




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       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]     ,							lowercase     :	Dict     ,							lowercase     :	Union[str, Any]=None					):


      '''simple docstring'''



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

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]     ,							lowercase     :	Dict     ,							lowercase     :	Any					):


      '''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(lowercase					).transpose(1     ,							2					).contiguous().view(-1     ,							lowercase					)     ,							)
      set_param(
          torch_layer.self_attention.value     ,							torch.tensor(lowercase					).transpose(1     ,							2					).contiguous().view(-1     ,							lowercase					)     ,							)
      set_param(
          torch_layer.output.dense     ,							torch.tensor(lowercase					).view(-1     ,							lowercase					).contiguous().transpose(0     ,							1					)     ,							)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	str     ,							lowercase     :	Dict     ,							lowercase     :	str					):


      '''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(lowercase					).transpose(1     ,							2					).contiguous().view(-1     ,							lowercase					)     ,							)
      set_param(
          torch_layer.self_attention.key     ,							torch.tensor(lowercase					).transpose(1     ,							2					).contiguous().view(-1     ,							lowercase					)     ,							)
      set_param(
          torch_layer.self_attention.value     ,							torch.tensor(lowercase					).transpose(1     ,							2					).contiguous().view(-1     ,							lowercase					)     ,							)
      set_param(
          torch_layer.output.dense     ,							torch.tensor(lowercase					).view(-1     ,							lowercase					).contiguous().transpose(0     ,							1					)     ,							)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Dict     ,							lowercase     :	Optional[int]     ,							lowercase     :	Dict					):


      '''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(lowercase					)     ,							torch.tensor(lowercase					)     ,							)

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

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

      # Chunked Feed Forward
      if len(lowercase					) == 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(lowercase					)     ,							torch.tensor(lowercase					)     ,							)

      # 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(lowercase					).transpose(0     ,							1					).contiguous()     ,							torch.tensor(lowercase					)     ,							)

      # 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(lowercase					).transpose(0     ,							1					).contiguous()     ,							torch.tensor(lowercase					)     ,							)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	List[str]     ,							lowercase     :	Optional[int]     ,							lowercase     :	List[str]					):


      '''simple docstring'''



      lowerCamelCase_							=   torch_model.reformer

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

      if isinstance(weights[3]     ,							lowercase					):
            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(lowercase					)					)

      lowerCamelCase_							=   weights[5]
      assert len(torch_model_reformer.encoder.layers					) * 4 == len(
          lowercase					), "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(lowercase     ,							lowercase     ,							lowercase					)

      # 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(lowercase					)     ,							torch.tensor(lowercase					)     ,							)

      # output embeddings
      lowerCamelCase_							=   np.asarray(weights[9][0]					)
      lowerCamelCase_							=   np.asarray(weights[9][1]					)
      set_param(
          torch_model.lm_head.decoder     ,							torch.tensor(lowercase					).transpose(0     ,							1					).contiguous()     ,							torch.tensor(lowercase					)     ,							)

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Union[str, Any]     ,							lowercase     :	Union[str, Any]     ,							lowercase     :	str					):


      '''simple docstring'''



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

      with open(lowercase     ,							'rb'					) as f:
            lowerCamelCase_							=   pickle.load(lowercase					)['weights']

      set_model_weights_in_torch(lowercase     ,							lowercase     ,							config.hidden_size					)

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


if __name__ == "__main__":
 lowerCamelCase    :		List[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."
 )
 lowerCamelCase    :		Optional[Any]			   =       parser.parse_args()
 convert_trax_checkpoint_to_pytorch(args.trax_model_pkl_path, args.config_file, args.pytorch_dump_path)









import argparse
import os

import transformers

from .convert_slow_tokenizer import SLOW_TO_FAST_CONVERTERS
from .utils import logging


logging.set_verbosity_info()

lowerCamelCase    :		Union[str, Any]			   =       logging.get_logger(__name__)


lowerCamelCase    :		str			   =       {name: getattr(transformers, name + "Fast") for name in SLOW_TO_FAST_CONVERTERS}

def       _SCREAMING_SNAKE_CASE      (							lowercase     :	Any     ,							lowercase     :	str     ,							lowercase     :	int     ,							lowercase     :	List[Any]					):


