





import argparse

import tensorflow as tf
import torch

from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertPooler,
    BertSelfAttention,
    BertSelfOutput,
)
from transformers.utils import logging


logging.set_verbosity_info()




def     A_      (	lowercase_   ,		lowercase_   ,		lowercase_	)      ->    Tuple:
       def get_masked_lm_array(lowercase_	):
              _snake_case   :				List[str]  					=    f'''masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _snake_case   :				Union[str, Any]  					=    tf.train.load_variable(lowercase_   ,		lowercase_	)

              if "kernel" in name:
                     _snake_case   :				Optional[int]  					=    array.transpose()

              return torch.from_numpy(lowercase_	)

       def get_encoder_array(lowercase_	):
              _snake_case   :				List[str]  					=    f'''encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _snake_case   :				Any  					=    tf.train.load_variable(lowercase_   ,		lowercase_	)

              if "kernel" in name:
                     _snake_case   :				List[str]  					=    array.transpose()

              return torch.from_numpy(lowercase_	)

       def get_encoder_layer_array(lowercase_   ,		lowercase_	):
              _snake_case   :				List[str]  					=    f'''encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _snake_case   :				Dict  					=    tf.train.load_variable(lowercase_   ,		lowercase_	)

              if "kernel" in name:
                     _snake_case   :				List[str]  					=    array.transpose()

              return torch.from_numpy(lowercase_	)

       def get_encoder_attention_layer_array(lowercase_   ,		lowercase_   ,		lowercase_	):
              _snake_case   :				str  					=    f'''encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'''
              _snake_case   :				int  					=    tf.train.load_variable(lowercase_   ,		lowercase_	)
              _snake_case   :				Any  					=    array.reshape(lowercase_	)

              if "kernel" in name:
                     _snake_case   :				Dict  					=    array.transpose()

              return torch.from_numpy(lowercase_	)

       print(f'''Loading model based on config from {config_path}...'''	)
       _snake_case   :				Optional[Any]  					=    BertConfig.from_json_file(lowercase_	)
       _snake_case   :				Union[str, Any]  					=    BertForMaskedLM(lowercase_	)

       # Layers
       for layer_index in range(0   ,		config.num_hidden_layers	):
              _snake_case   :				BertLayer  					=    model.bert.encoder.layer[layer_index]

              # Self-attention
              _snake_case   :				BertSelfAttention  					=    layer.attention.self

              _snake_case   :				str  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_query_dense/kernel'''   ,		self_attn.query.weight.data.shape	)
              _snake_case   :				Optional[Any]  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_query_dense/bias'''   ,		self_attn.query.bias.data.shape	)
              _snake_case   :				List[str]  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_key_dense/kernel'''   ,		self_attn.key.weight.data.shape	)
              _snake_case   :				Optional[int]  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_key_dense/bias'''   ,		self_attn.key.bias.data.shape	)
              _snake_case   :				List[str]  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_value_dense/kernel'''   ,		self_attn.value.weight.data.shape	)
              _snake_case   :				str  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_value_dense/bias'''   ,		self_attn.value.bias.data.shape	)

              # Self-attention Output
              _snake_case   :				BertSelfOutput  					=    layer.attention.output

              _snake_case   :				Dict  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_output_dense/kernel'''   ,		self_output.dense.weight.data.shape	)
              _snake_case   :				List[str]  					=    get_encoder_attention_layer_array(
                  lowercase_   ,		'''_output_dense/bias'''   ,		self_output.dense.bias.data.shape	)

              _snake_case   :				Union[str, Any]  					=    get_encoder_layer_array(lowercase_   ,		'''_attention_layer_norm/gamma'''	)
              _snake_case   :				Optional[int]  					=    get_encoder_layer_array(lowercase_   ,		'''_attention_layer_norm/beta'''	)

              # Intermediate
              _snake_case   :				BertIntermediate  					=    layer.intermediate

              _snake_case   :				Optional[int]  					=    get_encoder_layer_array(lowercase_   ,		'''_intermediate_dense/kernel'''	)
              _snake_case   :				List[Any]  					=    get_encoder_layer_array(lowercase_   ,		'''_intermediate_dense/bias'''	)

              # Output
              _snake_case   :				BertOutput  					=    layer.output

              _snake_case   :				Any  					=    get_encoder_layer_array(lowercase_   ,		'''_output_dense/kernel'''	)
              _snake_case   :				Union[str, Any]  					=    get_encoder_layer_array(lowercase_   ,		'''_output_dense/bias'''	)

              _snake_case   :				str  					=    get_encoder_layer_array(lowercase_   ,		'''_output_layer_norm/gamma'''	)
              _snake_case   :				Union[str, Any]  					=    get_encoder_layer_array(lowercase_   ,		'''_output_layer_norm/beta'''	)

