"""simple docstring"""



import argparse
import math
import os
from copy import deepcopy

import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn

from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel


SCREAMING_SNAKE_CASE__:Tuple									=			{
    """gwf-440k""": {
        """url""": """https://model-server.zqevans2.workers.dev/gwf-440k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 65536,
    },
    """jmann-small-190k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 65536,
    },
    """jmann-large-580k""": {
        """url""": """https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt""",
        """sample_rate""": 48000,
        """sample_size""": 131072,
    },
    """maestro-uncond-150k""": {
        """url""": """https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
    """unlocked-uncond-250k""": {
        """url""": """https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
    """honk-140k""": {
        """url""": """https://model-server.zqevans2.workers.dev/honk-140k.ckpt""",
        """sample_rate""": 16000,
        """sample_size""": 65536,
    },
}



def 	_lowerCamelCase(	a   ,    a				):
       return torch.atana(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				) / math.pi * 2



def 	_lowerCamelCase(	a				):
       __a       =    torch.sin(t * math.pi / 2				) ** 2
       __a       =    (1 - sigma**2) ** 0.5
       return alpha_sigma_to_t(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				)






class  snake_case__      (       snake_case_       ):
 pass






class  snake_case__      (       nn.Module       ):


 def __init__( self    ,		lowerCamelCase						):
        super().__init__()

        __a       =    DiffusionAttnUnetaD(_lowerCAmelCase    ,		n_attn_layers=4						)
        __a       =    deepcopy(self.diffusion						)
        __a       =    torch.quasirandom.SobolEngine(1    ,		scramble=_lowerCAmelCase						)



def 	_lowerCamelCase(	a				):
       __a       =    MODELS_MAP[model_name]["url"]
       os.system(F"wget {url} ./"				)

       return F"./{model_name}.ckpt"


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
SCREAMING_SNAKE_CASE__:Tuple									=			{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
SCREAMING_SNAKE_CASE__:List[Any]									=			{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
    """8""": """resnets.3""",
    """9""": """attentions.3""",
    """10""": """resnets.4""",
    """11""": """attentions.4""",
    """12""": """resnets.5""",
    """13""": """attentions.5""",
}
SCREAMING_SNAKE_CASE__:Dict									=			{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

SCREAMING_SNAKE_CASE__:List[Any]									=			{
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

SCREAMING_SNAKE_CASE__:List[str]									=			{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}



def 	_lowerCamelCase(	a				):





       if name.startswith("skip"				):
              return name.replace("skip"   ,    RES_CONV_MAP["skip"]				)

       # name has to be of format main.{digit}
       if not name.startswith("main."				):
              raise ValueError(F"ResConvBlock error with {name}"				)

       return name.replace(name[:6]   ,    RES_CONV_MAP[name[:6]]				)



def 	_lowerCamelCase(	a				):





       for key, value in ATTN_MAP.items():
              if name.startswith(SCREAMING_SNAKE_CASE_				) and not isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				):
                     return name.replace(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				)
              elif name.startswith(SCREAMING_SNAKE_CASE_				):
                     return [name.replace(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				) for v in value]
       raise ValueError(F"Attn error with {name}"				)



def 	_lowerCamelCase(	a   ,    a=1_3				):
       __a       =    input_string

       if string.split("."				)[0] == "timestep_embed":
              return string.replace("timestep_embed"   ,    "time_proj"				)

       __a       =    0
       if string.startswith("net.3."				):
              depth += 1
              __a       =    string[6:]
       elif string.startswith("net."				):
              __a       =    string[4:]

       while string.startswith("main.7."				):
              depth += 1
              __a       =    string[7:]

       if string.startswith("main."				):
              __a       =    string[5:]

       # mid block
       if string[:2].isdigit():
              __a       =    string[:2]
              __a       =    string[2:]
       else:
              __a       =    string[0]
              __a       =    string[1:]

       if depth == max_depth:
              __a       =    MID_NUM_TO_LAYER[layer_num]
              __a       =    "mid_block"
       elif depth > 0 and int(SCREAMING_SNAKE_CASE_				) < 7:
              __a       =    DOWN_NUM_TO_LAYER[layer_num]
              __a       =    F"down_blocks.{depth}"
       elif depth > 0 and int(SCREAMING_SNAKE_CASE_				) > 7:
              __a       =    UP_NUM_TO_LAYER[layer_num]
              __a       =    F"up_blocks.{max_depth - depth - 1}"
       elif depth == 0:
              __a       =    DEPTH_0_TO_LAYER[layer_num]
              __a       =    F"up_blocks.{max_depth - 1}" if int(SCREAMING_SNAKE_CASE_				) > 3 else "down_blocks.0"

       if not string_left.startswith("."				):
              raise ValueError(F"Naming error with {input_string} and string_left: {string_left}."				)

       __a       =    string_left[1:]

       if "resnets" in new_layer:
              __a       =    convert_resconv_naming(SCREAMING_SNAKE_CASE_				)
       elif "attentions" in new_layer:
              __a       =    convert_attn_naming(SCREAMING_SNAKE_CASE_				)
              __a       =    new_string_left

       if not isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				):
              __a       =    prefix + "." + new_layer + "." + string_left
       else:
              __a       =    [prefix + "." + new_layer + "." + s for s in string_left]
       return new_string



def 	_lowerCamelCase(	a				):
       __a       =    {}
       for k, v in state_dict.items():
              if k.endswith("kernel"				):
                     # up- and downsample layers, don't have trainable weights
                     continue

