



'''simple docstring'''





import argparse
import json
import os

import tensorstore as ts
import torch
from flax import serialization
from flax.traverse_util import flatten_dict, unflatten_dict
from tensorflow.io import gfile

from transformers.modeling_utils import dtype_byte_size
from transformers.models.switch_transformers.convert_switch_transformers_original_flax_checkpoint_to_pytorch import (
    rename_keys,
)
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
from transformers.utils.hub import convert_file_size_to_int

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      List[str]:
  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    __lowerCamelCase       :       Dict           = flax_key_tuple[:-1] + ('weight',)
    __lowerCamelCase       :       Tuple           = torch.permute(_lowerCAmelCase       ,(0, 2, 1)       )
  elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase       ):
    # linear layer
    __lowerCamelCase       :       Optional[Any]           = flax_key_tuple[:-1] + ('weight',)
    __lowerCamelCase       :       List[str]           = flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    __lowerCamelCase       :       List[Any]           = flax_key_tuple[:-1] + ('weight',)

  return flax_key_tuple, flax_tensor

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Union[str, Any]:
  if "metadata" in layer:
    __lowerCamelCase       :       Dict           = layer.split('metadata'       )
    __lowerCamelCase       :       Optional[int]           = ''.join(split_layer[0]       )[:-1]
    __lowerCamelCase       :       Any           = [tuple(('metadata' + split_layer[1]).split('/'       )       )]
  elif "kvstore" in layer:
    __lowerCamelCase       :       List[str]           = layer.split('kvstore'       )
    __lowerCamelCase       :       List[str]           = ''.join(split_layer[0]       )[:-1]
    __lowerCamelCase       :       List[Any]           = [tuple(('kvstore' + split_layer[1]).split('/'       )       )]

  else:
    __lowerCamelCase       :       str           = layer.split('/'       )
    __lowerCamelCase       :       Dict           = '/'.join(split_layer[:-1]       )
    __lowerCamelCase       :       Optional[int]           = (split_layer[-1],)

  if "kvstore/path" in layer:
    __lowerCamelCase       :       Dict           = F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
  elif "kvstore/driver" in layer:
    __lowerCamelCase       :       Any           = 'file'
  else:
    __lowerCamelCase       :       Any           = checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Any:
  __lowerCamelCase       :       Optional[int]           = rename_keys(_lowerCAmelCase       )
  __lowerCamelCase       :       str           = {}
  for k, v in current_block.items():
    __lowerCamelCase       :       Union[str, Any]           = v
  __lowerCamelCase       :       Union[str, Any]           = new_current_block
  torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase = WEIGHTS_NAME       )      ->      str:
  __lowerCamelCase       :       List[Any]           = convert_file_size_to_int(_lowerCAmelCase       )
  __lowerCamelCase       :       Dict           = []
  __lowerCamelCase       :       List[Any]           = {}
  __lowerCamelCase       :       Union[str, Any]           = 0
  __lowerCamelCase       :       Optional[int]           = 0

  os.makedirs(_lowerCAmelCase       ,exist_ok=_lowerCAmelCase       )
  with gfile.GFile(switch_checkpoint_path + '/checkpoint'       ,'rb'       ) as fp:
    __lowerCamelCase       :       str           = serialization.msgpack_restore(fp.read()       )['optimizer']['target']
    __lowerCamelCase       :       Optional[int]           = flatten_dict(_lowerCAmelCase       ,sep='/'       )

  __lowerCamelCase       :       Dict           = {}
  for layer in checkpoint_info.keys():
    __lowerCamelCase       ,__lowerCamelCase       ,__lowerCamelCase       :       Optional[Any]           = get_key_and_tensorstore_dict(
        _lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    if curr_real_layer_name in all_layers:
      __lowerCamelCase       :       Tuple           = content
    else:
      __lowerCamelCase       :       Union[str, Any]           = {split_layer[-1]: content}

  for key in all_layers.keys():
    # open tensorstore file
    __lowerCamelCase       :       int           = ts.open(unflatten_dict(all_layers[key]       )       ).result().read().result()
    __lowerCamelCase       :       int           = torch.tensor(_lowerCAmelCase       )
    __lowerCamelCase       :       Union[str, Any]           = raw_weights.numel() * dtype_byte_size(raw_weights.dtype       )

