
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig


__SCREAMING_SNAKE_CASE      : Any       =  {
    'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/config.json',
    'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/config.json',
    'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/config.json',
    'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/config.json',
    'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/config.json',
    'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/config.json',
    'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/config.json',
    'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/config.json',
}


class 		__magic_name__ (    _snake_case     ):
		_lowerCAmelCase		=						'albert'





		def __init__(				self     :						Dict    ,	lowerCamelCase__     :						str=3_0_0_0_0    ,	lowerCamelCase__     :						Union[str, Any]=1_2_8    ,	lowerCamelCase__     :						Optional[Any]=4_0_9_6    ,	lowerCamelCase__     :						Optional[int]=1_2    ,	lowerCamelCase__     :						int=1    ,	lowerCamelCase__     :						str=6_4    ,	lowerCamelCase__     :						List[Any]=1_6_3_8_4    ,	lowerCamelCase__     :						List[Any]=1    ,	lowerCamelCase__     :						Optional[Any]="gelu_new"    ,	lowerCamelCase__     :						Tuple=0    ,	lowerCamelCase__     :						int=0    ,	lowerCamelCase__     :						Tuple=5_1_2    ,	lowerCamelCase__     :						str=2    ,	lowerCamelCase__     :						Optional[int]=0.0_2    ,	lowerCamelCase__     :						Union[str, Any]=1E-12    ,	lowerCamelCase__     :						Any=0.1    ,	lowerCamelCase__     :						List[Any]="absolute"    ,	lowerCamelCase__     :						Any=0    ,	lowerCamelCase__     :						Tuple=2    ,	lowerCamelCase__     :						Optional[int]=3    ,	**lowerCamelCase__     :						Any    ,	):
							super().__init__(pad_token_id=lowerCamelCase__    ,	bos_token_id=lowerCamelCase__    ,	eos_token_id=lowerCamelCase__    ,	**lowerCamelCase__					)

							lowerCAmelCase		:       Union[str, Any]							=  vocab_size
							lowerCAmelCase		:       Optional[int]							=  embedding_size
							lowerCAmelCase		:       Tuple							=  hidden_size
							lowerCAmelCase		:       Tuple							=  num_hidden_layers
							lowerCAmelCase		:       Tuple							=  num_hidden_groups
							lowerCAmelCase		:       Any							=  num_attention_heads
							lowerCAmelCase		:       Tuple							=  inner_group_num
							lowerCAmelCase		:       str							=  hidden_act
							lowerCAmelCase		:       str							=  intermediate_size
							lowerCAmelCase		:       Optional[int]							=  hidden_dropout_prob
							lowerCAmelCase		:       Any							=  attention_probs_dropout_prob
							lowerCAmelCase		:       int							=  max_position_embeddings
							lowerCAmelCase		:       Union[str, Any]							=  type_vocab_size
							lowerCAmelCase		:       str							=  initializer_range
							lowerCAmelCase		:       Union[str, Any]							=  layer_norm_eps
							lowerCAmelCase		:       List[str]							=  classifier_dropout_prob
							lowerCAmelCase		:       int							=  position_embedding_type



class 		__magic_name__ (    _snake_case     ):





		@property
		def    _A				(				self     :						Optional[int]					):

							if self.task == "multiple-choice":
												lowerCAmelCase		:       Any							=  {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
							else:
												lowerCAmelCase		:       int							=  {0: '''batch''', 1: '''sequence'''}
							return OrderedDict(
							    [
							        ('''input_ids''', dynamic_axis),
							        ('''attention_mask''', dynamic_axis),
							        ('''token_type_ids''', dynamic_axis),
							    ]					)







'''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


a   	=    {
    "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     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







   return torch.atana(__UpperCAmelCase				,						__UpperCAmelCase   ) / math.pi * 2

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.sin(t * math.pi / 2   ) ** 2
   __SCREAMING_SNAKE_CASE   				=							(1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(__UpperCAmelCase				,						__UpperCAmelCase   )







class __a			( _snake_case      ):
   pass







class __a			( nn.Module      ):





   def __init__(       self					:      Union[str, Any]					,lowerCamelCase					:      Union[str, Any]     ):

      '''simple docstring'''

      super().__init__()

