






"""simple docstring"""






import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
    A_			  =    []

    A_			  =    os.path.join("tests", "models", "**", "test_modeling_*.py")
    A_			  =    glob.glob(pattern)
    # TODO: deal with TF/Flax too
    A_			  =    [
        x for x in test_files if not (x.startswith("test_modeling_tf_") or x.startswith("test_modeling_flax_"))
    ]

    for test_file in test_files:
        A_			  =    get_tester_classes(test_file)
        for tester_class in tester_classes:
            # A few tester classes don't have `parent` parameter in `__init__`.
            # TODO: deal this better
            try:
                A_			  =    tester_class(parent=None)
            except Exception:
                continue
            if hasattr(tester, "get_config"):
                A_			  =    tester.get_config()
                for k, v in config.to_dict().items():
                    if isinstance(v, int):
                        A_			  =    None
                        if k in ["vocab_size"]:
                            A_			  =    100
                        elif k in ["max_position_embeddings"]:
                            A_			  =    128
                        elif k in ["hidden_size", "d_model"]:
                            A_			  =    40
                        elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
                            A_			  =    5
                        if target is not None and v > target:
                            failures.append(
                                f'''{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'''
                                f''' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'''
                                f''' than {target}.'''
                            )

    if len(failures) > 0:
        raise Exception(f'''There were {len(failures)} failures:\n''' + "\n".join(failures))








"""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
