






"""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_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	str:
    """simple docstring"""

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

    return flax_key_tuple, flax_tensor







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	Tuple:
    """simple docstring"""

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

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

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

    return curr_real_layer_name, split_layer, content







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					->	List[str]:
    """simple docstring"""

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







def   lowerCAmelCase_( SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE = WEIGHTS_NAME					)					->	Union[str, Any]:
    """simple docstring"""

    UpperCamelCase__         =    convert_file_size_to_int(SCREAMING_SNAKE_CASE					)
    UpperCamelCase__         =    []
    UpperCamelCase__         =    {}
    UpperCamelCase__         =    0
    UpperCamelCase__         =    0

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

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

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

        # use the renaming pattern from the small conversion scripts
        UpperCamelCase__       ,						UpperCamelCase__         =    rename_base_flax_keys(tuple(key.split('/'					)					) ,	SCREAMING_SNAKE_CASE					)
        UpperCamelCase__         =    '/'.join(SCREAMING_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:
            UpperCamelCase__         =    os.path.join(
                SCREAMING_SNAKE_CASE ,	weights_name.replace('.bin' ,	F'-{len(SCREAMING_SNAKE_CASE					)+1:05d}-of-???.bin'					)					)
            rename_and_save_block(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)
            sharded_state_dicts.append(current_block.keys()					)
            del current_block
            UpperCamelCase__         =    {}
            UpperCamelCase__         =    0

        UpperCamelCase__         =    raw_weights.to(getattr(SCREAMING_SNAKE_CASE ,	SCREAMING_SNAKE_CASE					)					)
        current_block_size += weight_size
        total_size += weight_size

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

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

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

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

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

    return metadata, index


if __name__ == "__main__":
  A__					: 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.""",
  )
  A__					: 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_( )					->	Union[str, Any]:
    """simple docstring"""

    from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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












"""simple docstring"""





from __future__ import annotations

from typing import Generic, TypeVar

A__					: Any=	TypeVar("""T""")
class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self ,				snake_case_      )		->       None:
       UpperCamelCase__         =    data
       UpperCamelCase__         =    self
       UpperCamelCase__         =    0







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # map from node name to the node object
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # create a new set with x as its member
       UpperCamelCase__         =    DisjointSetTreeNode(snake_case_      )

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       DisjointSetTreeNode[T]:
       # find the set x belongs to (with path-compression)
       UpperCamelCase__         =    self.map[data]
       if elem_ref != elem_ref.parent:
           UpperCamelCase__         =    self.find_set(elem_ref.parent.data      )
       return elem_ref.parent

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # helper function for union operation
       if nodea.rank > nodea.rank:
           UpperCamelCase__         =    nodea
       else:
           UpperCamelCase__         =    nodea
           if nodea.rank == nodea.rank:
               nodea.rank += 1







   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_      )		->       None:
       # merge 2 disjoint sets
       self.link(self.find_set(snake_case_      ) ,				self.find_set(snake_case_      )      )







class    __lowerCamelCase    (  Generic[T]  ):

   def __init__(					self      )		->       None:
       # connections: map from the node to the neighbouring nodes (with weights)
       UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_      )		->       None:
       # add a node ONLY if its not present in the graph
       if node not in self.connections:
           UpperCamelCase__         =    {}

   def 						SCREAMING_SNAKE_CASE__ (					self ,				snake_case_ ,				snake_case_ ,				snake_case_      )		->       None:
       # add an edge with the given weight
       self.add_node(snake_case_      )
       self.add_node(snake_case_      )
       UpperCamelCase__         =    weight
       UpperCamelCase__         =    weight







   def 						SCREAMING_SNAKE_CASE__ (					self      )		->       GraphUndirectedWeighted[T]:
       UpperCamelCase__         =    []
       UpperCamelCase__         =    set()
       for start in self.connections:
           for end in self.connections[start]:
               if (start, end) not in seen:
                   seen.add((end, start)      )
                   edges.append((start, end, self.connections[start][end])      )
       edges.sort(key=lambda snake_case_      : x[2]      )

       # creating the disjoint set
       UpperCamelCase__         =    DisjointSetTree[T]()
       for node in self.connections:
           disjoint_set.make_set(snake_case_      )

       # MST generation
       UpperCamelCase__         =    0
       UpperCamelCase__         =    0
       UpperCamelCase__         =    GraphUndirectedWeighted[T]()
       while num_edges < len(self.connections      ) - 1:
           UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__         =    edges[index]
           index += 1
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           UpperCamelCase__         =    disjoint_set.find_set(snake_case_      )
           if parent_u != parent_v:
               num_edges += 1
               graph.add_edge(snake_case_ ,				snake_case_ ,				snake_case_      )
               disjoint_set.union(snake_case_ ,				snake_case_      )
       return graph