      '''simple docstring'''



      if tokenizer_name is not None and tokenizer_name not in TOKENIZER_CLASSES:
            raise ValueError(f"""Unrecognized tokenizer name, should be one of {list(TOKENIZER_CLASSES.keys()					)}."""					)

      if tokenizer_name is None:
            lowerCamelCase_							=   TOKENIZER_CLASSES
      else:
            lowerCamelCase_							=   {tokenizer_name: getattr(lowercase     ,							tokenizer_name + 'Fast'					)}

      logger.info(f"""Loading tokenizer classes: {tokenizer_names}"""					)

      for tokenizer_name in tokenizer_names:
            lowerCamelCase_							=   TOKENIZER_CLASSES[tokenizer_name]

            lowerCamelCase_							=   True
            if checkpoint_name is None:
                  lowerCamelCase_							=   list(tokenizer_class.max_model_input_sizes.keys()					)
            else:
                  lowerCamelCase_							=   [checkpoint_name]

            logger.info(f"""For tokenizer {tokenizer_class.__class__.__name__} loading checkpoints: {checkpoint_names}"""					)

            for checkpoint in checkpoint_names:
                  logger.info(f"""Loading {tokenizer_class.__class__.__name__} {checkpoint}"""					)

                  # Load tokenizer
                  lowerCamelCase_							=   tokenizer_class.from_pretrained(lowercase     ,							force_download=lowercase					)

                  # Save fast tokenizer
                  logger.info(f"""Save fast tokenizer to {dump_path} with prefix {checkpoint} add_prefix {add_prefix}"""					)

                  # For organization names we create sub-directories
                  if "/" in checkpoint:
                        lowerCamelCase_					,    lowerCamelCase_							=   checkpoint.split('/'					)
                        lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                  elif add_prefix:
                        lowerCamelCase_							=   checkpoint
                        lowerCamelCase_							=   dump_path
                  else:
                        lowerCamelCase_							=   None
                        lowerCamelCase_							=   dump_path

                  logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""					)

                  if checkpoint in list(tokenizer.pretrained_vocab_files_map.values()					)[0]:
                        lowerCamelCase_							=   list(tokenizer.pretrained_vocab_files_map.values()					)[0][checkpoint]
                        lowerCamelCase_							=   file_path.split(lowercase					)[-1][0]
                        if next_char == "/":
                              lowerCamelCase_							=   os.path.join(lowercase     ,							lowercase					)
                              lowerCamelCase_							=   None

                        logger.info(f"""=> {dump_path_full} with prefix {checkpoint_prefix_name}, add_prefix {add_prefix}"""					)

                  lowerCamelCase_							=   tokenizer.save_pretrained(
                      lowercase     ,							legacy_format=lowercase     ,							filename_prefix=lowercase					)
                  logger.info(f"""=> File names {file_names}"""					)

                  for file_name in file_names:
                        if not file_name.endswith('tokenizer.json'					):
                              os.remove(lowercase					)
                              logger.info(f"""=> removing {file_name}"""					)


if __name__ == "__main__":
 lowerCamelCase    :		Tuple			   =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--dump_path", default=None, type=str, required=True, help="Path to output generated fast tokenizer files."
 )
 parser.add_argument(
     "--tokenizer_name",
     default=None,
     type=str,
     help=(
         F"""Optional tokenizer type selected in the list of {list(TOKENIZER_CLASSES.keys())}. If not given, will """
         "download and convert all the checkpoints from AWS."
     ),
 )
 parser.add_argument(
     "--checkpoint_name",
     default=None,
     type=str,
     help="Optional checkpoint name. If not given, will download and convert the canonical checkpoints from AWS.",
 )
 parser.add_argument(
     "--force_download",
     action="store_true",
     help="Re-download checkpoints.",
 )
 lowerCamelCase    :		Union[str, Any]			   =       parser.parse_args()

 convert_slow_checkpoint_to_fast(args.tokenizer_name, args.checkpoint_name, args.dump_path, args.force_download)



