





'''simple docstring'''






import argparse
import pickle

import numpy as np
import torch
from torch import nn

from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging


logging.set_verbosity_info()
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase=None  ):





      """simple docstring"""


      assert torch_layer.weight.shape == weight.shape, f"{torch_layer} layer.weight does not match"
      _lowerCAmelCase												=							nn.Parameter(lowerCAmelCase  )
      if bias is not None:
            assert torch_layer.bias.shape == bias.shape, f"{torch_layer} layer.bias does not match"
            _lowerCAmelCase												=							nn.Parameter(lowerCAmelCase  )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							np.asarray(weights[0]  )
      _lowerCAmelCase												=							np.asarray(weights[1]  )
      _lowerCAmelCase												=							np.asarray(weights[2]  )

      set_param(
          torch_layer.self_attention.query_key							,     torch.tensor(lowerCAmelCase  ).transpose(1							,     2  ).contiguous().view(-1							,     lowerCAmelCase  )							,     )
      set_param(
          torch_layer.self_attention.value							,     torch.tensor(lowerCAmelCase  ).transpose(1							,     2  ).contiguous().view(-1							,     lowerCAmelCase  )							,     )
      set_param(
          torch_layer.output.dense							,     torch.tensor(lowerCAmelCase  ).view(-1							,     lowerCAmelCase  ).contiguous().transpose(0							,     1  )							,     )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							np.asarray(weights[0]  )
      _lowerCAmelCase												=							np.asarray(weights[1]  )
      _lowerCAmelCase												=							np.asarray(weights[2]  )
      _lowerCAmelCase												=							np.asarray(weights[3]  )

      set_param(
          torch_layer.self_attention.query							,     torch.tensor(lowerCAmelCase  ).transpose(1							,     2  ).contiguous().view(-1							,     lowerCAmelCase  )							,     )
      set_param(
          torch_layer.self_attention.key							,     torch.tensor(lowerCAmelCase  ).transpose(1							,     2  ).contiguous().view(-1							,     lowerCAmelCase  )							,     )
      set_param(
          torch_layer.self_attention.value							,     torch.tensor(lowerCAmelCase  ).transpose(1							,     2  ).contiguous().view(-1							,     lowerCAmelCase  )							,     )
      set_param(
          torch_layer.output.dense							,     torch.tensor(lowerCAmelCase  ).view(-1							,     lowerCAmelCase  ).contiguous().transpose(0							,     1  )							,     )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							weights[0][0][0]
      _lowerCAmelCase												=							np.asarray(layer_norm_a[0]  )
      _lowerCAmelCase												=							np.asarray(layer_norm_a[1]  )
      set_param(
          torch_block.attention.layer_norm							,     torch.tensor(lowerCAmelCase  )							,     torch.tensor(lowerCAmelCase  )							,     )

      # lsh weights + output
      _lowerCAmelCase												=							weights[0][1]
      if len(lowerCAmelCase  ) < 4:
            set_layer_weights_in_torch_lsh(lowerCAmelCase							,     torch_block.attention							,     lowerCAmelCase  )
      else:
            set_layer_weights_in_torch_local(lowerCAmelCase							,     torch_block.attention							,     lowerCAmelCase  )

      # intermediate weighs
      _lowerCAmelCase												=							weights[2][0][1][2]

      # Chunked Feed Forward
      if len(lowerCAmelCase  ) == 4:
            _lowerCAmelCase												=							intermediate_weights[2]

      # layernorm 2
      _lowerCAmelCase												=							np.asarray(intermediate_weights[0][0]  )
      _lowerCAmelCase												=							np.asarray(intermediate_weights[0][1]  )
      set_param(
          torch_block.feed_forward.layer_norm							,     torch.tensor(lowerCAmelCase  )							,     torch.tensor(lowerCAmelCase  )							,     )

      # intermediate dense
      _lowerCAmelCase												=							np.asarray(intermediate_weights[1][0]  )
      _lowerCAmelCase												=							np.asarray(intermediate_weights[1][1]  )
      set_param(
          torch_block.feed_forward.dense.dense							,     torch.tensor(lowerCAmelCase  ).transpose(0							,     1  ).contiguous()							,     torch.tensor(lowerCAmelCase  )							,     )

      # intermediate out
      _lowerCAmelCase												=							np.asarray(intermediate_weights[4][0]  )
      _lowerCAmelCase												=							np.asarray(intermediate_weights[4][1]  )
      set_param(
          torch_block.feed_forward.output.dense							,     torch.tensor(lowerCAmelCase  ).transpose(0							,     1  ).contiguous()							,     torch.tensor(lowerCAmelCase  )							,     )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							torch_model.reformer

