






'''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


UpperCamelCase__ =			{
    '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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    str           =     torch.sin(t * math.pi / 2							) ** 2
   lowercase_	:    List[Any]           =     (1 - sigma**2) ** 0.5
   return alpha_sigma_to_t(_UpperCamelCase						,		_UpperCamelCase							)


class        _UpperCAmelCase						(		snake_case			):
    pass


class        _UpperCAmelCase						(		nn.Module			):




    def __init__(			self  :       Optional[int]  ,     a  :       Any			):






       '''simple docstring'''






       super().__init__()

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




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Dict           =     MODELS_MAP[model_name]["url"]
   os.system(F"""wget {url} ./"""							)

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


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

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

UpperCamelCase__ =			{
    'norm': 'group_norm',
    'qkv_proj': ['query', 'key', 'value'],
    'out_proj': ['proj_attn'],
}




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase=13							):


   """simple docstring"""




   lowercase_	:    List[Any]           =     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_	:    int           =     string[6:]
   elif string.startswith("net."							):
      lowercase_	:    Optional[Any]           =     string[4:]

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

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

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

   if depth == max_depth:
      lowercase_	:    List[str]           =     MID_NUM_TO_LAYER[layer_num]
      lowercase_	:    Any           =     "mid_block"
   elif depth > 0 and int(_UpperCamelCase							) < 7:
      lowercase_	:    Optional[int]           =     DOWN_NUM_TO_LAYER[layer_num]
      lowercase_	:    List[str]           =     F"""down_blocks.{depth}"""
   elif depth > 0 and int(_UpperCamelCase							) > 7:
      lowercase_	:    str           =     UP_NUM_TO_LAYER[layer_num]
      lowercase_	:    Any           =     F"""up_blocks.{max_depth - depth - 1}"""
   elif depth == 0:
      lowercase_	:    int           =     DEPTH_0_TO_LAYER[layer_num]
      lowercase_	:    Optional[Any]           =     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}."""							)

   lowercase_	:    Tuple           =     string_left[1:]

   if "resnets" in new_layer:
      lowercase_	:    Tuple           =     convert_resconv_naming(_UpperCamelCase							)
   elif "attentions" in new_layer:
      lowercase_	:    Union[str, Any]           =     convert_attn_naming(_UpperCamelCase							)
      lowercase_	:    Tuple           =     new_string_left

   if not isinstance(_UpperCamelCase						,		_UpperCamelCase							):
      lowercase_	:    Optional[int]           =     prefix + "." + new_layer + "." + string_left
   else:
      lowercase_	:    Any           =     [prefix + "." + new_layer + "." + s for s in string_left]
   return new_string




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    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(_UpperCamelCase							)

      # check if we need to transform from Conv => Linear for attention
      if isinstance(_UpperCamelCase						,		_UpperCamelCase							):
         lowercase_	:    List[str]           =     transform_conv_attns(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							)
      else:
         lowercase_	:    Tuple           =     v

   return new_state_dict




def 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase							):


   """simple docstring"""




   if len(_UpperCamelCase							) == 1:
      if len(v.shape							) == 3:
         # weight
         lowercase_	:    List[Any]           =     v[:, :, 0]
      else:
         # bias
         lowercase_	:    Any           =     v
   else:
      # qkv matrices
      lowercase_	:    str           =     v.shape[0]
      lowercase_	:    Optional[int]           =     trippled_shape // 3
      for i in range(3							):
         if len(v.shape							) == 3:
            lowercase_	:    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 		__SCREAMING_SNAKE_CASE					(  _UpperCamelCase							):


   """simple docstring"""




   lowercase_	:    Optional[int]           =     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_	:    Optional[Any]           =     download(_UpperCamelCase							)

   lowercase_	:    Any           =     MODELS_MAP[model_name]["sample_rate"]
   lowercase_	:    List[str]           =     MODELS_MAP[model_name]["sample_size"]

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

   lowercase_	:    Any           =     UNetaDModel(sample_size=_UpperCamelCase						,		sample_rate=_UpperCamelCase							)
   lowercase_	:    Any           =     diffusers_model.state_dict()

   lowercase_	:    int           =     DiffusionUncond(_UpperCamelCase							)
   orig_model.load_state_dict(torch.load(args.model_path						,		map_location=_UpperCamelCase							)["state_dict"]							)
   lowercase_	:    Dict           =     orig_model.diffusion_ema.eval()
   lowercase_	:    Dict           =     orig_model.state_dict()
   lowercase_	:    str           =     rename_orig_weights(_UpperCamelCase							)

   lowercase_	:    int           =     set(renamed_state_dict.keys()							) - set(diffusers_state_dict.keys()							)
   lowercase_	:    int           =     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":
         lowercase_	:    int           =     value.squeeze()

      lowercase_	:    List[Any]           =     value

   diffusers_model.load_state_dict(_UpperCamelCase							)

