


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




		if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 3:
				# expert layer
				lowercase__	     =						flax_key_tuple[:-1] + ("""weight""",)
				lowercase__	     =						torch.permute(__magic_name__       ,       (0, 2, 1)	)
		elif flax_key_tuple[-1] == "kernel" and ".".join(__magic_name__	):
				# linear layer
				lowercase__	     =						flax_key_tuple[:-1] + ("""weight""",)
				lowercase__	     =						flax_tensor.T
		elif flax_key_tuple[-1] in ["scale", "embedding"]:
				lowercase__	     =						flax_key_tuple[:-1] + ("""weight""",)

		return flax_key_tuple, flax_tensor






def 					UpperCamelCase   (      __magic_name__	:				Optional[Any]       ,       __magic_name__	:				Dict       ,       __magic_name__	:				str	)  ->    Any:
		"""simple docstring"""




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

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

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

		return curr_real_layer_name, split_layer, content






def 					UpperCamelCase   (      __magic_name__	:				int       ,       __magic_name__	:				Dict	)  ->    List[str]:
		"""simple docstring"""




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






def 					UpperCamelCase   (      __magic_name__	:				str       ,       __magic_name__	:				Tuple       ,       __magic_name__	:				str       ,       __magic_name__	:				List[Any]       ,       __magic_name__	:				str = WEIGHTS_NAME	)  ->    Any:
		"""simple docstring"""




		lowercase__	     =						convert_file_size_to_int(__magic_name__	)
		lowercase__	     =						[]
		lowercase__	     =						{}
		lowercase__	     =						0
		lowercase__	     =						0

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

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

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

				# use the renaming pattern from the small conversion scripts
				lowercase__     ,      lowercase__	     =						rename_base_flax_keys(tuple(key.split("""/"""	)	)       ,       __magic_name__	)
				lowercase__	     =						"""/""".join(__magic_name__	)

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

				lowercase__	     =						raw_weights.to(getattr(__magic_name__       ,       __magic_name__	)	)
				current_block_size += weight_size
				total_size += weight_size

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

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

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

    # Add the metadata
		lowercase__	     =						{"""total_size""": total_size}
		lowercase__	     =						{"""metadata""": metadata, """weight_map""": weight_map}

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

		return metadata, index


if __name__ == "__main__":
	A			:						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.',
	)
	A			:						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   (      )  ->    Optional[int]:
		"""simple docstring"""




		from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

		lowercase__	     =						tokenizer(__magic_name__       ,       return_tensors="""pt"""	).input_ids
		lowercase__	     =						model.generate(__magic_name__       ,       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)

