


import argparse
import copy
import json
import multiprocessing
import os
import time

from create_dummy_models import COMPOSITE_MODELS, create_tiny_models
from huggingface_hub import ModelFilter, hf_api

import transformers
from transformers import AutoFeatureExtractor, AutoImageProcessor, AutoTokenizer
from transformers.image_processing_utils import BaseImageProcessor






def 	lowerCamelCase_       (			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   set()
		# Each auto modeling files contains multiple mappings. Let's get them in a dynamic way.
		for module_name in ["modeling_auto", "modeling_tf_auto", "modeling_flax_auto"]:
				lowerCAmelCase__  :     List[Any]       	=   getattr(transformers.models.auto      ,  _a      ,  _a			)
				if module is None:
						continue
				# all mappings in a single auto modeling file
				lowerCAmelCase__  :     Union[str, Any]       	=   [
				    x
				    for x in dir(_a			)
				    if x.endswith('''_MAPPING_NAMES'''			)
				    and (x.startswith('''MODEL_'''			) or x.startswith('''TF_MODEL_'''			) or x.startswith('''FLAX_MODEL_'''			))
				]
				for name in mapping_names:
						lowerCAmelCase__  :     List[Any]       	=   getattr(_a      ,  _a			)
						if mapping is not None:
								for v in mapping.values():
										if isinstance(_a      ,  (list, tuple)			):
												model_names.update(_a			)
										elif isinstance(_a      ,  _a			):
												model_names.add(_a			)

		return sorted(_a			)






def 	lowerCamelCase_       (			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   set(get_all_model_names()			)

		with open('''tests/utils/tiny_model_summary.json'''			) as fp:
				lowerCAmelCase__  :     List[Any]       	=   json.load(_a			)
		lowerCAmelCase__  :     Optional[int]       	=   set()
		for model_base_name in tiny_model_info:
				tiny_models_names.update(tiny_model_info[model_base_name]['''model_classes''']			)

		# Remove a tiny model name if one of its framework implementation hasn't yet a tiny version on the Hub.
		lowerCAmelCase__  :     Dict       	=   model_names.difference(_a			)
		for model_name in copy.copy(_a			):
				if not model_name.startswith('''TF'''			) and f'TF{model_name}' in not_on_hub:
						tiny_models_names.remove(_a			)
				elif model_name.startswith('''TF'''			) and model_name[2:] in not_on_hub:
						tiny_models_names.remove(_a			)

		return sorted(_a			)






def 	lowerCamelCase_       (			_a			):




		"""simple docstring"""






		lowerCAmelCase__  :     Dict       	=   COMPOSITE_MODELS.values()

		# All tiny model base names on Hub
		lowerCAmelCase__  :     Optional[int]       	=   get_all_model_names()
		lowerCAmelCase__  :     Optional[Any]       	=   hf_api.list_models(
		    filter=ModelFilter(
		        author='''hf-internal-testing'''      ,  )			)
		lowerCAmelCase__  :     Dict       	=   set()
		for x in models:
				lowerCAmelCase__  :     int       	=   x.modelId
				lowerCAmelCase__	,      lowerCAmelCase__  :     List[Any]       	=   model.split('''/'''			)
				if not model.startswith('''tiny-random-'''			):
						continue
				lowerCAmelCase__  :     List[Any]       	=   model.replace('''tiny-random-'''      ,  ''''''			)
				if not model[0].isupper():
						continue
				if model not in model_names and model not in special_models:
						continue
				_models.add(_a			)