    # use the renaming pattern from the small conversion scripts
    __lowerCamelCase       ,__lowerCamelCase       :       Union[str, Any]           = rename_base_flax_keys(tuple(key.split('/'       )       )       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = '/'.join(_lowerCAmelCase       )

    # If this weight is going to tip up over the maximal size, we split.
    if current_block_size + weight_size > max_shard_size:
      __lowerCamelCase       :       Optional[int]           = os.path.join(
          _lowerCAmelCase       ,weights_name.replace('.bin'       ,F'-{len(_lowerCAmelCase       )+1:05d}-of-???.bin'       )       )
      rename_and_save_block(_lowerCAmelCase       ,_lowerCAmelCase       )
      sharded_state_dicts.append(current_block.keys()       )
      del current_block
      __lowerCamelCase       :       int           = {}
      __lowerCamelCase       :       int           = 0

    __lowerCamelCase       :       Optional[Any]           = raw_weights.to(getattr(_lowerCAmelCase       ,_lowerCAmelCase       )       )
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  __lowerCamelCase       :       Any           = os.path.join(_lowerCAmelCase       ,weights_name.replace('.bin'       ,F'-{len(_lowerCAmelCase       )+1:05d}-of-???.bin'       )       )
  rename_and_save_block(_lowerCAmelCase       ,_lowerCAmelCase       )
  sharded_state_dicts.append(current_block.keys()       )

  # If we only have one shard, we return it
  if len(_lowerCAmelCase       ) == 1:
    return {weights_name: sharded_state_dicts[0]}, None

  # Otherwise, let's build the index
  __lowerCamelCase       :       Optional[int]           = {}
  __lowerCamelCase       :       Tuple           = {}
  for idx, shard in enumerate(_lowerCAmelCase       ):
    __lowerCamelCase       :       List[str]           = weights_name.replace(
        '.bin'       ,F'-{idx+1:05d}-of-{len(_lowerCAmelCase       ):05d}.bin'       )  # len(sharded_state_dicts):05d}
    __lowerCamelCase       :       Any           = os.path.join(_lowerCAmelCase       ,weights_name.replace('.bin'       ,F'-{idx+1:05d}-of-???.bin'       )       )
    os.rename(_lowerCAmelCase       ,os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )       )
    __lowerCamelCase       :       int           = shard
    for key in shard:
      __lowerCamelCase       :       Optional[int]           = shard_file

    # Add the metadata
  __lowerCamelCase       :       List[Any]           = {'total_size': total_size}
  __lowerCamelCase       :       Union[str, Any]           = {'metadata': metadata, 'weight_map': weight_map}

  with open(os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )       ,'w'       ,encoding='utf-8'       ) as f:
    __lowerCamelCase       :       Optional[int]           = json.dumps(_lowerCAmelCase       ,indent=2       ,sort_keys=_lowerCAmelCase       ) + '\n'
    f.write(_lowerCAmelCase       )

  return metadata, index


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--switch_t5x_checkpoint_path',
       default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128/checkpoint_634600',
       type=str,
       required=False,
       help='Path to a directory containing a folder per layer. Follows the original Google format.',
   )
   parser.add_argument('--max_shard_size', default='10GB', required=False, help='Max shard size')
   parser.add_argument('--dtype', default='bfloat16', type=str, required=False, help='dtype of the saved model')
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default='/mnt/disks/disk_switch/original_checkpoints/switch-xxl-128-converted',
       type=str,
       required=False,
       help='Path to the output pytorch model.',
   )
   _UpperCamelCase											=						parser.parse_args()
   shard_on_the_fly(
       args.switch_tax_checkpoint_path,
       args.pytorch_dump_folder_path,
       args.max_shard_size,
       args.dtype,
   )


def        a_     (							)      ->      List[Any]:
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  __lowerCamelCase       :       int           = SwitchTransformersConfig.from_pretrained('google/switch-base-8'       )
  config.save_pretrained('/home/arthur_huggingface_co/transformers/switch_converted'       )
  __lowerCamelCase       :       Union[str, Any]           = SwitchTransformersForConditionalGeneration.from_pretrained(
      '/home/arthur_huggingface_co/transformers/switch_converted'       ,device_map='auto'       )

  __lowerCamelCase       :       List[str]           = TaTokenizer.from_pretrained('t5-small'       )
  __lowerCamelCase       :       Dict           = 'A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'

