




"""simple docstring"""




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


lowerCAmelCase							:			Union[str, Any]     							=							logging.get_logger(__name__)

lowerCAmelCase							:			Any     							=							{
    """google/fnet-base""": """https://huggingface.co/google/fnet-base/resolve/main/config.json""",
    """google/fnet-large""": """https://huggingface.co/google/fnet-large/resolve/main/config.json"""
    # See all FNet models at https://huggingface.co/models?filter=fnet
}






class     __magic_name__							(       _UpperCAmelCase				):






				'''simple docstring'''
				__UpperCamelCase			  =						"fnet"





				def __init__(       self		, _a=32_000		, _a=768		, _a=12		, _a=3_072		, _a="gelu_new"		, _a=0.1		, _a=512		, _a=4		, _a=0.02		, _a=1e-1_2		, _a=False		, _a=512		, _a=3		, _a=1		, _a=2		, **_a		, ):





							"""simple docstring"""
							super().__init__(pad_token_id=lowercase_		, bos_token_id=lowercase_		, eos_token_id=lowercase_		, **lowercase_ )

							lowerCamelCase			      =					vocab_size
							lowerCamelCase			      =					max_position_embeddings
							lowerCamelCase			      =					hidden_size
							lowerCamelCase			      =					num_hidden_layers
							lowerCamelCase			      =					intermediate_size
							lowerCamelCase			      =					hidden_act
							lowerCamelCase			      =					hidden_dropout_prob
							lowerCamelCase			      =					initializer_range
							lowerCamelCase			      =					type_vocab_size
							lowerCamelCase			      =					layer_norm_eps
							lowerCamelCase			      =					use_tpu_fourier_optimizations
							lowerCamelCase			      =					tpu_short_seq_length






from typing import Union

import fire
import torch
from tqdm import tqdm
def 						_snake_case(    SCREAMING_SNAKE_CASE__			:	str    ,			SCREAMING_SNAKE_CASE__			:	str = "cpu"    ,			SCREAMING_SNAKE_CASE__			:	Union[str, None] = None      )							->      None:





    '''simple docstring'''






    A__          =   torch.load(SCREAMING_SNAKE_CASE__    ,			map_location=SCREAMING_SNAKE_CASE__      )
    for k, v in tqdm(state_dict.items()      ):
        if not isinstance(SCREAMING_SNAKE_CASE__    ,			torch.Tensor      ):
            raise TypeError('FP16 conversion only works on paths that are saved state dicts, like pytorch_model.bin'      )
        A__          =   v.half()
    if save_path is None:  # overwrite src_path
        A__          =   src_path
    torch.save(SCREAMING_SNAKE_CASE__    ,			SCREAMING_SNAKE_CASE__      )


if __name__ == "__main__":
     fire.Fire(convert)