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





def     __magic_name__							(				__UpperCAmelCase   )    ->       List[str]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							MODELS_MAP[model_name]["""url"""]
   os.system(f"""wget {url} ./"""   )

   return f"""./{model_name}.ckpt"""


a   	=    {
    "1": "resnets.0",
    "2": "attentions.0",
    "3": "resnets.1",
    "4": "attentions.1",
    "5": "resnets.2",
    "6": "attentions.2",
}
a   	=    {
    "8": "resnets.0",
    "9": "attentions.0",
    "10": "resnets.1",
    "11": "attentions.1",
    "12": "resnets.2",
    "13": "attentions.2",
}
a   	=    {
    "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",
}
a   	=    {
    "0": "resnets.0",
    "1": "resnets.1",
    "2": "resnets.2",
    "4": "resnets.0",
    "5": "resnets.1",
    "6": "resnets.2",
}

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

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

def     __magic_name__							(				__UpperCAmelCase   )    ->       int:

   '''simple docstring'''







   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     __magic_name__							(				__UpperCAmelCase   )    ->       Union[str, Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase=13   )    ->       Optional[int]:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							input_string

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

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

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

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

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

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

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

   __SCREAMING_SNAKE_CASE   				=							string_left[1:]

   if "resnets" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_resconv_naming(__UpperCAmelCase   )
   elif "attentions" in new_layer:
      __SCREAMING_SNAKE_CASE   				=							convert_attn_naming(__UpperCAmelCase   )
      __SCREAMING_SNAKE_CASE   				=							new_string_left

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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Optional[int]:

   '''simple docstring'''







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

      __SCREAMING_SNAKE_CASE   				=							rename(__UpperCAmelCase   )

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

   return new_state_dict

def     __magic_name__							(				__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase   )    ->       List[Any]:

   '''simple docstring'''







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

def     __magic_name__							(				__UpperCAmelCase   )    ->       Dict:

   '''simple docstring'''







   __SCREAMING_SNAKE_CASE   				=							torch.device("""cuda""" if torch.cuda.is_available() else """cpu"""   )

   __SCREAMING_SNAKE_CASE   				=							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()}"""
      __SCREAMING_SNAKE_CASE   				=							download(__UpperCAmelCase   )

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

   __SCREAMING_SNAKE_CASE   				=							Object()
   __SCREAMING_SNAKE_CASE   				=							sample_size
   __SCREAMING_SNAKE_CASE   				=							sample_rate
   __SCREAMING_SNAKE_CASE   				=							0

   __SCREAMING_SNAKE_CASE   				=							UNetaDModel(sample_size=__UpperCAmelCase				,						sample_rate=__UpperCAmelCase   )
   __SCREAMING_SNAKE_CASE   				=							diffusers_model.state_dict()

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

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

   assert len(__UpperCAmelCase   ) == 0, f"""Problem with {renamed_minus_diffusers}"""
   assert all(k.endswith("""kernel"""   ) for k in list(__UpperCAmelCase   )   ), 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":
         __SCREAMING_SNAKE_CASE   				=							value.squeeze()

      __SCREAMING_SNAKE_CASE   				=							value

   diffusers_model.load_state_dict(__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							100
   __SCREAMING_SNAKE_CASE   				=							33

   __SCREAMING_SNAKE_CASE   				=							IPNDMScheduler(num_train_timesteps=__UpperCAmelCase   )

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

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

   __SCREAMING_SNAKE_CASE   				=							DanceDiffusionPipeline(unet=__UpperCAmelCase				,						scheduler=__UpperCAmelCase   )

   __SCREAMING_SNAKE_CASE   				=							torch.manual_seed(33   )
   __SCREAMING_SNAKE_CASE   				=							pipe(num_inference_steps=__UpperCAmelCase				,						generator=__UpperCAmelCase   ).audios

   __SCREAMING_SNAKE_CASE   				=							sampling.iplms_sample(__UpperCAmelCase				,						__UpperCAmelCase				,						__UpperCAmelCase				,						{}   )
   __SCREAMING_SNAKE_CASE   				=							generated.clamp(-1				,						1   )

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

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

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

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

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


if __name__ == "__main__":
    a   	=    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.")
    a   	=    parser.parse_args()

    main(args)





