
import argparse
import json

import torch

from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    str=1							):



      """simple docstring"""




      if n_shave_prefix_segments >= 0:
            return ".".join(path.split("."							)[n_shave_prefix_segments:]							)
      else:
            return ".".join(path.split("."							)[:n_shave_prefix_segments]							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Optional[int]=0							):



      """simple docstring"""




      _lowerCamelCase				:   List[Any]								=   []
      for old_item in old_list:
            _lowerCamelCase				:   Union[str, Any]								=   old_item.replace("in_layers.0"				,	"norm1"							)
            _lowerCamelCase				:   List[str]								=   new_item.replace("in_layers.2"				,	"conv1"							)

            _lowerCamelCase				:   List[str]								=   new_item.replace("out_layers.0"				,	"norm2"							)
            _lowerCamelCase				:   Union[str, Any]								=   new_item.replace("out_layers.3"				,	"conv2"							)

            _lowerCamelCase				:   List[str]								=   new_item.replace("emb_layers.1"				,	"time_emb_proj"							)
            _lowerCamelCase				:   str								=   new_item.replace("skip_connection"				,	"conv_shortcut"							)

            _lowerCamelCase				:   int								=   shave_segments(_lowerCamelCase				,	n_shave_prefix_segments=_lowerCamelCase							)

            mapping.append({"old": old_item, "new": new_item}							)

      return mapping







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    List[Any]=0							):



      """simple docstring"""




      _lowerCamelCase				:   Any								=   []
      for old_item in old_list:
            _lowerCamelCase				:   List[str]								=   old_item

            _lowerCamelCase				:   Dict								=   new_item.replace("norm.weight"				,	"group_norm.weight"							)
            _lowerCamelCase				:   Optional[int]								=   new_item.replace("norm.bias"				,	"group_norm.bias"							)

            _lowerCamelCase				:   int								=   new_item.replace("proj_out.weight"				,	"proj_attn.weight"							)
            _lowerCamelCase				:   Any								=   new_item.replace("proj_out.bias"				,	"proj_attn.bias"							)

            _lowerCamelCase				:   Optional[Any]								=   shave_segments(_lowerCamelCase				,	n_shave_prefix_segments=_lowerCamelCase							)

            mapping.append({"old": old_item, "new": new_item}							)

      return mapping







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    Optional[Any]=None				,	_lowerCAmelCase   :    Optional[Any]=None				,	_lowerCAmelCase   :    Tuple=None							):



      """simple docstring"""




      assert isinstance(_lowerCamelCase				,	_lowerCamelCase							), "Paths should be a list of dicts containing 'old' and 'new' keys."

      # Splits the attention layers into three variables.
      if attention_paths_to_split is not None:
            for path, path_map in attention_paths_to_split.items():
                  _lowerCamelCase				:   Optional[Any]								=   old_checkpoint[path]
                  _lowerCamelCase				:   int								=   old_tensor.shape[0] // 3

                  _lowerCamelCase				:   Optional[int]								=   (-1, channels) if len(old_tensor.shape							) == 3 else (-1)

                  _lowerCamelCase				:   str								=   old_tensor.shape[0] // config["num_head_channels"] // 3

                  _lowerCamelCase				:   Union[str, Any]								=   old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:]							)
                  _lowerCamelCase						,		_lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   old_tensor.split(channels // num_heads				,	dim=1							)

                  _lowerCamelCase				:   Optional[Any]								=   query.reshape(_lowerCamelCase							)
                  _lowerCamelCase				:   int								=   key.reshape(_lowerCamelCase							)
                  _lowerCamelCase				:   str								=   value.reshape(_lowerCamelCase							)

      for path in paths:
            _lowerCamelCase				:   int								=   path["new"]

            # These have already been assigned
            if attention_paths_to_split is not None and new_path in attention_paths_to_split:
                  continue

            # Global renaming happens here
            _lowerCamelCase				:   Union[str, Any]								=   new_path.replace("middle_block.0"				,	"mid_block.resnets.0"							)
            _lowerCamelCase				:   Optional[Any]								=   new_path.replace("middle_block.1"				,	"mid_block.attentions.0"							)
            _lowerCamelCase				:   Any								=   new_path.replace("middle_block.2"				,	"mid_block.resnets.1"							)

            if additional_replacements is not None:
                  for replacement in additional_replacements:
                        _lowerCamelCase				:   Any								=   new_path.replace(replacement["old"]				,	replacement["new"]							)

