






import itertools
import os
import subprocess
from os.path import dirname

from parameterized import parameterized

from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
from transformers import is_torch_available
from transformers.testing_utils import (
    TestCasePlus,
    execute_subprocess_async,
    get_gpu_count,
    get_tests_dir,
    require_deepspeed,
    require_torch_gpu,
    slow,
)
from transformers.trainer_utils import set_seed


if is_torch_available():
		from tests.trainer.test_trainer import (  # noqa
		    RegressionModelConfig,
		    RegressionPreTrainedModel,
		    get_regression_trainer,
		)


set_seed(4_2)

lowerCamelCase_		  =						get_tests_dir("""fixtures""")
lowerCamelCase_		  =						os.path.join(dirname(get_tests_dir()))
lowerCamelCase_		  =						dirname(os.path.abspath(__file__))

# default torch.distributed port
lowerCamelCase_		  =						"""10999"""

lowerCamelCase_		  =						"""t5-small"""

# *** Working Models ***
lowerCamelCase_		  =						"""hf-internal-testing/tiny-albert"""
lowerCamelCase_		  =						"""sshleifer/bart-tiny-random"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-bert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-bigbird_pegasus"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-big_bird"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-blenderbot"""
lowerCamelCase_		  =						"""bigscience/bigscience-small-testing"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-deberta"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-deberta-v2"""
lowerCamelCase_		  =						"""sshleifer/tiny-distilbert-base-cased"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-electra"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-flaubert"""
lowerCamelCase_		  =						"""stas/tiny-wmt19-en-de"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-funnel"""
lowerCamelCase_		  =						"""sshleifer/tiny-gpt2"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-gptj"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-gpt_neo"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-layoutlm"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-led"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-longformer"""
lowerCamelCase_		  =						"""stas/tiny-m2m_100"""  # hf tiny model is unsuitable
lowerCamelCase_		  =						"""sshleifer/tiny-marian-en-de"""
lowerCamelCase_		  =						"""sshleifer/tiny-mbart"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-mobilebert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-mpnet"""
lowerCamelCase_		  =						"""stas/pegasus-cnn_dailymail-tiny-random"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-prophetnet"""
lowerCamelCase_		  =						"""sshleifer/tiny-distilroberta-base"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-squeezebert"""
lowerCamelCase_		  =						"""patrickvonplaten/t5-tiny-random"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-t5-v1.1"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-vit"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-xlm-roberta"""
lowerCamelCase_		  =						"""sshleifer/tiny-xlnet-base-cased"""


# *** To Fix ***


# *** tiny model issues ***
# missing model files:
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-mt5"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-camembert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-openai-gpt"""

# missing tokenizer files
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-convbert"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-layoutlmv2"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-hubert"""

# issues with tokenizer
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-ctrl"""
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-transfo-xl"""  # same as ctrl

# other issues with tiny models
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-ibert"""  # multiple issues with either mlm/qa/clas
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-reformer"""  # multiple issues with either mlm/qa/clas

# *** Lacking official examples to test with ***
# or not working with examples
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-dpr"""
# - "dpr"  examples/research_projects/rag-end2end-retriever/
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-rag"""
# - "rag" research_projects
lowerCamelCase_		  =						""""""
# - "luke" Entities classes - no plan to make such example
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-lxmert"""
# - "lxmert" doesn't work with run_qa.py
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-clip"""
# - "clip" nothing under pytorch examples - XXX: Suraj is working on adding some - check by end of Sep
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-speech_to_text"""
# - "speech_to_text", nothing under pytorch examples


# *** Reactive mode ***
# models with low usage, unstable API, things about to change - do nothing about the following until someone runs into a problem
lowerCamelCase_		  =						"""hf-internal-testing/tiny-random-tapas"""
def   lowerCamelCase      (		a_=False			)			->    Tuple:
							# 1. explicitly set --num_nodes=1 just in case these tests end up run on a multi-node setup
							# - it won't be able to handle that
							# 2. for now testing with just 2 gpus max (since some quality tests may give different
							# results with mode gpus because we use very little data)
							lowerCAmelCase_       		=  min(2				,		get_gpu_count()			) if distributed else 1
							lowerCAmelCase_       		=  os.environ.get('DS_TEST_PORT'				,		a_			)
							return F'''deepspeed --num_nodes 1 --num_gpus {num_gpus} --master_port {master_port}'''.split()
def   lowerCamelCase      (		)			->    Union[str, Any]:
							lowerCAmelCase_       		=  F'''{FIXTURE_DIRECTORY}/tests_samples'''
							lowerCAmelCase_       		=  F'''{data_dir_samples}/wmt_en_ro'''
							lowerCAmelCase_       		=  F'''{data_dir_samples}/xsum'''
							lowerCAmelCase_       		=  '\n        --do_train\n        --max_train_samples 4\n        --per_device_train_batch_size 2\n        --num_train_epochs 1\n        --fp16\n        --report_to none\n        --overwrite_output_dir\n        '.split()

