




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

			return flax_key_tuple, flax_tensor



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__       )		->      int:
			if "metadata" in layer:
						lowerCamelCase       =      layer.split("""metadata"""       )
						lowerCamelCase       =      """""".join(split_layer[0]       )[:-1]
						lowerCamelCase       =      [tuple(("""metadata""" + split_layer[1]).split("""/"""       )       )]
			elif "kvstore" in layer:
						lowerCamelCase       =      layer.split("""kvstore"""       )
						lowerCamelCase       =      """""".join(split_layer[0]       )[:-1]
						lowerCamelCase       =      [tuple(("""kvstore""" + split_layer[1]).split("""/"""       )       )]

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

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

			return curr_real_layer_name, split_layer, content



def 					a__				(       snake_case__ ,				snake_case__       )		->      List[Any]:
			lowerCamelCase       =      rename_keys(snake_case__       )
			lowerCamelCase       =      {}
			for k, v in current_block.items():
						lowerCamelCase       =      v
			lowerCamelCase       =      new_current_block
			torch.save(snake_case__ ,				snake_case__       )



def 					a__				(       snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ ,				snake_case__ = WEIGHTS_NAME       )		->      Union[str, Any]:
			lowerCamelCase       =      convert_file_size_to_int(snake_case__       )
			lowerCamelCase       =      []
			lowerCamelCase       =      {}
			lowerCamelCase       =      0
			lowerCamelCase       =      0

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

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

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

						# use the renaming pattern from the small conversion scripts
						lowerCamelCase   ,     lowerCamelCase       =      rename_base_flax_keys(tuple(key.split("""/"""       )       ) ,				snake_case__       )
						lowerCamelCase       =      """/""".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:
									lowerCamelCase       =      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
									lowerCamelCase       =      {}
									lowerCamelCase       =      0

						lowerCamelCase       =      raw_weights.to(getattr(snake_case__ ,				snake_case__       )       )
						current_block_size += weight_size
						total_size += weight_size

			# Add the last block
			lowerCamelCase       =      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
			lowerCamelCase       =      {}
			lowerCamelCase       =      {}
			for idx, shard in enumerate(snake_case__       ):
						lowerCamelCase       =      weights_name.replace(
						    """.bin""" ,				F'-{idx+1:05d}-of-{len(snake_case__       ):05d}.bin'       )  # len(sharded_state_dicts):05d}
						lowerCamelCase       =      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__       )       )
						lowerCamelCase       =      shard
						for key in shard:
									lowerCamelCase       =      shard_file

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

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

			return metadata, index


if __name__ == "__main__":
					lowerCAmelCase							:    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.""",
					)
					lowerCAmelCase							:    Union[str, 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 					a__				(       )		->      Optional[Any]:
			from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration, TaTokenizer

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

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

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









"""simple docstring"""




import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
					lowerCAmelCase							:    Dict  							=							argparse.ArgumentParser()

					parser.add_argument(
					    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
					)
					# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
					parser.add_argument(
					    """--original_config_file""",
					    default=None,
					    type=str,
					    help="""The YAML config file corresponding to the original architecture.""",
					)
					parser.add_argument(
					    """--num_in_channels""",
					    default=None,
					    type=int,
					    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
					)
					parser.add_argument(
					    """--scheduler_type""",
					    default="""pndm""",
					    type=str,
					    help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
					)
					parser.add_argument(
					    """--pipeline_type""",
					    default=None,
					    type=str,
					    help=(
					        """The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
					        """. If `None` pipeline will be automatically inferred."""
					    ),
					)
					parser.add_argument(
					    """--image_size""",
					    default=None,
					    type=int,
					    help=(
					        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
					        """ Base. Use 768 for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--prediction_type""",
					    default=None,
					    type=str,
					    help=(
					        """The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
					        """ Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
					    ),
					)
					parser.add_argument(
					    """--extract_ema""",
					    action="""store_true""",
					    help=(
					        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
					        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
					        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
					    ),
					)
					parser.add_argument(
					    """--upcast_attention""",
					    action="""store_true""",
					    help=(
					        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
					        """ diffusion 2.1."""
					    ),
					)
					parser.add_argument(
					    """--from_safetensors""",
					    action="""store_true""",
					    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
					)
					parser.add_argument(
					    """--to_safetensors""",
					    action="""store_true""",
					    help="""Whether to store pipeline in safetensors format or not.""",
					)
					parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
					parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
					parser.add_argument(
					    """--stable_unclip""",
					    type=str,
					    default=None,
					    required=False,
					    help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
					)
					parser.add_argument(
					    """--stable_unclip_prior""",
					    type=str,
					    default=None,
					    required=False,
					    help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
					)
					parser.add_argument(
					    """--clip_stats_path""",
					    type=str,
					    help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
					    required=False,
					)
					parser.add_argument(
					    """--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
					)
					parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
					parser.add_argument(
					    """--vae_path""",
					    type=str,
					    default=None,
					    required=False,
					    help="""Set to a path, hub id to an already converted vae to not convert it again.""",
					)
					lowerCAmelCase							:    Dict  							=							parser.parse_args()

					lowerCAmelCase							:    str  							=							download_from_original_stable_diffusion_ckpt(
					    checkpoint_path=args.checkpoint_path,
					    original_config_file=args.original_config_file,
					    image_size=args.image_size,
					    prediction_type=args.prediction_type,
					    model_type=args.pipeline_type,
					    extract_ema=args.extract_ema,
					    scheduler_type=args.scheduler_type,
					    num_in_channels=args.num_in_channels,
					    upcast_attention=args.upcast_attention,
					    from_safetensors=args.from_safetensors,
					    device=args.device,
					    stable_unclip=args.stable_unclip,
					    stable_unclip_prior=args.stable_unclip_prior,
					    clip_stats_path=args.clip_stats_path,
					    controlnet=args.controlnet,
					    vae_path=args.vae_path,
					)

					if args.half:
										pipe.to(torch_dtype=torch.floataa)

					if args.controlnet:
										# only save the controlnet model
										pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
					else:
										pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)