        # proj_attn.weight has to be converted from conv 1D to linear
            if "proj_attn.weight" in new_path:
                  _lowerCamelCase				:   Optional[Any]								=   old_checkpoint[path["old"]][:, :, 0]
            else:
                  _lowerCamelCase				:   List[str]								=   old_checkpoint[path["old"]]







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   {}

      _lowerCamelCase				:   Dict								=   checkpoint["time_embed.0.weight"]
      _lowerCamelCase				:   Tuple								=   checkpoint["time_embed.0.bias"]
      _lowerCamelCase				:   Tuple								=   checkpoint["time_embed.2.weight"]
      _lowerCamelCase				:   List[str]								=   checkpoint["time_embed.2.bias"]

      _lowerCamelCase				:   Optional[int]								=   checkpoint["input_blocks.0.0.weight"]
      _lowerCamelCase				:   Optional[int]								=   checkpoint["input_blocks.0.0.bias"]

      _lowerCamelCase				:   Any								=   checkpoint["out.0.weight"]
      _lowerCamelCase				:   Optional[Any]								=   checkpoint["out.0.bias"]
      _lowerCamelCase				:   Any								=   checkpoint["out.2.weight"]
      _lowerCamelCase				:   Union[str, Any]								=   checkpoint["out.2.bias"]

      # Retrieves the keys for the input blocks only
      _lowerCamelCase				:   List[str]								=   len({".".join(layer.split("."							)[:2]							) for layer in checkpoint if "input_blocks" in layer}							)
      _lowerCamelCase				:   Dict								=   {
          layer_id: [key for key in checkpoint if F'input_blocks.{layer_id}' in key]
          for layer_id in range(_lowerCamelCase							)
      }

      # Retrieves the keys for the middle blocks only
      _lowerCamelCase				:   Dict								=   len({".".join(layer.split("."							)[:2]							) for layer in checkpoint if "middle_block" in layer}							)
      _lowerCamelCase				:   List[Any]								=   {
          layer_id: [key for key in checkpoint if F'middle_block.{layer_id}' in key]
          for layer_id in range(_lowerCamelCase							)
      }

      # Retrieves the keys for the output blocks only
      _lowerCamelCase				:   Dict								=   len({".".join(layer.split("."							)[:2]							) for layer in checkpoint if "output_blocks" in layer}							)
      _lowerCamelCase				:   Dict								=   {
          layer_id: [key for key in checkpoint if F'output_blocks.{layer_id}' in key]
          for layer_id in range(_lowerCamelCase							)
      }

      for i in range(1				,	_lowerCamelCase							):
            _lowerCamelCase				:   Union[str, Any]								=   (i - 1) // (config["num_res_blocks"] + 1)
            _lowerCamelCase				:   Dict								=   (i - 1) % (config["num_res_blocks"] + 1)

            _lowerCamelCase				:   Union[str, Any]								=   [key for key in input_blocks[i] if F'input_blocks.{i}.0' in key]
            _lowerCamelCase				:   Any								=   [key for key in input_blocks[i] if F'input_blocks.{i}.1' in key]

            if F'input_blocks.{i}.0.op.weight' in checkpoint:
                  _lowerCamelCase				:   str								=   checkpoint[
                      F'input_blocks.{i}.0.op.weight'
                  ]
                  _lowerCamelCase				:   Tuple								=   checkpoint[
                      F'input_blocks.{i}.0.op.bias'
                  ]
                  continue

            _lowerCamelCase				:   str								=   renew_resnet_paths(_lowerCamelCase							)
            _lowerCamelCase				:   int								=   {"old": F'input_blocks.{i}.0', "new": F'down_blocks.{block_id}.resnets.{layer_in_block_id}'}
            _lowerCamelCase				:   Optional[Any]								=   {"old": "resnets.2.op", "new": "downsamplers.0.op"}
            assign_to_checkpoint(
                _lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	additional_replacements=[meta_path, resnet_op]				,	config=_lowerCamelCase							)

            if len(_lowerCamelCase							):
                  _lowerCamelCase				:   List[Any]								=   renew_attention_paths(_lowerCamelCase							)
                  _lowerCamelCase				:   List[str]								=   {
                      "old": F'input_blocks.{i}.1',
                      "new": F'down_blocks.{block_id}.attentions.{layer_in_block_id}',
                  }
                  _lowerCamelCase				:   Tuple								=   {
                      F'input_blocks.{i}.1.qkv.bias': {
                          "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                          "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                          "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                      },
                      F'input_blocks.{i}.1.qkv.weight': {
                          "key": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                          "query": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                          "value": F'down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                      },
                  }
                  assign_to_checkpoint(
                      _lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	additional_replacements=[meta_path]				,	attention_paths_to_split=_lowerCamelCase				,	config=_lowerCamelCase				,	)