		lowerCAmelCase__  :     Any       	=   sorted(_models			)
		# All tiny model names on Hub
		lowerCAmelCase__  :     Any       	=   {}
		for model in models:
				lowerCAmelCase__  :     Optional[int]       	=   f'hf-internal-testing/tiny-random-{model}'
				lowerCAmelCase__  :     Optional[int]       	=   model.split('''-'''			)[0]
				try:
						lowerCAmelCase__  :     Optional[int]       	=   hf_api.repo_info(_a			)
						lowerCAmelCase__  :     List[str]       	=   {
						    '''tokenizer_classes''': set(),
						    '''processor_classes''': set(),
						    '''model_classes''': set(),
						    '''sha''': repo_info.sha,
						}
				except Exception:
						continue
				try:
						time.sleep(1			)
						lowerCAmelCase__  :     Optional[Any]       	=   AutoTokenizer.from_pretrained(_a			)
						content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase__  :     int       	=   AutoTokenizer.from_pretrained(_a      ,  use_fast=_a			)
						content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase__  :     Optional[int]       	=   AutoImageProcessor.from_pretrained(_a			)
						content["processor_classes"].add(img_p.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase__  :     List[str]       	=   AutoFeatureExtractor.from_pretrained(_a			)
						if not isinstance(_a      ,  _a			):
								content["processor_classes"].add(feat_p.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase__  :     Any       	=   getattr(_a      ,  _a			)
						lowerCAmelCase__  :     Tuple       	=   model_class.from_pretrained(_a			)
						content["model_classes"].add(m.__class__.__name__			)
				except Exception:
						pass
				try:
						time.sleep(1			)
						lowerCAmelCase__  :     List[Any]       	=   getattr(_a      ,  f'TF{model}'			)
						lowerCAmelCase__  :     Optional[int]       	=   model_class.from_pretrained(_a			)
						content["model_classes"].add(m.__class__.__name__			)
				except Exception:
						pass

				lowerCAmelCase__  :     Union[str, Any]       	=   sorted(content['''tokenizer_classes''']			)
				lowerCAmelCase__  :     Optional[Any]       	=   sorted(content['''processor_classes''']			)
				lowerCAmelCase__  :     Any       	=   sorted(content['''model_classes''']			)

				lowerCAmelCase__  :     Optional[int]       	=   content
				with open(os.path.join(_a      ,  '''hub_tiny_model_summary.json'''			)      ,  '''w'''			) as fp:
						json.dump(_a      ,  _a      ,  ensure_ascii=_a      ,  indent=4			)


if __name__ == "__main__":
		lowerCamelCase		=						argparse.ArgumentParser()
		parser.add_argument('''--num_workers''', default=1, type=int, help='''The number of workers to run.''')
		lowerCamelCase		=						parser.parse_args()

		# This has to be `spawn` to avoid hanging forever!
		multiprocessing.set_start_method('''spawn''')

		lowerCamelCase		=						'''tiny_models'''
		lowerCamelCase		=						True
		lowerCamelCase		=						None
		lowerCamelCase		=						get_tiny_model_names_from_repo()
		lowerCamelCase		=						True
		lowerCamelCase		=						True
		lowerCamelCase		=						'''hf-internal-testing'''

		create_tiny_models(
		    output_path,
		    all,
		    model_types,
		    models_to_skip,
		    no_check,
		    upload,
		    organization,
		    token=os.environ.get('''TOKEN''', None),
		    num_workers=args.num_workers,
		)








from __future__ import annotations

from string import ascii_letters






def 	lowerCamelCase_       (			_a      ,  _a      ,  _a = None			):




		"""simple docstring"""






		lowerCAmelCase__  :     List[Any]       	=   alphabet or ascii_letters

		# The final result string
		lowerCAmelCase__  :     Optional[Any]       	=   ''''''

		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__  :     int       	=   (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			):




		"""simple docstring"""






		key *= -1

		return encrypt(_a      ,  _a      ,  _a			)






def 	lowerCamelCase_       (			_a      ,  _a = None			):




		"""simple docstring"""






		lowerCAmelCase__  :     Optional[Any]       	=   alphabet or ascii_letters

		# To store data on all the combinations
		lowerCAmelCase__  :     Optional[int]       	=   {}

		# Cycle through each combination
		for key in range(1      ,  len(_a			) + 1			):
				# Decrypt the message and store the result in the data
				lowerCAmelCase__  :     Optional[int]       	=   decrypt(_a      ,  _a      ,  _a			)

		return brute_force_data


if __name__ == "__main__":
		while True:
				print(f'''\n{"-" * 10}\n Menu\n{"-" * 10}''')
				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