      # word embeds
      _lowerCAmelCase												=							np.asarray(weights[1]  )
      set_param(
          torch_model_reformer.embeddings.word_embeddings							,     torch.tensor(lowerCAmelCase  )							,     )

      if isinstance(weights[3]							,     lowerCAmelCase  ):
            _lowerCAmelCase												=							torch_model_reformer.embeddings.position_embeddings
            for emb_idx in range(len(position_embeddings.weights  )  ):
                  _lowerCAmelCase												=							np.asarray(weights[3][emb_idx][0]  )
                  assert (
                      position_embeddings.weights[emb_idx].shape == emb_weights.shape
                  ), f"{position_embeddings[emb_idx]} emb does not match"
                  _lowerCAmelCase												=							nn.Parameter(torch.tensor(lowerCAmelCase  )  )

      _lowerCAmelCase												=							weights[5]
      assert len(torch_model_reformer.encoder.layers  ) * 4 == len(
          lowerCAmelCase  ), "HF and trax model do not have the same number of layers"
      for layer_idx, layer in enumerate(torch_model_reformer.encoder.layers  ):
            _lowerCAmelCase												=							trax_layer_weights[4 * layer_idx : 4 * (layer_idx + 1)]
            set_block_weights_in_torch(lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  )

      # output layer norm
      _lowerCAmelCase												=							np.asarray(weights[7][0]  )
      _lowerCAmelCase												=							np.asarray(weights[7][1]  )
      set_param(
          torch_model_reformer.encoder.layer_norm							,     torch.tensor(lowerCAmelCase  )							,     torch.tensor(lowerCAmelCase  )							,     )

      # output embeddings
      _lowerCAmelCase												=							np.asarray(weights[9][0]  )
      _lowerCAmelCase												=							np.asarray(weights[9][1]  )
      set_param(
          torch_model.lm_head.decoder							,     torch.tensor(lowerCAmelCase  ).transpose(0							,     1  ).contiguous()							,     torch.tensor(lowerCAmelCase  )							,     )
def        UpperCamelCase__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


      _lowerCAmelCase												=							ReformerConfig.from_json_file(lowerCAmelCase  )
      print(f"Building PyTorch model from configuration: {config}"  )
      _lowerCAmelCase												=							ReformerModelWithLMHead(lowerCAmelCase  )

      with open(lowerCAmelCase							,     """rb"""  ) as f:
            _lowerCAmelCase												=							pickle.load(lowerCAmelCase  )["""weights"""]

      set_model_weights_in_torch(lowerCAmelCase							,     lowerCAmelCase							,     config.hidden_size  )

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


if __name__ == "__main__":
      A__    :       Any              =argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          '''--trax_model_pkl_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
      )
      parser.add_argument(
          '''--config_file''',
          default=None,
          type=str,
          required=True,
          help=(
              '''The config json file corresponding to the pre-trained Reformer 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.'''
      )
      A__    :       Optional[int]              =parser.parse_args()
      convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)













'''simple docstring'''






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__				(		lowerCAmelCase							,     lowerCAmelCase							,     lowerCAmelCase  ):





      """simple docstring"""


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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            return torch.from_numpy(lowerCAmelCase  )

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

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

            # Self-attention
            _lowerCAmelCase												=							layer.attention.self

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/kernel"""							,     self_attn.query.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_query_dense/bias"""							,     self_attn.query.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/kernel"""							,     self_attn.key.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_key_dense/bias"""							,     self_attn.key.bias.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/kernel"""							,     self_attn.value.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_value_dense/bias"""							,     self_attn.value.bias.data.shape  )

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

            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/kernel"""							,     self_output.dense.weight.data.shape  )
            _lowerCAmelCase												=							get_encoder_attention_layer_array(
                lowerCAmelCase							,     """_output_dense/bias"""							,     self_output.dense.bias.data.shape  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_attention_layer_norm/beta"""  )

            # Intermediate
            _lowerCAmelCase												=							layer.intermediate

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_intermediate_dense/bias"""  )

            # Output
            _lowerCAmelCase												=							layer.output

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/kernel"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_dense/bias"""  )

            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/gamma"""  )
            _lowerCAmelCase												=							get_encoder_layer_array(lowerCAmelCase							,     """_output_layer_norm/beta"""  )

      # Embeddings
      _lowerCAmelCase												=							get_encoder_array("""_position_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_type_embedding_layer/embeddings"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/gamma"""  )
      _lowerCAmelCase												=							get_encoder_array("""_embedding_norm_layer/beta"""  )

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

      _lowerCAmelCase												=							get_masked_lm_array("""dense/kernel"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""dense/bias"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/gamma"""  )
      _lowerCAmelCase												=							get_masked_lm_array("""layer_norm/beta"""  )

      _lowerCAmelCase												=							get_masked_lm_array("""embedding_table"""  )

      # Pooling
      _lowerCAmelCase												=							BertPooler(config=lowerCAmelCase  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/kernel"""  )
      _lowerCAmelCase												=							get_encoder_array("""_pooler_layer/bias"""  )

      # Export final model
      model.save_pretrained(lowerCAmelCase  )

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

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


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






