

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       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=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"]:
          SCREAMING_SNAKE_CASE										=getattr(transformers.models.auto,   lowerCAmelCase_,   lowerCAmelCase_      )
          if module is None:
               continue
          # all mappings in a single auto modeling file
          SCREAMING_SNAKE_CASE										=[
              x
              for x in dir(lowerCAmelCase_      )
              if x.endswith('_MAPPING_NAMES'      )
              and (x.startswith('MODEL_'      ) or x.startswith('TF_MODEL_'      ) or x.startswith('FLAX_MODEL_'      ))
          ]
          for name in mapping_names:
               SCREAMING_SNAKE_CASE										=getattr(lowerCAmelCase_,   lowerCAmelCase_      )
               if mapping is not None:
                    for v in mapping.values():
                         if isinstance(lowerCAmelCase_,   (list, tuple)      ):
                              model_names.update(lowerCAmelCase_      )
                         elif isinstance(lowerCAmelCase_,   lowerCAmelCase_      ):
                              model_names.add(lowerCAmelCase_      )

     return sorted(lowerCAmelCase_      )







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=set(get_all_model_names()      )

     with open('tests/utils/tiny_model_summary.json'      ) as fp:
          SCREAMING_SNAKE_CASE										=json.load(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=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.
     SCREAMING_SNAKE_CASE										=model_names.difference(lowerCAmelCase_      )
     for model_name in copy.copy(lowerCAmelCase_      ):
          if not model_name.startswith('TF'      ) and F'TF{model_name}' in not_on_hub:
               tiny_models_names.remove(lowerCAmelCase_      )
          elif model_name.startswith('TF'      ) and model_name[2:] in not_on_hub:
               tiny_models_names.remove(lowerCAmelCase_      )

     return sorted(lowerCAmelCase_      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=COMPOSITE_MODELS.values()

     # All tiny model base names on Hub
     SCREAMING_SNAKE_CASE										=get_all_model_names()
     SCREAMING_SNAKE_CASE										=hf_api.list_models(
         filter=ModelFilter(
             author='hf-internal-testing',   )      )
     SCREAMING_SNAKE_CASE										=set()
     for x in models:
          SCREAMING_SNAKE_CASE										=x.modelId
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=model.split('/'      )
          if not model.startswith('tiny-random-'      ):
               continue
          SCREAMING_SNAKE_CASE										=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(lowerCAmelCase_      )

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

          SCREAMING_SNAKE_CASE										=sorted(content['tokenizer_classes']      )
          SCREAMING_SNAKE_CASE										=sorted(content['processor_classes']      )
          SCREAMING_SNAKE_CASE										=sorted(content['model_classes']      )

          SCREAMING_SNAKE_CASE										=content
          with open(os.path.join(lowerCAmelCase_,   'hub_tiny_model_summary.json'      ),   'w'      ) as fp:
               json.dump(lowerCAmelCase_,   lowerCAmelCase_,   ensure_ascii=lowerCAmelCase_,   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,
 )




import os
import sys
import time

from . import transposition_cipher as trans_cipher







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										='Prehistoric Men.txt'
     SCREAMING_SNAKE_CASE										='Output.txt'
     SCREAMING_SNAKE_CASE										=int(input('Enter key: '      )      )
     SCREAMING_SNAKE_CASE										=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]'      )
          SCREAMING_SNAKE_CASE										=input('> '      )
          if not response.lower().startswith('y'      ):
               sys.exit()

     SCREAMING_SNAKE_CASE										=time.time()
     if mode.lower().startswith('e'      ):
          with open(lowerCAmelCase_      ) as f:
               SCREAMING_SNAKE_CASE										=f.read()
          SCREAMING_SNAKE_CASE										=trans_cipher.encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif mode.lower().startswith('d'      ):
          with open(lowerCAmelCase_      ) as f:
               SCREAMING_SNAKE_CASE										=f.read()
          SCREAMING_SNAKE_CASE										=trans_cipher.decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     with open(lowerCAmelCase_,   'w'      ) as output_obj:
          output_obj.write(lowerCAmelCase_      )

     SCREAMING_SNAKE_CASE										=round(time.time() - start_time,   2      )
     print(('Done (', total_time, 'seconds )')      )


if __name__ == "__main__":
 main()

