




'''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()
_snake_case				:				Dict       							=		logging.get_logger(__name__)


_snake_case				:				int       							=		['model.decoder.embed_positions.weights']
def 		snake_case_   (UpperCamelCase	:  List[Any]				):





							'''simple docstring'''


							if "emb" in name:
														_a     					=    name.replace('''emb'''			,						'''model.decoder.embed_tokens'''				)
							if "transformer" in name:
														_a     					=    name.replace('''transformer'''			,						'''model.decoder'''				)
							if "cross_attention" in name:
														_a     					=    name.replace('''cross_attention'''			,						'''encoder_attn'''				)
							if "linear1" in name:
														_a     					=    name.replace('''linear1'''			,						'''fc1'''				)
							if "linear2" in name:
														_a     					=    name.replace('''linear2'''			,						'''fc2'''				)
							if "norm1" in name:
														_a     					=    name.replace('''norm1'''			,						'''self_attn_layer_norm'''				)
							if "norm_cross" in name:
														_a     					=    name.replace('''norm_cross'''			,						'''encoder_attn_layer_norm'''				)
							if "norm2" in name:
														_a     					=    name.replace('''norm2'''			,						'''final_layer_norm'''				)
							if "out_norm" in name:
														_a     					=    name.replace('''out_norm'''			,						'''model.decoder.layer_norm'''				)
							if "linears" in name:
														_a     					=    name.replace('''linears'''			,						'''lm_heads'''				)
							if "condition_provider.conditioners.description.output_proj" in name:
														_a     					=    name.replace('''condition_provider.conditioners.description.output_proj'''			,						'''enc_to_dec_proj'''				)
							return name
def 		snake_case_   (UpperCamelCase	:  OrderedDict			,						UpperCamelCase	:  int				):





							'''simple docstring'''


							_a     					=    list(state_dict.keys()				)
							_a     					=    {}
							for key in keys:
														_a     					=    state_dict.pop(UpperCamelCase				)
														_a     					=    rename_keys(UpperCamelCase				)
														if "in_proj_weight" in key:
																					# split fused qkv proj
																					_a     					=    val[:hidden_size, :]
																					_a     					=    val[hidden_size : 2 * hidden_size, :]
																					_a     					=    val[-hidden_size:, :]
														elif "enc_to_dec_proj" in key:
																					_a     					=    val
														else:
																					_a     					=    val
							return state_dict, enc_dec_proj_state_dict
def 		snake_case_   (UpperCamelCase	:  str				):





							'''simple docstring'''


							if checkpoint == "small":
														# default config values
														_a     					=    1024
														_a     					=    24
														_a     					=    16
							elif checkpoint == "medium":
														_a     					=    1536
														_a     					=    48
														_a     					=    24
							elif checkpoint == "large":
														_a     					=    2048
														_a     					=    48
														_a     					=    32
							else:
														raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'				)
							_a     					=    MusicgenDecoderConfig(
							    hidden_size=UpperCamelCase			,						ffn_dim=hidden_size * 4			,						num_hidden_layers=UpperCamelCase			,						num_attention_heads=UpperCamelCase			,						)
							return config
@torch.no_grad()
def 		snake_case_   (UpperCamelCase	:  List[str]			,						UpperCamelCase	:  List[Any]=None			,						UpperCamelCase	:  List[Any]=None			,						UpperCamelCase	:  Optional[int]="cpu"				):





							'''simple docstring'''


							_a     					=    MusicGen.get_pretrained(UpperCamelCase			,						device=UpperCamelCase				)
							_a     					=    decoder_config_from_checkpoint(UpperCamelCase				)

							_a     					=    fairseq_model.lm.state_dict()
							_a					,							_a     					=    rename_state_dict(
							    UpperCamelCase			,						hidden_size=decoder_config.hidden_size				)

							_a     					=    TaEncoderModel.from_pretrained('''t5-base'''				)
							_a     					=    EncodecModel.from_pretrained('''facebook/encodec_32khz'''				)
							_a     					=    MusicgenForCausalLM(UpperCamelCase				).eval()

							# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
							_a					,							_a     					=    decoder.load_state_dict(UpperCamelCase			,						strict=UpperCamelCase				)

