
"""simple docstring"""


from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

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


_lowercase				 =					logging.get_logger(__name__)  # pylint: disable=invalid-name

_lowercase				 =					'''
    Examples:
        ```py
        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
        >>> from diffusers.utils import load_image
        >>> import torch

        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
        ... )
        >>> pipe_prior.to("cuda")

        >>> prompt = "A red cartoon frog, 4k"
        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)

        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
        ... )
        >>> pipe.to("cuda")

        >>> init_image = load_image(
        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
        ...     "/kandinsky/frog.png"
        ... )

        >>> image = pipe(
        ...     image=init_image,
        ...     image_embeds=image_emb,
        ...     negative_image_embeds=zero_image_emb,
        ...     height=768,
        ...     width=768,
        ...     num_inference_steps=100,
        ...     strength=0.2,
        ... ).images

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






def    _snake_case					(		snake_case__ :  Union[str, Any]						,   snake_case__ :  Union[str, Any]						,   snake_case__ :  str=8  ):
	A     = height // scale_factor**2
	if height % scale_factor**2 != 0:
		new_height += 1
	A     = width // scale_factor**2
	if width % scale_factor**2 != 0:
		new_width += 1
	return new_height * scale_factor, new_width * scale_factor






def    _snake_case					(		snake_case__ :  List[Any]						,   snake_case__ :  Optional[int]=512						,   snake_case__ :  Tuple=512  ):
	A     = pil_image.resize((w, h)						,   resample=Image.BICUBIC						,   reducing_gap=1  )
	A     = np.array(pil_image.convert('RGB'  )  )
	A     = arr.astype(np.floataa  ) / 127.5 - 1
	A     = np.transpose(snake_case__						,   [2, 0, 1]  )
	A     = torch.from_numpy(snake_case__  ).unsqueeze(0  )
	return image







class 					lowerCAmelCase_					(   _lowercase     ):



						'''simple docstring'''


						def __init__(			self  :   Any	,A_  :   UNetaDConditionModel	,A_  :   DDPMScheduler	,A_  :   VQModel	,)  -> Union[str, Any]:
							super().__init__()

							self.register_modules(
							    unet=A_	,scheduler=A_	,movq=A_	,)
							A     = 2 ** (len(self.movq.config.block_out_channels   ) - 1)


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   int	,A_  :   Tuple	,A_  :   Tuple   )  -> List[Any]:
							# get the original timestep using init_timestep
							A     = min(int(num_inference_steps * strength   )	,A_   )

							A     = max(num_inference_steps - init_timestep	,0   )
							A     = self.scheduler.timesteps[t_start:]

							return timesteps, num_inference_steps - t_start


						def     _SCREAMING_SNAKE_CASE   (			self  :   str	,A_  :   Optional[int]	,A_  :   Tuple	,A_  :   Optional[int]	,A_  :   str	,A_  :   Union[str, Any]	,A_  :   Optional[Any]	,A_  :   Union[str, Any]=None   )  -> List[Any]:
							if not isinstance(A_	,(torch.Tensor, PIL.Image.Image, list)   ):
								raise ValueError(
								    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A_   )}'   )

							A     = image.to(device=A_	,dtype=A_   )

							A     = batch_size * num_images_per_prompt

							if image.shape[1] == 4:
								A     = image

							else:
								if isinstance(A_	,A_   ) and len(A_   ) != batch_size:
									raise ValueError(
									    F'You have passed a list of generators of length {len(A_   )}, but requested an effective batch'
									    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'   )

								elif isinstance(A_	,A_   ):
									A     = [
									    self.movq.encode(image[i : i + 1]   ).latent_dist.sample(generator[i]   ) for i in range(A_   )
									]
									A     = torch.cat(A_	,dim=0   )
								else:
									A     = self.movq.encode(A_   ).latent_dist.sample(A_   )

								A     = self.movq.config.scaling_factor * init_latents

							A     = torch.cat([init_latents]	,dim=0   )

