






from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCamelCase						= logging.get_logger(__name__)

__lowerCamelCase						= {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  '''swiftformer'''




						def __init__(    self		,	lowercase=3		,	lowercase=[3, 3, 6, 4]		,	lowercase=[48, 56, 112, 220]		,	lowercase=4		,	lowercase=[True, True, True, True]		,	lowercase="gelu"		,	lowercase=3		,	lowercase=2		,	lowercase=1		,	lowercase=0.0		,	lowercase=True		,	lowercase=1e-5		,	lowercase=1e-5		,	**lowercase		,	)			->						List[str]:
													super().__init__(**lowercase       )
													_a  :    Dict			=						num_channels
													_a  :    Tuple			=						depths
													_a  :    Optional[int]			=						embed_dims
													_a  :    Optional[Any]			=						mlp_ratio
													_a  :    List[str]			=						downsamples
													_a  :    int			=						hidden_act
													_a  :    List[str]			=						down_patch_size
													_a  :    List[str]			=						down_stride
													_a  :    Any			=						down_pad
													_a  :    str			=						drop_path_rate
													_a  :    List[str]			=						use_layer_scale
													_a  :    str			=						layer_scale_init_value
													_a  :    str			=						batch_norm_eps

class      UpperCamelCase_							(				UpperCamelCase       ):
						lowercase									=  version.parse('''1.11'''       )




						@property
						def      snake_case__(    self       )			->						Mapping[str, Mapping[int, str]]:
													return OrderedDict(
													    [
													        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
													    ]       )







						@property
						def      snake_case__(    self       )			->						float:
													return 1e-4







import argparse

import torch

from transformers import ElectraConfig, ElectraForMaskedLM, ElectraForPreTraining, load_tf_weights_in_electra
from transformers.utils import logging


logging.set_verbosity_info()






def    UpperCamelCase__  (   UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase     )     ->		str:







							"""simple docstring"""





							_a  :    Union[str, Any]			=						ElectraConfig.from_json_file(UpperCAmelCase     )
							print(F'Building PyTorch model from configuration: {config}'     )

							if discriminator_or_generator == "discriminator":
														_a  :    int			=						ElectraForPreTraining(UpperCAmelCase     )
							elif discriminator_or_generator == "generator":
														_a  :    Any			=						ElectraForMaskedLM(UpperCAmelCase     )
							else:
														raise ValueError('''The discriminator_or_generator argument should be either \'discriminator\' or \'generator\''''     )

							# Load weights from tf checkpoint
							load_tf_weights_in_electra(
							    UpperCAmelCase    ,	UpperCAmelCase    ,	UpperCAmelCase    ,	discriminator_or_generator=UpperCAmelCase     )

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


if __name__ == "__main__":
						__lowerCamelCase						= 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(
						    '--discriminator_or_generator',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'Whether to export the generator or the discriminator. Should be a string, either \'discriminator\' or '
						        '\'generator\'.'
						    ),
						)
						__lowerCamelCase						= parser.parse_args()
						convert_tf_checkpoint_to_pytorch(
						    args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path, args.discriminator_or_generator
						)