							# try to cover as many models as possible once (it's enough to run on one task per model)
							# but need a tiny model for each
							#
							# should have "{model_type.upper()}_TINY" corresponding vars defined, e.g., T5_TINY, etc.
							lowerCAmelCase_       		=  {
							    'trans': [
							        'bart',
							        'fsmt',
							        'm2m_100',
							        'marian',
							        'mbart',
							        't5',
							        't5_v1',
							        # "mt5", missing model files
							    ],
							    'sum': [
							        'pegasus',
							    ],
							    'clm': [
							        'big_bird',
							        'bigbird_pegasus',
							        'blenderbot',
							        'bloom',
							        'gpt2',
							        'gpt_neo',
							        'gptj',
							        'xlm-roberta',
							        'prophetnet',
							        # "camembert", missing model files
							    ],
							    'mlm': [
							        'albert',
							        'deberta',
							        'deberta-v2',
							        'distilbert',
							        'electra',
							        'flaubert',
							        'funnel',
							        'layoutlm',
							        # "reformer", # multiple issues with either mlm/qa/clas
							    ],
							    'qa': [
							        'led',
							        'longformer',
							        'mobilebert',
							        'mpnet',
							        'roberta',
							        'squeezebert',
							        # "convbert", # missing tokenizer files
							        # "layoutlmv2", missing model files
							    ],
							    'clas': [
							        'bert',
							        'xlnet',
							        # "hubert", # missing tokenizer files
							        # "ibert", # multiple issues with either mlm/qa/clas
							        # "transfo-xl", # tokenizer issues as ctrl
							        # "ctrl", # tokenizer issues
							        # "openai-gpt", missing model files
							        # "tapas", multiple issues
							    ],
							    'img_clas': [
							        'vit',
							    ],
							}

							lowerCAmelCase_       		=  F'''{ROOT_DIRECTORY}/examples/pytorch'''

							lowerCAmelCase_       		=  {
							    'trans': F'''
        {scripts_dir}/translation/run_translation.py
        --train_file {data_dir_wmt}/train.json
        --source_lang en
        --target_lang ro
        ''',
							    'sum': F'''
        {scripts_dir}/summarization/run_summarization.py
        --train_file {data_dir_xsum}/sample.json
        --max_source_length 12
        --max_target_length 12
        --lang en
        ''',
							    'clm': F'''
        {scripts_dir}/language-modeling/run_clm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        --block_size 8
        ''',
							    'mlm': F'''
        {scripts_dir}/language-modeling/run_mlm.py
        --train_file {FIXTURE_DIRECTORY}/sample_text.txt
        ''',
							    'qa': F'''
        {scripts_dir}/question-answering/run_qa.py
        --train_file {data_dir_samples}/SQUAD/sample.json
        ''',
							    'clas': F'''
        {scripts_dir}/text-classification/run_glue.py
        --train_file {data_dir_samples}/MRPC/train.csv
        --max_seq_length 12
        --task_name MRPC
        ''',
							    'img_clas': F'''
        {scripts_dir}/image-classification/run_image_classification.py
            --dataset_name hf-internal-testing/cats_vs_dogs_sample
            --remove_unused_columns False
            --max_steps 10
            --image_processor_name {DS_TESTS_DIRECTORY}/vit_feature_extractor.json
        ''',
							}

							lowerCAmelCase_       		=  get_launcher(distributed=a_			)

							lowerCAmelCase_       		=  {}
							for task, args in tasks.items():
														lowerCAmelCase_       		=  args.split()
														for model in tasksamodels[task]:
																					lowerCAmelCase_       		=  globals()[F'''{model.upper().replace("-"				,		"_"			)}_TINY''']
																					lowerCAmelCase_       		=  F'''--model_name_or_path {model_name}'''.split()
																					lowerCAmelCase_       		=  launcher + args + args_model + args_main

																					# # generation special case
																					# if task == "gen":
																					#     launcher = f"deepspeed --num_nodes 1 --num_gpus 1".split()
																					#     args_model += f"--model_type {model}".split()
																					#     cmds[f"{task}_{model}"] = launcher + args + args_model
																					# else:

							return cmds


lowerCamelCase_		  =						make_task_cmds()

lowerCamelCase_		  =						"""zero2"""
lowerCamelCase_		  =						"""zero3"""

lowerCamelCase_		  =						[ZEROa, ZEROa]
def   lowerCamelCase      (		a_				,		a_				,		a_			)			->    Tuple:
							# customize the test name generator function as we want both params to appear in the sub-test
							# name, as by default it shows only the first param
							lowerCAmelCase_       		=  parameterized.to_safe_name('_'.join(str(a_			) for x in param.args			)			)
							return F'''{func.__name__}_{param_based_name}'''


