



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      lowerCAmelCase_					(  _lowercase    :       Dict						,       _lowercase    :       Optional[Any]						,       _lowercase    :       Optional[Any]						,       _lowercase    :       List[str])							->	Optional[int]:







			"""simple docstring"""
			a__       :						Tuple													=  FunnelConfig.from_json_file(_lowercase)
			print(F'''Building PyTorch model from configuration: {config}''')
			a__       :						str													=  FunnelBaseModel(_lowercase) if base_model else FunnelModel(_lowercase)

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

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


if __name__ == "__main__":
		_lowercase		:				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 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."
		)
		_lowercase		:				List[str]      		=parser.parse_args()
		convert_tf_checkpoint_to_pytorch(
		    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.base_model
		)









import logging
from dataclasses import dataclass, field
from typing import Optional

from seqaseq_trainer import arg_to_scheduler

from transformers import TrainingArguments


_lowercase		:				str      		=logging.getLogger(__name__)

@dataclass
class        snake_case__    (A__		):







						"""simple docstring"""




						__lowerCAmelCase					:Optional[float]      =					field(
						    default=0.0 ,     metadata={"help": "The label smoothing epsilon to apply (if not zero)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "Whether to SortishSamler or not."}		)
						__lowerCAmelCase					:bool      =					field(
						    default=A__ ,     metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."}		)
						__lowerCAmelCase					:bool      =					field(default=A__ ,     metadata={"help": "whether to use adafactor"}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Encoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Decoder layer dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(default=A__ ,     metadata={"help": "Dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[float]      =					field(
						    default=A__ ,     metadata={"help": "Attention dropout probability. Goes into model.config."}		)
						__lowerCAmelCase					:Optional[str]      =					field(
						    default="linear" ,     metadata={"help": f"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys()		)}"} ,     )




