



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       SCREAMING_SNAKE_CASE__		(						self   :      Optional[Any]			)   ->					Union[str, 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       SCREAMING_SNAKE_CASE__		(						self   :      List[str]			)   ->					Optional[Any]:
									import transformers.commands.transformers_cli

									shutil.rmtree("/tmp/hf-internal-testing/tiny-random-gptj"						,						ignore_errors=__lowerCamelCase			)  # 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]			)   ->					Tuple:
									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=__lowerCamelCase			)

									# 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   :      List[Any]			)   ->					Optional[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=__lowerCamelCase			)

									# 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"			)			)




import math
from decimal import Decimal, getcontext




def 						UpperCAmelCase (    _lowerCamelCase = 400_0000     ):

			try:
						A				:       Any														=    int(_lowerCamelCase     )
			except (TypeError, ValueError):
						raise TypeError("Parameter n must be int or castable to int."     )
			if n <= 0:
						raise ValueError("Parameter n must be greater than or equal to one."     )
			A				:       int														=    100
			A				:       Tuple														=    (Decimal(5     ) ** Decimal(0.5     ) + 1) / Decimal(2     )

			A				:       str														=    (math.floor(math.log(n * (phi + 2)			,     _lowerCamelCase     ) - 1     ) // 3) * 3 + 2
			A				:       Optional[int]														=    Decimal(round(phi ** Decimal(index + 1     )     )     ) / (phi + 2)
			A				:       str														=    num // 2
			return int(_lowerCamelCase     )


if __name__ == "__main__":
							print(F"""{solution() = }""")