"""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


__SCREAMING_SNAKE_CASE             ={
    "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      lowercase__(			__SCREAMING_SNAKE_CASE				:	str	,     __SCREAMING_SNAKE_CASE				:	str ):
     return torch.atana(A__	,     A__ ) / math.pi * 2



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int] ):
     lowercase_      :							Optional[int]						=      torch.sin(t * math.pi / 2 ) ** 2
     lowercase_      :							List[Any]						=      (1 - sigma**2) ** 0.5
     return alpha_sigma_to_t(A__	,     A__ )







class 			UpperCamelCase						(     __A    ):
   pass







class 			UpperCamelCase						(     nn.Module    ):
   def __init__(     self	,__UpperCamelCase     )      -> Any:


        '''simple docstring'''


        super().__init__()

        lowercase_      :							Tuple						=      DiffusionAttnUnetaD(lowerCAmelCase_	,n_attn_layers=4     )
        lowercase_      :							Dict						=      deepcopy(self.diffusion     )
        lowercase_      :							Tuple						=      torch.quasirandom.SobolEngine(1	,scramble=lowerCAmelCase_     )






def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Union[str, Any] ):
     lowercase_      :							int						=      MODELS_MAP[model_name]["url"]
     os.system(F'''wget {url} ./''' )

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


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

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

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



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[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      lowercase__(			__SCREAMING_SNAKE_CASE				:	Optional[int] ):
     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      lowercase__(			__SCREAMING_SNAKE_CASE				:	Any	,     __SCREAMING_SNAKE_CASE				:	Optional[Any]=13 ):
     lowercase_      :							List[str]						=      input_string

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

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

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

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

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

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

     lowercase_      :							Any						=      string_left[1:]

     if "resnets" in new_layer:
          lowercase_      :							Dict						=      convert_resconv_naming(A__ )
     elif "attentions" in new_layer:
          lowercase_      :							Tuple						=      convert_attn_naming(A__ )
          lowercase_      :							Optional[Any]						=      new_string_left

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



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	List[Any] ):
     lowercase_      :							Dict						=      {}
     for k, v in state_dict.items():
          if k.endswith('kernel' ):
               # up- and downsample layers, don't have trainable weights
               continue

          lowercase_      :							Dict						=      rename(A__ )

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

     return new_state_dict



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	List[Any]	,     __SCREAMING_SNAKE_CASE				:	int ):
     if len(A__ ) == 1:
          if len(v.shape ) == 3:
               # weight
               lowercase_      :							Any						=      v[:, :, 0]
          else:
               # bias
               lowercase_      :							Any						=      v
     else:
          # qkv matrices
          lowercase_      :							Any						=      v.shape[0]
          lowercase_      :							Union[str, Any]						=      trippled_shape // 3
          for i in range(3 ):
               if len(v.shape ) == 3:
                    lowercase_      :							str						=      v[i * single_shape : (i + 1) * single_shape, :, 0]
               else:
                    lowercase_      :							Optional[Any]						=      v[i * single_shape : (i + 1) * single_shape]
     return new_state_dict



def      lowercase__(			__SCREAMING_SNAKE_CASE				:	str ):
     lowercase_      :							Union[str, Any]						=      torch.device('cuda' if torch.cuda.is_available() else 'cpu' )

     lowercase_      :							Optional[int]						=      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_      :							Dict						=      download(A__ )

     lowercase_      :							int						=      MODELS_MAP[model_name]["sample_rate"]
     lowercase_      :							Optional[int]						=      MODELS_MAP[model_name]["sample_size"]

     lowercase_      :							Optional[int]						=      Object()
     lowercase_      :							Optional[Any]						=      sample_size
     lowercase_      :							Dict						=      sample_rate
     lowercase_      :							str						=      0

     lowercase_      :							Union[str, Any]						=      UNetaDModel(sample_size=A__	,     sample_rate=A__ )
     lowercase_      :							List[Any]						=      diffusers_model.state_dict()

     lowercase_      :							List[Any]						=      DiffusionUncond(A__ )
     orig_model.load_state_dict(torch.load(args.model_path	,     map_location=A__ )['state_dict'] )
     lowercase_      :							Optional[Any]						=      orig_model.diffusion_ema.eval()
     lowercase_      :							Dict						=      orig_model.state_dict()
     lowercase_      :							Optional[Any]						=      rename_orig_weights(A__ )

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

          lowercase_      :							List[Any]						=      value

     diffusers_model.load_state_dict(A__ )

     lowercase_      :							Optional[int]						=      1_00
     lowercase_      :							Any						=      33

     lowercase_      :							Optional[Any]						=      IPNDMScheduler(num_train_timesteps=A__ )

     lowercase_      :							Dict						=      torch.manual_seed(A__ )
     lowercase_      :							Union[str, Any]						=      torch.randn([1, 2, config.sample_size]	,     generator=A__ ).to(A__ )

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

     lowercase_      :							Tuple						=      DanceDiffusionPipeline(unet=A__	,     scheduler=A__ )

     lowercase_      :							Optional[int]						=      torch.manual_seed(33 )
     lowercase_      :							int						=      pipe(num_inference_steps=A__	,     generator=A__ ).audios

     lowercase_      :							Union[str, Any]						=      sampling.iplms_sample(A__	,     A__	,     A__	,     {} )
     lowercase_      :							Dict						=      generated.clamp(-1	,     1 )

     lowercase_      :							str						=      (generated - audio).abs().sum()
     lowercase_      :							Optional[Any]						=      (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__":
       __SCREAMING_SNAKE_CASE             =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.")
       __SCREAMING_SNAKE_CASE             =parser.parse_args()

       main(args)









