





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__				:int				,     a__				:List[str]    ):





							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       _UpperCamelCase       (a__				:Tuple				,     a__				:List[str]				,     a__				:Tuple    ):





							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       _UpperCamelCase       (a__				:str				,     a__				:List[str]    ):
							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       _UpperCamelCase       (a__				:List[str]				,     a__				:List[Any]				,     a__				:Any				,     a__				:Optional[int]				,     a__				:str = WEIGHTS_NAME    ):
							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__			       =     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__			       =     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__":
					UpperCamelCase__     =	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__     =	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       ():
							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]    )    )











from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


UpperCamelCase__     =	{
    "configuration_swiftformer": [
        "SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "SwiftFormerConfig",
        "SwiftFormerOnnxConfig",
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCamelCase__     =	[
					    "SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "SwiftFormerForImageClassification",
					    "SwiftFormerModel",
					    "SwiftFormerPreTrainedModel",
					]

if TYPE_CHECKING:
					from .configuration_swiftformer import (
					    SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    SwiftFormerConfig,
					    SwiftFormerOnnxConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_swiftformer import (
										    SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    SwiftFormerForImageClassification,
										    SwiftFormerModel,
										    SwiftFormerPreTrainedModel,
										)

else:
					import sys

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