							A     = init_latents.shape
							A     = randn_tensor(A_	,generator=A_	,device=A_	,dtype=A_   )

							# get latents
							A     = self.scheduler.add_noise(A_	,A_	,A_   )

							A     = init_latents

							return latents


						def     _SCREAMING_SNAKE_CASE   (			self  :   Dict	,A_  :   str=0   )  -> Optional[Any]:
							if is_accelerate_available():
								from accelerate import cpu_offload
							else:
								raise ImportError('Please install accelerate via `pip install accelerate`'   )

							A     = torch.device(F'cuda:{gpu_id}'   )

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


						def     _SCREAMING_SNAKE_CASE   (			self  :   Optional[int]	,A_  :   Tuple=0   )  -> List[Any]:
							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.'   )

							A     = torch.device(F'cuda:{gpu_id}'   )

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

							A     = None
							for cpu_offloaded_model in [self.unet, self.movq]:
								A ,   A     = cpu_offload_with_hook(A_	,A_	,prev_module_hook=A_   )

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


						@property
						# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
						def     _SCREAMING_SNAKE_CASE   (			self  :   List[Any]   )  -> str:
							if not hasattr(self.unet	,'_hf_hook'   ):
								return self.device
							for module in self.unet.modules():
								if (
								    hasattr(A_	,'_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(A_   )
						def __call__(			self  :   Optional[Any]	,A_  :   Union[torch.FloatTensor, List[torch.FloatTensor]]	,A_  :   Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]	,A_  :   Union[torch.FloatTensor, List[torch.FloatTensor]]	,A_  :   int = 512	,A_  :   int = 512	,A_  :   int = 100	,A_  :   float = 4.0	,A_  :   float = 0.3	,A_  :   int = 1	,A_  :   Optional[Union[torch.Generator, List[torch.Generator]]] = None	,A_  :   Optional[str] = "pil"	,A_  :   bool = True	,)  -> List[Any]:
							A     = self._execution_device

							A     = guidance_scale > 1.0

							if isinstance(A_	,A_   ):
								A     = torch.cat(A_	,dim=0   )
							A     = image_embeds.shape[0]
							if isinstance(A_	,A_   ):
								A     = torch.cat(A_	,dim=0   )

							if do_classifier_free_guidance:
								A     = image_embeds.repeat_interleave(A_	,dim=0   )
								A     = negative_image_embeds.repeat_interleave(A_	,dim=0   )

							A     = torch.cat([negative_image_embeds, image_embeds]	,dim=0   ).to(dtype=self.unet.dtype	,device=A_   )

							if not isinstance(A_	,A_   ):
								A     = [image]
							if not all(isinstance(A_	,(PIL.Image.Image, torch.Tensor)   ) for i in image   ):
								raise ValueError(
								    F'Input is in incorrect format: {[type(A_   ) for i in image]}. Currently, we only support  PIL image and pytorch tensor'   )

							A     = torch.cat([prepare_image(A_	,A_	,A_   ) for i in image]	,dim=0   )
							A     = image.to(dtype=image_embeds.dtype	,device=A_   )

							A     = self.movq.encode(A_   )['latents']
							A     = latents.repeat_interleave(A_	,dim=0   )
							self.scheduler.set_timesteps(A_	,device=A_   )
							A ,   A     = self.get_timesteps(A_	,A_	,A_   )
							A     = timesteps[:1].repeat(batch_size * num_images_per_prompt   )
							A ,   A     = downscale_height_and_width(A_	,A_	,self.movq_scale_factor   )
							A     = self.prepare_latents(
							    A_	,A_	,A_	,A_	,image_embeds.dtype	,A_	,A_   )
							for i, t in enumerate(self.progress_bar(A_   )   ):
								# expand the latents if we are doing classifier free guidance
								A     = torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

								A     = {'image_embeds': image_embeds}
								A     = self.unet(
								    sample=A_	,timestep=A_	,encoder_hidden_states=A_	,added_cond_kwargs=A_	,return_dict=A_	,)[0]