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

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


lowerCamelCase_							      = logging.get_logger(__name__)

lowerCamelCase_							      = {
    '''huggingface/informer-tourism-monthly''': (
        '''https://huggingface.co/huggingface/informer-tourism-monthly/resolve/main/config.json'''
    ),
    # See all Informer models at https://huggingface.co/models?filter=informer
}







class      UpperCamelCase_			(__A	):
	__magic_name__       =							'''informer'''
	__magic_name__       =							{
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''encoder_attention_heads''',
	    '''num_hidden_layers''': '''encoder_layers''',
	}





	def __init__( self   :					Optional[Any]      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					Optional[int] = None      ,      lowerCAmelCase_   :					str = "student_t"      ,      lowerCAmelCase_   :					str = "nll"      ,      lowerCAmelCase_   :					int = 1      ,      lowerCAmelCase_   :					List[int] = None      ,      lowerCAmelCase_   :					Optional[Union[str, bool]] = "mean"      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					int = 0      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					Optional[List[int]] = None      ,      lowerCAmelCase_   :					int = 64      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 32      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					int = 2      ,      lowerCAmelCase_   :					bool = True      ,      lowerCAmelCase_   :					str = "gelu"      ,      lowerCAmelCase_   :					float = 0.0_5      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					float = 0.1      ,      lowerCAmelCase_   :					int = 100      ,      lowerCAmelCase_   :					float = 0.0_2      ,      lowerCAmelCase_   :					List[Any]=True      ,      lowerCAmelCase_   :					str = "prob"      ,      lowerCAmelCase_   :					int = 5      ,      lowerCAmelCase_   :					bool = True      ,      **lowerCAmelCase_   :					Tuple      ,      )		->     Tuple:
				# time series specific configuration
				UpperCAmelCase_	:					str     	=   prediction_length
				UpperCAmelCase_	:					Tuple     	=   context_length or prediction_length
				UpperCAmelCase_	:					Any     	=   distribution_output
				UpperCAmelCase_	:					Union[str, Any]     	=   loss
				UpperCAmelCase_	:					Any     	=   input_size
				UpperCAmelCase_	:					int     	=   num_time_features
				UpperCAmelCase_	:					Union[str, Any]     	=   lags_sequence if lags_sequence is not None else [1, 2, 3, 4, 5, 6, 7]
				UpperCAmelCase_	:					List[Any]     	=   scaling
				UpperCAmelCase_	:					List[str]     	=   num_dynamic_real_features
				UpperCAmelCase_	:					int     	=   num_static_real_features
				UpperCAmelCase_	:					Any     	=   num_static_categorical_features

				# set cardinality
				if cardinality and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The cardinality should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Optional[Any]     	=   cardinality
				else:
							UpperCAmelCase_	:					Optional[int]     	=   [0]

				# set embedding_dimension
				if embedding_dimension and num_static_categorical_features > 0:
							if len(lowerCAmelCase_		) != num_static_categorical_features:
										raise ValueError(
										    "The embedding dimension should be a list of the same length as `num_static_categorical_features`"		)
							UpperCAmelCase_	:					Any     	=   embedding_dimension
				else:
							UpperCAmelCase_	:					Optional[Any]     	=   [min(50      ,      (cat + 1) // 2		) for cat in self.cardinality]

				UpperCAmelCase_	:					Dict     	=   num_parallel_samples

				# Transformer architecture configuration
				UpperCAmelCase_	:					Optional[Any]     	=   input_size * len(self.lags_sequence		) + self._number_of_features
				UpperCAmelCase_	:					List[Any]     	=   d_model
				UpperCAmelCase_	:					List[str]     	=   encoder_attention_heads
				UpperCAmelCase_	:					List[str]     	=   decoder_attention_heads
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_ffn_dim
				UpperCAmelCase_	:					Union[str, Any]     	=   decoder_ffn_dim
				UpperCAmelCase_	:					Tuple     	=   encoder_layers
				UpperCAmelCase_	:					List[Any]     	=   decoder_layers

				UpperCAmelCase_	:					List[str]     	=   dropout
				UpperCAmelCase_	:					Optional[Any]     	=   attention_dropout
				UpperCAmelCase_	:					Any     	=   activation_dropout
				UpperCAmelCase_	:					Union[str, Any]     	=   encoder_layerdrop
				UpperCAmelCase_	:					Optional[int]     	=   decoder_layerdrop

				UpperCAmelCase_	:					Union[str, Any]     	=   activation_function
				UpperCAmelCase_	:					int     	=   init_std

				UpperCAmelCase_	:					Optional[Any]     	=   use_cache

				# Informer
				UpperCAmelCase_	:					int     	=   attention_type
				UpperCAmelCase_	:					List[str]     	=   sampling_factor
				UpperCAmelCase_	:					Union[str, Any]     	=   distil

				super().__init__(is_encoder_decoder=lowerCAmelCase_      ,      **lowerCAmelCase_		)





	@property
	def 	_SCREAMING_SNAKE_CASE ( self   :					List[Any]		)		->     int:
				return (
				    sum(self.embedding_dimension		)
				    + self.num_dynamic_real_features
				    + self.num_time_features
				    + self.num_static_real_features
				    + self.input_size * 2  # the log1p(abs(loc)) and log(scale) features
				)



