



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       (   snake_case			,		snake_case			,		snake_case    ):





     """simple docstring"""

     _lowerCAmelCase      			=					json.load(open(snake_case			,		"""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(snake_case    )

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

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


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








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


A__			   = {
    """configuration_videomae""": ["""VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """VideoMAEConfig"""],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = [
	    """VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """VideoMAEForPreTraining""",
	    """VideoMAEModel""",
	    """VideoMAEPreTrainedModel""",
	    """VideoMAEForVideoClassification""",
	]

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A__			   = ["""VideoMAEFeatureExtractor"""]
	A__			   = ["""VideoMAEImageProcessor"""]

if TYPE_CHECKING:
	from .configuration_videomae import VIDEOMAE_PRETRAINED_CONFIG_ARCHIVE_MAP, VideoMAEConfig

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_videomae import (
		    VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST,
		    VideoMAEForPreTraining,
		    VideoMAEForVideoClassification,
		    VideoMAEModel,
		    VideoMAEPreTrainedModel,
		)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_videomae import VideoMAEFeatureExtractor
		from .image_processing_videomae import VideoMAEImageProcessor

else:
	import sys

	A__			   = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



