
'''simple docstring'''






class  UpperCAmelCase__						:



    def __init__(	self      :       int     ):
          _lowerCamelCase				:   dict[str, TrieNode]								=   {}  # Mapping from char to TrieNode
          _lowerCamelCase				:   Optional[int]								=   False



    def   lowerCamelCase_  (	self      :       str,__A      :       list[str]     ):
          for word in words:
                self.insert(__A     )



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       str     ):
          _lowerCamelCase				:   List[Any]								=   self
          for char in word:
                if char not in curr.nodes:
                      _lowerCamelCase				:   Tuple								=   TrieNode()
                _lowerCamelCase				:   Any								=   curr.nodes[char]
          _lowerCamelCase				:   List[str]								=   True



    def   lowerCamelCase_  (	self      :       Any,__A      :       str     ):
          _lowerCamelCase				:   Dict								=   self
          for char in word:
                if char not in curr.nodes:
                      return False
                _lowerCamelCase				:   Optional[int]								=   curr.nodes[char]
          return curr.is_leaf



    def   lowerCamelCase_  (	self      :       Any,__A      :       str     ):

          def _delete(__A      :       TrieNode,__A      :       str,__A      :       int     ) -> bool:
                if index == len(__A     ):
                      # If word does not exist
                      if not curr.is_leaf:
                            return False
                      _lowerCamelCase				:   int								=   False
                      return len(curr.nodes     ) == 0
                _lowerCamelCase				:   Dict								=   word[index]
                _lowerCamelCase				:   Optional[Any]								=   curr.nodes.get(__A     )
                # If char not in current trie node
                if not char_node:
                      return False
                # Flag to check if node can be deleted
                _lowerCamelCase				:   List[Any]								=   _delete(__A,__A,index + 1     )
                if delete_curr:
                      del curr.nodes[char]
                      return len(curr.nodes     ) == 0
                return delete_curr

          _delete(self,__A,0     )







def 		A_       (	_lowerCAmelCase   :    TrieNode				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      if node.is_leaf:
            print(_lowerCAmelCase				,	end=" "							)

      for key, value in node.nodes.items():
            print_words(_lowerCAmelCase				,	word + key							)







def 		A_       (	):



      """simple docstring"""




      _lowerCamelCase				:   List[str]								=   "banana bananas bandana band apple all beast".split()
      _lowerCamelCase				:   str								=   TrieNode()
      root.insert_many(_lowerCAmelCase							)
      # print_words(root, "")
      assert all(root.find(_lowerCAmelCase							) for word in words							)
      assert root.find("banana"							)
      assert not root.find("bandanas"							)
      assert not root.find("apps"							)
      assert root.find("apple"							)
      assert root.find("all"							)
      root.delete("all"							)
      assert not root.find("all"							)
      root.delete("banana"							)
      assert not root.find("banana"							)
      assert root.find("bananas"							)
      return True







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    bool							):



      """simple docstring"""




      print(str(_lowerCAmelCase							)				,	"works!" if passes else "doesn't work :("							)







def 		A_       (	):



      """simple docstring"""




      assert test_trie()







def 		A_       (	):



      """simple docstring"""




      print_results("Testing trie functionality"				,	test_trie()							)


if __name__ == "__main__":
 main()

'''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   :    Any				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            _lowerCamelCase				:   List[str]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   Union[str, Any]								=   torch.permute(_lowerCAmelCase				,	(0, 2, 1)							)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase							):
            # linear layer
            _lowerCamelCase				:   Union[str, Any]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   int								=   flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            _lowerCamelCase				:   Tuple								=   flax_key_tuple[:-1] + ("weight",)

      return flax_key_tuple, flax_tensor







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Any							):



      """simple docstring"""




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

      else:
            _lowerCamelCase				:   Tuple								=   layer.split("/"							)
            _lowerCamelCase				:   int								=   "/".join(split_layer[:-1]							)
            _lowerCamelCase				:   Optional[Any]								=   (split_layer[-1],)

      if "kvstore/path" in layer:
            _lowerCamelCase				:   int								=   F'{switch_checkpoint_path}/{checkpoint_info[layer]}'
      elif "kvstore/driver" in layer:
            _lowerCamelCase				:   Optional[int]								=   "file"
      else:
            _lowerCamelCase				:   str								=   checkpoint_info[layer]

      return curr_real_layer_name, split_layer, content







def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   rename_keys(_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   {}
      for k, v in current_block.items():
            _lowerCamelCase				:   Union[str, Any]								=   v
      _lowerCamelCase				:   str								=   new_current_block
      torch.save(_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    str = WEIGHTS_NAME							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   convert_file_size_to_int(_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   []
      _lowerCamelCase				:   Dict								=   {}
      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   List[str]								=   0

      os.makedirs(_lowerCAmelCase				,	exist_ok=_lowerCAmelCase							)
      with gfile.GFile(switch_checkpoint_path + "/checkpoint"				,	"rb"							) as fp:
            _lowerCamelCase				:   Tuple								=   serialization.msgpack_restore(fp.read()							)["optimizer"]["target"]
            _lowerCamelCase				:   Union[str, Any]								=   flatten_dict(_lowerCAmelCase				,	sep="/"							)

      _lowerCamelCase				:   Optional[int]								=   {}
      for layer in checkpoint_info.keys():
            _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   int								=   get_key_and_tensorstore_dict(
                _lowerCAmelCase				,	_lowerCAmelCase				,	_lowerCAmelCase							)
            if curr_real_layer_name in all_layers:
                  _lowerCamelCase				:   Optional[int]								=   content
            else:
                  _lowerCamelCase				:   int								=   {split_layer[-1]: content}

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

            # use the renaming pattern from the small conversion scripts
            _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   rename_base_flax_keys(tuple(key.split("/"							)							)				,	_lowerCAmelCase							)
            _lowerCamelCase				:   Optional[Any]								=   "/".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				:   Tuple								=   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				:   Optional[int]								=   {}
                  _lowerCamelCase				:   Dict								=   0

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

      # Add the last block
      _lowerCamelCase				:   List[str]								=   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				:   Union[str, Any]								=   {}
      _lowerCamelCase				:   str								=   {}
      for idx, shard in enumerate(_lowerCAmelCase							):
            _lowerCamelCase				:   Dict								=   weights_name.replace(
                ".bin"				,	F'-{idx+1:05d}-of-{len(_lowerCAmelCase							):05d}.bin'							)  # len(sharded_state_dicts):05d}
            _lowerCamelCase				:   Union[str, Any]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{idx+1:05d}-of-???.bin'							)							)
            os.rename(_lowerCAmelCase				,	os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   Tuple								=   shard
            for key in shard:
                  _lowerCamelCase				:   str								=   shard_file

    # Add the metadata
      _lowerCamelCase				:   Optional[Any]								=   {"total_size": total_size}
      _lowerCamelCase				:   Dict								=   {"metadata": metadata, "weight_map": weight_map}

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

      return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_       :   List[Any]   =       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_       :   Dict   =       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_       (	):



      """simple docstring"""




      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      _lowerCamelCase				:   Optional[int]								=   SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
      config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
      _lowerCamelCase				:   int								=   SwitchTransformersForConditionalGeneration.from_pretrained(
          "/home/arthur_huggingface_co/transformers/switch_converted"				,	device_map="auto"							)

      _lowerCamelCase				:   List[str]								=   TaTokenizer.from_pretrained("t5-small"							)
      _lowerCamelCase				:   Tuple								=   "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				:   List[Any]								=   model.generate(_lowerCAmelCase				,	decoder_start_token_id=0							)
      print(tokenizer.decode(out[0]							)							)