





"""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 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Dict									=    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"]:
    A_    :			str									=    getattr(transformers.models.auto					,   lowerCamelCase__					,   lowerCamelCase__   )
    if module is None:
      continue
    # all mappings in a single auto modeling file
    A_    :			List[Any]									=    [
        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:
      A_    :			Union[str, Any]									=    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 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Tuple									=    set(get_all_model_names()   )

  with open('tests/utils/tiny_model_summary.json'   ) as fp:
    A_    :			Optional[Any]									=    json.load(lowerCamelCase__   )
  A_    :			Tuple									=    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.
  A_    :			List[Any]									=    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 		UpperCAmelCase__						(	_UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Any									=    COMPOSITE_MODELS.values()

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

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

    A_    :			Optional[Any]									=    sorted(content['tokenizer_classes']   )
    A_    :			Optional[int]									=    sorted(content['processor_classes']   )
    A_    :			str									=    sorted(content['model_classes']   )

    A_    :			Union[str, Any]									=    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_	:		List[Any]         =   argparse.ArgumentParser()
   parser.add_argument('--num_workers', default=1, type=int, help='The number of workers to run.')
   lowerCamelCase_	:		Any         =   parser.parse_args()

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

   lowerCamelCase_	:		int         =   'tiny_models'
   lowerCamelCase_	:		Any         =   True
   lowerCamelCase_	:		List[Any]         =   None
   lowerCamelCase_	:		List[str]         =   get_tiny_model_names_from_repo()
   lowerCamelCase_	:		Optional[int]         =   True
   lowerCamelCase_	:		Optional[Any]         =   True
   lowerCamelCase_	:		List[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,
   )
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     =     logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE     =     ["""UtteranceLevel"""]
@torch.no_grad()
def 					__lowercase						(    lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__			,					lowerCamelCase__       )					->int:

  '''simple docstring'''




  a__  		=  torch.load(lowerCamelCase__			,					map_location="cpu"       )
  if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
    raise NotImplementedError(f"The supported s3prl models are {SUPPORTED_MODELS}"       )

  a__  		=  checkpoint["Downstream"]

  a__  		=  HubertConfig.from_pretrained(lowerCamelCase__       )
  a__  		=  HubertForSequenceClassification.from_pretrained(lowerCamelCase__			,					config=lowerCamelCase__       )
  a__  		=  WavaVecaFeatureExtractor.from_pretrained(
      lowerCamelCase__			,					return_attention_mask=lowerCamelCase__			,					do_normalize=lowerCamelCase__       )

  if hf_congfig.use_weighted_layer_sum:
    a__  		=  checkpoint["Featurizer"]["weights"]

  a__  		=  downstream_dict["projector.weight"]
  a__  		=  downstream_dict["projector.bias"]
  a__  		=  downstream_dict["model.post_net.linear.weight"]
  a__  		=  downstream_dict["model.post_net.linear.bias"]

  hf_feature_extractor.save_pretrained(lowerCamelCase__       )
  hf_model.save_pretrained(lowerCamelCase__       )


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE     =     argparse.ArgumentParser()
     parser.add_argument(
         """--base_model_name""", default=None, type=str, help="""Name of the huggingface pretrained base model."""
     )
     parser.add_argument("""--config_path""", default=None, type=str, help="""Path to the huggingface classifier config.""")
     parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to the s3prl checkpoint.""")
     parser.add_argument("""--model_dump_path""", default=None, type=str, help="""Path to the final converted model.""")
     _SCREAMING_SNAKE_CASE     =     parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

