






"""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			(  ):
   UpperCamelCase_     =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_     =getattr(transformers.models.auto    ,       A    ,       A			)
      if module is None:
         continue
      # all mappings in a single auto modeling file
      UpperCamelCase_     =[
          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:
         UpperCamelCase_     =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 						_UpperCamelCase			(  ):
   # All model names defined in auto mappings
   UpperCamelCase_     =set(get_all_model_names()			)

   with open("tests/utils/tiny_model_summary.json"			) as fp:
      UpperCamelCase_     =json.load(A			)
   UpperCamelCase_     =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_     =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 						_UpperCamelCase			(  A			):
   UpperCamelCase_     =COMPOSITE_MODELS.values()

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

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

      UpperCamelCase_     =sorted(content["tokenizer_classes"]			)
      UpperCamelCase_     =sorted(content["processor_classes"]			)
      UpperCamelCase_     =sorted(content["model_classes"]			)

      UpperCamelCase_     =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__":
    A_			  =    argparse.ArgumentParser()
    parser.add_argument("--num_workers", default=1, type=int, help="The number of workers to run.")
    A_			  =    parser.parse_args()

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

    A_			  =    "tiny_models"
    A_			  =    True
    A_			  =    None
    A_			  =    get_tiny_model_names_from_repo()
    A_			  =    True
    A_			  =    True
    A_			  =    "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 warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 						__lowerCAmelCase   (			UpperCAmelCase       ):







  '''simple docstring'''


  __lowerCamelCase       :    Optional[Any]     	=			["image_processor", "tokenizer"]
  __lowerCamelCase       :    List[str]     	=			"VideoMAEImageProcessor"
  __lowerCamelCase       :    Dict     	=			("CLIPTokenizer", "CLIPTokenizerFast")
  def __init__(      self:		str				,			UpperCamelCase_:		Dict=None				,			UpperCamelCase_:		str=None				,			**UpperCamelCase_:		str					):
     UpperCamelCase_     =None
     if "feature_extractor" in kwargs:
        warnings.warn(
            "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
            " instead."				,			UpperCamelCase_				,			)
        UpperCamelCase_     =kwargs.pop("feature_extractor"					)

     UpperCamelCase_     =image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
        raise ValueError("You need to specify an `image_processor`."					)
     if tokenizer is None:
        raise ValueError("You need to specify a `tokenizer`."					)

     super().__init__(UpperCamelCase_				,			UpperCamelCase_					)
     UpperCamelCase_     =self.image_processor
  def __call__(      self:		Tuple				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		str=None				,			UpperCamelCase_:		Optional[Any]=None				,			**UpperCamelCase_:		int					):

     if text is None and videos is None:
        raise ValueError("You have to specify either text or videos. Both cannot be none."					)

     if text is not None:
        UpperCamelCase_     =self.tokenizer(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)

     if videos is not None:
        UpperCamelCase_     =self.image_processor(UpperCamelCase_				,			return_tensors=UpperCamelCase_				,			**UpperCamelCase_					)

     if text is not None and videos is not None:
        UpperCamelCase_     =image_features.pixel_values
        return encoding
     elif text is not None:
        return encoding
     else:
        return BatchEncoding(data=dict(**UpperCamelCase_					)				,			tensor_type=UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		Any				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Optional[int]					):
     return self.tokenizer.batch_decode(*UpperCamelCase_				,			**UpperCamelCase_					)
  def 					UpperCamelCase__   (      self:		Optional[Any]				,			*UpperCamelCase_:		List[str]				,			**UpperCamelCase_:		Tuple					):
     return self.tokenizer.decode(*UpperCamelCase_				,			**UpperCamelCase_					)
  @property
  def 					UpperCamelCase__   (      self:		List[Any]					):
     return ["input_ids", "attention_mask", "position_ids", "pixel_values"]
  @property
  def 					UpperCamelCase__   (      self:		str					):
     warnings.warn(
         "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."				,			UpperCamelCase_				,			)
     return self.image_processor_class





  @property
  def 					UpperCamelCase__   (      self:		Optional[Any]					):
     warnings.warn(
         "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."				,			UpperCamelCase_				,			)
     return self.image_processor
