





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

 return flax_key_tuple, flax_tensor



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

 else:
  UpperCamelCase      :   Union[str, Any]			      =  layer.split("/"	)
  UpperCamelCase      :   List[str]			      =  "/".join(split_layer[:-1]	)
  UpperCamelCase      :   int			      =  (split_layer[-1],)

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

 return curr_real_layer_name, split_layer, content



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



def 						A_							(	_lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase				,  _lowerCAmelCase = WEIGHTS_NAME	)		->					Dict:
 UpperCamelCase      :   Union[str, Any]			      =  convert_file_size_to_int(_lowerCAmelCase	)
 UpperCamelCase      :   Optional[Any]			      =  []
 UpperCamelCase      :   Tuple			      =  {}
 UpperCamelCase      :   List[str]			      =  0
 UpperCamelCase      :   Optional[Any]			      =  0

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

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

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

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

  UpperCamelCase      :   str			      =  raw_weights.to(getattr(_lowerCAmelCase				,  _lowerCAmelCase	)	)
  current_block_size += weight_size
  total_size += weight_size

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

    # Add the metadata
 UpperCamelCase      :   int			      =  {"total_size": total_size}
 UpperCamelCase      :   List[Any]			      =  {"metadata": metadata, "weight_map": weight_map}

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

 return metadata, index


if __name__ == "__main__":
    __lowerCamelCase			:	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.""",
    )
    __lowerCamelCase			:	Union[str, Any]					    =	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

 UpperCamelCase      :   Tuple			      =  SwitchTransformersConfig.from_pretrained("google/switch-base-8"	)
 config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"	)
 UpperCamelCase      :   Any			      =  SwitchTransformersForConditionalGeneration.from_pretrained(
     "/home/arthur_huggingface_co/transformers/switch_converted"				,  device_map="auto"	)

 UpperCamelCase      :   Optional[Any]			      =  TaTokenizer.from_pretrained("t5-small"	)
 UpperCamelCase      :   Optional[int]			      =  "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

 UpperCamelCase      :   List[Any]			      =  tokenizer(_lowerCAmelCase				,  return_tensors="pt"	).input_ids
 UpperCamelCase      :   List[str]			      =  model.generate(_lowerCAmelCase				,  decoder_start_token_id=0	)
 print(tokenizer.decode(out[0]	)	)







def 						A_							(	_lowerCAmelCase = 1000	)		->					int:
 UpperCamelCase      :   Optional[int]			      =  -1
 UpperCamelCase      :   int			      =  0
 for a in range(1				,  n // 3	):
  # Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
  UpperCamelCase      :   Optional[Any]			      =  (n * n - 2 * a * n) // (2 * n - 2 * a)
  UpperCamelCase      :   List[str]			      =  n - a - b
  if c * c == (a * a + b * b):
   UpperCamelCase      :   Union[str, Any]			      =  a * b * c
   if candidate >= product:
    UpperCamelCase      :   Tuple			      =  candidate
 return product


if __name__ == "__main__":
    print(f"""{solution() = }""")
