
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


lowercase  :					List[str]						    =							{
    """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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    Optional[int]:
       return torch.atana(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) / math.pi * 2







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     str       	=      torch.sin(t * math.pi / 2				) ** 2
       lowercase							:     Any       	=      (1 - sigma**2) ** 0.5
       return alpha_sigma_to_t(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)

class __snake_case		(      lowerCAmelCase     ):
    pass






class __snake_case		(      nn.Module     ):
    def __init__(  self		,snake_case      ):



           '''simple docstring'''







           super().__init__()

           lowercase							:     List[str]       	=      DiffusionAttnUnetaD(snake_case		,n_attn_layers=4      )
           lowercase							:     int       	=      deepcopy(self.diffusion      )
           lowercase							:     Dict       	=      torch.quasirandom.SobolEngine(1		,scramble=snake_case      )







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Optional[Any]:
       lowercase							:     Tuple       	=      MODELS_MAP[model_name]["""url"""]
       os.system(f"wget {url} ./"				)

       return f"./{model_name}.ckpt"


lowercase  :					Any						    =							{
    """1""": """resnets.0""",
    """2""": """attentions.0""",
    """3""": """resnets.1""",
    """4""": """attentions.1""",
    """5""": """resnets.2""",
    """6""": """attentions.2""",
}
lowercase  :					Union[str, Any]						    =							{
    """8""": """resnets.0""",
    """9""": """attentions.0""",
    """10""": """resnets.1""",
    """11""": """attentions.1""",
    """12""": """resnets.2""",
    """13""": """attentions.2""",
}
lowercase  :					str						    =							{
    """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""",
}
lowercase  :					int						    =							{
    """0""": """resnets.0""",
    """1""": """resnets.1""",
    """2""": """resnets.2""",
    """4""": """resnets.0""",
    """5""": """resnets.1""",
    """6""": """resnets.2""",
}

lowercase  :					str						    =							{
    """skip""": """conv_skip""",
    """main.0""": """conv_1""",
    """main.1""": """group_norm_1""",
    """main.3""": """conv_2""",
    """main.4""": """group_norm_2""",
}

lowercase  :					Any						    =							{
    """norm""": """group_norm""",
    """qkv_proj""": ["""query""", """key""", """value"""],
    """out_proj""": ["""proj_attn"""],
}







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       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 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Any:
       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 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__=13				) ->    Tuple:
       lowercase							:     Optional[int]       	=      input_string

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

       lowercase							:     Optional[int]       	=      0
       if string.startswith("""net.3."""				):
              depth += 1
              lowercase							:     List[str]       	=      string[6:]
       elif string.startswith("""net."""				):
              lowercase							:     str       	=      string[4:]

       while string.startswith("""main.7."""				):
              depth += 1
              lowercase							:     Union[str, Any]       	=      string[7:]

       if string.startswith("""main."""				):
              lowercase							:     List[str]       	=      string[5:]

       # mid block
       if string[:2].isdigit():
              lowercase							:     List[str]       	=      string[:2]
              lowercase							:     Tuple       	=      string[2:]
       else:
              lowercase							:     Optional[Any]       	=      string[0]
              lowercase							:     Any       	=      string[1:]

       if depth == max_depth:
              lowercase							:     str       	=      MID_NUM_TO_LAYER[layer_num]
              lowercase							:     Dict       	=      """mid_block"""
       elif depth > 0 and int(SCREAMING_SNAKE_CASE__				) < 7:
              lowercase							:     str       	=      DOWN_NUM_TO_LAYER[layer_num]
              lowercase							:     int       	=      f"down_blocks.{depth}"
       elif depth > 0 and int(SCREAMING_SNAKE_CASE__				) > 7:
              lowercase							:     Dict       	=      UP_NUM_TO_LAYER[layer_num]
              lowercase							:     Any       	=      f"up_blocks.{max_depth - depth - 1}"
       elif depth == 0:
              lowercase							:     Dict       	=      DEPTH_0_TO_LAYER[layer_num]
              lowercase							:     str       	=      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}."				)

       lowercase							:     Any       	=      string_left[1:]

       if "resnets" in new_layer:
              lowercase							:     Dict       	=      convert_resconv_naming(SCREAMING_SNAKE_CASE__				)
       elif "attentions" in new_layer:
              lowercase							:     List[Any]       	=      convert_attn_naming(SCREAMING_SNAKE_CASE__				)
              lowercase							:     List[str]       	=      new_string_left

       if not isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
              lowercase							:     Dict       	=      prefix + """.""" + new_layer + """.""" + string_left
       else:
              lowercase							:     int       	=      [prefix + """.""" + new_layer + """.""" + s for s in string_left]
       return new_string







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    Union[str, Any]:
       lowercase							:     Optional[Any]       	=      {}
       for k, v in state_dict.items():
              if k.endswith("""kernel"""				):
                     # up- and downsample layers, don't have trainable weights
                     continue

              lowercase							:     List[str]       	=      rename(SCREAMING_SNAKE_CASE__				)

