

'''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 	UpperCamelCase   (       lowercase_						:    Optional[int]     ,					lowercase_						:    Union[str, Any]	)		->	Tuple:




  '''simple docstring'''



  if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
    # expert layer
    lowercase									=flax_key_tuple[:-1] + ('''weight''',)
    lowercase									=torch.permute(lowerCAmelCase_     ,					(0, 2, 1)	)
  elif flax_key_tuple[-1] == "kernel" and ".".join(lowerCAmelCase_	):
    # linear layer
    lowercase									=flax_key_tuple[:-1] + ('''weight''',)
    lowercase									=flax_tensor.T
  elif flax_key_tuple[-1] in ["scale", "embedding"]:
    lowercase									=flax_key_tuple[:-1] + ('''weight''',)

  return flax_key_tuple, flax_tensor




def 	UpperCamelCase   (       lowercase_						:    List[Any]     ,					lowercase_						:    str     ,					lowercase_						:    str	)		->	str:




  '''simple docstring'''



  if "metadata" in layer:
    lowercase									=layer.split('''metadata'''	)
    lowercase									=''''''.join(split_layer[0]	)[:-1]
    lowercase									=[tuple(('''metadata''' + split_layer[1]).split('''/'''	)	)]
  elif "kvstore" in layer:
    lowercase									=layer.split('''kvstore'''	)
    lowercase									=''''''.join(split_layer[0]	)[:-1]
    lowercase									=[tuple(('''kvstore''' + split_layer[1]).split('''/'''	)	)]

  else:
    lowercase									=layer.split('''/'''	)
    lowercase									='''/'''.join(split_layer[:-1]	)
    lowercase									=(split_layer[-1],)

  if "kvstore/path" in layer:
    lowercase									=f'{switch_checkpoint_path}/{checkpoint_info[layer]}'
  elif "kvstore/driver" in layer:
    lowercase									='''file'''
  else:
    lowercase									=checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content




def 	UpperCamelCase   (       lowercase_						:    str     ,					lowercase_						:    List[str]	)		->	int:




  '''simple docstring'''



  lowercase									=rename_keys(lowerCAmelCase_	)
  lowercase									={}
  for k, v in current_block.items():
    lowercase									=v
  lowercase									=new_current_block
  torch.save(lowerCAmelCase_     ,					lowerCAmelCase_	)




def 	UpperCamelCase   (       lowercase_						:    List[str]     ,					lowercase_						:    List[str]     ,					lowercase_						:    Dict     ,					lowercase_						:    int     ,					lowercase_						:    Tuple = WEIGHTS_NAME	)		->	Optional[int]:




  '''simple docstring'''



  lowercase									=convert_file_size_to_int(lowerCAmelCase_	)
  lowercase									=[]
  lowercase									={}
  lowercase									=0
  lowercase									=0

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

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

  for key in all_layers.keys():
    # open tensorstore file
    lowercase									=ts.open(unflatten_dict(all_layers[key]	)	).result().read().result()
    lowercase									=torch.tensor(lowerCAmelCase_	)
    lowercase									=raw_weights.numel() * dtype_byte_size(raw_weights.dtype	)

    # use the renaming pattern from the small conversion scripts
    lowercase							, lowercase									=rename_base_flax_keys(tuple(key.split('''/'''	)	)     ,					lowerCAmelCase_	)
    lowercase									='''/'''.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:
      lowercase									=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
      lowercase									={}
      lowercase									=0

    lowercase									=raw_weights.to(getattr(lowerCAmelCase_     ,					lowerCAmelCase_	)	)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  lowercase									=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
  lowercase									={}
  lowercase									={}
  for idx, shard in enumerate(lowerCAmelCase_	):
    lowercase									=weights_name.replace(
        '''.bin'''     ,					f'-{idx+1:05d}-of-{len(lowerCAmelCase_	):05d}.bin'	)  # len(sharded_state_dicts):05d}
    lowercase									=os.path.join(lowerCAmelCase_     ,					weights_name.replace('''.bin'''     ,					f'-{idx+1:05d}-of-???.bin'	)	)
    os.rename(lowerCAmelCase_     ,					os.path.join(lowerCAmelCase_     ,					lowerCAmelCase_	)	)
    lowercase									=shard
    for key in shard:
      lowercase									=shard_file

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

  with open(os.path.join(lowerCAmelCase_     ,					lowerCAmelCase_	)     ,					'''w'''     ,					encoding='''utf-8'''	) as f:
    lowercase									=json.dumps(lowerCAmelCase_     ,					indent=2     ,					sort_keys=lowerCAmelCase_	) + '''\n'''
    f.write(lowerCAmelCase_	)

  return metadata, index


if __name__ == "__main__":
     _UpperCAmelCase  :		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.''',
     )
     _UpperCAmelCase  :		Tuple  =		parser.parse_args()
     shard_on_the_fly(
         args.switch_tax_checkpoint_path,
         args.pytorch_dump_folder_path,
         args.max_shard_size,
         args.dtype,
     )




def 	UpperCamelCase   (       )		->	Optional[int]:




  '''simple docstring'''



  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

  lowercase									=tokenizer(lowerCAmelCase_     ,					return_tensors='''pt'''	).input_ids
  lowercase									=model.generate(lowerCAmelCase_     ,					decoder_start_token_id=0	)
  print(tokenizer.decode(out[0]	)	)




from __future__ import annotations
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Optional[int]:
   # Checks if the entire collection has been sorted
   if len(lowerCAmelCase_ ) <= 1 or n <= 1:
      return

   insert_next(lowerCAmelCase_	,    n - 1 )
   rec_insertion_sort(lowerCAmelCase_	,    n - 1 )
def 	_UpperCamelCase					(    lowerCAmelCase_	,    lowerCAmelCase_ )    ->Dict:
   # Checks order between adjacent elements
   if index >= len(lowerCAmelCase_ ) or collection[index - 1] <= collection[index]:
      return

   # Swaps adjacent elements since they are not in ascending order
   UpperCAmelCase					,	UpperCAmelCase      					=  (
       collection[index],
       collection[index - 1],
   )

   insert_next(lowerCAmelCase_	,    index + 1 )


if __name__ == "__main__":
 __a	 =      input("""Enter integers separated by spaces: """)
 __a	 =      [int(num) for num in numbers.split()]
 rec_insertion_sort(number_list, len(number_list))
 print(number_list)