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

								# compute the previous noisy sample x_t -> x_t-1
								A     = self.scheduler.step(
								    A_	,A_	,A_	,generator=A_	,)[0]

							# post-processing
							A     = self.movq.decode(A_	,force_not_quantize=A_   )['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"]:
								A     = image * 0.5 + 0.5
								A     = image.clamp(0	,1   )
								A     = image.cpu().permute(0	,2	,3	,1   ).float().numpy()

							if output_type == "pil":
								A     = self.numpy_to_pil(A_   )

							if not return_dict:
								return (image,)

							return ImagePipelineOutput(images=A_   )




"""simple docstring"""




import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple

import torch
from audiocraft.models import MusicGen

from transformers import (
    AutoFeatureExtractor,
    AutoTokenizer,
    EncodecModel,
    MusicgenDecoderConfig,
    MusicgenForConditionalGeneration,
    MusicgenProcessor,
    TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase							:   Optional[Any]				=				logging.get_logger(__name__)


_lowerCAmelCase							:   List[str]				=				["model.decoder.embed_positions.weights"]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

     if "emb" in name:
          _UpperCAmelCase     :      int														=       name.replace("emb"		,    "model.decoder.embed_tokens"							)
     if "transformer" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("transformer"		,    "model.decoder"							)
     if "cross_attention" in name:
          _UpperCAmelCase     :      Any														=       name.replace("cross_attention"		,    "encoder_attn"							)
     if "linear1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("linear1"		,    "fc1"							)
     if "linear2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("linear2"		,    "fc2"							)
     if "norm1" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("norm1"		,    "self_attn_layer_norm"							)
     if "norm_cross" in name:
          _UpperCAmelCase     :      List[Any]														=       name.replace("norm_cross"		,    "encoder_attn_layer_norm"							)
     if "norm2" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("norm2"		,    "final_layer_norm"							)
     if "out_norm" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("out_norm"		,    "model.decoder.layer_norm"							)
     if "linears" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("linears"		,    "lm_heads"							)
     if "condition_provider.conditioners.description.output_proj" in name:
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("condition_provider.conditioners.description.output_proj"		,    "enc_to_dec_proj"							)
     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		OrderedDict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple[Dict, Dict]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       list(state_dict.keys()							)
     _UpperCAmelCase     :      Dict														=       {}
     for key in keys:
          _UpperCAmelCase     :      Optional[Any]														=       state_dict.pop(SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Any														=       rename_keys(SCREAMING_SNAKE_CASE__							)
          if "in_proj_weight" in key:
               # split fused qkv proj
               _UpperCAmelCase     :      Optional[int]														=       val[:hidden_size, :]
               _UpperCAmelCase     :      str														=       val[hidden_size : 2 * hidden_size, :]
               _UpperCAmelCase     :      List[Any]														=       val[-hidden_size:, :]
          elif "enc_to_dec_proj" in key:
               _UpperCAmelCase     :      Union[str, Any]														=       val
          else:
               _UpperCAmelCase     :      int														=       val
     return state_dict, enc_dec_proj_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  MusicgenDecoderConfig:





     '''simple docstring'''

     if checkpoint == "small":
          # default config values
          _UpperCAmelCase     :      Union[str, Any]														=       1_024
          _UpperCAmelCase     :      Tuple														=       24
          _UpperCAmelCase     :      List[str]														=       16
     elif checkpoint == "medium":
          _UpperCAmelCase     :      List[str]														=       1_536
          _UpperCAmelCase     :      List[str]														=       48
          _UpperCAmelCase     :      str														=       24
     elif checkpoint == "large":
          _UpperCAmelCase     :      Dict														=       2_048
          _UpperCAmelCase     :      Optional[int]														=       48
          _UpperCAmelCase     :      Tuple														=       32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenDecoderConfig(
         hidden_size=SCREAMING_SNAKE_CASE__		,    ffn_dim=hidden_size * 4		,    num_hidden_layers=SCREAMING_SNAKE_CASE__		,    num_attention_heads=SCREAMING_SNAKE_CASE__		,    )
     return config