      _lowerCamelCase				:   str								=   middle_blocks[0]
      _lowerCamelCase				:   Optional[int]								=   middle_blocks[1]
      _lowerCamelCase				:   Dict								=   middle_blocks[2]

      _lowerCamelCase				:   Dict								=   renew_resnet_paths(_lowerCamelCase							)
      assign_to_checkpoint(_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	config=_lowerCamelCase							)

      _lowerCamelCase				:   Tuple								=   renew_resnet_paths(_lowerCamelCase							)
      assign_to_checkpoint(_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	config=_lowerCamelCase							)

      _lowerCamelCase				:   Optional[int]								=   renew_attention_paths(_lowerCamelCase							)
      _lowerCamelCase				:   int								=   {
          "middle_block.1.qkv.bias": {
              "key": "mid_block.attentions.0.key.bias",
              "query": "mid_block.attentions.0.query.bias",
              "value": "mid_block.attentions.0.value.bias",
          },
          "middle_block.1.qkv.weight": {
              "key": "mid_block.attentions.0.key.weight",
              "query": "mid_block.attentions.0.query.weight",
              "value": "mid_block.attentions.0.value.weight",
          },
      }
      assign_to_checkpoint(
          _lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	attention_paths_to_split=_lowerCamelCase				,	config=_lowerCamelCase							)

      for i in range(_lowerCamelCase							):
            _lowerCamelCase				:   Optional[int]								=   i // (config["num_res_blocks"] + 1)
            _lowerCamelCase				:   List[str]								=   i % (config["num_res_blocks"] + 1)
            _lowerCamelCase				:   int								=   [shave_segments(_lowerCamelCase				,	2							) for name in output_blocks[i]]
            _lowerCamelCase				:   Tuple								=   {}

            for layer in output_block_layers:
                  _lowerCamelCase						,		_lowerCamelCase				:   str								=   layer.split("."							)[0], shave_segments(_lowerCamelCase				,	1							)
                  if layer_id in output_block_list:
                        output_block_list[layer_id].append(_lowerCamelCase							)
                  else:
                        _lowerCamelCase				:   str								=   [layer_name]

            if len(_lowerCamelCase							) > 1:
                  _lowerCamelCase				:   Optional[Any]								=   [key for key in output_blocks[i] if F'output_blocks.{i}.0' in key]
                  _lowerCamelCase				:   Tuple								=   [key for key in output_blocks[i] if F'output_blocks.{i}.1' in key]

                  _lowerCamelCase				:   Tuple								=   renew_resnet_paths(_lowerCamelCase							)
                  _lowerCamelCase				:   int								=   renew_resnet_paths(_lowerCamelCase							)

                  _lowerCamelCase				:   Optional[int]								=   {"old": F'output_blocks.{i}.0', "new": F'up_blocks.{block_id}.resnets.{layer_in_block_id}'}
                  assign_to_checkpoint(_lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	additional_replacements=[meta_path]				,	config=_lowerCamelCase							)

                  if ["conv.weight", "conv.bias"] in output_block_list.values():
                        _lowerCamelCase				:   List[str]								=   list(output_block_list.values()							).index(["conv.weight", "conv.bias"]							)
                        _lowerCamelCase				:   Optional[int]								=   checkpoint[
                            F'output_blocks.{i}.{index}.conv.weight'
                        ]
                        _lowerCamelCase				:   List[Any]								=   checkpoint[
                            F'output_blocks.{i}.{index}.conv.bias'
                        ]

                        # Clear attentions as they have been attributed above.
                        if len(_lowerCamelCase							) == 2:
                              _lowerCamelCase				:   str								=   []

                  if len(_lowerCamelCase							):
                        _lowerCamelCase				:   Dict								=   renew_attention_paths(_lowerCamelCase							)
                        _lowerCamelCase				:   List[str]								=   {
                            "old": F'output_blocks.{i}.1',
                            "new": F'up_blocks.{block_id}.attentions.{layer_in_block_id}',
                        }
                        _lowerCamelCase				:   Any								=   {
                            F'output_blocks.{i}.1.qkv.bias': {
                                "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias',
                                "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias',
                                "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias',
                            },
                            F'output_blocks.{i}.1.qkv.weight': {
                                "key": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight',
                                "query": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight',
                                "value": F'up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight',
                            },
                        }
                        assign_to_checkpoint(
                            _lowerCamelCase				,	_lowerCamelCase				,	_lowerCamelCase				,	additional_replacements=[meta_path]				,	attention_paths_to_split=to_split if any("qkv" in key for key in attentions							) else None				,	config=_lowerCamelCase				,	)
            else:
                  _lowerCamelCase				:   Union[str, Any]								=   renew_resnet_paths(_lowerCamelCase				,	n_shave_prefix_segments=1							)
                  for path in resnet_0_paths:
                        _lowerCamelCase				:   int								=   ".".join(["output_blocks", str(_lowerCamelCase							), path["old"]]							)
                        _lowerCamelCase				:   Union[str, Any]								=   ".".join(["up_blocks", str(_lowerCamelCase							), "resnets", str(_lowerCamelCase							), path["new"]]							)

