

"""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  (  UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	Tuple)     ->						Optional[Any]:
   '''simple docstring'''


   if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
      # expert layer
      __lowercase								=						flax_key_tuple[:-1] + ("weight",)
      __lowercase								=						torch.permute(UpperCamelCase_,      (0, 2, 1))
   elif flax_key_tuple[-1] == "kernel" and ".".join(UpperCamelCase_):
      # linear layer
      __lowercase								=						flax_key_tuple[:-1] + ("weight",)
      __lowercase								=						flax_tensor.T
   elif flax_key_tuple[-1] in ["scale", "embedding"]:
      __lowercase								=						flax_key_tuple[:-1] + ("weight",)

   return flax_key_tuple, flax_tensor




def   _A  (  UpperCamelCase_					:	Tuple,      UpperCamelCase_					:	Dict,      UpperCamelCase_					:	Tuple)     ->						List[str]:
   '''simple docstring'''


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

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

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

   return curr_real_layer_name, split_layer, content




def   _A  (  UpperCamelCase_					:	Union[str, Any],      UpperCamelCase_					:	Union[str, Any])     ->						Dict:
   '''simple docstring'''


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




def   _A  (  UpperCamelCase_					:	Any,      UpperCamelCase_					:	List[str],      UpperCamelCase_					:	Optional[int],      UpperCamelCase_					:	str,      UpperCamelCase_					:	str = WEIGHTS_NAME)     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						convert_file_size_to_int(UpperCamelCase_)
   __lowercase								=						[]
   __lowercase								=						{}
   __lowercase								=						0
   __lowercase								=						0

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

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

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

      # use the renaming pattern from the small conversion scripts
      __lowercase		,__lowercase								=						rename_base_flax_keys(tuple(key.split("/")),      UpperCamelCase_)
      __lowercase								=						"/".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:
         __lowercase								=						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
         __lowercase								=						{}
         __lowercase								=						0

      __lowercase								=						raw_weights.to(getattr(UpperCamelCase_,      UpperCamelCase_))
      current_block_size += weight_size
      total_size += weight_size

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

    # Add the metadata
   __lowercase								=						{"total_size": total_size}
   __lowercase								=						{"metadata": metadata, "weight_map": weight_map}

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

   return metadata, index


if __name__ == "__main__":
   _a							=							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							=							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  (  )     ->						Union[str, Any]:
   '''simple docstring'''


   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

   __lowercase								=						tokenizer(UpperCamelCase_,      return_tensors="pt").input_ids
   __lowercase								=						model.generate(UpperCamelCase_,      decoder_start_token_id=0)
   print(tokenizer.decode(out[0]))







"""simple docstring"""





# Copyright 2021 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.

import argparse
import os

from accelerate.utils import ComputeEnvironment

from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file  # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment  # noqa: F401
from .sagemaker import get_sagemaker_input


_a							=							'Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine'




def   _A  (  )     ->						Tuple:
   '''simple docstring'''


   __lowercase								=						_ask_options(
       "In which compute environment are you running?",      ["This machine", "AWS (Amazon SageMaker)"],      _convert_compute_environment,      )
   if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
      __lowercase								=						get_sagemaker_input()
   else:
      __lowercase								=						get_cluster_input()
   return config




def   _A  (  UpperCamelCase_					:	Union[str, Any]=None)     ->						Union[str, Any]:
   '''simple docstring'''


   if subparsers is not None:
      __lowercase								=						subparsers.add_parser("config",      description=UpperCamelCase_)
   else:
      __lowercase								=						argparse.ArgumentParser("Accelerate config command",      description=UpperCamelCase_)

   parser.add_argument(
       "--config_file",      default=UpperCamelCase_,      help=(
           "The path to use to store the config file. Will default to a file named default_config.yaml in the cache "
           "location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have "
           "such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed "
           "with 'huggingface'."
       ),      )

   if subparsers is not None:
      parser.set_defaults(func=UpperCamelCase_)
   return parser




def   _A  (  UpperCamelCase_					:	Dict)     ->						str:
   '''simple docstring'''


   __lowercase								=						get_user_input()
   if args.config_file is not None:
      __lowercase								=						args.config_file
   else:
      if not os.path.isdir(UpperCamelCase_):
         os.makedirs(UpperCamelCase_)
      __lowercase								=						default_yaml_config_file

   if config_file.endswith(".json"):
      config.to_json_file(UpperCamelCase_)
   else:
      config.to_yaml_file(UpperCamelCase_)
   print(F"""accelerate configuration saved at {config_file}""")




def   _A  (  )     ->						Optional[Any]:
   '''simple docstring'''


   __lowercase								=						config_command_parser()
   __lowercase								=						parser.parse_args()
   config_command(UpperCamelCase_)


if __name__ == "__main__":
   main()




