

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

import torch
from transformers import (
    XLMRobertaTokenizer,
)

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)
from .text_encoder import MultilingualCLIP


_UpperCAmelCase  :		Union[str, Any]  =		logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase  :		Dict  =		'''
    Examples:
        ```py
        >>> from diffusers import KandinskyPipeline, KandinskyPriorPipeline
        >>> import torch

        >>> pipe_prior = KandinskyPriorPipeline.from_pretrained("kandinsky-community/Kandinsky-2-1-prior")
        >>> pipe_prior.to("cuda")

        >>> prompt = "red cat, 4k photo"
        >>> out = pipe_prior(prompt)
        >>> image_emb = out.image_embeds
        >>> negative_image_emb = out.negative_image_embeds

        >>> pipe = KandinskyPipeline.from_pretrained("kandinsky-community/kandinsky-2-1")
        >>> pipe.to("cuda")

        >>> image = pipe(
        ...     prompt,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=negative_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ... ).images

        >>> image[0].save("cat.png")
        ```
'''




def 	UpperCamelCase   (       lowercase_						:    Dict     ,					lowercase_						:    Tuple     ,					lowercase_						:    int=8	)		->	Union[str, Any]:




  '''simple docstring'''



  lowercase									=h // scale_factor**2
  if h % scale_factor**2 != 0:
    new_h += 1
  lowercase									=w // scale_factor**2
  if w % scale_factor**2 != 0:
    new_w += 1
  return new_h * scale_factor, new_w * scale_factor




class 						__magic_name__   (							__SCREAMING_SNAKE_CASE ):







 def __init__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , ):
   super().__init__()

   self.register_modules(
       text_encoder=snake_case_  , tokenizer=snake_case_  , unet=snake_case_  , scheduler=snake_case_  , movq=snake_case_  , )
   lowercase									=2 ** (len(self.movq.config.block_out_channels   ) - 1)







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   ):
   if latents is None:
     lowercase									=randn_tensor(snake_case_  , generator=snake_case_  , device=snake_case_  , dtype=snake_case_   )
   else:
     if latents.shape != shape:
       raise ValueError(f'Unexpected latents shape, got {latents.shape}, expected {shape}'   )
     lowercase									=latents.to(snake_case_   )

   lowercase									=latents * scheduler.init_noise_sigma
   return latents







 def 						_A(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_=None  , ):
   lowercase									=len(snake_case_   ) if isinstance(snake_case_  , snake_case_   ) else 1
   # get prompt text embeddings
   lowercase									=self.tokenizer(
       snake_case_  , padding='''max_length'''  , truncation=snake_case_  , max_length=77  , return_attention_mask=snake_case_  , add_special_tokens=snake_case_  , return_tensors='''pt'''  , )

   lowercase									=text_inputs.input_ids
   lowercase									=self.tokenizer(snake_case_  , padding='''longest'''  , return_tensors='''pt'''   ).input_ids

   if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal(snake_case_  , snake_case_   ):
     lowercase									=self.tokenizer.batch_decode(untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1]   )
     logger.warning(
         '''The following part of your input was truncated because CLIP can only handle sequences up to'''
         f' {self.tokenizer.model_max_length} tokens: {removed_text}'   )

   lowercase									=text_input_ids.to(snake_case_   )
   lowercase									=text_inputs.attention_mask.to(snake_case_   )

   lowercase							, lowercase									=self.text_encoder(
       input_ids=snake_case_  , attention_mask=snake_case_   )

   lowercase									=prompt_embeds.repeat_interleave(snake_case_  , dim=0   )
   lowercase									=text_encoder_hidden_states.repeat_interleave(snake_case_  , dim=0   )
   lowercase									=text_mask.repeat_interleave(snake_case_  , dim=0   )

