


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        snake_case_					(  snake_case      ,							snake_case      ,							snake_case					)  ->    int:
       def get_masked_lm_array(snake_case					):
              lowercase__:       Optional[Any]        =       f'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
              lowercase__:       Optional[int]        =       tf.train.load_variable(snake_case      ,							snake_case					)

              if "kernel" in name:
                     lowercase__:       Tuple        =       array.transpose()

              return torch.from_numpy(snake_case					)

       def get_encoder_array(snake_case					):
              lowercase__:       Optional[int]        =       f'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
              lowercase__:       int        =       tf.train.load_variable(snake_case      ,							snake_case					)

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

              return torch.from_numpy(snake_case					)

       def get_encoder_layer_array(snake_case      ,							snake_case					):
              lowercase__:       int        =       f'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
              lowercase__:       int        =       tf.train.load_variable(snake_case      ,							snake_case					)

              if "kernel" in name:
                     lowercase__:       str        =       array.transpose()

              return torch.from_numpy(snake_case					)

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

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

              return torch.from_numpy(snake_case					)

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

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

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

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

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

              lowercase__:       str        =       get_encoder_attention_layer_array(
                  snake_case      ,							'_output_dense/kernel'      ,							self_output.dense.weight.data.shape					)
              lowercase__:       Optional[int]        =       get_encoder_attention_layer_array(
                  snake_case      ,							'_output_dense/bias'      ,							self_output.dense.bias.data.shape					)

              lowercase__:       Any        =       get_encoder_layer_array(snake_case      ,							'_attention_layer_norm/gamma'					)
              lowercase__:       Optional[int]        =       get_encoder_layer_array(snake_case      ,							'_attention_layer_norm/beta'					)

              # Intermediate
              lowercase__:       BertIntermediate        =       layer.intermediate

              lowercase__:       Tuple        =       get_encoder_layer_array(snake_case      ,							'_intermediate_dense/kernel'					)
              lowercase__:       str        =       get_encoder_layer_array(snake_case      ,							'_intermediate_dense/bias'					)

              # Output
              lowercase__:       BertOutput        =       layer.output

              lowercase__:       Union[str, Any]        =       get_encoder_layer_array(snake_case      ,							'_output_dense/kernel'					)
              lowercase__:       List[Any]        =       get_encoder_layer_array(snake_case      ,							'_output_dense/bias'					)

              lowercase__:       Optional[Any]        =       get_encoder_layer_array(snake_case      ,							'_output_layer_norm/gamma'					)
              lowercase__:       List[Any]        =       get_encoder_layer_array(snake_case      ,							'_output_layer_norm/beta'					)

       # Embeddings
       lowercase__:       Dict        =       get_encoder_array('_position_embedding_layer/embeddings'					)
       lowercase__:       Union[str, Any]        =       get_encoder_array('_type_embedding_layer/embeddings'					)
       lowercase__:       str        =       get_encoder_array('_embedding_norm_layer/gamma'					)
       lowercase__:       Dict        =       get_encoder_array('_embedding_norm_layer/beta'					)

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

       lowercase__:       Optional[Any]        =       get_masked_lm_array('dense/kernel'					)
       lowercase__:       str        =       get_masked_lm_array('dense/bias'					)

       lowercase__:       Dict        =       get_masked_lm_array('layer_norm/gamma'					)
       lowercase__:       Dict        =       get_masked_lm_array('layer_norm/beta'					)

       lowercase__:       Optional[Any]        =       get_masked_lm_array('embedding_table'					)

       # Pooling
       lowercase__:       Optional[int]        =       BertPooler(config=snake_case					)
       lowercase__:       BertPooler        =       get_encoder_array('_pooler_layer/kernel'					)
       lowercase__:       BertPooler        =       get_encoder_array('_pooler_layer/bias'					)

       # Export final model
       model.save_pretrained(snake_case					)

       # Integration test - should load without any errors ;)
       lowercase__:       int        =       BertForMaskedLM.from_pretrained(snake_case					)
       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)





from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase			=							logging.get_logger(__name__)

__lowerCAmelCase			=							{
    '''google/pegasus-large''': '''https://huggingface.co/google/pegasus-large/resolve/main/config.json''',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}





class   __a      (   __UpperCamelCase				):
 __lowercase			:							Any       					=			'pegasus'
 __lowercase			:							Union[str, Any]       					=			['past_key_values']
 __lowercase			:							Any       					=			{'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
 def __init__(       self    ,			lowerCAmelCase__=50_265    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=12    ,			lowerCAmelCase__=4_096    ,			lowerCAmelCase__=16    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__=True    ,			lowerCAmelCase__="gelu"    ,			lowerCAmelCase__=1_024    ,			lowerCAmelCase__=0.1    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0    ,			lowerCAmelCase__=0.0_2    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=False    ,			lowerCAmelCase__=0    ,			lowerCAmelCase__=1    ,			lowerCAmelCase__=1    ,			**lowerCAmelCase__    ,			) ->    Union[str, Any]:




        '''simple docstring'''
        lowercase__:       int        =       vocab_size
        lowercase__:       Optional[int]        =       max_position_embeddings
        lowercase__:       List[str]        =       d_model
        lowercase__:       Optional[Any]        =       encoder_ffn_dim
        lowercase__:       Optional[Any]        =       encoder_layers
        lowercase__:       Union[str, Any]        =       encoder_attention_heads
        lowercase__:       Optional[int]        =       decoder_ffn_dim
        lowercase__:       Tuple        =       decoder_layers
        lowercase__:       Union[str, Any]        =       decoder_attention_heads
        lowercase__:       Dict        =       dropout
        lowercase__:       List[str]        =       attention_dropout
        lowercase__:       List[str]        =       activation_dropout
        lowercase__:       Optional[int]        =       activation_function
        lowercase__:       Dict        =       init_std
        lowercase__:       Optional[Any]        =       encoder_layerdrop
        lowercase__:       List[str]        =       decoder_layerdrop
        lowercase__:       Union[str, Any]        =       use_cache
        lowercase__:       Any        =       encoder_layers
        lowercase__:       List[str]        =       scale_embedding  # scale factor will be sqrt(d_model) if True
        super().__init__(
            pad_token_id=lowerCAmelCase__    ,			eos_token_id=lowerCAmelCase__    ,			is_encoder_decoder=lowerCAmelCase__    ,			decoder_start_token_id=lowerCAmelCase__    ,			forced_eos_token_id=lowerCAmelCase__    ,			**lowerCAmelCase__    ,			)
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.encoder_attention_heads
 @property
 def 		SCREAMING_SNAKE_CASE__       (       self			) ->    int:




        '''simple docstring'''
        return self.d_model


