






from ...configuration_utils import PretrainedConfig
class     lowerCamelCase_					(   lowerCamelCase			):
				a__       							=	'''bert-generation'''







				def __init__(    self							,       __lowerCAmelCase=5_0_3_5_8							,       __lowerCAmelCase=1_0_2_4							,       __lowerCAmelCase=2_4							,       __lowerCAmelCase=1_6							,       __lowerCAmelCase=4_0_9_6							,       __lowerCAmelCase="gelu"							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=0.1							,       __lowerCAmelCase=5_1_2							,       __lowerCAmelCase=0.02							,       __lowerCAmelCase=1E-12							,       __lowerCAmelCase=0							,       __lowerCAmelCase=2							,       __lowerCAmelCase=1							,       __lowerCAmelCase="absolute"							,       __lowerCAmelCase=True							,       **__lowerCAmelCase							,       ):
								"""simple docstring"""
								super().__init__(pad_token_id=__lowerCAmelCase							,       bos_token_id=__lowerCAmelCase							,       eos_token_id=__lowerCAmelCase							,       **__lowerCAmelCase  )

								__magic_name__		:int   =   vocab_size
								__magic_name__		:Tuple   =   hidden_size
								__magic_name__		:Optional[int]   =   num_hidden_layers
								__magic_name__		:str   =   num_attention_heads
								__magic_name__		:Any   =   hidden_act
								__magic_name__		:int   =   intermediate_size
								__magic_name__		:Dict   =   hidden_dropout_prob
								__magic_name__		:Dict   =   attention_probs_dropout_prob
								__magic_name__		:str   =   max_position_embeddings
								__magic_name__		:Optional[int]   =   initializer_range
								__magic_name__		:Union[str, Any]   =   layer_norm_eps
								__magic_name__		:Tuple   =   position_embedding_type
								__magic_name__		:Dict   =   use_cache












import argparse

import torch

from transformers import FunnelBaseModel, FunnelConfig, FunnelModel, load_tf_weights_in_funnel
from transformers.utils import logging


logging.set_verbosity_info()

def lowercase    (  SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )			->							Union[str, Any]:


			'''simple docstring'''







			SCREAMING_SNAKE_CASE_								=						FunnelConfig.from_json_file(SCREAMING_SNAKE_CASE )
			print(F'Building PyTorch model from configuration: {config}' )
			SCREAMING_SNAKE_CASE_								=						FunnelBaseModel(SCREAMING_SNAKE_CASE ) if base_model else FunnelModel(SCREAMING_SNAKE_CASE )

			# Load weights from tf checkpoint
			load_tf_weights_in_funnel(SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE       ,    SCREAMING_SNAKE_CASE )

			# Save pytorch-model
			print(F'Save PyTorch model to {pytorch_dump_path}' )
			torch.save(model.state_dict()       ,    SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
				SCREAMING_SNAKE_CASE__     :     Union[str, Any]		    =       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 model. \nThis specifies the model architecture.",
				)
				parser.add_argument(
				    "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
				)
				parser.add_argument(
				    "--base_model", action="store_true", help="Whether you want just the base model (no decoder) or not."
				)
				SCREAMING_SNAKE_CASE__     :     List[Any]		    =       parser.parse_args()
				convert_tf_checkpoint_to_pytorch(
				    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
				)
