



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__   :					Optional[Any] 						=      {
    "gwf-440k": {
        "url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-small-190k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 6_55_36,
    },
    "jmann-large-580k": {
        "url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
        "sample_rate": 4_80_00,
        "sample_size": 13_10_72,
    },
    "maestro-uncond-150k": {
        "url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "unlocked-uncond-250k": {
        "url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
    "honk-140k": {
        "url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
        "sample_rate": 1_60_00,
        "sample_size": 6_55_36,
    },
}

def 	_lowerCAmelCase (    A__    ,	A__						):
      return torch.atana(__lowercase    ,	__lowercase						) / math.pi * 2

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	torch.sin(t * math.pi / 2						) ** 2
      lowercase__  =	(1 - sigma**2) ** 0.5
      return alpha_sigma_to_t(__lowercase    ,	__lowercase						)



class    UpperCAmelCase__(				A__						):

   '''simple docstring'''




   pass



class    UpperCAmelCase__(				nn.Module						):

   '''simple docstring'''




   def __init__(    self	:      Any      ,					lowerCAmelCase	:      Tuple)						->					Union[str, Any]:




         """simple docstring"""

         super().__init__()

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

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	MODELS_MAP[model_name]['url']
      os.system(F'''wget {url} ./'''						)

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


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

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

a__   :					Tuple 						=      {
    "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(__lowercase						) and not isinstance(__lowercase    ,	__lowercase						):
                  return name.replace(__lowercase    ,	__lowercase						)
            elif name.startswith(__lowercase						):
                  return [name.replace(__lowercase    ,	__lowercase						) for v in value]
      raise ValueError(F'''Attn error with {name}'''						)

def 	_lowerCAmelCase (    A__    ,	A__=13						):
      lowercase__  =	input_string

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

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

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

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

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

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

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

      lowercase__  =	string_left[1:]

      if "resnets" in new_layer:
            lowercase__  =	convert_resconv_naming(__lowercase						)
      elif "attentions" in new_layer:
            lowercase__  =	convert_attn_naming(__lowercase						)
            lowercase__  =	new_string_left

      if not isinstance(__lowercase    ,	__lowercase						):
            lowercase__  =	prefix + '.' + new_layer + '.' + string_left
      else:
            lowercase__  =	[prefix + '.' + new_layer + '.' + s for s in string_left]
      return new_string

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

            lowercase__  =	rename(__lowercase						)

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

      return new_state_dict

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

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	torch.device('cuda' if torch.cuda.is_available() else 'cpu'						)

      lowercase__  =	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__  =	download(__lowercase						)

      lowercase__  =	MODELS_MAP[model_name]['sample_rate']
      lowercase__  =	MODELS_MAP[model_name]['sample_size']

      lowercase__  =	Object()
      lowercase__  =	sample_size
      lowercase__  =	sample_rate
      lowercase__  =	0

      lowercase__  =	UNetaDModel(sample_size=__lowercase    ,	sample_rate=__lowercase						)
      lowercase__  =	diffusers_model.state_dict()

      lowercase__  =	DiffusionUncond(__lowercase						)
      orig_model.load_state_dict(torch.load(args.model_path    ,	map_location=__lowercase						)['state_dict']						)
      lowercase__  =	orig_model.diffusion_ema.eval()
      lowercase__  =	orig_model.state_dict()
      lowercase__  =	rename_orig_weights(__lowercase						)

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

      assert len(__lowercase						) == 0, F'''Problem with {renamed_minus_diffusers}'''
      assert all(k.endswith('kernel'						) for k in list(__lowercase						)						), 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__  =	value.squeeze()

            lowercase__  =	value

      diffusers_model.load_state_dict(__lowercase						)

      lowercase__  =	100
      lowercase__  =	33

      lowercase__  =	IPNDMScheduler(num_train_timesteps=__lowercase						)