       # Embeddings
       _snake_case   :				Tuple  					=    get_encoder_array('''_position_embedding_layer/embeddings'''	)
       _snake_case   :				List[Any]  					=    get_encoder_array('''_type_embedding_layer/embeddings'''	)
       _snake_case   :				Union[str, Any]  					=    get_encoder_array('''_embedding_norm_layer/gamma'''	)
       _snake_case   :				Optional[int]  					=    get_encoder_array('''_embedding_norm_layer/beta'''	)

       # LM Head
       _snake_case   :				int  					=    model.cls.predictions.transform

       _snake_case   :				str  					=    get_masked_lm_array('''dense/kernel'''	)
       _snake_case   :				Any  					=    get_masked_lm_array('''dense/bias'''	)

       _snake_case   :				Optional[int]  					=    get_masked_lm_array('''layer_norm/gamma'''	)
       _snake_case   :				Optional[int]  					=    get_masked_lm_array('''layer_norm/beta'''	)

       _snake_case   :				Union[str, Any]  					=    get_masked_lm_array('''embedding_table'''	)

       # Pooling
       _snake_case   :				Tuple  					=    BertPooler(config=lowercase_	)
       _snake_case   :				BertPooler  					=    get_encoder_array('''_pooler_layer/kernel'''	)
       _snake_case   :				BertPooler  					=    get_encoder_array('''_pooler_layer/bias'''	)

       # Export final model
       model.save_pretrained(lowercase_	)

       # Integration test - should load without any errors ;)
       _snake_case   :				Union[str, Any]  					=    BertForMaskedLM.from_pretrained(lowercase_	)
       print(new_model.eval()	)

       print('''Model conversion was done sucessfully!'''	)


if __name__ == "__main__":
   lowerCAmelCase_				= argparse.ArgumentParser()
   parser.add_argument(
       "--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
   )
   parser.add_argument(
       "--bert_config_file",
       type=str,
       required=True,
       help="The config json file corresponding to the BERT model. This specifies the model architecture.",
   )
   parser.add_argument(
       "--pytorch_dump_path",
       type=str,
       required=True,
       help="Path to the output PyTorch model.",
   )
   lowerCAmelCase_				= parser.parse_args()
   convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)









import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


_lowerCAmelCase  :      int			 ="""scheduler_config.json"""

class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   1
					__magic_name__        =   2
					__magic_name__        =   3
					__magic_name__        =   4
					__magic_name__        =   5
					__magic_name__        =   6
					__magic_name__        =   7
					__magic_name__        =   8
					__magic_name__        =   9
					__magic_name__        =   1_0
					__magic_name__        =   1_1
					__magic_name__        =   1_2
					__magic_name__        =   1_3
					__magic_name__        =   1_4

@dataclass
class __UpperCamelCase     (				_a		):






					'''simple docstring'''



					__magic_name__        =   42

class __UpperCamelCase     :






					'''simple docstring'''



					__magic_name__        =   SCHEDULER_CONFIG_NAME
					__magic_name__        =   []
					__magic_name__        =   True





					@classmethod
					def    _UpperCAmelCase    (				cls				,      lowerCamelCase__ = None				,      lowerCamelCase__ = None				,      lowerCamelCase__=False				,      **lowerCamelCase__				,      ):
						UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__:	Tuple									= cls.load_config(
						    pretrained_model_name_or_path=lowerCamelCase__				,      subfolder=lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      return_commit_hash=lowerCamelCase__				,      **lowerCamelCase__				,      )
						return cls.from_config(lowerCamelCase__				,      return_unused_kwargs=lowerCamelCase__				,      **lowerCamelCase__						)





					def    _UpperCAmelCase    (				self				,      lowerCamelCase__				,      lowerCamelCase__ = False				,      **lowerCamelCase__						):
						self.save_config(save_directory=lowerCamelCase__				,      push_to_hub=lowerCamelCase__				,      **lowerCamelCase__						)





					@property
					def    _UpperCAmelCase    (				self						):
						return self._get_compatibles()





					@classmethod
					def    _UpperCAmelCase    (				cls						):
						UpperCAmelCase__:	List[str]									= list(set([cls.__name__] + cls._compatibles						)						)
						UpperCAmelCase__:	Union[str, Any]									= importlib.import_module(__name__.split("."						)[0]						)
						UpperCAmelCase__:	int									= [
						    getattr(lowerCamelCase__				,      lowerCamelCase__						) for c in compatible_classes_str if hasattr(lowerCamelCase__				,      lowerCamelCase__						)
						]
						return compatible_classes