
'''simple docstring'''
import copy

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


lowercase__						=			logging.get_logger(__name__)






class   snake_case__					(			__SCREAMING_SNAKE_CASE				):







		"""simple docstring"""

		lowerCamelCase           =	"""hybrid-clip"""
		lowerCamelCase           =	True



		def __init__(			self							: Any     ,  UpperCamelCase__							: Optional[Any]=512     ,  **UpperCamelCase__							: Dict			)	-> Dict:






				"""simple docstring"""







				super().__init__(**UpperCamelCase__			)

				if "text_config" not in kwargs:
						raise ValueError('''`text_config` can not be `None`.'''			)

				if "vision_config" not in kwargs:
						raise ValueError('''`vision_config` can not be `None`.'''			)

				snake_case     :					Any     =	kwargs.pop('''text_config'''			)
				snake_case     :					str     =	kwargs.pop('''vision_config'''			)

				snake_case     :					int     =	text_config.pop('''model_type'''			)
				snake_case     :					str     =	vision_config.pop('''model_type'''			)

				from transformers import AutoConfig

				snake_case     :					str     =	AutoConfig.for_model(UpperCamelCase__     ,  **UpperCamelCase__			)

				if vision_model_type == "clip":
						snake_case     :					Optional[int]     =	AutoConfig.for_model(UpperCamelCase__     ,  **UpperCamelCase__			).vision_config
				elif vision_model_type == "clip_vision_model":
						from transformers import CLIPVisionConfig

						snake_case     :					Dict     =	CLIPVisionConfig(**UpperCamelCase__			)
				else:
						snake_case     :					Tuple     =	AutoConfig.for_model(UpperCamelCase__     ,  **UpperCamelCase__			)

				snake_case     :					Optional[Any]     =	projection_dim
				snake_case     :					Optional[int]     =	1.0



		@classmethod
		def 			lowerCAmelCase							(			cls							: List[str]     ,  UpperCamelCase__							: PretrainedConfig     ,  UpperCamelCase__							: PretrainedConfig     ,  **UpperCamelCase__							: List[Any]			)	-> Union[str, Any]:






				"""simple docstring"""







				return cls(text_config=text_config.to_dict()     ,  vision_config=vision_config.to_dict()     ,  **UpperCamelCase__			)



		def 			lowerCAmelCase							(			self							: int			)	-> Optional[Any]:






				"""simple docstring"""







				snake_case     :					Dict     =	copy.deepcopy(self.__dict__			)
				snake_case     :					List[str]     =	self.text_config.to_dict()
				snake_case     :					List[str]     =	self.vision_config.to_dict()
				snake_case     :					Any     =	self.__class__.model_type
				return output




'''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       _UpperCamelCase	(					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__   ,					SCREAMING_SNAKE_CASE__ )     ->       Dict:


		'''simple docstring'''


		snake_case     :					Tuple     =	json.load(open(SCREAMING_SNAKE_CASE__   ,					'''r''' ) )
		snake_case     :					Any     =	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}' )
		snake_case     :					Union[str, Any]     =	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__":
						lowercase__						=			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."
						)
						lowercase__						=			parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