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

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

      lowercase__  =	DanceDiffusionPipeline(unet=__lowercase    ,	scheduler=__lowercase						)

      lowercase__  =	torch.manual_seed(33						)
      lowercase__  =	pipe(num_inference_steps=__lowercase    ,	generator=__lowercase						).audios

      lowercase__  =	sampling.iplms_sample(__lowercase    ,	__lowercase    ,	__lowercase    ,	{}						)
      lowercase__  =	generated.clamp(-1    ,	1						)

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

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

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

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

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


if __name__ == "__main__":
       a__   :					Dict 						=      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__   :					str 						=      parser.parse_args()

       main(args)


"""simple docstring"""






import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__SCREAMING_SNAKE_CASE							=     logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE							=     {
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_vision_model'''



     def __init__(							self :int      ,  _lowerCamelCase :Optional[int]=1_408      ,  _lowerCamelCase :int=6_144      ,  _lowerCamelCase :str=39      ,  _lowerCamelCase :Tuple=16      ,  _lowerCamelCase :List[Any]=224      ,  _lowerCamelCase :int=14      ,  _lowerCamelCase :int="gelu"      ,  _lowerCamelCase :Optional[Any]=1E-6      ,  _lowerCamelCase :str=0.0      ,  _lowerCamelCase :Optional[Any]=1E-1_0      ,  _lowerCamelCase :str=True      ,  **_lowerCamelCase :Optional[Any]      ,  ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      UpperCamelCase_     :					Dict   	=hidden_size
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[int]   	=num_hidden_layers
      UpperCamelCase_     :					Union[str, Any]   	=num_attention_heads
      UpperCamelCase_     :					Optional[Any]   	=patch_size
      UpperCamelCase_     :					List[Any]   	=image_size
      UpperCamelCase_     :					Dict   	=initializer_range
      UpperCamelCase_     :					List[Any]   	=attention_dropout
      UpperCamelCase_     :					List[str]   	=layer_norm_eps
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=qkv_bias




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :Any   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					List[str]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the vision config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					Tuple   	=config_dict['vision_config']

      if "model_type" in config_dict and hasattr(cls      ,  'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
           f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

      return cls.from_dict(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip_qformer'''



     def __init__(							self :Dict      ,  _lowerCamelCase :List[Any]=30_522      ,  _lowerCamelCase :Optional[int]=768      ,  _lowerCamelCase :Tuple=12      ,  _lowerCamelCase :List[str]=12      ,  _lowerCamelCase :int=3_072      ,  _lowerCamelCase :Tuple="gelu"      ,  _lowerCamelCase :Dict=0.1      ,  _lowerCamelCase :List[Any]=0.1      ,  _lowerCamelCase :Optional[int]=512      ,  _lowerCamelCase :List[Any]=0.02      ,  _lowerCamelCase :int=1E-1_2      ,  _lowerCamelCase :str=0      ,  _lowerCamelCase :Dict="absolute"      ,  _lowerCamelCase :Optional[int]=2      ,  _lowerCamelCase :Union[str, Any]=1_408      ,  **_lowerCamelCase :Tuple      ,  ):







      '''simple docstring'''



      super().__init__(pad_token_id=_lowerCamelCase      ,  **_lowerCamelCase   )

      UpperCamelCase_     :					Optional[Any]   	=vocab_size
      UpperCamelCase_     :					Tuple   	=hidden_size
      UpperCamelCase_     :					Union[str, Any]   	=num_hidden_layers
      UpperCamelCase_     :					List[str]   	=num_attention_heads
      UpperCamelCase_     :					List[str]   	=hidden_act
      UpperCamelCase_     :					Tuple   	=intermediate_size
      UpperCamelCase_     :					Optional[Any]   	=hidden_dropout_prob
      UpperCamelCase_     :					List[str]   	=attention_probs_dropout_prob
      UpperCamelCase_     :					List[str]   	=max_position_embeddings
      UpperCamelCase_     :					List[str]   	=initializer_range
      UpperCamelCase_     :					str   	=layer_norm_eps
      UpperCamelCase_     :					int   	=position_embedding_type
      UpperCamelCase_     :					Tuple   	=cross_attention_frequency
      UpperCamelCase_     :					List[Any]   	=encoder_hidden_size




