




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 			_SCREAMING_SNAKE_CASE			(							lowercase	:       List[str]	,							lowercase	:       Union[str, Any]     ):


      '''simple docstring'''



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

      return flax_key_tuple, flax_tensor

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Dict	,							lowercase	:       List[Any]	,							lowercase	:       List[Any]     ):


      '''simple docstring'''



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

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

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

      return curr_real_layer_name, split_layer, content

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       Optional[Any]	,							lowercase	:       str     ):


      '''simple docstring'''



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

def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       str	,							lowercase	:       Dict	,							lowercase	:       Optional[int]	,							lowercase	:       Union[str, Any]	,							lowercase	:       str = WEIGHTS_NAME     ):


      '''simple docstring'''



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

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

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

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

            # use the renaming pattern from the small conversion scripts
            lowerCamelCase_     ,				lowerCamelCase_		     =    rename_base_flax_keys(tuple(key.split('/'     )     )	,							lowercase     )
            lowerCamelCase_		     =    '/'.join(lowercase     )

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

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

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

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

      # Otherwise, let's build the index
      lowerCamelCase_		     =    {}
      lowerCamelCase_		     =    {}
      for idx, shard in enumerate(lowercase     ):
            lowerCamelCase_		     =    weights_name.replace(
                '.bin'	,							f"""-{idx+1:05d}-of-{len(lowercase     ):05d}.bin"""     )  # len(sharded_state_dicts):05d}
            lowerCamelCase_		     =    os.path.join(lowercase	,							weights_name.replace('.bin'	,							f"""-{idx+1:05d}-of-???.bin"""     )     )
            os.rename(lowercase	,							os.path.join(lowercase	,							lowercase     )     )
            lowerCamelCase_		     =    shard
            for key in shard:
                  lowerCamelCase_		     =    shard_file

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

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

      return metadata, index


if __name__ == "__main__":
 lowerCamelCase				:		str     =   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				:		int     =   parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )






def 			_SCREAMING_SNAKE_CASE			(							):


      '''simple docstring'''



      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









def 			_SCREAMING_SNAKE_CASE			(							lowercase	:       list     ):


      '''simple docstring'''



      for i in range(len(lowercase     ) - 1	,							0	,							-1     ):
            lowerCamelCase_		     =    False

            for j in range(lowercase	,							0	,							-1     ):
                  if unsorted[j] < unsorted[j - 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j - 1], unsorted[j]
                        lowerCamelCase_		     =    True

            for j in range(lowercase     ):
                  if unsorted[j] > unsorted[j + 1]:
                        lowerCamelCase_     ,				lowerCamelCase_		     =    unsorted[j + 1], unsorted[j]
                        lowerCamelCase_		     =    True

            if not swapped:
                  break
      return unsorted


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 lowerCamelCase				:		Any     =   input("Enter numbers separated by a comma:\n").strip()
 lowerCamelCase				:		Dict     =   [int(item) for item in user_input.split(",")]
 print(F"""{cocktail_shaker_sort(unsorted) = }""")