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

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

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

							# init the composite model
							_a     					=    MusicgenForConditionalGeneration(text_encoder=UpperCamelCase			,						audio_encoder=UpperCamelCase			,						decoder=UpperCamelCase				)

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

							# check we can do a forward pass
							_a     					=    torch.arange(0			,						8			,						dtype=torch.long				).reshape(2			,						-1				)
							_a     					=    input_ids.reshape(2 * 4			,						-1				)

							with torch.no_grad():
														_a     					=    model(input_ids=UpperCamelCase			,						decoder_input_ids=UpperCamelCase				).logits

							if logits.shape != (8, 1, 2048):
														raise ValueError('''Incorrect shape for logits'''				)

							# now construct the processor
							_a     					=    AutoTokenizer.from_pretrained('''t5-base'''				)
							_a     					=    AutoFeatureExtractor.from_pretrained('''facebook/encodec_32khz'''			,						padding_side='''left'''				)

							_a     					=    MusicgenProcessor(feature_extractor=UpperCamelCase			,						tokenizer=UpperCamelCase				)

							# set the appropriate bos/pad token ids
							_a     					=    2048
							_a     					=    2048

							# set other default generation config params
							_a     					=    int(30 * audio_encoder.config.frame_rate				)
							_a     					=    True
							_a     					=    3.0

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

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


if __name__ == "__main__":
				_snake_case				:				Optional[Any]       							=		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.'
				)

				_snake_case				:				int       							=		parser.parse_args()
				convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)









'''simple docstring'''


from typing import Callable, List, Optional, Union

import PIL
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPTextModel,
    CLIPTokenizer,
)

from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging


_snake_case				:				List[Any]       							=		logging.get_logger(__name__)  # pylint: disable=invalid-name

class       A   (		_a   ):







							def __init__(  self	:  Dict							,      lowerCAmelCase_	:  CLIPSegForImageSegmentation							,      lowerCAmelCase_	:  CLIPSegProcessor							,      lowerCAmelCase_	:  AutoencoderKL							,      lowerCAmelCase_	:  CLIPTextModel							,      lowerCAmelCase_	:  CLIPTokenizer							,      lowerCAmelCase_	:  UNetaDConditionModel							,      lowerCAmelCase_	:  Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler]							,      lowerCAmelCase_	:  StableDiffusionSafetyChecker							,      lowerCAmelCase_	:  CLIPImageProcessor							,      )      ->							int:


														"""simple docstring"""


														super().__init__()

														if hasattr(scheduler.config							,      '''steps_offset''' ) and scheduler.config.steps_offset != 1:
																					_a     					=    (
																					    F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
																					    F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
																					    '''to update the config accordingly as leaving `steps_offset` might led to incorrect results'''
																					    ''' in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,'''
																					    ''' it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`'''
																					    ''' file'''
																					)
																					deprecate('''steps_offset!=1'''							,      '''1.0.0'''							,      lowerCAmelCase_							,      standard_warn=lowerCAmelCase_ )
																					_a     					=    dict(scheduler.config )
																					_a     					=    1
																					_a     					=    FrozenDict(lowerCAmelCase_ )

														if hasattr(scheduler.config							,      '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
																					_a     					=    (
																					    F'The configuration file of this scheduler: {scheduler} has not set the configuration'
																					    ''' `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make'''
																					    ''' sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to'''
																					    ''' incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face'''
																					    ''' Hub, it would be very nice if you could open a Pull request for the'''
																					    ''' `scheduler/scheduler_config.json` file'''
																					)
																					deprecate('''skip_prk_steps not set'''							,      '''1.0.0'''							,      lowerCAmelCase_							,      standard_warn=lowerCAmelCase_ )
																					_a     					=    dict(scheduler.config )
																					_a     					=    True
																					_a     					=    FrozenDict(lowerCAmelCase_ )

														if safety_checker is None:
																					logger.warning(
																					    F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
																					    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
																					    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
																					    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
																					    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
																					    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .''' )

														self.register_modules(
														    segmentation_model=lowerCAmelCase_							,      segmentation_processor=lowerCAmelCase_							,      vae=lowerCAmelCase_							,      text_encoder=lowerCAmelCase_							,      tokenizer=lowerCAmelCase_							,      unet=lowerCAmelCase_							,      scheduler=lowerCAmelCase_							,      safety_checker=lowerCAmelCase_							,      feature_extractor=lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  str							,      lowerCAmelCase_	:  Optional[Union[str, int]] = "auto" )      ->							Any:


