"""simple docstring"""
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
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase__   			=      logging.get_logger(__name__)

lowercase__   			=      {
    'facebook/convnext-tiny-224': 'https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}




class   __snake_case			(	__lowerCAmelCase			, __lowerCAmelCase							):
      a__								=						"""convnext"""



      def __init__(		self       ,     lowercase=3       ,     lowercase=4       ,     lowercase=4       ,     lowercase=None       ,     lowercase=None       ,     lowercase="gelu"       ,     lowercase=0.02       ,     lowercase=1e-12       ,     lowercase=1e-6       ,     lowercase=0.0       ,     lowercase=2_24       ,     lowercase=None       ,     lowercase=None       ,     **lowercase       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__(**lowercase)

        a__:	List[str]     				=      num_channels
        a__:	Tuple     				=      patch_size
        a__:	str     				=      num_stages
        a__:	Any     				=      [96, 1_92, 3_84, 7_68] if hidden_sizes is None else hidden_sizes
        a__:	Any     				=      [3, 3, 9, 3] if depths is None else depths
        a__:	List[Any]     				=      hidden_act
        a__:	Dict     				=      initializer_range
        a__:	int     				=      layer_norm_eps
        a__:	Optional[int]     				=      layer_scale_init_value
        a__:	Optional[int]     				=      drop_path_rate
        a__:	Union[str, Any]     				=      image_size
        a__:	List[Any]     				=      ['stem'] + [f'stage{idx}' for idx in range(1       ,     len(self.depths) + 1)]
        a__				,       a__:	Optional[int]     				=      get_aligned_output_features_output_indices(
            out_features=lowercase       ,     out_indices=lowercase       ,     stage_names=self.stage_names)







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						version.parse("""1.11"""							)



      @property
      def     lowerCamelCase_  (		self)      ->     Mapping[str, Mapping[int, str]]:





        '''simple docstring'''





        return OrderedDict(
            [
                ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
            ])



      @property
      def     lowerCamelCase_  (		self)      ->     float:





        '''simple docstring'''





        return 1e-5




'''simple docstring'''





import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)->      Union[str, Any]:







      """simple docstring"""
      # Initialise PyTorch model
      __A													=      RoFormerConfig.from_json_file(UpperCAmelCase						)
      print(f'Building PyTorch model from configuration: {config}'						)
      __A													=      RoFormerForMaskedLM(UpperCAmelCase						)

      # Load weights from tf checkpoint
      load_tf_weights_in_roformer(UpperCAmelCase   ,	UpperCAmelCase   ,	UpperCAmelCase						)

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


if __name__ == "__main__":
 a__		: 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(
     "--bert_config_file",
     default=None,
     type=str,
     required=True,
     help=(
         "The config json file corresponding to the pre-trained BERT 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."
 )
 a__		: Union[str, Any]  							=							parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