     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :Union[str, os.PathLike]      ,  **_lowerCamelCase :List[Any]   ):







      '''simple docstring'''



      cls._set_token_in_kwargs(_lowerCamelCase   )

      UpperCamelCase_	,     UpperCamelCase_     :					Optional[Any]   	=cls.get_config_dict(_lowerCamelCase      ,  **_lowerCamelCase   )

      # get the qformer config dict if we are loading from InstructBlipConfig
      if config_dict.get('model_type'   ) == "instructblip":
       UpperCamelCase_     :					List[Any]   	=config_dict['qformer_config']

      if "model_type" in config_dict and hasattr(cls      ,  'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
           f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''   )

      return cls.from_dict(_lowerCamelCase      ,  **_lowerCamelCase   )





class 				a__					(						A__       ):
     UpperCAmelCase__         =      '''instructblip'''
     UpperCAmelCase__         =      True



     def __init__(							self :str      ,  _lowerCamelCase :Optional[Any]=None      ,  _lowerCamelCase :List[str]=None      ,  _lowerCamelCase :List[Any]=None      ,  _lowerCamelCase :Union[str, Any]=32      ,  **_lowerCamelCase :Tuple   ):







      '''simple docstring'''



      super().__init__(**_lowerCamelCase   )

      if vision_config is None:
       UpperCamelCase_     :					Optional[int]   	={}
       logger.info('vision_config is None. initializing the InstructBlipVisionConfig with default values.'   )

      if qformer_config is None:
       UpperCamelCase_     :					Union[str, Any]   	={}
       logger.info('qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'   )

      if text_config is None:
       UpperCamelCase_     :					str   	={}
       logger.info('text_config is None. Initializing the text config with default values (`OPTConfig`).'   )

      UpperCamelCase_     :					Any   	=InstructBlipVisionConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					Optional[Any]   	=InstructBlipQFormerConfig(**_lowerCamelCase   )
      UpperCamelCase_     :					str   	=text_config['model_type'] if 'model_type' in text_config else 'opt'
      UpperCamelCase_     :					int   	=CONFIG_MAPPING[text_model_type](**_lowerCamelCase   )

      UpperCamelCase_     :					Tuple   	=self.text_config.tie_word_embeddings
      UpperCamelCase_     :					Any   	=self.text_config.is_encoder_decoder

      UpperCamelCase_     :					List[Any]   	=num_query_tokens
      UpperCamelCase_     :					Tuple   	=self.vision_config.hidden_size
      UpperCamelCase_     :					Tuple   	=self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
      UpperCamelCase_     :					int   	=1.0
      UpperCamelCase_     :					List[str]   	=0.02



     @classmethod
     def  lowerCamelCase_						(							cls :Optional[Any]      ,  _lowerCamelCase :InstructBlipVisionConfig      ,  _lowerCamelCase :InstructBlipQFormerConfig      ,  _lowerCamelCase :PretrainedConfig      ,  **_lowerCamelCase :Dict      ,  ):







      '''simple docstring'''



      return cls(
          vision_config=vision_config.to_dict()      ,  qformer_config=qformer_config.to_dict()      ,  text_config=text_config.to_dict()      ,  **_lowerCamelCase      ,  )




     def  lowerCamelCase_						(							self :Optional[Any]   ):







      '''simple docstring'''



      UpperCamelCase_     :					str   	=copy.deepcopy(self.__dict__   )
      UpperCamelCase_     :					Dict   	=self.vision_config.to_dict()
      UpperCamelCase_     :					Tuple   	=self.qformer_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.text_config.to_dict()
      UpperCamelCase_     :					List[str]   	=self.__class__.model_type
      return output
