

'''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 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					int    )				->      Dict:
     """simple docstring"""




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

     return flax_key_tuple, flax_tensor


def 	_lowerCAmelCase (						_UpperCamelCase						:					List[Any]							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Optional[int]    )				->      List[str]:
     """simple docstring"""




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

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

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

     return curr_real_layer_name, split_layer, content


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Tuple    )				->      Dict:
     """simple docstring"""




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


def 	_lowerCAmelCase (						_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					Union[str, Any]							,			_UpperCamelCase						:					Optional[Any]							,			_UpperCamelCase						:					int							,			_UpperCamelCase						:					str = WEIGHTS_NAME    )				->      List[Any]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =convert_file_size_to_int(_UpperCamelCase    )
     _SCREAMING_SNAKE_CASE							   =[]
     _SCREAMING_SNAKE_CASE							   ={}
     _SCREAMING_SNAKE_CASE							   =0
     _SCREAMING_SNAKE_CASE							   =0

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

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

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

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

          # 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							   =os.path.join(
                   _UpperCamelCase							,			weights_name.replace('.bin'							,			f"-{len(_UpperCamelCase    )+1:05d}-of-???.bin"    )    )
               rename_and_save_block(_UpperCamelCase							,			_UpperCamelCase    )
               sharded_state_dicts.append(current_block.keys()    )
               del current_block
               _SCREAMING_SNAKE_CASE							   ={}
               _SCREAMING_SNAKE_CASE							   =0

          _SCREAMING_SNAKE_CASE							   =raw_weights.to(getattr(_UpperCamelCase							,			_UpperCamelCase    )    )
          current_block_size += weight_size
          total_size += weight_size

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

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

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

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

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

     return metadata, index


if __name__ == "__main__":
     lowerCamelCase			:				List[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			:				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 	_lowerCAmelCase (						)				->      Union[str, Any]:
     """simple docstring"""




     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

     _SCREAMING_SNAKE_CASE							   =TaTokenizer.from_pretrained('t5-small'    )
     _SCREAMING_SNAKE_CASE							   ='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							   =tokenizer(_UpperCamelCase							,			return_tensors='pt'    ).input_ids
     _SCREAMING_SNAKE_CASE							   =model.generate(_UpperCamelCase							,			decoder_start_token_id=0    )
     print(tokenizer.decode(out[0]    )    )



'''simple docstring'''
def 	_lowerCAmelCase (						_UpperCamelCase						:					int = 50    )				->      int:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							   =[[0] * 3 for _ in range(length + 1    )]

     for row_length in range(length + 1    ):
          for tile_length in range(2							,			5    ):
               for tile_start in range(row_length - tile_length + 1    ):
                    different_colour_ways_number[row_length][tile_length - 2] += (
                        different_colour_ways_number[row_length - tile_start - tile_length][
                            tile_length - 2
                        ]
                        + 1
                    )

     return sum(different_colour_ways_number[length]    )


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