
import argparse

import torch

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt


if __name__ == "__main__":
 __lowerCAmelCase    :	Union[str, Any]						=						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    :	Union[str, Any]						=						parser.parse_args()

 __lowerCAmelCase    :	Any						=						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)










from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCamelCase	:							Optional[int]				=logging.get_logger(__name__)

lowerCamelCase	:							Dict				={
    '''facebook/s2t-wav2vec2-large-en-de''': (
        '''https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'''
    ),
    # See all Speech2Text models at https://huggingface.co/models?filter=speech2text2
}





class 			__a  (    A__       ):
 _lowerCAmelCase       :       Tuple        = '''speech_to_text_2'''
 _lowerCAmelCase       :       Dict        = ['''past_key_values''']
 _lowerCAmelCase       :       Any        = {'''num_attention_heads''': '''decoder_attention_heads''', '''hidden_size''': '''d_model'''}







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=1_00_00							,   SCREAMING_SNAKE_CASE					:   List[Any]=6							,   SCREAMING_SNAKE_CASE					:   List[Any]=20_48							,   SCREAMING_SNAKE_CASE					:   Dict=4							,   SCREAMING_SNAKE_CASE					:   List[Any]=0.0							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   List[Any]="relu"							,   SCREAMING_SNAKE_CASE					:   Tuple=2_56							,   SCREAMING_SNAKE_CASE					:   List[str]=0.1							,   SCREAMING_SNAKE_CASE					:   Tuple=0.0							,   SCREAMING_SNAKE_CASE					:   Any=0.0							,   SCREAMING_SNAKE_CASE					:   int=0.0_2							,   SCREAMING_SNAKE_CASE					:   Dict=2							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   Any=1							,   SCREAMING_SNAKE_CASE					:   List[Any]=0							,   SCREAMING_SNAKE_CASE					:   Any=2							,   SCREAMING_SNAKE_CASE					:   str=10_24							,   **SCREAMING_SNAKE_CASE					:   int							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  vocab_size
      UpperCamelCase__			:   Optional[Any]       				=  d_model
      UpperCamelCase__			:   Optional[Any]       				=  decoder_ffn_dim
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Any       				=  decoder_attention_heads
      UpperCamelCase__			:   List[str]       				=  dropout
      UpperCamelCase__			:   int       				=  attention_dropout
      UpperCamelCase__			:   Optional[int]       				=  activation_dropout
      UpperCamelCase__			:   Union[str, Any]       				=  activation_function
      UpperCamelCase__			:   Tuple       				=  init_std
      UpperCamelCase__			:   Optional[int]       				=  decoder_layerdrop
      UpperCamelCase__			:   Dict       				=  use_cache
      UpperCamelCase__			:   str       				=  decoder_layers
      UpperCamelCase__			:   Union[str, Any]       				=  scale_embedding  # scale factor will be sqrt(d_model) if True
      UpperCamelCase__			:   Optional[Any]       				=  max_target_positions

      super().__init__(
          pad_token_id=SCREAMING_SNAKE_CASE							,   bos_token_id=SCREAMING_SNAKE_CASE							,   eos_token_id=SCREAMING_SNAKE_CASE							,   decoder_start_token_id=SCREAMING_SNAKE_CASE							,   **SCREAMING_SNAKE_CASE							,   )