   if do_classifier_free_guidance:
     lowercase									=42
     if negative_prompt is None:
       lowercase									=[''''''] * batch_size
     elif type(snake_case_   ) is not type(snake_case_   ):
       raise TypeError(
           f'`negative_prompt` should be the same type to `prompt`, but got {type(snake_case_   )} !='
           f' {type(snake_case_   )}.'   )
     elif isinstance(snake_case_  , snake_case_   ):
       lowercase									=[negative_prompt]
     elif batch_size != len(snake_case_   ):
       raise ValueError(
           f'`negative_prompt`: {negative_prompt} has batch size {len(snake_case_   )}, but `prompt`:'
           f' {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches'
           ''' the batch size of `prompt`.'''   )
     else:
       lowercase									=negative_prompt

     lowercase									=self.tokenizer(
         snake_case_  , padding='''max_length'''  , max_length=77  , truncation=snake_case_  , return_attention_mask=snake_case_  , add_special_tokens=snake_case_  , return_tensors='''pt'''  , )
     lowercase									=uncond_input.input_ids.to(snake_case_   )
     lowercase									=uncond_input.attention_mask.to(snake_case_   )

     lowercase							, lowercase									=self.text_encoder(
         input_ids=snake_case_  , attention_mask=snake_case_   )

     # duplicate unconditional embeddings for each generation per prompt, using mps friendly method

     lowercase									=negative_prompt_embeds.shape[1]
     lowercase									=negative_prompt_embeds.repeat(1  , snake_case_   )
     lowercase									=negative_prompt_embeds.view(batch_size * num_images_per_prompt  , snake_case_   )

     lowercase									=uncond_text_encoder_hidden_states.shape[1]
     lowercase									=uncond_text_encoder_hidden_states.repeat(1  , snake_case_  , 1   )
     lowercase									=uncond_text_encoder_hidden_states.view(
         batch_size * num_images_per_prompt  , snake_case_  , -1   )
     lowercase									=uncond_text_mask.repeat_interleave(snake_case_  , dim=0   )

     # done duplicates

     # For classifier free guidance, we need to do two forward passes.
     # Here we concatenate the unconditional and text embeddings into a single batch
     # to avoid doing two forward passes
     lowercase									=torch.cat([negative_prompt_embeds, prompt_embeds]   )
     lowercase									=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]   )

     lowercase									=torch.cat([uncond_text_mask, text_mask]   )

   return prompt_embeds, text_encoder_hidden_states, text_mask







 def 						_A(       self  , snake_case_=0   ):
   if is_accelerate_available():
     from accelerate import cpu_offload
   else:
     raise ImportError('''Please install accelerate via `pip install accelerate`'''   )

   lowercase									=torch.device(f'cuda:{gpu_id}'   )

   lowercase									=[
       self.unet,
       self.text_encoder,
       self.movq,
   ]
   for cpu_offloaded_model in models:
     if cpu_offloaded_model is not None:
       cpu_offload(snake_case_  , snake_case_   )







 def 						_A(       self  , snake_case_=0   ):
   if is_accelerate_available() and is_accelerate_version('''>='''  , '''0.17.0.dev0'''   ):
     from accelerate import cpu_offload_with_hook
   else:
     raise ImportError('''`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'''   )

   lowercase									=torch.device(f'cuda:{gpu_id}'   )

   if self.device.type != "cpu":
     self.to('''cpu'''  , silence_dtype_warnings=snake_case_   )
     torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

   lowercase									=None
   for cpu_offloaded_model in [self.text_encoder, self.unet, self.movq]:
     lowercase							, lowercase									=cpu_offload_with_hook(snake_case_  , snake_case_  , prev_module_hook=snake_case_   )

   if self.safety_checker is not None:
     lowercase							, lowercase									=cpu_offload_with_hook(self.safety_checker  , snake_case_  , prev_module_hook=snake_case_   )