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

   lowercase_	:    Union[str, Any]           =     IPNDMScheduler(num_train_timesteps=_UpperCamelCase							)

   lowercase_	:    List[Any]           =     torch.manual_seed(_UpperCamelCase							)
   lowercase_	:    Any           =     torch.randn([1, 2, config.sample_size]						,		generator=_UpperCamelCase							).to(_UpperCamelCase							)

   lowercase_	:    int           =     torch.linspace(1						,		0						,		steps + 1						,		device=_UpperCamelCase							)[:-1]
   lowercase_	:    int           =     get_crash_schedule(_UpperCamelCase							)

   lowercase_	:    str           =     DanceDiffusionPipeline(unet=_UpperCamelCase						,		scheduler=_UpperCamelCase							)

   lowercase_	:    Optional[Any]           =     torch.manual_seed(33							)
   lowercase_	:    List[Any]           =     pipe(num_inference_steps=_UpperCamelCase						,		generator=_UpperCamelCase							).audios

   lowercase_	:    int           =     sampling.iplms_sample(_UpperCamelCase						,		_UpperCamelCase						,		_UpperCamelCase						,		{}							)
   lowercase_	:    Any           =     generated.clamp(-1						,		1							)

   lowercase_	:    Tuple           =     (generated - audio).abs().sum()
   lowercase_	:    Tuple           =     (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__":
 UpperCamelCase__ =			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__ =			parser.parse_args()

 main(args)








'''simple docstring'''
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType


class        _UpperCAmelCase						(		snake_case			):
    __lowerCamelCase:   str		       =    ['image_processor', 'tokenizer']
    __lowerCamelCase:   Dict		       =    'Pix2StructImageProcessor'
    __lowerCamelCase:   Union[str, Any]		       =    ('T5Tokenizer', 'T5TokenizerFast')




    def __init__(			self  :       str  ,     a  :       Dict  ,     a  :       List[str]			):






       '''simple docstring'''






       lowercase_	:    Optional[Any]           =     False
       super().__init__(a  ,     a			)




    def __call__(			self  :       Tuple  ,     a  :       int=None  ,     a  :       Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None  ,     a  :       bool = True  ,     a  :       Union[bool, str, PaddingStrategy] = False  ,     a  :       Union[bool, str, TruncationStrategy] = None  ,     a  :       Optional[int] = None  ,     a  :       Optional[int] = 2_0_4_8  ,     a  :       int = 0  ,     a  :       Optional[int] = None  ,     a  :       Optional[bool] = None  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = False  ,     a  :       bool = True  ,     a  :       Optional[Union[str, TensorType]] = None  ,     **a  :       List[str]  ,     ):






       '''simple docstring'''






       if images is None and text is None:
          raise ValueError("You have to specify either images or text."			)

       # Get only text
       if images is None and not self.image_processor.is_vqa:
          lowercase_	:    Dict           =     self.tokenizer
          lowercase_	:    Tuple           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )
          return text_encoding

       if not self.image_processor.is_vqa:
          # add pixel_values
          lowercase_	:    Optional[int]           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     **a			)
       else:
          # add pixel_values and bbox
          lowercase_	:    Any           =     self.image_processor(
              a  ,     return_tensors=a  ,     max_patches=a  ,     header_text=a  ,     **a			)

       if text is not None and not self.image_processor.is_vqa:
          lowercase_	:    int           =     self.tokenizer(
              text=a  ,     add_special_tokens=a  ,     padding=a  ,     truncation=a  ,     max_length=a  ,     stride=a  ,     pad_to_multiple_of=a  ,     return_attention_mask=a  ,     return_overflowing_tokens=a  ,     return_special_tokens_mask=a  ,     return_offsets_mapping=a  ,     return_token_type_ids=a  ,     return_length=a  ,     verbose=a  ,     return_tensors=a  ,     **a  ,     )

          if "attention_mask" in text_encoding:
             lowercase_	:    str           =     text_encoding.pop("attention_mask"			)
          if "input_ids" in text_encoding:
             lowercase_	:    Dict           =     text_encoding.pop("input_ids"			)
       else:
          lowercase_	:    str           =     None

       if text_encoding is not None:
          encoding_image_processor.update(a			)

       return encoding_image_processor




    def 				lowerCAmelCase__				(			self  :       Any  ,     *a  :       str  ,     **a  :       Tuple			):






       '''simple docstring'''






       return self.tokenizer.batch_decode(*a  ,     **a			)




    def 				lowerCAmelCase__				(			self  :       str  ,     *a  :       Optional[int]  ,     **a  :       Any			):






       '''simple docstring'''






       return self.tokenizer.decode(*a  ,     **a			)





    @property
    def 				lowerCAmelCase__				(			self  :       str			):






       '''simple docstring'''






       lowercase_	:    Tuple           =     self.tokenizer.model_input_names
       lowercase_	:    Dict           =     self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)




