
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


__lowerCamelCase				=			logging.get_logger(__name__)






class     UpperCAmelCase						(		A_			,A_					):
   A__   :						Optional[int]			=	"maskformer-swin"

   A__   :						Optional[Any]			=	{
       "num_attention_heads": "num_heads",
       "num_hidden_layers": "num_layers",
   }



   def __init__(self						:   List[str]       ,		snake_case__						:   str=2_24       ,		snake_case__						:   Optional[Any]=4       ,		snake_case__						:   Optional[Any]=3       ,		snake_case__						:   Optional[int]=96       ,		snake_case__						:   List[Any]=[2, 2, 6, 2]       ,		snake_case__						:   Union[str, Any]=[3, 6, 12, 24]       ,		snake_case__						:   Any=7       ,		snake_case__						:   Dict=4.0       ,		snake_case__						:   int=True       ,		snake_case__						:   Optional[Any]=0.0       ,		snake_case__						:   List[Any]=0.0       ,		snake_case__						:   List[str]=0.1       ,		snake_case__						:   Tuple="gelu"       ,		snake_case__						:   Tuple=False       ,		snake_case__						:   Optional[Any]=0.02       ,		snake_case__						:   int=1e-5       ,		snake_case__						:   int=None       ,		snake_case__						:   str=None       ,		**snake_case__						:   Optional[Any]       ,		)						->      List[Any]:


          '''simple docstring'''


          super().__init__(**snake_case__					)

          snake_case			:  List[str]     							= image_size
          snake_case			:  str     							= patch_size
          snake_case			:  int     							= num_channels
          snake_case			:  Union[str, Any]     							= embed_dim
          snake_case			:  List[str]     							= depths
          snake_case			:  str     							= len(snake_case__					)
          snake_case			:  Optional[Any]     							= num_heads
          snake_case			:  List[Any]     							= window_size
          snake_case			:  Any     							= mlp_ratio
          snake_case			:  List[str]     							= qkv_bias
          snake_case			:  Optional[Any]     							= hidden_dropout_prob
          snake_case			:  Optional[Any]     							= attention_probs_dropout_prob
          snake_case			:  Union[str, Any]     							= drop_path_rate
          snake_case			:  str     							= hidden_act
          snake_case			:  List[Any]     							= use_absolute_embeddings
          snake_case			:  Any     							= layer_norm_eps
          snake_case			:  Any     							= initializer_range
          # we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
          # this indicates the channel dimension after the last stage of the model
          snake_case			:  List[Any]     							= int(embed_dim * 2 ** (len(snake_case__					) - 1)					)
          snake_case			:  Any     							= ["stem"] + [f"""stage{idx}""" for idx in range(1       ,		len(snake_case__					) + 1					)]
          snake_case							,   snake_case			:  Union[str, Any]     							= get_aligned_output_features_output_indices(
              out_features=snake_case__       ,		out_indices=snake_case__       ,		stage_names=self.stage_names					)







import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()


def      UpperCamelCase   (       __lowerCamelCase	:					List[str]							,   __lowerCamelCase	:					Union[str, Any]							,   __lowerCamelCase	:					Union[str, Any]       ):
       # Initialise PyTorch model
       snake_case			:  Optional[int]     							= BertConfig.from_json_file(__lowerCamelCase       )
       print(f"""Building PyTorch model from configuration: {config}"""       )
       snake_case			:  Any     							= BertForPreTraining(__lowerCamelCase       )

       # Load weights from tf checkpoint
       load_tf_weights_in_bert(__lowerCamelCase							,   __lowerCamelCase							,   __lowerCamelCase       )

       # Save pytorch-model
       print(f"""Save PyTorch model to {pytorch_dump_path}"""       )
       torch.save(model.state_dict()							,   __lowerCamelCase       )


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(
       """--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."""
   )
   __lowerCamelCase				=			parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





