"""simple docstring"""


import argparse
import os

import numpy as np
import tensorflow as tf
import torch

from transformers import BertModel







def 					__a   (		A    ,				A    ,				A      ):






     '''simple docstring'''



     lowercase__				 =							("dense.weight", "attention.self.query", "attention.self.key", "attention.self.value")

     lowercase__				 =							(
         ("layer.", "layer_"),
         ("word_embeddings.weight", "word_embeddings"),
         ("position_embeddings.weight", "position_embeddings"),
         ("token_type_embeddings.weight", "token_type_embeddings"),
         (".", "/"),
         ("LayerNorm/weight", "LayerNorm/gamma"),
         ("LayerNorm/bias", "LayerNorm/beta"),
         ("weight", "kernel"),
     )

     if not os.path.isdir(A      ):
          os.makedirs(A      )

     lowercase__				 =							model.state_dict()

     def to_tf_var_name(A      ):
          for patt, repl in iter(A      ):
               lowercase__				 =							name.replace(A    ,				A      )
          return f'''bert/{name}'''

     def create_tf_var(A    ,				A    ,				A      ):
          lowercase__				 =							tf.dtypes.as_dtype(tensor.dtype      )
          lowercase__				 =							tf.get_variable(dtype=A    ,				shape=tensor.shape    ,				name=A    ,				initializer=tf.zeros_initializer()      )
          session.run(tf.variables_initializer([tf_var]      )      )
          session.run(A      )
          return tf_var

     tf.reset_default_graph()
     with tf.Session() as session:
          for var_name in state_dict:
               lowercase__				 =							to_tf_var_name(A      )
               lowercase__				 =							state_dict[var_name].numpy()
               if any(x in var_name for x in tensors_to_transpose      ):
                    lowercase__				 =							torch_tensor.T
               lowercase__				 =							create_tf_var(tensor=A    ,				name=A    ,				session=A      )
               tf.keras.backend.set_value(A    ,				A      )
               lowercase__				 =							session.run(A      )
               print(f'''Successfully created {tf_name}: {np.allclose(A    ,				A      )}'''      )

          lowercase__				 =							tf.train.Saver(tf.trainable_variables()      )
          saver.save(A    ,				os.path.join(A    ,				model_name.replace("-"    ,				"_"      ) + ".ckpt"      )      )







def 					__a   (		A=None      ):






     '''simple docstring'''



     lowercase__				 =							argparse.ArgumentParser()
     parser.add_argument("--model_name"    ,				type=A    ,				required=A    ,				help="model name e.g. bert-base-uncased"      )
     parser.add_argument(
         "--cache_dir"    ,				type=A    ,				default=A    ,				required=A    ,				help="Directory containing pytorch model"      )
     parser.add_argument("--pytorch_model_path"    ,				type=A    ,				required=A    ,				help="/path/to/<pytorch-model-name>.bin"      )
     parser.add_argument("--tf_cache_dir"    ,				type=A    ,				required=A    ,				help="Directory in which to save tensorflow model"      )
     lowercase__				 =							parser.parse_args(A      )

     lowercase__				 =							BertModel.from_pretrained(
         pretrained_model_name_or_path=args.model_name    ,				state_dict=torch.load(args.pytorch_model_path      )    ,				cache_dir=args.cache_dir    ,				)

     convert_pytorch_checkpoint_to_tf(model=A    ,				ckpt_dir=args.tf_cache_dir    ,				model_name=args.model_name      )


if __name__ == "__main__":
 main()





"""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


lowerCAmelCase_:					str     =					logging.get_logger(__name__)

lowerCAmelCase_:					List[Any]     =					{
    "facebook/data2vec-vision-base-ft": (
        "https://huggingface.co/facebook/data2vec-vision-base-ft/resolve/main/config.json"
    ),
}






class    a__    (   _a	):
   snake_case_							=      "data2vec-vision"


   def __init__(     self,				_UpperCAmelCase=768,				_UpperCAmelCase=12,				_UpperCAmelCase=12,				_UpperCAmelCase=3072,				_UpperCAmelCase="gelu",				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.0,				_UpperCAmelCase=0.02,				_UpperCAmelCase=1E-12,				_UpperCAmelCase=224,				_UpperCAmelCase=16,				_UpperCAmelCase=3,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=False,				_UpperCAmelCase=0.1,				_UpperCAmelCase=0.1,				_UpperCAmelCase=True,				_UpperCAmelCase=[3, 5, 7, 11],				_UpperCAmelCase=[1, 2, 3, 6],				_UpperCAmelCase=True,				_UpperCAmelCase=0.4,				_UpperCAmelCase=256,				_UpperCAmelCase=1,				_UpperCAmelCase=False,				_UpperCAmelCase=255,				**_UpperCAmelCase,				):
        '''simple docstring'''



        super().__init__(**_UpperCAmelCase  )

        lowercase__				 =							hidden_size
        lowercase__				 =							num_hidden_layers
        lowercase__				 =							num_attention_heads
        lowercase__				 =							intermediate_size
        lowercase__				 =							hidden_act
        lowercase__				 =							hidden_dropout_prob
        lowercase__				 =							attention_probs_dropout_prob
        lowercase__				 =							initializer_range
        lowercase__				 =							layer_norm_eps

        lowercase__				 =							image_size
        lowercase__				 =							patch_size
        lowercase__				 =							num_channels
        lowercase__				 =							use_mask_token
        lowercase__				 =							use_absolute_position_embeddings
        lowercase__				 =							use_relative_position_bias
        lowercase__				 =							use_shared_relative_position_bias
        lowercase__				 =							layer_scale_init_value
        lowercase__				 =							drop_path_rate
        lowercase__				 =							use_mean_pooling
        # decode head attributes (semantic segmentation)
        lowercase__				 =							out_indices
        lowercase__				 =							pool_scales
        # auxiliary head attributes (semantic segmentation)
        lowercase__				 =							use_auxiliary_head
        lowercase__				 =							auxiliary_loss_weight
        lowercase__				 =							auxiliary_channels
        lowercase__				 =							auxiliary_num_convs
        lowercase__				 =							auxiliary_concat_input
        lowercase__				 =							semantic_loss_ignore_index






class    a__    (   _a	):
   snake_case_							=      version.parse("1.11"	)


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



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


   @property
   def 	snake_case__	(     self  ):
        '''simple docstring'''



        return 1E-4