														"""simple docstring"""


														if slice_size == "auto":
																					# half the attention head size is usually a good trade-off between
																					# speed and memory
																					_a     					=    self.unet.config.attention_head_dim // 2
														self.unet.set_attention_slice(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Any:


														"""simple docstring"""


														self.enable_attention_slicing(lowerCAmelCase_ )







							def        __lowerCAmelCase      (  self	:  Union[str, Any] )      ->							Optional[Any]:


														"""simple docstring"""


														if is_accelerate_available():
																					from accelerate import cpu_offload
														else:
																					raise ImportError('''Please install accelerate via `pip install accelerate`''' )

														_a     					=    torch.device('''cuda''' )

														for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
																					if cpu_offloaded_model is not None:
																												cpu_offload(lowerCAmelCase_							,      lowerCAmelCase_ )







							@property
							# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
							def        __lowerCAmelCase      (  self	:  Dict )      ->							List[str]:


														"""simple docstring"""


														if self.device != torch.device('''meta''' ) or not hasattr(self.unet							,      '''_hf_hook''' ):
																					return self.device
														for module in self.unet.modules():
																					if (
																					    hasattr(lowerCAmelCase_							,      '''_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()
							def __call__(  self	:  List[Any]							,      lowerCAmelCase_	:  Union[str, List[str]]							,      lowerCAmelCase_	:  Union[torch.FloatTensor, PIL.Image.Image]							,      lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  int = 5_12							,      lowerCAmelCase_	:  int = 5_12							,      lowerCAmelCase_	:  int = 50							,      lowerCAmelCase_	:  float = 7.5							,      lowerCAmelCase_	:  Optional[Union[str, List[str]]] = None							,      lowerCAmelCase_	:  Optional[int] = 1							,      lowerCAmelCase_	:  float = 0.0							,      lowerCAmelCase_	:  Optional[torch.Generator] = None							,      lowerCAmelCase_	:  Optional[torch.FloatTensor] = None							,      lowerCAmelCase_	:  Optional[str] = "pil"							,      lowerCAmelCase_	:  bool = True							,      lowerCAmelCase_	:  Optional[Callable[[int, int, torch.FloatTensor], None]] = None							,      lowerCAmelCase_	:  int = 1							,      **lowerCAmelCase_	:  Any							,      )      ->							Tuple:


														"""simple docstring"""


														_a     					=    self.segmentation_processor(
														    text=[text]							,      images=[image]							,      padding='''max_length'''							,      return_tensors='''pt''' ).to(self.device )
														_a     					=    self.segmentation_model(**lowerCAmelCase_ )
														_a     					=    torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
														_a     					=    self.numpy_to_pil(lowerCAmelCase_ )[0].resize(image.size )

														# Run inpainting pipeline with the generated mask
														_a     					=    StableDiffusionInpaintPipeline(
														    vae=self.vae							,      text_encoder=self.text_encoder							,      tokenizer=self.tokenizer							,      unet=self.unet							,      scheduler=self.scheduler							,      safety_checker=self.safety_checker							,      feature_extractor=self.feature_extractor							,      )
														return inpainting_pipeline(
														    prompt=lowerCAmelCase_							,      image=lowerCAmelCase_							,      mask_image=lowerCAmelCase_							,      height=lowerCAmelCase_							,      width=lowerCAmelCase_							,      num_inference_steps=lowerCAmelCase_							,      guidance_scale=lowerCAmelCase_							,      negative_prompt=lowerCAmelCase_							,      num_images_per_prompt=lowerCAmelCase_							,      eta=lowerCAmelCase_							,      generator=lowerCAmelCase_							,      latents=lowerCAmelCase_							,      output_type=lowerCAmelCase_							,      return_dict=lowerCAmelCase_							,      callback=lowerCAmelCase_							,      callback_steps=lowerCAmelCase_							,      )