@torch.no_grad()
def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		int=None		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Dict="cpu"							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       MusicGen.get_pretrained(SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       fairseq_model.lm.state_dict()
     _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       rename_state_dict(
         SCREAMING_SNAKE_CASE__		,    hidden_size=decoder_config.hidden_size							)

     _UpperCAmelCase     :      Tuple														=       TaEncoderModel.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      List[str]														=       EncodecModel.from_pretrained("facebook/encodec_32khz"							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenForCausalLM(SCREAMING_SNAKE_CASE__							).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       decoder.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)

     for key in missing_keys.copy():
          if key.startswith(("text_encoder", "audio_encoder")							) or key in EXPECTED_MISSING_KEYS:
               missing_keys.remove(SCREAMING_SNAKE_CASE__							)

     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError(f'Missing key(s) in state_dict: {missing_keys}'							)

     if len(SCREAMING_SNAKE_CASE__							) > 0:
          raise ValueError(f'Unexpected key(s) in state_dict: {unexpected_keys}'							)

     # init the composite model
     _UpperCAmelCase     :      Dict														=       MusicgenForConditionalGeneration(text_encoder=SCREAMING_SNAKE_CASE__		,    audio_encoder=SCREAMING_SNAKE_CASE__		,    decoder=SCREAMING_SNAKE_CASE__							)

     # load the pre-trained enc-dec projection (from the decoder state dict)
     model.enc_to_dec_proj.load_state_dict(SCREAMING_SNAKE_CASE__							)

     # check we can do a forward pass
     _UpperCAmelCase     :      int														=       torch.arange(0		,    8		,    dtype=torch.long							).reshape(2		,    -1							)
     _UpperCAmelCase     :      List[Any]														=       input_ids.reshape(2 * 4		,    -1							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       model(input_ids=SCREAMING_SNAKE_CASE__		,    decoder_input_ids=SCREAMING_SNAKE_CASE__							).logits

     if logits.shape != (8, 1, 2_048):
          raise ValueError("Incorrect shape for logits"							)

     # now construct the processor
     _UpperCAmelCase     :      Optional[int]														=       AutoTokenizer.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      str														=       AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,    padding_side="left"							)

     _UpperCAmelCase     :      Optional[Any]														=       MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)

     # set the appropriate bos/pad token ids
     _UpperCAmelCase     :      Optional[int]														=       2_048
     _UpperCAmelCase     :      Union[str, Any]														=       2_048

     # set other default generation config params
     _UpperCAmelCase     :      Optional[int]														=       int(30 * audio_encoder.config.frame_rate							)
     _UpperCAmelCase     :      Union[str, Any]														=       True
     _UpperCAmelCase     :      Dict														=       3.0

     if pytorch_dump_folder is not None:
          Path(SCREAMING_SNAKE_CASE__							).mkdir(exist_ok=SCREAMING_SNAKE_CASE__							)
          logger.info(f'Saving model {checkpoint} to {pytorch_dump_folder}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)
          processor.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if repo_id:
          logger.info(f'Pushing model {checkpoint} to {repo_id}'							)
          model.push_to_hub(SCREAMING_SNAKE_CASE__							)
          processor.push_to_hub(SCREAMING_SNAKE_CASE__							)


if __name__ == "__main__":
 _lowerCAmelCase							:   int				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint",
     default="small",
     type=str,
     help="Checkpoint size of the MusicGen model you'd like to convert. Can be one of: `['small', 'medium', 'large']`.",
 )
 parser.add_argument(
     "--pytorch_dump_folder",
     required=True,
     default=None,
     type=str,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument(
     "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
 )
 parser.add_argument(
     "--device", default="cpu", type=str, help="Torch device to run the conversion, either cpu or cuda."
 )

 _lowerCAmelCase							:   List[str]				=				parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