                        _lowerCamelCase				:   str								=   checkpoint[old_path]

      return new_checkpoint


if __name__ == "__main__":
 UpperCAmelCase_       :   Union[str, Any]   =       argparse.ArgumentParser()

 parser.add_argument(
     '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
 )

 parser.add_argument(
     '--config_file',
     default=None,
     type=str,
     required=True,
     help='The config json file corresponding to the architecture.',
 )

 parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

 UpperCAmelCase_       :   Dict   =       parser.parse_args()

 UpperCAmelCase_       :   int   =       torch.load(args.checkpoint_path)

 with open(args.config_file) as f:
  UpperCAmelCase_       :   List[str]   =       json.loads(f.read())

 UpperCAmelCase_       :   Tuple   =       convert_ldm_checkpoint(checkpoint, config)

 if "ldm" in config:
  del config["ldm"]

 UpperCAmelCase_       :   Union[str, Any]   =       UNetaDModel(**config)
 model.load_state_dict(converted_checkpoint)

 try:
  UpperCAmelCase_       :   Optional[int]   =       DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
  UpperCAmelCase_       :   Optional[Any]   =       VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))

  UpperCAmelCase_       :   Optional[int]   =       LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
  pipe.save_pretrained(args.dump_path)
 except:  # noqa: E722
  model.save_pretrained(args.dump_path)

'''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 		A_       (	_lowerCAmelCase   :    Any				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




      if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
            # expert layer
            _lowerCamelCase				:   List[str]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   Union[str, Any]								=   torch.permute(_lowerCAmelCase				,	(0, 2, 1)							)
      elif flax_key_tuple[-1] == "kernel" and ".".join(_lowerCAmelCase							):
            # linear layer
            _lowerCamelCase				:   Union[str, Any]								=   flax_key_tuple[:-1] + ("weight",)
            _lowerCamelCase				:   int								=   flax_tensor.T
      elif flax_key_tuple[-1] in ["scale", "embedding"]:
            _lowerCamelCase				:   Tuple								=   flax_key_tuple[:-1] + ("weight",)

      return flax_key_tuple, flax_tensor







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    int				,	_lowerCAmelCase   :    Any							):



      """simple docstring"""




      if "metadata" in layer:
            _lowerCamelCase				:   Optional[int]								=   layer.split("metadata"							)
            _lowerCamelCase				:   Union[str, Any]								=   "".join(split_layer[0]							)[:-1]
            _lowerCamelCase				:   Dict								=   [tuple(("metadata" + split_layer[1]).split("/"							)							)]
      elif "kvstore" in layer:
            _lowerCamelCase				:   List[str]								=   layer.split("kvstore"							)
            _lowerCamelCase				:   Optional[int]								=   "".join(split_layer[0]							)[:-1]
            _lowerCamelCase				:   List[Any]								=   [tuple(("kvstore" + split_layer[1]).split("/"							)							)]

      else:
            _lowerCamelCase				:   Tuple								=   layer.split("/"							)
            _lowerCamelCase				:   int								=   "/".join(split_layer[:-1]							)
            _lowerCamelCase				:   Optional[Any]								=   (split_layer[-1],)

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

      return curr_real_layer_name, split_layer, content







def 		A_       (	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   rename_keys(_lowerCAmelCase							)
      _lowerCamelCase				:   Dict								=   {}
      for k, v in current_block.items():
            _lowerCamelCase				:   Union[str, Any]								=   v
      _lowerCamelCase				:   str								=   new_current_block
      torch.save(_lowerCAmelCase				,	_lowerCAmelCase							)







def 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    Tuple				,	_lowerCAmelCase   :    str = WEIGHTS_NAME							):



      """simple docstring"""




