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__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		List[Any]   ):
 if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
  # expert layer
  SCREAMING_SNAKE_CASE__: Optional[Any]=   flax_key_tuple[:-1] + ('''weight''',)
  SCREAMING_SNAKE_CASE__: Union[str, Any]=   torch.permute(snake_case_							,					(0, 2, 1)   )
 elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case_   ):
  # linear layer
  SCREAMING_SNAKE_CASE__: List[str]=   flax_key_tuple[:-1] + ('''weight''',)
  SCREAMING_SNAKE_CASE__: Optional[int]=   flax_tensor.T
 elif flax_key_tuple[-1] in ["scale", "embedding"]:
  SCREAMING_SNAKE_CASE__: List[str]=   flax_key_tuple[:-1] + ('''weight''',)

 return flax_key_tuple, flax_tensor







def       A__   (						snake_case_			:		Optional[int]							,					snake_case_			:		int							,					snake_case_			:		Any   ):
 if "metadata" in layer:
  SCREAMING_SNAKE_CASE__: str=   layer.split('''metadata'''   )
  SCREAMING_SNAKE_CASE__: Optional[int]=   ''''''.join(split_layer[0]   )[:-1]
  SCREAMING_SNAKE_CASE__: Optional[Any]=   [tuple(('''metadata''' + split_layer[1]).split('''/'''   )   )]
 elif "kvstore" in layer:
  SCREAMING_SNAKE_CASE__: int=   layer.split('''kvstore'''   )
  SCREAMING_SNAKE_CASE__: List[Any]=   ''''''.join(split_layer[0]   )[:-1]
  SCREAMING_SNAKE_CASE__: Tuple=   [tuple(('''kvstore''' + split_layer[1]).split('''/'''   )   )]

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

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

 return curr_real_layer_name, split_layer, content







def       A__   (						snake_case_			:		Optional[Any]							,					snake_case_			:		str   ):
 SCREAMING_SNAKE_CASE__: str=   rename_keys(snake_case_   )
 SCREAMING_SNAKE_CASE__: List[str]=   {}
 for k, v in current_block.items():
  SCREAMING_SNAKE_CASE__: List[Any]=   v
 SCREAMING_SNAKE_CASE__: Tuple=   new_current_block
 torch.save(snake_case_							,					snake_case_   )







def       A__   (						snake_case_			:		List[str]							,					snake_case_			:		Dict							,					snake_case_			:		Union[str, Any]							,					snake_case_			:		Optional[int]							,					snake_case_			:		str = WEIGHTS_NAME   ):
 SCREAMING_SNAKE_CASE__: Dict=   convert_file_size_to_int(snake_case_   )
 SCREAMING_SNAKE_CASE__: Optional[Any]=   []
 SCREAMING_SNAKE_CASE__: List[str]=   {}
 SCREAMING_SNAKE_CASE__: Optional[int]=   0
 SCREAMING_SNAKE_CASE__: Dict=   0

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

 SCREAMING_SNAKE_CASE__: List[Any]=   {}
 for layer in checkpoint_info.keys():
  SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Dict=   get_key_and_tensorstore_dict(
      snake_case_							,					snake_case_							,					snake_case_   )
  if curr_real_layer_name in all_layers:
   SCREAMING_SNAKE_CASE__: Tuple=   content
  else:
   SCREAMING_SNAKE_CASE__: List[Any]=   {split_layer[-1]: content}

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

  # use the renaming pattern from the small conversion scripts
  SCREAMING_SNAKE_CASE__,						SCREAMING_SNAKE_CASE__: Optional[Any]=   rename_base_flax_keys(tuple(key.split('''/'''   )   )							,					snake_case_   )
  SCREAMING_SNAKE_CASE__: str=   '''/'''.join(snake_case_   )

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

  SCREAMING_SNAKE_CASE__: List[str]=   raw_weights.to(getattr(snake_case_							,					snake_case_   )   )
  current_block_size += weight_size
  total_size += weight_size

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

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

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

    # Add the metadata
 SCREAMING_SNAKE_CASE__: str=   {'''total_size''': total_size}
 SCREAMING_SNAKE_CASE__: str=   {'''metadata''': metadata, '''weight_map''': weight_map}

 with open(os.path.join(snake_case_							,					snake_case_   )							,					'''w'''							,					encoding='''utf-8'''   ) as f:
  SCREAMING_SNAKE_CASE__: List[Any]=   json.dumps(snake_case_							,					indent=2							,					sort_keys=snake_case_   ) + '''\n'''
  f.write(snake_case_   )

 return metadata, index


if __name__ == "__main__":
      lowercase_  : Optional[int]								=					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.',
      )
      lowercase_  : str								=					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__   (						):
 from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

 SCREAMING_SNAKE_CASE__: Any=   TaTokenizer.from_pretrained('''t5-small'''   )
 SCREAMING_SNAKE_CASE__: Any=   '''A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.'''

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









from __future__ import annotations





def    __lowerCAmelCase   (    __snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # We need to create solution object to save path.
     __lowerCAmelCase             =       [[0 for _ in range(__snake_case							)] for _ in range(__snake_case							)]
     __lowerCAmelCase             =       run_maze(__snake_case       ,			0       ,			0       ,			__snake_case							)
     if solved:
          print("\n".join(str(__snake_case							) for row in solutions							)							)
     else:
          print("No solution exists!"							)
     return solved





def    __lowerCAmelCase   (    __snake_case       ,			__snake_case       ,			__snake_case       ,			__snake_case							):
     __lowerCAmelCase             =       len(__snake_case							)
     # Final check point.
     if i == j == (size - 1):
          __lowerCAmelCase             =       1
          return True

     __lowerCAmelCase             =       (not i < 0) and (not j < 0)  # Check lower bounds
     __lowerCAmelCase             =       (i < size) and (j < size)  # Check upper bounds

     if lower_flag and upper_flag:
          # check for already visited and block points.
          __lowerCAmelCase             =       (not solutions[i][j]) and (not maze[i][j])
          if block_flag:
               # check visited
               __lowerCAmelCase             =       1

               # check for directions
               if (
                   run_maze(__snake_case       ,			i + 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j + 1       ,			__snake_case							)
                   or run_maze(__snake_case       ,			i - 1       ,			__snake_case       ,			__snake_case							)
                   or run_maze(__snake_case       ,			__snake_case       ,			j - 1       ,			__snake_case							)
               ):
                    return True

               __lowerCAmelCase             =       0
               return False
     return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()






