


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


__UpperCamelCase				:  List[str]      					=							{
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 6_5536,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_8000,
        "sample_size": 13_1072,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_6000,
        "sample_size": 6_5536,
    },
}
def      snake_case_						( __lowercase     :   List[str]			,       __lowercase     :   Any       ):
   return torch.atana(a_			,       a_       ) / math.pi * 2
def      snake_case_						( __lowercase     :   List[str]       ):
   UpperCAmelCase_							:      List[str]						=   torch.sin(t * math.pi / 2       ) ** 2
   UpperCAmelCase_							:      Tuple						=   (1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(a_			,       a_       )





class  lowerCAmelCase__(    _UpperCAmelCase						):






    '''simple docstring'''





    pass





class  lowerCAmelCase__(    nn.Module						):






    '''simple docstring'''





    def __init__(	self				:   Tuple       ,   __snake_case				:   Tuple	):





       '''simple docstring'''
       super().__init__()

       UpperCAmelCase_							:      Optional[Any]						=   DiffusionAttnUnetaD(lowerCamelCase_       ,   n_attn_layers=4	)
       UpperCAmelCase_							:      int						=   deepcopy(self.diffusion	)
       UpperCAmelCase_							:      List[str]						=   torch.quasirandom.SobolEngine(1       ,   scramble=lowerCamelCase_	)





def      snake_case_						( __lowercase     :   Tuple       ):
   UpperCAmelCase_							:      int						=   MODELS_MAP[model_name]['''url''']
   os.system(F'''wget {url} ./'''       )

   return F'''./{model_name}.ckpt'''


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

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

__UpperCamelCase				:  List[Any]      					=							{
    "norm": "group_norm",
    "qkv_proj": ["query", "key", "value"],
    "out_proj": ["proj_attn"],
}
def      snake_case_						( __lowercase     :   Dict       ):






   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_						( __lowercase     :   str       ):






   for key, value in ATTN_MAP.items():
      if name.startswith(a_       ) and not isinstance(a_			,       a_       ):
         return name.replace(a_			,       a_       )
      elif name.startswith(a_       ):
         return [name.replace(a_			,       a_       ) for v in value]
   raise ValueError(F'''Attn error with {name}'''       )
def      snake_case_						( __lowercase     :   Dict			,       __lowercase     :   Optional[Any]=1_3       ):
   UpperCAmelCase_							:      Tuple						=   input_string

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

   UpperCAmelCase_							:      Tuple						=   0
   if string.startswith('''net.3.'''       ):
      depth += 1
      UpperCAmelCase_							:      Dict						=   string[6:]
   elif string.startswith('''net.'''       ):
      UpperCAmelCase_							:      Dict						=   string[4:]

   while string.startswith('''main.7.'''       ):
      depth += 1
      UpperCAmelCase_							:      List[str]						=   string[7:]

   if string.startswith('''main.'''       ):
      UpperCAmelCase_							:      str						=   string[5:]

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

   if depth == max_depth:
      UpperCAmelCase_							:      Tuple						=   MID_NUM_TO_LAYER[layer_num]
      UpperCAmelCase_							:      Optional[Any]						=   '''mid_block'''
   elif depth > 0 and int(a_       ) < 7:
      UpperCAmelCase_							:      str						=   DOWN_NUM_TO_LAYER[layer_num]
      UpperCAmelCase_							:      Any						=   F'''down_blocks.{depth}'''
   elif depth > 0 and int(a_       ) > 7:
      UpperCAmelCase_							:      Tuple						=   UP_NUM_TO_LAYER[layer_num]
      UpperCAmelCase_							:      List[Any]						=   F'''up_blocks.{max_depth - depth - 1}'''
   elif depth == 0:
      UpperCAmelCase_							:      Dict						=   DEPTH_0_TO_LAYER[layer_num]
      UpperCAmelCase_							:      Any						=   F'''up_blocks.{max_depth - 1}''' if int(a_       ) > 3 else '''down_blocks.0'''

