UpperCAmelCase ) == find_set(_UpperCAmelCase )


if __name__ == "__main__":
						test_disjoint_set()

