

'''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 		UpperCamelCase_		(					unittest.TestCase   ):





						"""simple docstring"""




						@patch("sys.argv"	,				["fakeprogrampath", "env"]  )
						def 					__A					( self				:					Optional[Any]  )							->					List[str]:
										# 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 					__A					( self				:					int  )							->					Union[str, 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 					__A					( self				:					Union[str, Any]  )							->					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=_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 					__A					( self				:					Tuple  )							->					Any:
										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"  )  )







'''simple docstring'''
import os
import sys
import time

from . import transposition_cipher as trans_cipher




def      __snake_case       (       ):
				'''simple docstring'''




				__magic_name__	  = "Prehistoric Men.txt"
				__magic_name__	  = "Output.txt"
				__magic_name__	  = int(input("Enter key: "						)						)
				__magic_name__	  = input("Encrypt/Decrypt [e/d]: "						)

				if not os.path.exists(lowerCamelCase_						):
								print(F'File {input_file} does not exist. Quitting...'						)
								sys.exit()
				if os.path.exists(lowerCamelCase_						):
								print(F'Overwrite {output_file}? [y/n]'						)
								__magic_name__	  = input("> "						)
								if not response.lower().startswith("y"						):
												sys.exit()

				__magic_name__	  = time.time()
				if mode.lower().startswith("e"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.encrypt_message(lowerCamelCase_	,  lowerCamelCase_						)
				elif mode.lower().startswith("d"						):
								with open(lowerCamelCase_						) as f:
												__magic_name__	  = f.read()
								__magic_name__	  = trans_cipher.decrypt_message(lowerCamelCase_	,  lowerCamelCase_						)

				with open(lowerCamelCase_	,  "w"						) as output_obj:
								output_obj.write(lowerCamelCase_						)

				__magic_name__	  = round(time.time() - start_time	,  2						)
				print(("Done (", total_time, "seconds )")						)


if __name__ == "__main__":
						main()



