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  __UpperCamelCase    (						A ,							A ,							A							):
     def get_masked_lm_array(A							):
          UpperCamelCase__										=      f"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__										=      tf.train.load_variable(A ,							A							)

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

          return torch.from_numpy(A							)

     def get_encoder_array(A							):
          UpperCamelCase__										=      f"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__										=      tf.train.load_variable(A ,							A							)

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

          return torch.from_numpy(A							)

     def get_encoder_layer_array(A ,							A							):
          UpperCamelCase__										=      f"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__										=      tf.train.load_variable(A ,							A							)

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

          return torch.from_numpy(A							)

     def get_encoder_attention_layer_array(A ,							A ,							A							):
          UpperCamelCase__										=      f"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
          UpperCamelCase__										=      tf.train.load_variable(A ,							A							)
          UpperCamelCase__										=      array.reshape(A							)

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

          return torch.from_numpy(A							)

     print(f"Loading model based on config from {config_path}..."							)
     UpperCamelCase__										=      BertConfig.from_json_file(A							)
     UpperCamelCase__										=      BertForMaskedLM(A							)

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

          # Self-attention
          UpperCamelCase__										=      layer.attention.self

          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_query_dense/kernel''' ,							self_attn.query.weight.data.shape							)
          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_query_dense/bias''' ,							self_attn.query.bias.data.shape							)
          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_key_dense/kernel''' ,							self_attn.key.weight.data.shape							)
          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_key_dense/bias''' ,							self_attn.key.bias.data.shape							)
          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_value_dense/kernel''' ,							self_attn.value.weight.data.shape							)
          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_value_dense/bias''' ,							self_attn.value.bias.data.shape							)

          # Self-attention Output
          UpperCamelCase__										=      layer.attention.output

          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_output_dense/kernel''' ,							self_output.dense.weight.data.shape							)
          UpperCamelCase__										=      get_encoder_attention_layer_array(
              A ,							'''_output_dense/bias''' ,							self_output.dense.bias.data.shape							)

          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_attention_layer_norm/gamma'''							)
          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_attention_layer_norm/beta'''							)

          # Intermediate
          UpperCamelCase__										=      layer.intermediate

          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_intermediate_dense/kernel'''							)
          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_intermediate_dense/bias'''							)

          # Output
          UpperCamelCase__										=      layer.output

          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_output_dense/kernel'''							)
          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_output_dense/bias'''							)

          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_output_layer_norm/gamma'''							)
          UpperCamelCase__										=      get_encoder_layer_array(A ,							'''_output_layer_norm/beta'''							)

     # Embeddings
     UpperCamelCase__										=      get_encoder_array('''_position_embedding_layer/embeddings'''							)
     UpperCamelCase__										=      get_encoder_array('''_type_embedding_layer/embeddings'''							)
     UpperCamelCase__										=      get_encoder_array('''_embedding_norm_layer/gamma'''							)
     UpperCamelCase__										=      get_encoder_array('''_embedding_norm_layer/beta'''							)

     # LM Head
     UpperCamelCase__										=      model.cls.predictions.transform

     UpperCamelCase__										=      get_masked_lm_array('''dense/kernel'''							)
     UpperCamelCase__										=      get_masked_lm_array('''dense/bias'''							)

     UpperCamelCase__										=      get_masked_lm_array('''layer_norm/gamma'''							)
     UpperCamelCase__										=      get_masked_lm_array('''layer_norm/beta'''							)

     UpperCamelCase__										=      get_masked_lm_array('''embedding_table'''							)

     # Pooling
     UpperCamelCase__										=      BertPooler(config=A							)
     UpperCamelCase__										=      get_encoder_array('''_pooler_layer/kernel'''							)
     UpperCamelCase__										=      get_encoder_array('''_pooler_layer/bias'''							)

     # Export final model
     model.save_pretrained(A							)

     # Integration test - should load without any errors ;)
     UpperCamelCase__										=      BertForMaskedLM.from_pretrained(A							)
     print(new_model.eval()							)

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


if __name__ == "__main__":
     __magic_name__								=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.''',
     )
     __magic_name__								=parser.parse_args()
     convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)




import math

import tensorflow as tf
from packaging import version







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0							) ,							x.dtype							)							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(math.pi ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi							) * (x + coeff * tf.pow(A ,							3							))							))

     return x * cdf







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)

     return x * tf.tanh(tf.math.softplus(A							)							)







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(0.04_47_15 ,							x.dtype							)
     UpperCamelCase__										=      tf.cast(0.79_78_84_56_08 ,							x.dtype							)

     return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x)							))







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      tf.convert_to_tensor(A							)
     UpperCamelCase__										=      tf.cast(1.7_02 ,							x.dtype							)
     return x * tf.math.sigmoid(coeff * x							)







def  __UpperCamelCase    (						A							):
     return tf.clip_by_value(_gelu(A							) ,							-10 ,							10							)







def  __UpperCamelCase    (						A ,							A=-1							):
     UpperCamelCase__						,       UpperCamelCase__										=      tf.split(A ,							2 ,							axis=A							)
     return a * tf.math.sigmoid(A							)


if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):







     def  __UpperCamelCase    (						A							):
          return tf.keras.activations.gelu(A ,							approximate=A							)

     __magic_name__								=tf.keras.activations.gelu



     __magic_name__								=approximate_gelu_wrap
else:
     __magic_name__								=_gelu
     __magic_name__								=_gelu_new


__magic_name__								={
    '''gelu''': gelu,
    '''gelu_10''': gelu_aa,
    '''gelu_fast''': gelu_fast,
    '''gelu_new''': gelu_new,
    '''glu''': glu,
    '''mish''': mish,
    '''quick_gelu''': quick_gelu,
    '''relu''': tf.keras.activations.relu,
    '''sigmoid''': tf.keras.activations.sigmoid,
    '''silu''': tf.keras.activations.swish,
    '''swish''': tf.keras.activations.swish,
    '''tanh''': tf.keras.activations.tanh,
}







def  __UpperCamelCase    (						A							):
     if activation_string in ACTaFN:
          return ACTaFN[activation_string]
     else:
          raise KeyError(f"function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()							)}"							)



