
'''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 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: List[str]							):



						"""simple docstring"""





						if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
												# expert layer
												lowercase_	:     str													=       flax_key_tuple[:-1] + ('''weight''',)
												lowercase_	:     Tuple													=       torch.permute(__SCREAMING_SNAKE_CASE      ,      (0, 2, 1)							)
						elif flax_key_tuple[-1] == "kernel" and ".".join(__SCREAMING_SNAKE_CASE							):
												# linear layer
												lowercase_	:     List[str]													=       flax_key_tuple[:-1] + ('''weight''',)
												lowercase_	:     Union[str, Any]													=       flax_tensor.T
						elif flax_key_tuple[-1] in ["scale", "embedding"]:
												lowercase_	:     Optional[int]													=       flax_key_tuple[:-1] + ('''weight''',)

						return flax_key_tuple, flax_tensor
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[str]      ,      __SCREAMING_SNAKE_CASE					: Any      ,      __SCREAMING_SNAKE_CASE					: Optional[Any]							):



						"""simple docstring"""





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

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

						if "kvstore/path" in layer:
												lowercase_	:     str													=       F'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
						elif "kvstore/driver" in layer:
												lowercase_	:     List[Any]													=       '''file'''
						else:
												lowercase_	:     Dict													=       checkpoint_info[layer]

						return curr_real_layer_name, split_layer, content
def 					snake_case_		(				__SCREAMING_SNAKE_CASE					: List[Any]      ,      __SCREAMING_SNAKE_CASE					: Union[str, Any]							):



						"""simple docstring"""





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



						"""simple docstring"""





						lowercase_	:     Dict													=       convert_file_size_to_int(__SCREAMING_SNAKE_CASE							)
						lowercase_	:     List[str]													=       []
						lowercase_	:     Dict													=       {}
						lowercase_	:     List[str]													=       0
						lowercase_	:     str													=       0

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

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

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

												# use the renaming pattern from the small conversion scripts
												lowercase_						, lowercase_	:     Tuple													=       rename_base_flax_keys(tuple(key.split('''/'''							)							)      ,      __SCREAMING_SNAKE_CASE							)
												lowercase_	:     Any													=       '''/'''.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:
																		lowercase_	:     Optional[Any]													=       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
																		lowercase_	:     List[Any]													=       {}
																		lowercase_	:     Any													=       0

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

						# Add the last block
						lowercase_	:     Dict													=       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
						lowercase_	:     Optional[Any]													=       {}
						lowercase_	:     List[str]													=       {}
						for idx, shard in enumerate(__SCREAMING_SNAKE_CASE							):
												lowercase_	:     str													=       weights_name.replace(
												    '''.bin'''      ,      F'''-{idx+1:05d}-of-{len(__SCREAMING_SNAKE_CASE							):05d}.bin'''							)  # len(sharded_state_dicts):05d}
												lowercase_	:     Any													=       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							)							)
												lowercase_	:     int													=       shard
												for key in shard:
																		lowercase_	:     int													=       shard_file

    # Add the metadata
						lowercase_	:     Tuple													=       {'''total_size''': total_size}
						lowercase_	:     Any													=       {'''metadata''': metadata, '''weight_map''': weight_map}

						with open(os.path.join(__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE							)      ,      '''w'''      ,      encoding='''utf-8'''							) as f:
												lowercase_	:     Dict													=       json.dumps(__SCREAMING_SNAKE_CASE      ,      indent=2      ,      sort_keys=__SCREAMING_SNAKE_CASE							) + '''\n'''
												f.write(__SCREAMING_SNAKE_CASE							)

						return metadata, index


if __name__ == "__main__":
							_lowercase :  List[str]    							=       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.",
							)
							_lowercase :  Optional[Any]    							=       parser.parse_args()
							shard_on_the_fly(
							    args.switch_tax_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    args.max_shard_size,
							    args.dtype,
							)




def 					snake_case_		(				):



						"""simple docstring"""





						from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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





'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_lowercase :  Union[str, Any]    							=       {
    "configuration_mask2former": [
        "MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "Mask2FormerConfig",
    ],
}

try:
							if not is_vision_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  Optional[int]    							=       ["Mask2FormerImageProcessor"]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_lowercase :  str    							=       [
							    "MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
							    "Mask2FormerForUniversalSegmentation",
							    "Mask2FormerModel",
							    "Mask2FormerPreTrainedModel",
							]

if TYPE_CHECKING:
							from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig

							try:
														if not is_vision_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .image_processing_maskaformer import MaskaFormerImageProcessor

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_maskaformer import (
														    MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    MaskaFormerForUniversalSegmentation,
														    MaskaFormerModel,
														    MaskaFormerPreTrainedModel,
														)


else:
							import sys

							_lowercase :  Dict    							=       _LazyModule(__name__, globals()["__file__"], _import_structure)



