



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(      A       :    Any ,     A       :    Dict       )		->       Optional[Any]:
					"""simple docstring"""





					if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
										# expert layer
										a					     =   flax_key_tuple[:-1] + ('''weight''',)
										a					     =   torch.permute(__snake_case ,     (0, 2, 1)       )
					elif flax_key_tuple[-1] == "kernel" and ".".join(__snake_case       ):
										# linear layer
										a					     =   flax_key_tuple[:-1] + ('''weight''',)
										a					     =   flax_tensor.T
					elif flax_key_tuple[-1] in ["scale", "embedding"]:
										a					     =   flax_key_tuple[:-1] + ('''weight''',)

					return flax_key_tuple, flax_tensor






def     a(      A       :    str ,     A       :    str ,     A       :    Dict       )		->       Optional[int]:
					"""simple docstring"""





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

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

					if "kvstore/path" in layer:
										a					     =   f'''{switch_checkpoint_path}/{checkpoint_info[layer]}'''
					elif "kvstore/driver" in layer:
										a					     =   '''file'''
					else:
										a					     =   checkpoint_info[layer]

					return curr_real_layer_name, split_layer, content






def     a(      A       :    Any ,     A       :    Optional[Any]       )		->       Tuple:
					"""simple docstring"""





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






def     a(      A       :    List[Any] ,     A       :    Any ,     A       :    List[str] ,     A       :    Dict ,     A       :    str = WEIGHTS_NAME       )		->       Tuple:
					"""simple docstring"""





					a					     =   convert_file_size_to_int(__snake_case       )
					a					     =   []
					a					     =   {}
					a					     =   0
					a					     =   0

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

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

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

										# use the renaming pattern from the small conversion scripts
										a					     =   rename_base_flax_keys(tuple(key.split("/"       )       ) ,     __snake_case       )
										a					     =   '''/'''.join(__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:
															a					     =   os.path.join(
															    __snake_case ,     weights_name.replace(".bin" ,     f'''-{len(__snake_case       )+1:05d}-of-???.bin'''       )       )
															rename_and_save_block(__snake_case ,     __snake_case       )
															sharded_state_dicts.append(current_block.keys()       )
															del current_block
															a					     =   {}
															a					     =   0

										a					     =   raw_weights.to(getattr(__snake_case ,     __snake_case       )       )
										current_block_size += weight_size
										total_size += weight_size

					# Add the last block
					a					     =   os.path.join(__snake_case ,     weights_name.replace(".bin" ,     f'''-{len(__snake_case       )+1:05d}-of-???.bin'''       )       )
					rename_and_save_block(__snake_case ,     __snake_case       )
					sharded_state_dicts.append(current_block.keys()       )

					# If we only have one shard, we return it
					if len(__snake_case       ) == 1:
										return {weights_name: sharded_state_dicts[0]}, None

					# Otherwise, let's build the index
					a					     =   {}
					a					     =   {}
					for idx, shard in enumerate(__snake_case       ):
										a					     =   weights_name.replace(
										    ".bin" ,     f'''-{idx+1:05d}-of-{len(__snake_case       ):05d}.bin'''       )  # len(sharded_state_dicts):05d}
										a					     =   os.path.join(__snake_case ,     weights_name.replace(".bin" ,     f'''-{idx+1:05d}-of-???.bin'''       )       )
										os.rename(__snake_case ,     os.path.join(__snake_case ,     __snake_case       )       )
										a					     =   shard
										for key in shard:
															a					     =   shard_file

    # Add the metadata
					a					     =   {'''total_size''': total_size}
					a					     =   {'''metadata''': metadata, '''weight_map''': weight_map}

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

					return metadata, index


if __name__ == "__main__":
				_lowercase: List[Any]      			=		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: 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     a(      )		->       Tuple:
					"""simple docstring"""





					from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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








"""simple docstring"""



from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A		:       Dict  				=		logging.get_logger(__name__)

__A		:       Union[str, Any]  				=		{
    '''facebook/vit-mae-base''': '''https://huggingface.co/facebook/vit-mae-base/resolve/main/config.json''',
    # See all ViT MAE models at https://huggingface.co/models?filter=vit-mae
}





class 					_UpperCAmelCase       ( _A							):
					SCREAMING_SNAKE_CASE_		:				Union[str, Any]							    =   "vit_mae"




					def __init__(				self     :						Dict    ,						A     :						List[str]=7_68    ,						A     :						Any=12    ,						A     :						Union[str, Any]=12    ,						A     :						Tuple=30_72    ,						A     :						Any="gelu"    ,						A     :						Tuple=0.0    ,						A     :						List[str]=0.0    ,						A     :						Tuple=0.02    ,						A     :						Tuple=1e-12    ,						A     :						int=2_24    ,						A     :						Dict=16    ,						A     :						int=3    ,						A     :						Tuple=True    ,						A     :						Tuple=16    ,						A     :						Optional[Any]=5_12    ,						A     :						Union[str, Any]=8    ,						A     :						List[Any]=20_48    ,						A     :						Dict=0.75    ,						A     :						Any=False    ,						**A     :						Optional[int]    ,						)  ->  Union[str, Any]:
							super().__init__(**A   )

							lowercase_ : List[Any]							       =   hidden_size
							lowercase_ : str							       =   num_hidden_layers
							lowercase_ : List[Any]							       =   num_attention_heads
							lowercase_ : Any							       =   intermediate_size
							lowercase_ : Optional[int]							       =   hidden_act
							lowercase_ : List[Any]							       =   hidden_dropout_prob
							lowercase_ : int							       =   attention_probs_dropout_prob
							lowercase_ : int							       =   initializer_range
							lowercase_ : Dict							       =   layer_norm_eps
							lowercase_ : Optional[Any]							       =   image_size
							lowercase_ : str							       =   patch_size
							lowercase_ : Dict							       =   num_channels
							lowercase_ : Any							       =   qkv_bias
							lowercase_ : Union[str, Any]							       =   decoder_num_attention_heads
							lowercase_ : Optional[Any]							       =   decoder_hidden_size
							lowercase_ : List[str]							       =   decoder_num_hidden_layers
							lowercase_ : List[Any]							       =   decoder_intermediate_size
							lowercase_ : Optional[Any]							       =   mask_ratio
							lowercase_ : Optional[Any]							       =   norm_pix_loss



