

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCamelCase__     :					Optional[int]								=			logging.get_logger(__name__)

UpperCamelCase__     :					List[Any]								=			{
    "MCG-NJU/videomae-base": "https://huggingface.co/MCG-NJU/videomae-base/resolve/main/config.json",
}



class 					__snake_case     (			lowerCAmelCase__				):
  __lowerCAmelCase   :		Dict         =     'videomae'

  def __init__(    self  ,						_A=224  ,						_A=16  ,						_A=3  ,						_A=16  ,						_A=2  ,						_A=768  ,						_A=12  ,						_A=12  ,						_A=3072  ,						_A="gelu"  ,						_A=0.0  ,						_A=0.0  ,						_A=0.0_2  ,						_A=1E-12  ,						_A=True  ,						_A=True  ,						_A=6  ,						_A=384  ,						_A=4  ,						_A=1536  ,						_A=True  ,						**_A  ,						):
      super().__init__(**_A)

      SCREAMING_SNAKE_CASE_													=  image_size
      SCREAMING_SNAKE_CASE_													=  patch_size
      SCREAMING_SNAKE_CASE_													=  num_channels
      SCREAMING_SNAKE_CASE_													=  num_frames
      SCREAMING_SNAKE_CASE_													=  tubelet_size

      SCREAMING_SNAKE_CASE_													=  hidden_size
      SCREAMING_SNAKE_CASE_													=  num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  num_attention_heads
      SCREAMING_SNAKE_CASE_													=  intermediate_size
      SCREAMING_SNAKE_CASE_													=  hidden_act
      SCREAMING_SNAKE_CASE_													=  hidden_dropout_prob
      SCREAMING_SNAKE_CASE_													=  attention_probs_dropout_prob
      SCREAMING_SNAKE_CASE_													=  initializer_range
      SCREAMING_SNAKE_CASE_													=  layer_norm_eps
      SCREAMING_SNAKE_CASE_													=  qkv_bias
      SCREAMING_SNAKE_CASE_													=  use_mean_pooling

      SCREAMING_SNAKE_CASE_													=  decoder_num_attention_heads
      SCREAMING_SNAKE_CASE_													=  decoder_hidden_size
      SCREAMING_SNAKE_CASE_													=  decoder_num_hidden_layers
      SCREAMING_SNAKE_CASE_													=  decoder_intermediate_size
      SCREAMING_SNAKE_CASE_													=  norm_pix_loss





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     _UpperCAmelCase				(     _SCREAMING_SNAKE_CASE    :						int       ,      _SCREAMING_SNAKE_CASE    :						Tuple       ,      _SCREAMING_SNAKE_CASE    :						Union[str, Any]				):





    """simple docstring"""


    SCREAMING_SNAKE_CASE_													=  json.load(open(_SCREAMING_SNAKE_CASE       ,      'r'				)				)
    SCREAMING_SNAKE_CASE_													=  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}"""				)
    SCREAMING_SNAKE_CASE_													=  GPTNeoForCausalLM(_SCREAMING_SNAKE_CASE				)

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

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


if __name__ == "__main__":
       UpperCamelCase__     :					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."
       )
       UpperCamelCase__     :					Any								=			parser.parse_args()
       convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