              __a       =    rename(SCREAMING_SNAKE_CASE_				)

              # check if we need to transform from Conv => Linear for attention
              if isinstance(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				):
                     __a       =    transform_conv_attns(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_				)
              else:
                     __a       =    v

       return new_state_dict



def 	_lowerCamelCase(	a   ,    a   ,    a				):





       if len(SCREAMING_SNAKE_CASE_				) == 1:
              if len(v.shape				) == 3:
                     # weight
                     __a       =    v[:, :, 0]
              else:
                     # bias
                     __a       =    v
       else:
              # qkv matrices
              __a       =    v.shape[0]
              __a       =    trippled_shape // 3
              for i in range(3				):
                     if len(v.shape				) == 3:
                            __a       =    v[i * single_shape : (i + 1) * single_shape, :, 0]
                     else:
                            __a       =    v[i * single_shape : (i + 1) * single_shape]
       return new_state_dict



def 	_lowerCamelCase(	a				):
       __a       =    torch.device("cuda" if torch.cuda.is_available() else "cpu"				)

       __a       =    args.model_path.split("/"				)[-1].split("."				)[0]
       if not os.path.isfile(args.model_path				):
              assert (
                  model_name == args.model_path
              ), F"Make sure to provide one of the official model names {MODELS_MAP.keys()}"
              __a       =    download(SCREAMING_SNAKE_CASE_				)

       __a       =    MODELS_MAP[model_name]["sample_rate"]
       __a       =    MODELS_MAP[model_name]["sample_size"]

       __a       =    Object()
       __a       =    sample_size
       __a       =    sample_rate
       __a       =    0

       __a       =    UNetaDModel(sample_size=SCREAMING_SNAKE_CASE_   ,    sample_rate=SCREAMING_SNAKE_CASE_				)
       __a       =    diffusers_model.state_dict()

       __a       =    DiffusionUncond(SCREAMING_SNAKE_CASE_				)
       orig_model.load_state_dict(torch.load(args.model_path   ,    map_location=SCREAMING_SNAKE_CASE_				)["state_dict"]				)
       __a       =    orig_model.diffusion_ema.eval()
       __a       =    orig_model.state_dict()
       __a       =    rename_orig_weights(SCREAMING_SNAKE_CASE_				)

       __a       =    set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
       __a       =    set(diffusers_state_dict.keys()				) - set(renamed_state_dict.keys()				)

       assert len(SCREAMING_SNAKE_CASE_				) == 0, F"Problem with {renamed_minus_diffusers}"
       assert all(k.endswith("kernel"				) for k in list(SCREAMING_SNAKE_CASE_				)				), F"Problem with {diffusers_minus_renamed}"

       for key, value in renamed_state_dict.items():
              assert (
                  diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
              ), F"Shape for {key} doesn\'t match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"
              if key == "time_proj.weight":
                     __a       =    value.squeeze()

              __a       =    value

       diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE_				)

       __a       =    1_0_0
       __a       =    3_3

       __a       =    IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE_				)

       __a       =    torch.manual_seed(SCREAMING_SNAKE_CASE_				)
       __a       =    torch.randn([1, 2, config.sample_size]   ,    generator=SCREAMING_SNAKE_CASE_				).to(SCREAMING_SNAKE_CASE_				)

       __a       =    torch.linspace(1   ,    0   ,    steps + 1   ,    device=SCREAMING_SNAKE_CASE_				)[:-1]
       __a       =    get_crash_schedule(SCREAMING_SNAKE_CASE_				)

       __a       =    DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE_   ,    scheduler=SCREAMING_SNAKE_CASE_				)

       __a       =    torch.manual_seed(3_3				)
       __a       =    pipe(num_inference_steps=SCREAMING_SNAKE_CASE_   ,    generator=SCREAMING_SNAKE_CASE_				).audios

       __a       =    sampling.iplms_sample(SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    SCREAMING_SNAKE_CASE_   ,    {}				)
       __a       =    generated.clamp(-1   ,    1				)

       __a       =    (generated - audio).abs().sum()
       __a       =    (generated - audio).abs().max()

       if args.save:
              pipe.save_pretrained(args.checkpoint_path				)

       print("Diff sum"   ,    SCREAMING_SNAKE_CASE_				)
       print("Diff max"   ,    SCREAMING_SNAKE_CASE_				)

       assert diff_max < 1E-3, F"Diff max: {diff_max} is too much :-/"

       print(F"Conversion for {model_name} successful!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:Union[str, Any]									=			argparse.ArgumentParser()

   parser.add_argument("""--model_path""", default=None, type=str, required=True, help="""Path to the model to convert.""")
   parser.add_argument(
       """--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
   )
   parser.add_argument("""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the output model.""")
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()

   main(args)







'''simple docstring'''


import warnings

from ...utils import logging
from .image_processing_yolos import YolosImageProcessor


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

class  lowerCAmelCase_		(		__magic_name__		):




 def __init__(						self      ,  *_lowerCAmelCase      ,  **_lowerCAmelCase		)     ->				None:
     warnings.warn(
         "The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
         " use YolosImageProcessor instead."      ,  _lowerCAmelCase      ,  )
     super().__init__(*_lowerCAmelCase      ,  **_lowerCAmelCase		)