   # We'll offload the last model manually.
   lowercase									=hook







 @property
 # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
 def 						_A(       self   ):
   if not hasattr(self.unet  , '''_hf_hook'''   ):
     return self.device
   for module in self.unet.modules():
     if (
         hasattr(snake_case_  , '''_hf_hook'''   )
         and hasattr(module._hf_hook  , '''execution_device'''   )
         and module._hf_hook.execution_device is not None
     ):
       return torch.device(module._hf_hook.execution_device   )
   return self.device







 @torch.no_grad()
 @replace_example_docstring(snake_case_   )
 def __call__(       self  , snake_case_  , snake_case_  , snake_case_  , snake_case_ = None  , snake_case_ = 5_12  , snake_case_ = 5_12  , snake_case_ = 1_00  , snake_case_ = 4.0  , snake_case_ = 1  , snake_case_ = None  , snake_case_ = None  , snake_case_ = "pil"  , snake_case_ = True  , ):

   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=1
   elif isinstance(snake_case_  , snake_case_   ):
     lowercase									=len(snake_case_   )
   else:
     raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(snake_case_   )}'   )

   lowercase									=self._execution_device

   lowercase									=batch_size * num_images_per_prompt
   lowercase									=guidance_scale > 1.0

   lowercase							, lowercase							, lowercase									=self._encode_prompt(
       snake_case_  , snake_case_  , snake_case_  , snake_case_  , snake_case_   )

   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=torch.cat(snake_case_  , dim=0   )
   if isinstance(snake_case_  , snake_case_   ):
     lowercase									=torch.cat(snake_case_  , dim=0   )

   if do_classifier_free_guidance:
     lowercase									=image_embeds.repeat_interleave(snake_case_  , dim=0   )
     lowercase									=negative_image_embeds.repeat_interleave(snake_case_  , dim=0   )

   lowercase									=torch.cat([negative_image_embeds, image_embeds]  , dim=0   ).to(
       dtype=prompt_embeds.dtype  , device=snake_case_   )

   self.scheduler.set_timesteps(snake_case_  , device=snake_case_   )
   lowercase									=self.scheduler.timesteps

   lowercase									=self.unet.config.in_channels

   lowercase							, lowercase									=get_new_h_w(snake_case_  , snake_case_  , self.movq_scale_factor   )

   # create initial latent
   lowercase									=self.prepare_latents(
       (batch_size, num_channels_latents, height, width)  , text_encoder_hidden_states.dtype  , snake_case_  , snake_case_  , snake_case_  , self.scheduler  , )

   for i, t in enumerate(self.progress_bar(snake_case_   )   ):
     # expand the latents if we are doing classifier free guidance
     lowercase									=torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

     lowercase									={'''text_embeds''': prompt_embeds, '''image_embeds''': image_embeds}
     lowercase									=self.unet(
         sample=snake_case_  , timestep=snake_case_  , encoder_hidden_states=snake_case_  , added_cond_kwargs=snake_case_  , return_dict=snake_case_  , )[0]

     if do_classifier_free_guidance:
       lowercase							, lowercase									=noise_pred.split(latents.shape[1]  , dim=1   )
       lowercase							, lowercase									=noise_pred.chunk(2   )
       lowercase							, lowercase									=variance_pred.chunk(2   )
       lowercase									=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
       lowercase									=torch.cat([noise_pred, variance_pred_text]  , dim=1   )

     if not (
         hasattr(self.scheduler.config  , '''variance_type'''   )
         and self.scheduler.config.variance_type in ["learned", "learned_range"]
     ):
       lowercase							, lowercase									=noise_pred.split(latents.shape[1]  , dim=1   )

     # compute the previous noisy sample x_t -> x_t-1
     lowercase									=self.scheduler.step(
         snake_case_  , snake_case_  , snake_case_  , generator=snake_case_  , ).prev_sample
   # post-processing
   lowercase									=self.movq.decode(snake_case_  , force_not_quantize=snake_case_   )['''sample''']

   if output_type not in ["pt", "np", "pil"]:
     raise ValueError(f'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'   )

   if output_type in ["np", "pil"]:
     lowercase									=image * 0.5 + 0.5
     lowercase									=image.clamp(0  , 1   )
     lowercase									=image.cpu().permute(0  , 2  , 3  , 1   ).float().numpy()

   if output_type == "pil":
     lowercase									=self.numpy_to_pil(snake_case_   )

   if not return_dict:
     return (image,)

   return ImagePipelineOutput(images=snake_case_   )



