


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      UpperCamelCase     (   _a							,		_a		)       ->				Optional[Any]:
     '''simple docstring'''





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

     return flax_key_tuple, flax_tensor




def      UpperCamelCase     (   _a							,		_a							,		_a		)       ->				List[Any]:
     '''simple docstring'''





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

     else:
          lowercase_							:Any			     =							layer.split('''/'''		)
          lowercase_							:Union[str, Any]			     =							"/".join(split_layer[:-1]		)
          lowercase_							:Tuple			     =							(split_layer[-1],)

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

     return curr_real_layer_name, split_layer, content




def      UpperCamelCase     (   _a							,		_a		)       ->				Optional[int]:
     '''simple docstring'''





     lowercase_							:List[Any]			     =							rename_keys(__UpperCamelCase		)
     lowercase_							:Tuple			     =							{}
     for k, v in current_block.items():
          lowercase_							:str			     =							v
     lowercase_							:Optional[int]			     =							new_current_block
     torch.save(__UpperCamelCase							,		__UpperCamelCase		)




def      UpperCamelCase     (   _a							,		_a							,		_a							,		_a							,		_a = WEIGHTS_NAME		)       ->				Any:
     '''simple docstring'''





     lowercase_							:Optional[Any]			     =							convert_file_size_to_int(__UpperCamelCase		)
     lowercase_							:Tuple			     =							[]
     lowercase_							:Dict			     =							{}
     lowercase_							:str			     =							0
     lowercase_							:Tuple			     =							0

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

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

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

          # use the renaming pattern from the small conversion scripts
          lowercase_							:int			     =							rename_base_flax_keys(tuple(key.split('''/'''		)		)							,		__UpperCamelCase		)
          lowercase_							:Union[str, Any]			     =							"/".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_							:int			     =							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_							:int			     =							{}
               lowercase_							:Optional[int]			     =							0

          lowercase_							:List[str]			     =							raw_weights.to(getattr(__UpperCamelCase							,		__UpperCamelCase		)		)
          current_block_size += weight_size
          total_size += weight_size

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

    # Add the metadata
     lowercase_							:int			     =							{"total_size": total_size}
     lowercase_							:List[str]			     =							{"metadata": metadata, "weight_map": weight_map}

     with open(os.path.join(__UpperCamelCase							,		__UpperCamelCase		)							,		'''w'''							,		encoding='''utf-8'''		) as f:
          lowercase_							:List[str]			     =							json.dumps(__UpperCamelCase							,		indent=2							,		sort_keys=__UpperCamelCase		) + "\n"
          f.write(__UpperCamelCase		)

     return metadata, index


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE							:		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.",
   )
   SCREAMING_SNAKE_CASE							:		str       =					parser.parse_args()
   shard_on_the_fly(
       args.switch_tax_checkpoint_path,
       args.pytorch_dump_folder_path,
       args.max_shard_size,
       args.dtype,
   )




def      UpperCamelCase     (   )       ->				int:
     '''simple docstring'''





     from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

     lowercase_							:Optional[int]			     =							tokenizer(__UpperCamelCase							,		return_tensors='''pt'''		).input_ids
     lowercase_							:Optional[Any]			     =							model.generate(__UpperCamelCase							,		decoder_start_token_id=0		)
     print(tokenizer.decode(out[0]		)		)






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCamelCase		       =      {
    """configuration_instructblip""": [
        """INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InstructBlipConfig""",
        """InstructBlipQFormerConfig""",
        """InstructBlipVisionConfig""",
    ],
    """processing_instructblip""": ["""InstructBlipProcessor"""],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCamelCase		       =      [
						    """INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
						    """InstructBlipQFormerModel""",
						    """InstructBlipPreTrainedModel""",
						    """InstructBlipForConditionalGeneration""",
						    """InstructBlipVisionModel""",
						]

if TYPE_CHECKING:
						from .configuration_instructblip import (
						    INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    InstructBlipConfig,
						    InstructBlipQFormerConfig,
						    InstructBlipVisionConfig,
						)
						from .processing_instructblip import InstructBlipProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_instructblip import (
												    INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
												    InstructBlipForConditionalGeneration,
												    InstructBlipPreTrainedModel,
												    InstructBlipQFormerModel,
												    InstructBlipVisionModel,
												)

else:
						import sys

						lowerCamelCase		       =      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)


