





"""simple docstring"""


import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()




def     lowercase__			(    lowercase_						,lowercase_						,lowercase_	)    ->						Union[str, Any]:




      """simple docstring"""

      _UpperCamelCase					:		Optional[int]  =				{"small": (512, 8, 24), "medium": (1_024, 8, 36), "large": (1_536, 16, 48)}
      _UpperCamelCase,     _UpperCamelCase,     _UpperCamelCase					:		List[Any]  =				MODELS[model_size]  # set model hyperparameters
      _UpperCamelCase					:		str  =				ImageGPTConfig(n_embd=lowercase_						,n_layer=lowercase_						,n_head=lowercase_	)
      _UpperCamelCase					:		Dict  =				ImageGPTForCausalLM(lowercase_	)

      # Load weights from numpy
      load_tf_weights_in_imagegpt(lowercase_						,lowercase_						,lowercase_	)

      # Save pytorch-model
      _UpperCamelCase					:		str  =				pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      _UpperCamelCase					:		Any  =				pytorch_dump_folder_path + "/" + CONFIG_NAME
      print(F'''Save PyTorch model to {pytorch_weights_dump_path}'''	)
      torch.save(model.state_dict()						,lowercase_	)
      print(F'''Save configuration file to {pytorch_config_dump_path}'''	)
      with open(lowercase_						,"w"						,encoding="utf-8"	) as f:
            f.write(config.to_json_string()	)


if __name__ == "__main__":
  lowerCamelCase__      =      argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      "--imagegpt_checkpoint_path",
      default=None,
      type=str,
      required=True,
      help="Path to the TensorFlow checkpoint path.",
  )
  parser.add_argument(
      "--model_size",
      default=None,
      type=str,
      required=True,
      help="Size of the model (can be either 'small', 'medium' or 'large').",
  )
  parser.add_argument(
      "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
  )
  lowerCamelCase__      =      parser.parse_args()
  convert_imagegpt_checkpoint_to_pytorch(
      args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
  )







"""simple docstring"""


import glob
import os

from get_test_info import get_tester_classes


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

  lowerCamelCase__      =      os.path.join("tests", "models", "**", "test_modeling_*.py")
  lowerCamelCase__      =      glob.glob(pattern)
  # TODO: deal with TF/Flax too
  lowerCamelCase__      =      [
      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:
    lowerCamelCase__      =      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:
        lowerCamelCase__      =      tester_class(parent=None)
      except Exception:
        continue
      if hasattr(tester, "get_config"):
        lowerCamelCase__      =      tester.get_config()
        for k, v in config.to_dict().items():
          if isinstance(v, int):
            lowerCamelCase__      =      None
            if k in ["vocab_size"]:
              lowerCamelCase__      =      100
            elif k in ["max_position_embeddings"]:
              lowerCamelCase__      =      128
            elif k in ["hidden_size", "d_model"]:
              lowerCamelCase__      =      40
            elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
              lowerCamelCase__      =      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))