'''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						:  Union[str, Any]							       =				{
    "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   (	snake_case      : int					,		snake_case      : Tuple			) ->  Optional[int]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : str			) ->  Optional[int]:


    """simple docstring"""


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



class 				_snake_case					(   a_			):
  pass



class 				_snake_case					(   nn.Module			):

  def __init__(				self      , _SCREAMING_SNAKE_CASE	):





      '''simple docstring'''

      super().__init__()

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



def       snake_case   (	snake_case      : Any			) ->  List[Any]:


    """simple docstring"""


    lowerCAmelCase 						=							MODELS_MAP[model_name]['url']
    os.system(F'wget {url} ./'			)

    return F'./{model_name}.ckpt'


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

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

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


def       snake_case   (	snake_case      : str			) ->  List[str]:


    """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       snake_case   (	snake_case      : Optional[Any]			) ->  Optional[Any]:


    """simple docstring"""


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


def       snake_case   (	snake_case      : str					,		snake_case      : Union[str, Any]=13			) ->  int:


    """simple docstring"""


    lowerCAmelCase 						=							input_string

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

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

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

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

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

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

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

    lowerCAmelCase 						=							string_left[1:]

    if "resnets" in new_layer:
        lowerCAmelCase 						=							convert_resconv_naming(snake_case			)
    elif "attentions" in new_layer:
        lowerCAmelCase 						=							convert_attn_naming(snake_case			)
        lowerCAmelCase 						=							new_string_left

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


def       snake_case   (	snake_case      : str			) ->  Dict:


    """simple docstring"""


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

        lowerCAmelCase 						=							rename(snake_case			)

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

    return new_state_dict


def       snake_case   (	snake_case      : int					,		snake_case      : int					,		snake_case      : Optional[int]			) ->  Any:


    """simple docstring"""


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


def       snake_case   (	snake_case      : Optional[int]			) ->  Dict:


    """simple docstring"""


    lowerCAmelCase 						=							torch.device('cuda' if torch.cuda.is_available() else 'cpu'			)

    lowerCAmelCase 						=							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()}'
        lowerCAmelCase 						=							download(snake_case			)

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

    lowerCAmelCase 						=							Object()
    lowerCAmelCase 						=							sample_size
    lowerCAmelCase 						=							sample_rate
    lowerCAmelCase 						=							0

    lowerCAmelCase 						=							UNetaDModel(sample_size=snake_case					,		sample_rate=snake_case			)
    lowerCAmelCase 						=							diffusers_model.state_dict()

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

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

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

        lowerCAmelCase 						=							value

    diffusers_model.load_state_dict(snake_case			)

    lowerCAmelCase 						=							100
    lowerCAmelCase 						=							33

    lowerCAmelCase 						=							IPNDMScheduler(num_train_timesteps=snake_case			)

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

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

    lowerCAmelCase 						=							DanceDiffusionPipeline(unet=snake_case					,		scheduler=snake_case			)

    lowerCAmelCase 						=							torch.manual_seed(33			)
    lowerCAmelCase 						=							pipe(num_inference_steps=snake_case					,		generator=snake_case			).audios

    lowerCAmelCase 						=							sampling.iplms_sample(snake_case					,		snake_case					,		snake_case					,		{}			)
    lowerCAmelCase 						=							generated.clamp(-1					,		1			)

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

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

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

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

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


if __name__ == "__main__":
     _UpperCamelCase						:  Optional[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						:  List[str]							       =				parser.parse_args()

     main(args)





