






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__				(   ):
  __UpperCAmelCase							:     Optional[int]									=  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"]:
    __UpperCAmelCase							:     Optional[Any]									=  getattr(transformers.models.auto  ,		__lowerCamelCase  ,		__lowerCamelCase		)
    if module is None:
      continue
    # all mappings in a single auto modeling file
    __UpperCAmelCase							:     Dict									=  [
        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:
      __UpperCAmelCase							:     str									=  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     lowerCamelCase__				(   ):
  # All model names defined in auto mappings
  __UpperCAmelCase							:     List[Any]									=  set(get_all_model_names()		)

  with open("""tests/utils/tiny_model_summary.json"""		) as fp:
    __UpperCAmelCase							:     Optional[Any]									=  json.load(__lowerCamelCase		)
  __UpperCAmelCase							:     List[str]									=  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.
  __UpperCAmelCase							:     int									=  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     lowerCamelCase__				(   __lowerCamelCase      :					Dict		):
  __UpperCAmelCase							:     Tuple									=  COMPOSITE_MODELS.values()

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

  __UpperCAmelCase							:     Any									=  sorted(_models		)
  # All tiny model names on Hub
  __UpperCAmelCase							:     str									=  {}
  for model in models:
    __UpperCAmelCase							:     int									=  f"""hf-internal-testing/tiny-random-{model}"""
    __UpperCAmelCase							:     Optional[Any]									=  model.split("""-"""		)[0]
    try:
      __UpperCAmelCase							:     Dict									=  hf_api.repo_info(__lowerCamelCase		)
      __UpperCAmelCase							:     Optional[Any]									=  {
          """tokenizer_classes""": set(),
          """processor_classes""": set(),
          """model_classes""": set(),
          """sha""": repo_info.sha,
      }
    except Exception:
      continue
    try:
      time.sleep(1		)
      __UpperCAmelCase							:     int									=  AutoTokenizer.from_pretrained(__lowerCamelCase		)
      content["tokenizer_classes"].add(tokenizer_fast.__class__.__name__		)
    except Exception:
      pass
    try:
      time.sleep(1		)
      __UpperCAmelCase							:     int									=  AutoTokenizer.from_pretrained(__lowerCamelCase  ,		use_fast=__lowerCamelCase		)
      content["tokenizer_classes"].add(tokenizer_slow.__class__.__name__		)
    except Exception:
      pass
    try:
      time.sleep(1		)
      __UpperCAmelCase							:     Dict									=  AutoImageProcessor.from_pretrained(__lowerCamelCase		)
      content["processor_classes"].add(img_p.__class__.__name__		)
    except Exception:
      pass
    try:
      time.sleep(1		)
      __UpperCAmelCase							:     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		)
      __UpperCAmelCase							:     Tuple									=  getattr(__lowerCamelCase  ,		__lowerCamelCase		)
      __UpperCAmelCase							:     Optional[int]									=  model_class.from_pretrained(__lowerCamelCase		)
      content["model_classes"].add(m.__class__.__name__		)
    except Exception:
      pass
    try:
      time.sleep(1		)
      __UpperCAmelCase							:     Optional[Any]									=  getattr(__lowerCamelCase  ,		f"""TF{model}"""		)
      __UpperCAmelCase							:     Tuple									=  model_class.from_pretrained(__lowerCamelCase		)
      content["model_classes"].add(m.__class__.__name__		)
    except Exception:
      pass

    __UpperCAmelCase							:     Tuple									=  sorted(content["""tokenizer_classes"""]		)
    __UpperCAmelCase							:     Optional[Any]									=  sorted(content["""processor_classes"""]		)
    __UpperCAmelCase							:     Any									=  sorted(content["""model_classes"""]		)

    __UpperCAmelCase							:     int									=  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__":
       a		:	str      					=			argparse.ArgumentParser()
       parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
       a		:	List[str]      					=			parser.parse_args()

