


'''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    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





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

   return flax_key_tuple, flax_tensor


def    UpperCAmelCase_  (       A		,	A		,	A      ):
   '''simple docstring'''





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

   else:
      _a					:  Optional[int]						=  layer.split('/'      )
      _a					:  Optional[Any]						=  '/'.join(split_layer[:-1]      )
      _a					:  int						=  (split_layer[-1],)

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

   return curr_real_layer_name, split_layer, content


def    UpperCAmelCase_  (       A		,	A      ):
   '''simple docstring'''





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


def    UpperCAmelCase_  (       A		,	A		,	A		,	A		,	A = WEIGHTS_NAME      ):
   '''simple docstring'''





   _a					:  str						=  convert_file_size_to_int(A      )
   _a					:  Dict						=  []
   _a					:  Optional[Any]						=  {}
   _a					:  Tuple						=  0
   _a					:  Union[str, Any]						=  0

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

   _a					:  Tuple						=  {}
   for layer in checkpoint_info.keys():
      _a		,						_a		,						_a					:  int						=  get_key_and_tensorstore_dict(
          A		,	A		,	A      )
      if curr_real_layer_name in all_layers:
         _a					:  str						=  content
      else:
         _a					:  Optional[int]						=  {split_layer[-1]: content}

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

      # use the renaming pattern from the small conversion scripts
      _a		,						_a					:  List[Any]						=  rename_base_flax_keys(tuple(key.split('/'      )      )		,	A      )
      _a					:  Dict						=  '/'.join(A      )

      # If this weight is going to tip up over the maximal size, we split.
      if current_block_size + weight_size > max_shard_size:
         _a					:  int						=  os.path.join(
             A		,	weights_name.replace('.bin'		,	f'''-{len(A      )+1:05d}-of-???.bin'''      )      )
         rename_and_save_block(A		,	A      )
         sharded_state_dicts.append(current_block.keys()      )
         del current_block
         _a					:  Any						=  {}
         _a					:  Dict						=  0

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

   # Add the last block
   _a					:  Union[str, Any]						=  os.path.join(A		,	weights_name.replace('.bin'		,	f'''-{len(A      )+1:05d}-of-???.bin'''      )      )
   rename_and_save_block(A		,	A      )
   sharded_state_dicts.append(current_block.keys()      )

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

   # Otherwise, let's build the index
   _a					:  str						=  {}
   _a					:  List[Any]						=  {}
   for idx, shard in enumerate(A      ):
      _a					:  Tuple						=  weights_name.replace(
          '.bin'		,	f'''-{idx+1:05d}-of-{len(A      ):05d}.bin'''      )  # len(sharded_state_dicts):05d}
      _a					:  str						=  os.path.join(A		,	weights_name.replace('.bin'		,	f'''-{idx+1:05d}-of-???.bin'''      )      )
      os.rename(A		,	os.path.join(A		,	A      )      )
      _a					:  Optional[Any]						=  shard
      for key in shard:
         _a					:  Any						=  shard_file

    # Add the metadata
   _a					:  Optional[int]						=  {'total_size': total_size}
   _a					:  Optional[int]						=  {'metadata': metadata, 'weight_map': weight_map}

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

   return metadata, index


if __name__ == "__main__":
 UpperCAmelCase_      :		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.",
 )
 UpperCAmelCase_      :		Tuple          =		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_  (       ):
   '''simple docstring'''





   from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

   _a					:  str						=  tokenizer(A		,	return_tensors='pt'      ).input_ids
   _a					:  str						=  model.generate(A		,	decoder_start_token_id=0      )
   print(tokenizer.decode(out[0]      )      )






import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline






def 					UpperCamelCase   (      __magic_name__	:				Union[str, Any]       ,       __magic_name__	:				Any       ,       __magic_name__	:				List[str]       ,       __magic_name__	:				Optional[int]       ,       __magic_name__	:				Tuple	)  ->    List[Any]:
		"""simple docstring"""




		lowercase__	     =						StableDiffusionPipeline.from_pretrained(__magic_name__       ,       torch_dtype=torch.floataa	)

		# load LoRA weight from .safetensors
		lowercase__	     =						load_file(__magic_name__	)

		lowercase__	     =						[]

		# directly update weight in diffusers model
		for key in state_dict:
				# it is suggested to print out the key, it usually will be something like below
				# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

				# as we have set the alpha beforehand, so just skip
				if ".alpha" in key or key in visited:
						continue

				if "text" in key:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_TEXT_ENCODER + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.text_encoder
				else:
						lowercase__	     =						key.split("""."""	)[0].split(LORA_PREFIX_UNET + """_"""	)[-1].split("""_"""	)
						lowercase__	     =						pipeline.unet

				# find the target layer
				lowercase__	     =						layer_infos.pop(0	)
				while len(__magic_name__	) > -1:
						try:
								lowercase__	     =						curr_layer.__getattr__(__magic_name__	)
								if len(__magic_name__	) > 0:
										lowercase__	     =						layer_infos.pop(0	)
								elif len(__magic_name__	) == 0:
										break
						except Exception:
								if len(__magic_name__	) > 0:
										temp_name += "_" + layer_infos.pop(0	)
								else:
										lowercase__	     =						layer_infos.pop(0	)

				lowercase__	     =						[]
				if "lora_down" in key:
						pair_keys.append(key.replace("""lora_down"""       ,       """lora_up"""	)	)
						pair_keys.append(__magic_name__	)
				else:
						pair_keys.append(__magic_name__	)
						pair_keys.append(key.replace("""lora_up"""       ,       """lora_down"""	)	)

				# update weight
				if len(state_dict[pair_keys[0]].shape	) == 4:
						lowercase__	     =						state_dict[pair_keys[0]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].squeeze(3	).squeeze(2	).to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	).unsqueeze(2	).unsqueeze(3	)
				else:
						lowercase__	     =						state_dict[pair_keys[0]].to(torch.floataa	)
						lowercase__	     =						state_dict[pair_keys[1]].to(torch.floataa	)
						curr_layer.weight.data += alpha * torch.mm(__magic_name__       ,       __magic_name__	)

				# update visited list
				for item in pair_keys:
						visited.append(__magic_name__	)

		return pipeline


if __name__ == "__main__":
	A			:						int        =  argparse.ArgumentParser()

	parser.add_argument(
	    '--base_model_path', default=None, type=str, required=True, help='Path to the base model in diffusers format.'
	)
	parser.add_argument(
	    '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
	)
	parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
	parser.add_argument(
	    '--lora_prefix_unet', default='lora_unet', type=str, help='The prefix of UNet weight in safetensors'
	)
	parser.add_argument(
	    '--lora_prefix_text_encoder',
	    default='lora_te',
	    type=str,
	    help='The prefix of text encoder weight in safetensors',
	)
	parser.add_argument('--alpha', default=0.75, type=float, help='The merging ratio in W = W0 + alpha * deltaW')
	parser.add_argument(
	    '--to_safetensors', action='store_true', help='Whether to store pipeline in safetensors format or not.'
	)
	parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')

	A			:						str        =  parser.parse_args()

	A			:						Tuple        =  args.base_model_path
	A			:						List[str]        =  args.checkpoint_path
	A			:						Optional[int]        =  args.dump_path
	A			:						Optional[int]        =  args.lora_prefix_unet
	A			:						Any        =  args.lora_prefix_text_encoder
	A			:						Any        =  args.alpha

	A			:						List[str]        =  convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

	A			:						int        =  pipe.to(args.device)
	pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)