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

   UpperCAmelCase_							:      int						=   string_left[1:]

   if "resnets" in new_layer:
      UpperCAmelCase_							:      List[str]						=   convert_resconv_naming(a_       )
   elif "attentions" in new_layer:
      UpperCAmelCase_							:      Tuple						=   convert_attn_naming(a_       )
      UpperCAmelCase_							:      str						=   new_string_left

   if not isinstance(a_			,       a_       ):
      UpperCAmelCase_							:      int						=   prefix + '''.''' + new_layer + '''.''' + string_left
   else:
      UpperCAmelCase_							:      List[Any]						=   [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
   return new_string
def      snake_case_						( __lowercase     :   Tuple       ):
   UpperCAmelCase_							:      Optional[int]						=   {}
   for k, v in state_dict.items():
      if k.endswith('''kernel'''       ):
         # up- and downsample layers, don't have trainable weights
         continue

      UpperCAmelCase_							:      Optional[int]						=   rename(a_       )

      # check if we need to transform from Conv => Linear for attention
      if isinstance(a_			,       a_       ):
         UpperCAmelCase_							:      Union[str, Any]						=   transform_conv_attns(a_			,       a_			,       a_       )
      else:
         UpperCAmelCase_							:      Dict						=   v

   return new_state_dict
def      snake_case_						( __lowercase     :   Any			,       __lowercase     :   Tuple			,       __lowercase     :   List[Any]       ):






   if len(a_       ) == 1:
      if len(v.shape       ) == 3:
         # weight
         UpperCAmelCase_							:      Dict						=   v[:, :, 0]
      else:
         # bias
         UpperCAmelCase_							:      Dict						=   v
   else:
      # qkv matrices
      UpperCAmelCase_							:      List[Any]						=   v.shape[0]
      UpperCAmelCase_							:      Tuple						=   trippled_shape // 3
      for i in range(3       ):
         if len(v.shape       ) == 3:
            UpperCAmelCase_							:      int						=   v[i * single_shape : (i + 1) * single_shape, :, 0]
         else:
            UpperCAmelCase_							:      List[str]						=   v[i * single_shape : (i + 1) * single_shape]
   return new_state_dict
def      snake_case_						( __lowercase     :   Dict       ):
   UpperCAmelCase_							:      List[str]						=   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''       )

   UpperCAmelCase_							:      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()}'''
      UpperCAmelCase_							:      Tuple						=   download(a_       )

   UpperCAmelCase_							:      Union[str, Any]						=   MODELS_MAP[model_name]['''sample_rate''']
   UpperCAmelCase_							:      Optional[Any]						=   MODELS_MAP[model_name]['''sample_size''']

   UpperCAmelCase_							:      int						=   Object()
   UpperCAmelCase_							:      int						=   sample_size
   UpperCAmelCase_							:      Optional[int]						=   sample_rate
   UpperCAmelCase_							:      Dict						=   0

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

   UpperCAmelCase_							:      int						=   DiffusionUncond(a_       )
   orig_model.load_state_dict(torch.load(args.model_path			,       map_location=a_       )['''state_dict''']       )
   UpperCAmelCase_							:      Tuple						=   orig_model.diffusion_ema.eval()
   UpperCAmelCase_							:      Optional[Any]						=   orig_model.state_dict()
   UpperCAmelCase_							:      List[str]						=   rename_orig_weights(a_       )

   UpperCAmelCase_							:      List[str]						=   set(renamed_state_dict.keys()       ) - set(diffusers_state_dict.keys()       )
   UpperCAmelCase_							:      str						=   set(diffusers_state_dict.keys()       ) - set(renamed_state_dict.keys()       )

   assert len(a_       ) == 0, F'''Problem with {renamed_minus_diffusers}'''
   assert all(k.endswith('''kernel'''       ) for k in list(a_       )       ), 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":
         UpperCAmelCase_							:      str						=   value.squeeze()

