






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 						lowerCamelCase   (unittest.TestCase    ):







		'''simple docstring'''



		@patch('sys.argv'       ,	['fakeprogrampath', 'env']     )
		def       __UpperCAmelCase    (							self     )			->							Dict:
							# 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     )			->							Any:
							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       __UpperCAmelCase    (							self     )			->							Optional[int]:
							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       __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=_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'     )     )














from __future__ import annotations



class 						lowerCamelCase   :







		'''simple docstring'''



		def __init__(							self       ,	_UpperCamelCase = 0     )			->							None:
							UpperCAmelCase_					:   int   =		value
							UpperCAmelCase_					:   Node | None   =		None
							UpperCAmelCase_					:   Node | None   =		None





def   lowercase__   (  __snake_case					:							Node | None  , __snake_case					:							Node | None						):


					'''simple docstring'''


					if treea is None:
										return treea
					if treea is None:
										return treea

					UpperCAmelCase_					:   List[Any]   =		treea.value + treea.value
					UpperCAmelCase_					:   Dict   =		merge_two_binary_trees(treea.left  , treea.left						)
					UpperCAmelCase_					:   str   =		merge_two_binary_trees(treea.right  , treea.right						)
					return treea




def   lowercase__   (  __snake_case					:							Node | None						):


					'''simple docstring'''


					if root:
										print(root.value						)
										print_preorder(root.left						)
										print_preorder(root.right						)


if __name__ == "__main__":
		__UpperCAmelCase										=		Node(1)
		__UpperCAmelCase										=		Node(2)
		__UpperCAmelCase										=		Node(3)
		__UpperCAmelCase										=		Node(4)

		__UpperCAmelCase										=		Node(2)
		__UpperCAmelCase										=		Node(4)
		__UpperCAmelCase										=		Node(6)
		__UpperCAmelCase										=		Node(9)
		__UpperCAmelCase										=		Node(5)

		print('Tree1 is: ')
		print_preorder(treea)
		print('Tree2 is: ')
		print_preorder(treea)
		__UpperCAmelCase										=		merge_two_binary_trees(treea, treea)
		print('Merged Tree is: ')
		print_preorder(merged_tree)