              # check if we need to transform from Conv => Linear for attention
              if isinstance(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				):
                     lowercase							:     int       	=      transform_conv_attns(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				)
              else:
                     lowercase							:     Optional[int]       	=      v

       return new_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__				) ->    List[Any]:
       if len(SCREAMING_SNAKE_CASE__				) == 1:
              if len(v.shape				) == 3:
                     # weight
                     lowercase							:     Optional[Any]       	=      v[:, :, 0]
              else:
                     # bias
                     lowercase							:     Tuple       	=      v
       else:
              # qkv matrices
              lowercase							:     Tuple       	=      v.shape[0]
              lowercase							:     Union[str, Any]       	=      trippled_shape // 3
              for i in range(3				):
                     if len(v.shape				) == 3:
                            lowercase							:     Union[str, Any]       	=      v[i * single_shape : (i + 1) * single_shape, :, 0]
                     else:
                            lowercase							:     List[str]       	=      v[i * single_shape : (i + 1) * single_shape]
       return new_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__				) ->    List[str]:
       lowercase							:     List[Any]       	=      torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""				)

       lowercase							:     List[Any]       	=      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()}"
              lowercase							:     List[str]       	=      download(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Any       	=      MODELS_MAP[model_name]["""sample_rate"""]
       lowercase							:     str       	=      MODELS_MAP[model_name]["""sample_size"""]

       lowercase							:     List[Any]       	=      Object()
       lowercase							:     str       	=      sample_size
       lowercase							:     List[str]       	=      sample_rate
       lowercase							:     Any       	=      0

       lowercase							:     Dict       	=      UNetaDModel(sample_size=SCREAMING_SNAKE_CASE__					,	sample_rate=SCREAMING_SNAKE_CASE__				)
       lowercase							:     Optional[int]       	=      diffusers_model.state_dict()

       lowercase							:     Optional[int]       	=      DiffusionUncond(SCREAMING_SNAKE_CASE__				)
       orig_model.load_state_dict(torch.load(args.model_path					,	map_location=SCREAMING_SNAKE_CASE__				)["""state_dict"""]				)
       lowercase							:     Any       	=      orig_model.diffusion_ema.eval()
       lowercase							:     int       	=      orig_model.state_dict()
       lowercase							:     int       	=      rename_orig_weights(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Optional[Any]       	=      set(renamed_state_dict.keys()				) - set(diffusers_state_dict.keys()				)
       lowercase							:     Tuple       	=      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":
                     lowercase							:     str       	=      value.squeeze()

              lowercase							:     int       	=      value

       diffusers_model.load_state_dict(SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[Any]       	=      100
       lowercase							:     int       	=      33

       lowercase							:     Tuple       	=      IPNDMScheduler(num_train_timesteps=SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      torch.manual_seed(SCREAMING_SNAKE_CASE__				)
       lowercase							:     str       	=      torch.randn([1, 2, config.sample_size]					,	generator=SCREAMING_SNAKE_CASE__				).to(SCREAMING_SNAKE_CASE__				)

       lowercase							:     List[str]       	=      torch.linspace(1					,	0					,	steps + 1					,	device=SCREAMING_SNAKE_CASE__				)[:-1]
       lowercase							:     List[Any]       	=      get_crash_schedule(SCREAMING_SNAKE_CASE__				)

       lowercase							:     Any       	=      DanceDiffusionPipeline(unet=SCREAMING_SNAKE_CASE__					,	scheduler=SCREAMING_SNAKE_CASE__				)

       lowercase							:     Dict       	=      torch.manual_seed(33				)
       lowercase							:     List[str]       	=      pipe(num_inference_steps=SCREAMING_SNAKE_CASE__					,	generator=SCREAMING_SNAKE_CASE__				).audios

       lowercase							:     int       	=      sampling.iplms_sample(SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	{}				)
       lowercase							:     str       	=      generated.clamp(-1					,	1				)

       lowercase							:     Any       	=      (generated - audio).abs().sum()
       lowercase							:     Dict       	=      (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__":
  lowercase  :					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.""")
  lowercase  :					Optional[int]						    =							parser.parse_args()

  main(args)


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


lowercase  :					List[str]						    =							logging.get_logger(__name__)

lowercase  :					Any						    =							{
    """uclanlp/visualbert-vqa""": """https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-pre""": """https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vqa-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-vcr""": """https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-pre""": """https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json""",
    """uclanlp/visualbert-vcr-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"""
    ),
    """uclanlp/visualbert-nlvr2""": """https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-pre""": """https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json""",
    """uclanlp/visualbert-nlvr2-coco-pre""": (
        """https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"""
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}

class __snake_case		(      lowerCAmelCase     ):
    _a		:						Union[str, Any]=				"visual_bert"
    def __init__(  self		,snake_case=30522		,snake_case=768		,snake_case=512		,snake_case=12		,snake_case=12		,snake_case=3072		,snake_case="gelu"		,snake_case=0.1		,snake_case=0.1		,snake_case=512		,snake_case=2		,snake_case=0.02		,snake_case=1e-12		,snake_case=False		,snake_case=True		,snake_case=1		,snake_case=0		,snake_case=2		,**snake_case		,):



           '''simple docstring'''







           super().__init__(pad_token_id=snake_case		,bos_token_id=snake_case		,eos_token_id=snake_case		,**snake_case      )

           lowercase							:     Tuple       	=      vocab_size
           lowercase							:     int       	=      max_position_embeddings
           lowercase							:     Optional[Any]       	=      hidden_size
           lowercase							:     int       	=      visual_embedding_dim
           lowercase							:     Tuple       	=      num_hidden_layers
           lowercase							:     str       	=      num_attention_heads
           lowercase							:     Optional[Any]       	=      intermediate_size
           lowercase							:     str       	=      hidden_act
           lowercase							:     Tuple       	=      hidden_dropout_prob
           lowercase							:     List[Any]       	=      attention_probs_dropout_prob
           lowercase							:     Union[str, Any]       	=      initializer_range
           lowercase							:     int       	=      type_vocab_size
           lowercase							:     Union[str, Any]       	=      layer_norm_eps
           lowercase							:     Union[str, Any]       	=      bypass_transformer
           lowercase							:     int       	=      special_visual_initialize