# Cartesian-product of zero stages with models to test
lowerCamelCase_		  =						list(itertools.product(stages, task_cmds.keys()))




@slow
@require_deepspeed
@require_torch_gpu
class 							a_				(		a_	):


							'''simple docstring'''






							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Any:
														'''simple docstring'''

														if task not in task_cmds:
																					raise ValueError(f'''don\'t know of task {task}, have {task_cmds.keys()}'''     )

														lowerCAmelCase_       		=  task_cmds[task]
														lowerCAmelCase_       		=  f'''--deepspeed {self.test_file_dir_str}/ds_config_{stage}.json'''.split()

														lowerCAmelCase_       		=  self.get_auto_remove_tmp_dir()
														lowerCAmelCase_       		=  f'''--output_dir {output_dir}'''.split()

														cmd += args_ds + args_out

														return cmd, output_dir






							@parameterized.expand(lowercase_    ,    name_func=lowercase_     )
							def   _lowercase (	self    ,    lowercase_    ,    lowercase_     )      ->      Optional[int]:
														'''simple docstring'''

														lowerCAmelCase_		,     lowerCAmelCase_       		=  self.get_task_cmd(lowercase_    ,    lowercase_     )

														# 1. generate the checkpoint
														cmd += "--save_steps 1".split()
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] + cmd)); die
														execute_subprocess_async(lowercase_    ,    env=self.get_env()     )

														# 2. test that the fp32 weights get reconsolidated
														lowerCAmelCase_       		=  f'''{output_dir}/checkpoint-1'''
														lowerCAmelCase_       		=  f'''{chkpt_dir}/out.bin'''
														lowerCAmelCase_       		=  f'''{chkpt_dir}/zero_to_fp32.py {chkpt_dir} {recovered_model_path}'''
														# keep for quick debug
														# print(" ".join([f"\nPYTHONPATH={self.src_dir_str}"] +cmd)); die
														subprocess.check_call(lowercase_    ,    shell=lowercase_     )
														assert os.path.exists(lowercase_     ), f'''{recovered_model_path} was not found'''

														# possibly could also test that the resulting saved model is usable but given that we use
														# random models we won't know if it's any good











from __future__ import annotations

from string import ascii_letters
def   lowerCamelCase      (		a_				,		a_				,		a_ = None			)			->    str:
							lowerCAmelCase_       		=  alphabet or ascii_letters

							# The final result string
							lowerCAmelCase_       		=  ''

							for character in input_string:
														if character not in alpha:
																					# Append without encryption if character is not in the alphabet
																					result += character
														else:
																					# Get the index of the new key and make sure it isn't too large
																					lowerCAmelCase_       		=  (alpha.index(a_			) + key) % len(a_			)

																					# Append the encoded character to the alphabet
																					result += alpha[new_key]

							return result
def   lowerCamelCase      (		a_				,		a_				,		a_ = None			)			->    str:
							key *= -1

							return encrypt(a_				,		a_				,		a_			)
def   lowerCamelCase      (		a_				,		a_ = None			)			->    dict[int, str]:
							lowerCAmelCase_       		=  alphabet or ascii_letters

							# To store data on all the combinations
							lowerCAmelCase_       		=  {}

							# Cycle through each combination
							for key in range(1				,		len(a_			) + 1			):
														# Decrypt the message and store the result in the data
														lowerCAmelCase_       		=  decrypt(a_				,		a_				,		a_			)

							return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 1_0}\n Menu\n{"-" * 1_0}''')
				print(*["""1.Encrypt""", """2.Decrypt""", """3.BruteForce""", """4.Quit"""], sep="""\n""")

				# get user input
				lowerCamelCase_		  =						input("""\nWhat would you like to do?: """).strip() or """4"""

				# run functions based on what the user chose
				if choice not in ("1", "2", "3", "4"):
						print("""Invalid choice, please enter a valid choice""")
				elif choice == "1":
						lowerCamelCase_		  =						input("""Please enter the string to be encrypted: """)
						lowerCamelCase_		  =						int(input("""Please enter off-set: """).strip())

						print(encrypt(input_string, key))
				elif choice == "2":
						lowerCamelCase_		  =						input("""Please enter the string to be decrypted: """)
						lowerCamelCase_		  =						int(input("""Please enter off-set: """).strip())

						print(decrypt(input_string, key))
				elif choice == "3":
						lowerCamelCase_		  =						input("""Please enter the string to be decrypted: """)
						lowerCamelCase_		  =						brute_force(input_string)

						for key, value in brute_force_data.items():
								print(f'''Key: {key} | Message: {value}''')

				elif choice == "4":
						print("""Goodbye.""")
						break



