






from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__lowerCamelCase	:		Tuple										=   logging.get_logger(__name__)

__lowerCamelCase	:		Any										=   {
    """EleutherAI/gpt-j-6B""": """https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json""",
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							'gptj'
      _a							:						Optional[int]										=							{
          'max_position_embeddings': 'n_positions',
          'hidden_size': 'n_embd',
          'num_attention_heads': 'n_head',
          'num_hidden_layers': 'n_layer',
      }
      def __init__(     self				,			a=5_0_4_0_0				,			a=2_0_4_8				,			a=4_0_9_6				,			a=2_8				,			a=1_6				,			a=6_4				,			a=None				,			a="gelu_new"				,			a=0.0				,			a=0.0				,			a=0.0				,			a=1e-5				,			a=0.02				,			a=True				,			a=5_0_2_5_6				,			a=5_0_2_5_6				,			a=False				,			**a				,			):
       snake_case__  :    Union[str, Any]							       =vocab_size
       snake_case__  :    Union[str, Any]							       =n_positions
       snake_case__  :    Tuple							       =n_embd
       snake_case__  :    List[str]							       =n_layer
       snake_case__  :    str							       =n_head
       snake_case__  :    Any							       =n_inner
       snake_case__  :    Optional[int]							       =rotary_dim
       snake_case__  :    List[Any]							       =activation_function
       snake_case__  :    List[Any]							       =resid_pdrop
       snake_case__  :    int							       =embd_pdrop
       snake_case__  :    Optional[Any]							       =attn_pdrop
       snake_case__  :    Any							       =layer_norm_epsilon
       snake_case__  :    Optional[int]							       =initializer_range
       snake_case__  :    Optional[Any]							       =use_cache

       snake_case__  :    Tuple							       =bos_token_id
       snake_case__  :    Optional[Any]							       =eos_token_id

       super().__init__(
           bos_token_id=a				,			eos_token_id=a				,			tie_word_embeddings=a				,			**a   )







class  _lowercase   (   _A						):
      def __init__(     self				,			a				,			a = "default"				,			a = None				,			a = False				,			):
       super().__init__(a				,			task=a				,			patching_specs=a				,			use_past=a   )
       if not getattr(self._config				,			"""pad_token_id"""				,			a   ):
        # TODO: how to do that better?
        snake_case__  :    str							       =0
      @property
      def 						lowercase__					(     self   ):
       snake_case__  :    Tuple							       =OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}   )
       if self.use_past:
        self.fill_with_past_key_values_(a				,			direction="""inputs"""   )
        snake_case__  :    Dict							       ={0: """batch""", 1: """past_sequence + sequence"""}
       else:
        snake_case__  :    List[str]							       ={0: """batch""", 1: """sequence"""}

       return common_inputs
      @property
      def 						lowercase__					(     self   ):
       return self._config.n_layer
      @property
      def 						lowercase__					(     self   ):
       return self._config.n_head
      def 						lowercase__					(     self				,			a				,			a = -1				,			a = -1				,			a = False				,			a = None				,			):
       snake_case__  :    Dict							       =super(a				,			self   ).generate_dummy_inputs(
           a				,			batch_size=a				,			seq_length=a				,			is_pair=a				,			framework=a   )

       # We need to order the input in the way they appears in the forward()
       snake_case__  :    int							       =OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}   )

       # Need to add the past_keys
       if self.use_past:
        if not is_torch_available():
         raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""   )
        else:
         import torch

         snake_case__       ,   snake_case__  :    Tuple							       =common_inputs["""input_ids"""].shape
         # Not using the same length for past_key_values
         snake_case__  :    Any							       =seqlen + 2
         snake_case__  :    Union[str, Any]							       =(
             batch,
             self.num_attention_heads,
             past_key_values_length,
             self._config.hidden_size // self.num_attention_heads,
         )
         snake_case__  :    Any							       =[
             (torch.zeros(a   ), torch.zeros(a   )) for _ in range(self.num_layers   )
         ]

       snake_case__  :    Dict							       =common_inputs["""attention_mask"""]
       if self.use_past:
        snake_case__  :    Tuple							       =ordered_inputs["""attention_mask"""].dtype
        snake_case__  :    List[Any]							       =torch.cat(
            [ordered_inputs["""attention_mask"""], torch.ones(a				,			a				,			dtype=a   )]				,			dim=1   )

       return ordered_inputs



      @property
      def 						lowercase__					(     self   ):
       return 1_3













import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
       __lowerCamelCase	:		List[str]										=   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	:		Optional[Any]										=   parser.parse_args()

       __lowerCamelCase	:		Optional[int]										=   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)





