



"""simple docstring"""
import argparse
import json

from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
from transformers.utils import logging


logging.set_verbosity_info()



def 				lowerCAmelCase_(      lowercase_   :							Dict  ,      lowercase_   :							Any  ,      lowercase_   :							int							)		->				Union[str, Any]:
				# Initialise PyTorch model
				_lowerCamelCase					     = json.load(open(lowercase_  ,      '''r'''							)							)
				_lowerCamelCase					     = GPTNeoConfig(
				    hidden_size=config_json['''n_embd''']  ,      num_layers=config_json['''n_layer''']  ,      num_heads=config_json['''n_head''']  ,      attention_types=config_json['''attention_types''']  ,      max_position_embeddings=config_json['''n_positions''']  ,      resid_dropout=config_json['''res_dropout''']  ,      embed_dropout=config_json['''embed_dropout''']  ,      attention_dropout=config_json['''attn_dropout''']  ,      )
				print(F"""Building PyTorch model from configuration: {config}"""							)
				_lowerCamelCase					     = GPTNeoForCausalLM(lowercase_							)

				# Load weights from tf checkpoint
				load_tf_weights_in_gpt_neo(lowercase_  ,      lowercase_  ,      lowercase_							)

				# Save pytorch-model
				print(F"""Save PyTorch model to {pytorch_dump_path}"""							)
				model.save_pretrained(lowercase_							)


if __name__ == "__main__":
			__SCREAMING_SNAKE_CASE     :  Tuple										=					argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
			)
			parser.add_argument(
			    '''--config_file''',
			    default=None,
			    type=str,
			    required=True,
			    help=(
			        '''The config json file corresponding to the pre-trained mesh-tf model. \n'''
			        '''This specifies the model architecture.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
			)
			__SCREAMING_SNAKE_CASE     :  Optional[int]										=					parser.parse_args()
			convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_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))
