





"""simple docstring"""
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						(      )		->				List[str]:
   lowercase    :			List[str]  =       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"]:
      lowercase    :			Tuple  =       getattr(transformers.models.auto ,__A ,__A    )
      if module is None:
         continue
      # all mappings in a single auto modeling file
      lowercase    :			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:
         lowercase    :			Tuple  =       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 			__snake_case						(      )		->				Any:
   # All model names defined in auto mappings
   lowercase    :			Tuple  =       set(get_all_model_names()    )

   with open("""tests/utils/tiny_model_summary.json"""    ) as fp:
      lowercase    :			str  =       json.load(__A    )
   lowercase    :			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.
   lowercase    :			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 			__snake_case						(      __A    )		->				int:
   lowercase    :			List[str]  =       COMPOSITE_MODELS.values()

   # All tiny model base names on Hub
   lowercase    :			Optional[Any]  =       get_all_model_names()
   lowercase    :			List[str]  =       hf_api.list_models(
       filter=ModelFilter(
           author="""hf-internal-testing""" ,)    )
   lowercase    :			str  =       set()
   for x in models:
      lowercase    :			int  =       x.modelId
      lowercase    :			List[Any]  =       model.split("""/"""    )
      if not model.startswith("""tiny-random-"""    ):
         continue
      lowercase    :			List[str]  =       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    )

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

      lowercase    :			str  =       sorted(content["""tokenizer_classes"""]    )
      lowercase    :			Dict  =       sorted(content["""processor_classes"""]    )
      lowercase    :			Dict  =       sorted(content["""model_classes"""]    )

      lowercase    :			Any  =       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:	Tuple							      =argparse.ArgumentParser()
   parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
   lowerCAmelCase:	Tuple							      =parser.parse_args()

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

   lowerCAmelCase:	Optional[int]							      ="tiny_models"
   lowerCAmelCase:	str							      =True
   lowerCAmelCase:	Dict							      =None
   lowerCAmelCase:	Any							      =get_tiny_model_names_from_repo()
   lowerCAmelCase:	Dict							      =True
   lowerCAmelCase:	Optional[Any]							      =True
   lowerCAmelCase:	str							      ="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,
   )









"""simple docstring"""
import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()




def 			__snake_case						(      __A ,__A ,__A ,__A ,__A    )		->				Optional[int]:
   # Initialise PyTorch model
   lowercase    :			Optional[Any]  =       BertConfig.from_pretrained(
       """bert-large-cased""" ,vocab_size=__A ,max_position_embeddings=512 ,is_decoder=__A ,add_cross_attention=__A ,)
   lowercase    :			List[str]  =       bert_config.to_dict()
   del bert_config_dict["type_vocab_size"]
   lowercase    :			Tuple  =       BertGenerationConfig(**__A    )
   if is_encoder:
      lowercase    :			Dict  =       BertGenerationEncoder(__A    )
   else:
      lowercase    :			Dict  =       BertGenerationDecoder(__A    )
   print(F'''Building PyTorch model from configuration: {config}'''    )

   # Load weights from tf checkpoint
   load_tf_weights_in_bert_generation(
       __A ,__A ,model_class="""bert""" ,is_encoder_named_decoder=__A ,is_encoder=__A ,)

   # Save pytorch-model
   print(F'''Save PyTorch model and config to {pytorch_dump_path}'''    )
   model.save_pretrained(__A    )


if __name__ == "__main__":
   lowerCAmelCase:	Dict							      =argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--tf_hub_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
   )
   parser.add_argument(
       "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   parser.add_argument(
       "--is_encoder_named_decoder",
       action="store_true",
       help="If decoder has to be renamed to encoder in PyTorch model.",
   )
   parser.add_argument("--is_encoder", action="store_true", help="If model is an encoder.")
   parser.add_argument("--vocab_size", default=50358, type=int, help="Vocab size of model")
   lowerCAmelCase:	Optional[int]							      =parser.parse_args()
   convert_tf_checkpoint_to_pytorch(
       args.tf_hub_path,
       args.pytorch_dump_path,
       args.is_encoder_named_decoder,
       args.vocab_size,
       is_encoder=args.is_encoder,
   )


