

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 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase    ):






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

    return flax_key_tuple, flax_tensor





def 					__snake_case     (      _UpperCAmelCase       ,			_UpperCAmelCase       ,			_UpperCAmelCase    ):






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

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

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

    return curr_real_layer_name, split_layer, content





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





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

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

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

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

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

        __a							=    raw_weights.to(getattr(_UpperCAmelCase       ,			_UpperCAmelCase    )    )
        current_block_size += weight_size
        total_size += weight_size

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

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

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

    return metadata, index


if __name__ == "__main__":
 __snake_case						:Union[str, Any]						=					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.''',
 )
 __snake_case						:Optional[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 					__snake_case     (      ):
    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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










from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass

@dataclass
class    _A	:
       UpperCamelCase__  :   int
       UpperCamelCase__  :   TreeNode | None						 =						None
       UpperCamelCase__  :   TreeNode | None						 =						None


__snake_case						:Optional[Any]						=					namedtuple('''CoinsDistribResult''', '''moves excess''')





def 					__snake_case     (      _UpperCAmelCase    ):

    if root is None:
        return 0

    # Validation
    def count_nodes(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_nodes(node.left    ) + count_nodes(node.right    ) + 1

    def count_coins(_UpperCAmelCase    ) -> int:
        if node is None:
            return 0

        return count_coins(node.left    ) + count_coins(node.right    ) + node.data

    if count_nodes(_UpperCAmelCase    ) != count_coins(_UpperCAmelCase    ):
        raise ValueError('''The nodes number should be same as the number of coins'''    )

    # Main calculation
    def get_distrib(_UpperCAmelCase    ) -> CoinsDistribResult:

        if node is None:
            return CoinsDistribResult(0       ,			1    )

        __a	,       __a							=    get_distrib(node.left    )
        __a	,       __a							=    get_distrib(node.right    )

        __a							=    1 - left_distrib_excess
        __a							=    1 - right_distrib_excess

        __a							=    (
            left_distrib_moves
            + right_distrib_moves
            + abs(_UpperCAmelCase    )
            + abs(_UpperCAmelCase    )
        )
        __a							=    node.data - coins_to_left - coins_to_right

        return CoinsDistribResult(_UpperCAmelCase       ,			_UpperCAmelCase    )

    return get_distrib(_UpperCAmelCase    )[0]


if __name__ == "__main__":
 import doctest

 doctest.testmod()





