






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       __lowerCAmelCase      (						__snake_case					, __snake_case       ):

     if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
          # expert layer
          __lowerCAmelCase								=					flax_key_tuple[:-1] + ("weight",)
          __lowerCAmelCase								=					torch.permute(__snake_case					, (0, 2, 1)       )
     elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case       ):
          # 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       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case       ):

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





def       __lowerCAmelCase      (						__snake_case					, __snake_case					, __snake_case					, __snake_case					, __snake_case = WEIGHTS_NAME       ):
     __lowerCAmelCase								=					convert_file_size_to_int(__snake_case       )
     __lowerCAmelCase								=					[]
     __lowerCAmelCase								=					{}
     __lowerCAmelCase								=					0
     __lowerCAmelCase								=					0

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

     __lowerCAmelCase								=					{}
     for layer in checkpoint_info.keys():
          __lowerCAmelCase			, __lowerCAmelCase			, __lowerCAmelCase								=					get_key_and_tensorstore_dict(
              __snake_case					, __snake_case					, __snake_case       )
          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(__snake_case       )
          __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("/"       )       )					, __snake_case       )
          __lowerCAmelCase								=					"/".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:
               __lowerCAmelCase								=					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
               __lowerCAmelCase								=					{}
               __lowerCAmelCase								=					0

          __lowerCAmelCase								=					raw_weights.to(getattr(__snake_case					, __snake_case       )       )
          current_block_size += weight_size
          total_size += weight_size

     # Add the last block
     __lowerCAmelCase								=					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
     __lowerCAmelCase								=					{}
     __lowerCAmelCase								=					{}
     for idx, shard in enumerate(__snake_case       ):
          __lowerCAmelCase								=					weights_name.replace(
              ".bin"					, F"""-{idx+1:05d}-of-{len(__snake_case       ):05d}.bin"""       )  # len(sharded_state_dicts):05d}
          __lowerCAmelCase								=					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       )       )
          __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(__snake_case					, __snake_case       )					, "w"					, encoding="utf-8"       ) as f:
          __lowerCAmelCase								=					json.dumps(__snake_case					, indent=2					, sort_keys=__snake_case       ) + "\n"
          f.write(__snake_case       )

     return metadata, index


if __name__ == "__main__":
 lowerCamelCase			:					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.''',
 )
 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       __lowerCAmelCase      (						):
     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(__snake_case					, return_tensors="pt"       ).input_ids
     __lowerCAmelCase								=					model.generate(__snake_case					, decoder_start_token_id=0       )
     print(tokenizer.decode(out[0]       )       )














import random



class 							_UpperCamelCase			:


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase	)->	tuple[list[int], list[int]]:
           __lowerCAmelCase								=					[ord(__UpperCamelCase	) for i in text]
           __lowerCAmelCase								=					[]
           __lowerCAmelCase								=					[]
           for i in plain:
                __lowerCAmelCase								=					random.randint(1					,						3_0_0	)
                __lowerCAmelCase								=					(i + k) * k
                cipher.append(__UpperCamelCase	)
                key.append(__UpperCamelCase	)
           return cipher, key


      @staticmethod
      def  __UpperCAmelCase		(	__UpperCamelCase					,						__UpperCamelCase	)->	str:
           __lowerCAmelCase								=					[]
           for i in range(len(__UpperCamelCase	)	):
                __lowerCAmelCase								=					int((cipher[i] - (key[i]) ** 2) / key[i]	)
                plain.append(chr(__UpperCamelCase	)	)
           return "".join(__UpperCamelCase	)


if __name__ == "__main__":
 lowerCamelCase      ,					lowerCamelCase			:					List[Any]         =	Onepad().encrypt('''Hello''')
 print(c, k)
 print(Onepad().decrypt(c, k))