  __lowerCamelCase       :       Optional[int]           = tokenizer(_lowerCAmelCase       ,return_tensors='pt'       ).input_ids
  __lowerCamelCase       :       str           = model.generate(_lowerCAmelCase       ,decoder_start_token_id=0       )
  print(tokenizer.decode(out[0]       )       )











'''simple docstring'''





import argparse
import os
import pickle
import sys

import torch

from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()

# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
_UpperCamelCase											=						data_utils.TransfoXLTokenizer
_UpperCamelCase											=						data_utils.TransfoXLCorpus
_UpperCamelCase											=						data_utils
_UpperCamelCase											=						data_utils

def        a_     (							_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )      ->      Optional[Any]:
  if transfo_xl_dataset_file:
    # Convert a pre-processed corpus (see original TensorFlow repo)
    with open(_lowerCAmelCase       ,'rb'       ) as fp:
      __lowerCamelCase       :       Optional[Any]           = pickle.load(_lowerCAmelCase       ,encoding='latin1'       )
    # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
    __lowerCamelCase       :       Tuple           = pytorch_dump_folder_path + '/' + VOCAB_FILES_NAMES['pretrained_vocab_file']
    print(F'Save vocabulary to {pytorch_vocab_dump_path}'       )
    __lowerCamelCase       :       str           = corpus.vocab.__dict__
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

    __lowerCamelCase       :       Optional[Any]           = corpus.__dict__
    corpus_dict_no_vocab.pop('vocab'       ,_lowerCAmelCase       )
    __lowerCamelCase       :       Optional[Any]           = pytorch_dump_folder_path + '/' + CORPUS_NAME
    print(F'Save dataset to {pytorch_dataset_dump_path}'       )
    torch.save(_lowerCAmelCase       ,_lowerCAmelCase       )

  if tf_checkpoint_path:
    # Convert a pre-trained TensorFlow model
    __lowerCamelCase       :       int           = os.path.abspath(_lowerCAmelCase       )
    __lowerCamelCase       :       Any           = os.path.abspath(_lowerCAmelCase       )

    print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.'       )
    # Initialise PyTorch model
    if transfo_xl_config_file == "":
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig()
    else:
      __lowerCamelCase       :       Optional[int]           = TransfoXLConfig.from_json_file(_lowerCAmelCase       )
    print(F'Building PyTorch model from configuration: {config}'       )
    __lowerCamelCase       :       List[str]           = TransfoXLLMHeadModel(_lowerCAmelCase       )

    __lowerCamelCase       :       Dict           = load_tf_weights_in_transfo_xl(_lowerCAmelCase       ,_lowerCAmelCase       ,_lowerCAmelCase       )
    # Save pytorch-model
    __lowerCamelCase       :       List[str]           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    __lowerCamelCase       :       int           = os.path.join(_lowerCAmelCase       ,_lowerCAmelCase       )
    print(F'Save PyTorch model to {os.path.abspath(_lowerCAmelCase       )}'       )
    torch.save(model.state_dict()       ,_lowerCAmelCase       )
    print(F'Save configuration file to {os.path.abspath(_lowerCAmelCase       )}'       )
    with open(_lowerCAmelCase       ,'w'       ,encoding='utf-8'       ) as f:
      f.write(config.to_json_string()       )


if __name__ == "__main__":
   _UpperCamelCase											=						argparse.ArgumentParser()
   parser.add_argument(
       '--pytorch_dump_folder_path',
       default=None,
       type=str,
       required=True,
       help='Path to the folder to store the PyTorch model or dataset/vocab.',
   )
   parser.add_argument(
       '--tf_checkpoint_path',
       default='',
       type=str,
       help='An optional path to a TensorFlow checkpoint path to be converted.',
   )
   parser.add_argument(
       '--transfo_xl_config_file',
       default='',
       type=str,
       help=(
           'An optional config json file corresponding to the pre-trained BERT model. \n'
           'This specifies the model architecture.'
       ),
   )
   parser.add_argument(
       '--transfo_xl_dataset_file',
       default='',
       type=str,
       help='An optional dataset file to be converted in a vocabulary.',
   )
   _UpperCamelCase											=						parser.parse_args()
   convert_transfo_xl_checkpoint_to_pytorch(
       args.tf_checkpoint_path,
       args.transfo_xl_config_file,
       args.pytorch_dump_folder_path,
       args.transfo_xl_dataset_file,
   )






