





import os
import re
import shutil
import sys
import tempfile
import unittest

import black


SCREAMING_SNAKE_CASE_		     = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
SCREAMING_SNAKE_CASE_		     = """    def __init__(self, config):
        super().__init__()
        self.transform = BertPredictionHeadTransform(config)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)

        self.bias = nn.Parameter(torch.zeros(config.vocab_size))

        # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`
        self.decoder.bias = self.bias

    def forward(self, hidden_states):
        hidden_states = self.transform(hidden_states)
        hidden_states = self.decoder(hidden_states)
        return hidden_states
"""

class 	UpperCamelCase__				(   unittest.TestCase     ):



     '''simple docstring'''

     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		tempfile.mkdtemp()
           os.makedirs(os.path.join(self.transformer_dir		,"""models/bert/""" ) )
           SCREAMING_SNAKE_CASE	=		self.transformer_dir
           shutil.copy(
               os.path.join(lowerCamelCase__		,"""src/transformers/models/bert/modeling_bert.py""" )		,os.path.join(self.transformer_dir		,"""models/bert/modeling_bert.py""" )		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	Dict:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		"""src/transformers"""
           shutil.rmtree(self.transformer_dir )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					List[str]		,lowerCamelCase__		:					int		,lowerCamelCase__		:					str		,lowerCamelCase__		:					str=None )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		comment + F"""\nclass {class_name}(nn.Module):\n""" + class_code
           if overwrite_result is not None:
                 SCREAMING_SNAKE_CASE	=		comment + F"""\nclass {class_name}(nn.Module):\n""" + overwrite_result
           SCREAMING_SNAKE_CASE	=		black.Mode(target_versions={black.TargetVersion.PYaa}		,line_length=119 )
           SCREAMING_SNAKE_CASE	=		black.format_str(lowerCamelCase__		,mode=lowerCamelCase__ )
           SCREAMING_SNAKE_CASE	=		os.path.join(self.transformer_dir		,"""new_code.py""" )
           with open(lowerCamelCase__		,"""w"""		,newline="""\n""" ) as f:
                 f.write(lowerCamelCase__ )
           if overwrite_result is None:
                 self.assertTrue(len(check_copies.is_copy_consistent(lowerCamelCase__ ) ) == 0 )
           else:
                 check_copies.is_copy_consistent(f.name		,overwrite=lowerCamelCase__ )
                 with open(lowerCamelCase__		,"""r""" ) as f:
                       self.assertTrue(f.read()		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any )  ->	Optional[int]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		check_copies.find_code_in_transformers("""models.bert.modeling_bert.BertLMPredictionHead""" )
           self.assertEqual(lowerCamelCase__		,lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	List[str]:




           '''simple docstring'''


           self.check_copy_consistency(
               """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""		,"""BertLMPredictionHead"""		,REFERENCE_CODE + """\n"""		,)

           # With no empty line at the end
           self.check_copy_consistency(
               """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead"""		,"""BertLMPredictionHead"""		,lowerCamelCase__		,)

           # Copy consistency with rename
           self.check_copy_consistency(
               """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""		,"""TestModelLMPredictionHead"""		,re.sub("""Bert"""		,"""TestModel"""		,lowerCamelCase__ )		,)

           # Copy consistency with a really long name
           SCREAMING_SNAKE_CASE	=		"""TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"""
           self.check_copy_consistency(
               F"""# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}"""		,F"""{long_class_name}LMPredictionHead"""		,re.sub("""Bert"""		,lowerCamelCase__		,lowerCamelCase__ )		,)

           # Copy consistency with overwrite
           self.check_copy_consistency(
               """# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel"""		,"""TestModelLMPredictionHead"""		,lowerCamelCase__		,overwrite_result=re.sub("""Bert"""		,"""TestModel"""		,lowerCamelCase__ )		,)






     def        SCREAMING_SNAKE_CASE__						(							self		:					Union[str, Any] )  ->	Union[str, Any]:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		check_copies.LOCALIZED_READMES["""README_zh-hans.md"""]

           SCREAMING_SNAKE_CASE	=		(
               """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
               """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
               """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
               """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."""
               """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"""
               """ released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
               """ lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"""
               """ method has been applied to compress GPT2 into"""
               """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
               """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
               """ Multilingual BERT into"""
               """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
               """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"""
               """ (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"""
               """ as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"""
               """ Luong, Quoc V. Le, Christopher D. Manning."""
           )
           SCREAMING_SNAKE_CASE	=		(
               """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
               """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
               """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
               """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
           )
           SCREAMING_SNAKE_CASE	=		(
               """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
               """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
               """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
               """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."""
               """ **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"""
               """ [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"""
               """ lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"""
               """ method has been applied to compress GPT2 into"""
               """ [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"""
               """ [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"""
               """ Multilingual BERT into"""
               """ [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"""
               """ version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"""
               """ Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"""
               """ than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"""
               """ Christopher D. Manning 发布。\n"""
           )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		check_copies.convert_to_localized_md(
               lowerCamelCase__		,lowerCamelCase__		,localized_readme["""format_model_list"""] )

           self.assertFalse(lowerCamelCase__ )
           self.assertEqual(lowerCamelCase__		,lowerCamelCase__ )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		check_copies.convert_to_localized_md(
               lowerCamelCase__		,lowerCamelCase__		,localized_readme["""format_model_list"""] )

           # Check whether the number of models is equal to README.md after conversion.
           self.assertTrue(lowerCamelCase__ )

           SCREAMING_SNAKE_CASE	=		(
               """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"""
               """ Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"""
               """ Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"""
               """ Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."""
           )
           SCREAMING_SNAKE_CASE	=		(
               """1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"""
               """ the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
               """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
               """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
           )
           SCREAMING_SNAKE_CASE	=		(
               """1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"""
               """ Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"""
               """ Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"""
               """ Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"""
           )

           SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		check_copies.convert_to_localized_md(
               lowerCamelCase__		,lowerCamelCase__		,localized_readme["""format_model_list"""] )

           # Check if the model link is synchronized.
           self.assertEqual(lowerCamelCase__		,lowerCamelCase__ )









import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = 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.""",
      )
      SCREAMING_SNAKE_CASE_		     = parser.parse_args()

      SCREAMING_SNAKE_CASE_		     = 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)


