'''simple docstring'''





import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
    SCREAMING_SNAKE_CASE_:					Any  =argparse.ArgumentParser()

    parser.add_argument(
        '--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
    )
    parser.add_argument(
        '--original_config_file',
        type=str,
        required=True,
        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(
        '--image_size',
        default=5_12,
        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(
        '--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.)')





    def      lowerCAmelCase_    ( snake_case_	:			str      )   ->				Any:


         '''simple docstring'''




         if string == "True":
              return True
         elif string == "False":
              return False
         else:
              raise ValueError(f"""could not parse string as bool {string}"""      )

    parser.add_argument(
        '--use_linear_projection', help='Override for use linear projection', required=False, type=parse_bool
    )

    parser.add_argument('--cross_attention_dim', help='Override for cross attention_dim', required=False, type=int)

    SCREAMING_SNAKE_CASE_:					Tuple  =parser.parse_args()

    SCREAMING_SNAKE_CASE_:					Any  =download_controlnet_from_original_ckpt(
        checkpoint_path=args.checkpoint_path,
        original_config_file=args.original_config_file,
        image_size=args.image_size,
        extract_ema=args.extract_ema,
        num_in_channels=args.num_in_channels,
        upcast_attention=args.upcast_attention,
        from_safetensors=args.from_safetensors,
        device=args.device,
        use_linear_projection=args.use_linear_projection,
        cross_attention_dim=args.cross_attention_dim,
    )






    controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





'''simple docstring'''





# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
    import jax
    import jaxlib

SCREAMING_SNAKE_CASE_:					List[str]  =get_logger()

SCREAMING_SNAKE_CASE_:					Optional[dict]  =None




class 	__A				(			TensorFormatter[Mapping, """jax.Array""", Mapping]     ):



    def __init__(self   :   List[Any]       ,      __a   :   Optional[int]=None       ,      __a   :   Any=None       ,      **__a   :   Dict			):
         super().__init__(features=__a			)
         import jax
         from jaxlib.xla_client import Device

         if isinstance(__a       ,      __a			):
              raise ValueError(
                  f"""Expected {device} to be a `str` not {type(__a			)}, as `jaxlib.xla_extension.Device` """
                  "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
                  "the device with `str()` to get its string identifier that will be internally mapped "
                  "to the actual `jaxlib.xla_extension.Device`."			)
         UpperCAmelCase_	   =       device if isinstance(__a       ,      __a			) else str(jax.devices()[0]			)
         # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
         # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()
         if self.device not in list(DEVICE_MAPPING.keys()			):
              logger.warning(
                  f"""Device with string identifier {self.device} not listed among the available """
                  f"""devices: {list(DEVICE_MAPPING.keys()			)}, so falling back to the default """
                  f"""device: {str(jax.devices()[0]			)}."""			)
              UpperCAmelCase_	   =       str(jax.devices()[0]			)
         UpperCAmelCase_	   =       jnp_array_kwargs



    @staticmethod
    def  _lowercase						():
         import jax

         return {str(__a			): device for device in jax.devices()}



    def  _lowercase						(self   :   str       ,      __a   :   Tuple			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      __a			) and column:
              if all(
                  isinstance(__a       ,      jax.Array			) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column			):
                   return jnp.stack(__a       ,      axis=0			)
         return column



    def  _lowercase						(self   :   Any       ,      __a   :   Optional[int]			):
         import jax
         import jax.numpy as jnp

         if isinstance(__a       ,      (str, bytes, type(__a			))			):
              return value
         elif isinstance(__a       ,      (np.character, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.character			):
              return value.tolist()

         UpperCAmelCase_	   =       {}

         if isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.integer			):
              # the default int precision depends on the jax config
              # see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
              if jax.config.jax_enable_xaa:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
              else:
                   UpperCAmelCase_	   =       {"dtype": jnp.intaa}
         elif isinstance(__a       ,      (np.number, np.ndarray)			) and np.issubdtype(value.dtype       ,      np.floating			):
              UpperCAmelCase_	   =       {"dtype": jnp.floataa}
         elif config.PIL_AVAILABLE and "PIL" in sys.modules:
              import PIL.Image

              if isinstance(__a       ,      PIL.Image.Image			):
                   UpperCAmelCase_	   =       np.asarray(__a			)

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
         global DEVICE_MAPPING
         if DEVICE_MAPPING is None:
              UpperCAmelCase_	   =       self._map_devices_to_str()

         with jax.default_device(DEVICE_MAPPING[self.device]			):
              # calling jnp.array on a np.ndarray does copy the data
              # see https://github.com/google/jax/issues/4486
              return jnp.array(__a       ,      **{**default_dtype, **self.jnp_array_kwargs}			)



    def  _lowercase						(self   :   int       ,      __a   :   Any			):
         import jax

         # support for torch, tf, jax etc.
         if config.TORCH_AVAILABLE and "torch" in sys.modules:
              import torch

              if isinstance(__a       ,      torch.Tensor			):
                   return self._tensorize(data_struct.detach().cpu().numpy()[()]			)
         if hasattr(__a       ,      "__array__"			) and not isinstance(__a       ,      jax.Array			):
              UpperCAmelCase_	   =       data_struct.__array__()
         # support for nested types like struct of list of struct
         if isinstance(__a       ,      np.ndarray			):
              if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
                   return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         elif isinstance(__a       ,      (list, tuple)			):
              return self._consolidate([self.recursive_tensorize(__a			) for substruct in data_struct]			)
         return self._tensorize(__a			)



    def  _lowercase						(self   :   Union[str, Any]       ,      __a   :   dict			):
         return map_nested(self._recursive_tensorize       ,      __a       ,      map_list=__a			)



    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_row(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_row(__a			)
         return self.recursive_tensorize(__a			)



    def  _lowercase						(self   :   Tuple       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_column(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_column(__a       ,      pa_table.column_names[0]			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         UpperCAmelCase_	   =       self._consolidate(__a			)
         return column






    def  _lowercase						(self   :   str       ,      __a   :   pa.Table			):
         UpperCAmelCase_	   =       self.numpy_arrow_extractor().extract_batch(__a			)
         UpperCAmelCase_	   =       self.python_features_decoder.decode_batch(__a			)
         UpperCAmelCase_	   =       self.recursive_tensorize(__a			)
         for column_name in batch:
              UpperCAmelCase_	   =       self._consolidate(batch[column_name]			)
         return batch




