'''simple docstring'''

import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
  __a: Optional[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 			__UpperCamelCase       (   UpperCAmelCase     ):
   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)

  __a: Union[str, Any]          =   parser.parse_args()

  __a: Union[str, 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'''

import json
import os
import unittest

from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			UpperCAmelCase  (						a__    ,						unittest.TestCase					):





       '''simple docstring'''



       SCREAMING_SNAKE_CASE							=    CTRLTokenizer
       SCREAMING_SNAKE_CASE							=    False
       SCREAMING_SNAKE_CASE							=    False

       def _lowerCAmelCase(						self )  ->  Dict:
        super().setUp()

        # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
        lowercase__				:    Optional[Any]  =					['''adapt''', '''re@@''', '''a@@''', '''apt''', '''c@@''', '''t''', '''<unk>''']
        lowercase__				:    str  =					dict(zip(__lowerCAmelCase	,   range(len(__lowerCAmelCase ) ) ) )
        lowercase__				:    Tuple  =					['''#version: 0.2''', '''a p''', '''ap t</w>''', '''r e''', '''a d''', '''ad apt</w>''', '''''']
        lowercase__				:    Optional[Any]  =					{'''unk_token''': '''<unk>'''}

        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''vocab_file'''] )
        lowercase__				:    Dict  =					os.path.join(self.tmpdirname	,   VOCAB_FILES_NAMES['''merges_file'''] )
        with open(self.vocab_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write(json.dumps(__lowerCAmelCase ) + '''\n''' )
        with open(self.merges_file	,   '''w'''	,   encoding='''utf-8''' ) as fp:
         fp.write('''\n'''.join(__lowerCAmelCase ) )

       def _lowerCAmelCase(						self	,   **__lowerCAmelCase )  ->  List[str]:
        kwargs.update(self.special_tokens_map )
        return CTRLTokenizer.from_pretrained(self.tmpdirname	,   **__lowerCAmelCase )

       def _lowerCAmelCase(						self	,   __lowerCAmelCase )  ->  List[str]:
        lowercase__				:    List[str]  =					'''adapt react readapt apt'''
        lowercase__				:    Union[str, Any]  =					'''adapt react readapt apt'''
        return input_text, output_text


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Union[str, Any]  =					CTRLTokenizer(self.vocab_file	,   self.merges_file	,   **self.special_tokens_map )
        lowercase__				:    Optional[Any]  =					'''adapt react readapt apt'''
        lowercase__				:    Dict  =					'''adapt re@@ a@@ c@@ t re@@ adapt apt'''.split()
        lowercase__				:    Union[str, Any]  =					tokenizer.tokenize(__lowerCAmelCase )
        self.assertListEqual(__lowerCAmelCase	,   __lowerCAmelCase )

        lowercase__				:    int  =					tokens + [tokenizer.unk_token]

        lowercase__				:    List[str]  =					[0, 1, 2, 4, 5, 1, 0, 3, 6]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(__lowerCAmelCase )	,   __lowerCAmelCase )