      _lowerCamelCase				:   Dict								=   convert_file_size_to_int(_lowerCAmelCase							)
      _lowerCamelCase				:   Union[str, Any]								=   []
      _lowerCamelCase				:   Dict								=   {}
      _lowerCamelCase				:   Optional[int]								=   0
      _lowerCamelCase				:   List[str]								=   0

      os.makedirs(_lowerCAmelCase				,	exist_ok=_lowerCAmelCase							)
      with gfile.GFile(switch_checkpoint_path + "/checkpoint"				,	"rb"							) as fp:
            _lowerCamelCase				:   Tuple								=   serialization.msgpack_restore(fp.read()							)["optimizer"]["target"]
            _lowerCamelCase				:   Union[str, Any]								=   flatten_dict(_lowerCAmelCase				,	sep="/"							)

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

      for key in all_layers.keys():
            # open tensorstore file
            _lowerCamelCase				:   Any								=   ts.open(unflatten_dict(all_layers[key]							)							).result().read().result()
            _lowerCamelCase				:   int								=   torch.tensor(_lowerCAmelCase							)
            _lowerCamelCase				:   str								=   raw_weights.numel() * dtype_byte_size(raw_weights.dtype							)

            # use the renaming pattern from the small conversion scripts
            _lowerCamelCase						,		_lowerCamelCase				:   Optional[Any]								=   rename_base_flax_keys(tuple(key.split("/"							)							)				,	_lowerCAmelCase							)
            _lowerCamelCase				:   Optional[Any]								=   "/".join(_lowerCAmelCase							)

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

            _lowerCamelCase				:   Optional[int]								=   raw_weights.to(getattr(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            current_block_size += weight_size
            total_size += weight_size

      # Add the last block
      _lowerCamelCase				:   List[str]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{len(_lowerCAmelCase							)+1:05d}-of-???.bin'							)							)
      rename_and_save_block(_lowerCAmelCase				,	_lowerCAmelCase							)
      sharded_state_dicts.append(current_block.keys()							)

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

      # Otherwise, let's build the index
      _lowerCamelCase				:   Union[str, Any]								=   {}
      _lowerCamelCase				:   str								=   {}
      for idx, shard in enumerate(_lowerCAmelCase							):
            _lowerCamelCase				:   Dict								=   weights_name.replace(
                ".bin"				,	F'-{idx+1:05d}-of-{len(_lowerCAmelCase							):05d}.bin'							)  # len(sharded_state_dicts):05d}
            _lowerCamelCase				:   Union[str, Any]								=   os.path.join(_lowerCAmelCase				,	weights_name.replace(".bin"				,	F'-{idx+1:05d}-of-???.bin'							)							)
            os.rename(_lowerCAmelCase				,	os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)							)
            _lowerCamelCase				:   Tuple								=   shard
            for key in shard:
                  _lowerCamelCase				:   str								=   shard_file

    # Add the metadata
      _lowerCamelCase				:   Optional[Any]								=   {"total_size": total_size}
      _lowerCamelCase				:   Dict								=   {"metadata": metadata, "weight_map": weight_map}

      with open(os.path.join(_lowerCAmelCase				,	_lowerCAmelCase							)				,	"w"				,	encoding="utf-8"							) as f:
            _lowerCamelCase				:   int								=   json.dumps(_lowerCAmelCase				,	indent=2				,	sort_keys=_lowerCAmelCase							) + "\n"
            f.write(_lowerCAmelCase							)

      return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_       :   List[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.',
 )
 UpperCAmelCase_       :   Dict   =       parser.parse_args()
 shard_on_the_fly(
     args.switch_tax_checkpoint_path,
     args.pytorch_dump_folder_path,
     args.max_shard_size,
     args.dtype,
 )







def 		A_       (	):



      """simple docstring"""




      from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

      _lowerCamelCase				:   Optional[int]								=   SwitchTransformersConfig.from_pretrained("google/switch-base-8"							)
      config.save_pretrained("/home/arthur_huggingface_co/transformers/switch_converted"							)
      _lowerCamelCase				:   int								=   SwitchTransformersForConditionalGeneration.from_pretrained(
          "/home/arthur_huggingface_co/transformers/switch_converted"				,	device_map="auto"							)

      _lowerCamelCase				:   List[str]								=   TaTokenizer.from_pretrained("t5-small"							)
      _lowerCamelCase				:   Tuple								=   "A <extra_id_0> walks into a bar a orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>."

      _lowerCamelCase				:   Optional[int]								=   tokenizer(_lowerCAmelCase				,	return_tensors="pt"							).input_ids
      _lowerCamelCase				:   List[Any]								=   model.generate(_lowerCAmelCase				,	decoder_start_token_id=0							)
      print(tokenizer.decode(out[0]							)							)