      UpperCAmelCase_							:      Dict						=   value

   diffusers_model.load_state_dict(a_       )

   UpperCAmelCase_							:      Tuple						=   1_0_0
   UpperCAmelCase_							:      int						=   3_3

   UpperCAmelCase_							:      Optional[int]						=   IPNDMScheduler(num_train_timesteps=a_       )

   UpperCAmelCase_							:      Tuple						=   torch.manual_seed(a_       )
   UpperCAmelCase_							:      List[str]						=   torch.randn([1, 2, config.sample_size]			,       generator=a_       ).to(a_       )

   UpperCAmelCase_							:      Tuple						=   torch.linspace(1			,       0			,       steps + 1			,       device=a_       )[:-1]
   UpperCAmelCase_							:      str						=   get_crash_schedule(a_       )

   UpperCAmelCase_							:      Optional[Any]						=   DanceDiffusionPipeline(unet=a_			,       scheduler=a_       )

   UpperCAmelCase_							:      Union[str, Any]						=   torch.manual_seed(3_3       )
   UpperCAmelCase_							:      str						=   pipe(num_inference_steps=a_			,       generator=a_       ).audios

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

   UpperCAmelCase_							:      Dict						=   (generated - audio).abs().sum()
   UpperCAmelCase_							:      Dict						=   (generated - audio).abs().max()

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

   print('''Diff sum'''			,       a_       )
   print('''Diff max'''			,       a_       )

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

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


if __name__ == "__main__":
       __UpperCamelCase				:  int      					=							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.')
       __UpperCamelCase				:  Dict      					=							parser.parse_args()

       main(args)



import math

import qiskit
def      snake_case_						( __lowercase = 1			,       __lowercase = 1			,       __lowercase = 1       ):
   if (
       isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
       or isinstance(__lowercase			,       __lowercase       )
   ):
      raise TypeError('''inputs must be integers.'''       )

   if (input_a < 0) or (input_a < 0) or (carry_in < 0):
      raise ValueError('''inputs must be positive.'''       )

   if (
       (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != input_a)
       or (math.floor(__lowercase       ) != carry_in)
   ):
      raise ValueError('''inputs must be exact integers.'''       )

   if (input_a > 2) or (input_a > 2) or (carry_in > 2):
      raise ValueError('''inputs must be less or equal to 2.'''       )

   # build registers
   UpperCAmelCase_							:      Any						=   qiskit.QuantumRegister(4			,       '''qr'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.ClassicalRegister(2			,       '''cr'''       )
   # list the entries
   UpperCAmelCase_							:      Any						=   [input_a, input_a, carry_in]

   UpperCAmelCase_							:      Dict						=   qiskit.QuantumCircuit(__lowercase			,       __lowercase       )

   for i in range(0			,       3       ):
      if entry[i] == 2:
         quantum_circuit.h(__lowercase       )  # for hadamard entries
      elif entry[i] == 1:
         quantum_circuit.x(__lowercase       )  # for 1 entries
      elif entry[i] == 0:
         quantum_circuit.i(__lowercase       )  # for 0 entries

    # build the circuit
   quantum_circuit.ccx(0			,       1			,       3       )  # ccx = toffoli gate
   quantum_circuit.cx(0			,       1       )
   quantum_circuit.ccx(1			,       2			,       3       )
   quantum_circuit.cx(1			,       2       )
   quantum_circuit.cx(0			,       1       )

   quantum_circuit.measure([2, 3]			,       __lowercase       )  # measure the last two qbits

   UpperCAmelCase_							:      Optional[int]						=   qiskit.Aer.get_backend('''aer_simulator'''       )
   UpperCAmelCase_							:      List[str]						=   qiskit.execute(__lowercase			,       __lowercase			,       shots=1_0_0_0       )

   return job.result().get_counts(__lowercase       )


if __name__ == "__main__":
       print(F'Total sum count for state is: {quantum_full_adder(1, 1, 1)}')