






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

  return flax_key_tuple, flax_tensor






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

  else:
    __UpperCAmelCase							:	Any           =  layer.split("""/"""		)
    __UpperCAmelCase							:	Dict           =  """/""".join(split_layer[:-1]		)
    __UpperCAmelCase							:	List[str]           =  (split_layer[-1],)

  if "kvstore/path" in layer:
    __UpperCAmelCase							:	List[str]           =  f"""{switch_checkpoint_path}/{checkpoint_info[layer]}"""
  elif "kvstore/driver" in layer:
    __UpperCAmelCase							:	List[str]           =  """file"""
  else:
    __UpperCAmelCase							:	int           =  checkpoint_info[layer]

  return curr_real_layer_name, split_layer, content






def 				lowerCamelCase__      (    __lowerCamelCase			:     Dict  ,		__lowerCamelCase			:     Optional[Any]		):
  __UpperCAmelCase							:	List[Any]           =  rename_keys(__lowerCamelCase		)
  __UpperCAmelCase							:	Optional[int]           =  {}
  for k, v in current_block.items():
    __UpperCAmelCase							:	Union[str, Any]           =  v
  __UpperCAmelCase							:	str           =  new_current_block
  torch.save(__lowerCamelCase  ,		__lowerCamelCase		)






def 				lowerCamelCase__      (    __lowerCamelCase			:     Tuple  ,		__lowerCamelCase			:     str  ,		__lowerCamelCase			:     Union[str, Any]  ,		__lowerCamelCase			:     List[str]  ,		__lowerCamelCase			:     str = WEIGHTS_NAME		):
  __UpperCAmelCase							:	int           =  convert_file_size_to_int(__lowerCamelCase		)
  __UpperCAmelCase							:	Union[str, Any]           =  []
  __UpperCAmelCase							:	Dict           =  {}
  __UpperCAmelCase							:	List[Any]           =  0
  __UpperCAmelCase							:	List[str]           =  0

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

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

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

    # use the renaming pattern from the small conversion scripts
    __UpperCAmelCase      , __UpperCAmelCase							:	Any           =  rename_base_flax_keys(tuple(key.split("""/"""		)		)  ,		__lowerCamelCase		)
    __UpperCAmelCase							:	str           =  """/""".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:
      __UpperCAmelCase							:	Optional[Any]           =  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
      __UpperCAmelCase							:	List[Any]           =  {}
      __UpperCAmelCase							:	Optional[Any]           =  0

    __UpperCAmelCase							:	Union[str, Any]           =  raw_weights.to(getattr(__lowerCamelCase  ,		__lowerCamelCase		)		)
    current_block_size += weight_size
    total_size += weight_size

  # Add the last block
  __UpperCAmelCase							:	Any           =  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
  __UpperCAmelCase							:	str           =  {}
  __UpperCAmelCase							:	Dict           =  {}
  for idx, shard in enumerate(__lowerCamelCase		):
    __UpperCAmelCase							:	Optional[Any]           =  weights_name.replace(
        """.bin"""  ,		f"""-{idx+1:05d}-of-{len(__lowerCamelCase		):05d}.bin"""		)  # len(sharded_state_dicts):05d}
    __UpperCAmelCase							:	int           =  os.path.join(__lowerCamelCase  ,		weights_name.replace(""".bin"""  ,		f"""-{idx+1:05d}-of-???.bin"""		)		)
    os.rename(__lowerCamelCase  ,		os.path.join(__lowerCamelCase  ,		__lowerCamelCase		)		)
    __UpperCAmelCase							:	Union[str, Any]           =  shard
    for key in shard:
      __UpperCAmelCase							:	Tuple           =  shard_file

    # Add the metadata
  __UpperCAmelCase							:	Tuple           =  {"""total_size""": total_size}
  __UpperCAmelCase							:	int           =  {"""metadata""": metadata, """weight_map""": weight_map}

  with open(os.path.join(__lowerCamelCase  ,		__lowerCamelCase		)  ,		"""w"""  ,		encoding="""utf-8"""		) as f:
    __UpperCAmelCase							:	Optional[Any]           =  json.dumps(__lowerCamelCase  ,		indent=2  ,		sort_keys=__lowerCamelCase		) + """\n"""
    f.write(__lowerCamelCase		)

  return metadata, index


if __name__ == "__main__":
       a		:       Tuple			     = 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__      (    ):
  from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

  __UpperCAmelCase							:	Union[str, Any]           =  SwitchTransformersConfig.from_pretrained("""google/switch-base-8"""		)
  config.save_pretrained("""/home/arthur_huggingface_co/transformers/switch_converted"""		)
  __UpperCAmelCase							:	List[Any]           =  SwitchTransformersForConditionalGeneration.from_pretrained(
      """/home/arthur_huggingface_co/transformers/switch_converted"""  ,		device_map="""auto"""		)

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

  __UpperCAmelCase							:	Tuple           =  tokenizer(__lowerCamelCase  ,		return_tensors="""pt"""		).input_ids
  __UpperCAmelCase							:	List[str]           =  model.generate(__lowerCamelCase  ,		decoder_start_token_id=0		)
  print(tokenizer.decode(out[0]		)		)










# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a		:       Union[str, Any]			     = {"configuration_timm_backbone": ["TimmBackboneConfig"]}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a		:       Tuple			     = ["TimmBackbone"]


if TYPE_CHECKING:
       from .configuration_timm_backbone import TimmBackboneConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_timm_backbone import TimmBackbone

else:
       import sys

       a		:       List[Any]			     = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