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

       a		:	List[Any]      					=			"tiny_models"
       a		:	str      					=			True
       a		:	List[str]      					=			None
       a		:	int      					=			get_tiny_model_names_from_repo()
       a		:	Optional[int]      					=			True
       a		:	int      					=			True
       a		:	Union[str, Any]      					=			"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 copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


a		:	Optional[Any]      					=			logging.get_logger(__name__)

a		:	int      					=			{
    "facebook/musicgen-small": "https://huggingface.co/facebook/musicgen-small/resolve/main/config.json",
    # See all Musicgen models at https://huggingface.co/models?filter=musicgen
}
class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Optional[int]								=     'musicgen_decoder'
      a  :			Optional[int]								=     ['past_key_values']
      def __init__(				self :      Optional[Any]		,				__lowercase :      str=2048		,				__lowercase :      Optional[int]=2048		,				__lowercase :      List[Any]=24		,				__lowercase :      Optional[Any]=4096		,				__lowercase :      Dict=16		,				__lowercase :      List[Any]=0.0		,				__lowercase :      Tuple=True		,				__lowercase :      List[Any]="gelu"		,				__lowercase :      Any=1024		,				__lowercase :      Optional[Any]=0.1		,				__lowercase :      List[Any]=0.0		,				__lowercase :      int=0.0		,				__lowercase :      List[str]=0.02		,				__lowercase :      Dict=False		,				__lowercase :      List[str]=4		,				__lowercase :      List[str]=2048		,				__lowercase :      List[Any]=2048		,				__lowercase :      Union[str, Any]=None		,				__lowercase :      List[str]=False		,				**__lowercase :      int		,				)							->				str:
        __UpperCAmelCase							:     Union[str, Any]									=  vocab_size
        __UpperCAmelCase							:     Dict									=  max_position_embeddings
        __UpperCAmelCase							:     str									=  hidden_size
        __UpperCAmelCase							:     Optional[Any]									=  ffn_dim
        __UpperCAmelCase							:     List[Any]									=  num_hidden_layers
        __UpperCAmelCase							:     Any									=  num_attention_heads
        __UpperCAmelCase							:     List[Any]									=  dropout
        __UpperCAmelCase							:     List[str]									=  attention_dropout
        __UpperCAmelCase							:     List[Any]									=  activation_dropout
        __UpperCAmelCase							:     int									=  activation_function
        __UpperCAmelCase							:     Optional[int]									=  initializer_factor
        __UpperCAmelCase							:     List[Any]									=  layerdrop
        __UpperCAmelCase							:     Tuple									=  use_cache
        __UpperCAmelCase							:     Tuple									=  scale_embedding  # scale factor will be sqrt(d_model) if True
        __UpperCAmelCase							:     int									=  num_codebooks
        super().__init__(
            pad_token_id=__lowercase		,				bos_token_id=__lowercase		,				eos_token_id=__lowercase		,				tie_word_embeddings=__lowercase		,				**__lowercase		,				)


class    a   (			lowercase__       ):



      """simple docstring"""


      a  :			Tuple								=     'musicgen'
      a  :			int								=     True
      def __init__(				self :      Any		,				**__lowercase :      Tuple )							->				Union[str, Any]:
        super().__init__(**__lowercase )
        if "text_encoder" not in kwargs or "audio_encoder" not in kwargs or "decoder" not in kwargs:
          raise ValueError("""Config has to be initialized with text_encoder, audio_encoder and decoder config""" )

        __UpperCAmelCase							:     int									=  kwargs.pop("""text_encoder""" )
        __UpperCAmelCase							:     str									=  text_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Tuple									=  kwargs.pop("""audio_encoder""" )
        __UpperCAmelCase							:     Optional[Any]									=  audio_encoder_config.pop("""model_type""" )

        __UpperCAmelCase							:     Optional[Any]									=  kwargs.pop("""decoder""" )

        __UpperCAmelCase							:     Any									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Dict									=  AutoConfig.for_model(__lowercase		,				**__lowercase )
        __UpperCAmelCase							:     Any									=  MusicgenDecoderConfig(**__lowercase )
        __UpperCAmelCase							:     int									=  True
      @classmethod
      def      UpperCAmelCase				(				cls :      Optional[Any]		,				__lowercase :      PretrainedConfig		,				__lowercase :      PretrainedConfig		,				__lowercase :      MusicgenDecoderConfig		,				**__lowercase :      List[Any]		,				)							->				List[str]:
        return cls(
            text_encoder=text_encoder_config.to_dict()		,				audio_encoder=audio_encoder_config.to_dict()		,				decoder=decoder_config.to_dict()		,				**__lowercase		,				)




      def      UpperCAmelCase				(				self :      Any )							->				int:
        __UpperCAmelCase							:     Optional[Any]									=  copy.deepcopy(self.__dict__ )
        __UpperCAmelCase							:     List[Any]									=  self.text_encoder.to_dict()
        __UpperCAmelCase							:     str									=  self.audio_encoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.decoder.to_dict()
        __UpperCAmelCase							:     List[str]									=  self.__class__.model_type
        return output


