






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


SCREAMING_SNAKE_CASE__				     =			logging.get_logger(__name__)






class 			_UpperCamelCase(       __lowerCamelCase		):
		__SCREAMING_SNAKE_CASE  :       int							=							['''pixel_values''']

		def __init__(					self		:    Tuple    ,       SCREAMING_SNAKE_CASE__		:    bool = True    ,       SCREAMING_SNAKE_CASE__		:    Optional[Dict[str, int]] = None    ,       SCREAMING_SNAKE_CASE__		:    PILImageResampling = PILImageResampling.BILINEAR    ,       SCREAMING_SNAKE_CASE__		:    bool = True    ,       SCREAMING_SNAKE_CASE__		:    Dict[str, int] = None    ,       SCREAMING_SNAKE_CASE__		:    bool = True    ,       SCREAMING_SNAKE_CASE__		:    Union[int, float] = 1 / 2_5_5    ,       SCREAMING_SNAKE_CASE__		:    bool = True    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[float, List[float]]] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[float, List[float]]] = None    ,       **SCREAMING_SNAKE_CASE__		:    Optional[Any]    ,       ):



					'''simple docstring'''



					super().__init__(**SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     size if size is not None else {'shortest_edge': 2_5_6}
					__a						:  str									=     get_size_dict(SCREAMING_SNAKE_CASE__    ,       default_to_square=SCREAMING_SNAKE_CASE__    )
					__a						:  int									=     crop_size if crop_size is not None else {'height': 2_2_4, 'width': 2_2_4}
					__a						:  Union[str, Any]									=     get_size_dict(SCREAMING_SNAKE_CASE__    )
					__a						:  Dict									=     do_resize
					__a						:  Union[str, Any]									=     size
					__a						:  Any									=     resample
					__a						:  List[Any]									=     do_center_crop
					__a						:  Any									=     crop_size
					__a						:  Tuple									=     do_rescale
					__a						:  Optional[int]									=     rescale_factor
					__a						:  Optional[int]									=     do_normalize
					__a						:  int									=     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
					__a						:  Optional[Any]									=     image_std if image_std is not None else IMAGENET_STANDARD_STD

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    np.ndarray    ,       SCREAMING_SNAKE_CASE__		:    Dict[str, int]    ,       SCREAMING_SNAKE_CASE__		:    PILImageResampling = PILImageResampling.BICUBIC    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[str, ChannelDimension]] = None    ,       **SCREAMING_SNAKE_CASE__		:    List[Any]    ,       ):



					'''simple docstring'''



					__a						:  List[Any]									=     get_size_dict(SCREAMING_SNAKE_CASE__    ,       default_to_square=SCREAMING_SNAKE_CASE__    )
					if "shortest_edge" not in size:
								raise ValueError(f'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''    )
					__a						:  int									=     get_resize_output_image_size(SCREAMING_SNAKE_CASE__    ,       size=size['shortest_edge']    ,       default_to_square=SCREAMING_SNAKE_CASE__    )
					return resize(SCREAMING_SNAKE_CASE__    ,       size=SCREAMING_SNAKE_CASE__    ,       resample=SCREAMING_SNAKE_CASE__    ,       data_format=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Dict    ,       SCREAMING_SNAKE_CASE__		:    np.ndarray    ,       SCREAMING_SNAKE_CASE__		:    Dict[str, int]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[str, ChannelDimension]] = None    ,       **SCREAMING_SNAKE_CASE__		:    List[str]    ,       ):



					'''simple docstring'''



					__a						:  str									=     get_size_dict(SCREAMING_SNAKE_CASE__    )
					return center_crop(SCREAMING_SNAKE_CASE__    ,       size=(size['height'], size['width'])    ,       data_format=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Optional[Any]    ,       SCREAMING_SNAKE_CASE__		:    np.ndarray    ,       SCREAMING_SNAKE_CASE__		:    float    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[str, ChannelDimension]] = None    ,       **SCREAMING_SNAKE_CASE__		:    Dict    ):



					'''simple docstring'''



					return rescale(SCREAMING_SNAKE_CASE__    ,       scale=SCREAMING_SNAKE_CASE__    ,       data_format=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )

		def    __lowerCAmelCase  (					self		:    Optional[int]    ,       SCREAMING_SNAKE_CASE__		:    np.ndarray    ,       SCREAMING_SNAKE_CASE__		:    Union[float, List[float]]    ,       SCREAMING_SNAKE_CASE__		:    Union[float, List[float]]    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[str, ChannelDimension]] = None    ,       **SCREAMING_SNAKE_CASE__		:    Any    ,       ):



					'''simple docstring'''



					return normalize(SCREAMING_SNAKE_CASE__    ,       mean=SCREAMING_SNAKE_CASE__    ,       std=SCREAMING_SNAKE_CASE__    ,       data_format=SCREAMING_SNAKE_CASE__    ,       **SCREAMING_SNAKE_CASE__    )



		def    __lowerCAmelCase  (					self		:    int    ,       SCREAMING_SNAKE_CASE__		:    ImageInput    ,       SCREAMING_SNAKE_CASE__		:    Optional[bool] = None    ,       SCREAMING_SNAKE_CASE__		:    Dict[str, int] = None    ,       SCREAMING_SNAKE_CASE__		:    PILImageResampling = None    ,       SCREAMING_SNAKE_CASE__		:    bool = None    ,       SCREAMING_SNAKE_CASE__		:    Dict[str, int] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[bool] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[float] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[bool] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[float, List[float]]] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[float, List[float]]] = None    ,       SCREAMING_SNAKE_CASE__		:    Optional[Union[str, TensorType]] = None    ,       SCREAMING_SNAKE_CASE__		:    Union[str, ChannelDimension] = ChannelDimension.FIRST    ,       **SCREAMING_SNAKE_CASE__		:    Tuple    ,       ):



					'''simple docstring'''



					__a						:  Tuple									=     do_resize if do_resize is not None else self.do_resize
					__a						:  Optional[int]									=     size if size is not None else self.size
					__a						:  int									=     get_size_dict(SCREAMING_SNAKE_CASE__    ,       default_to_square=SCREAMING_SNAKE_CASE__    )
					__a						:  Dict									=     resample if resample is not None else self.resample
					__a						:  Optional[int]									=     do_center_crop if do_center_crop is not None else self.do_center_crop
					__a						:  Optional[int]									=     crop_size if crop_size is not None else self.crop_size
					__a						:  List[Any]									=     get_size_dict(SCREAMING_SNAKE_CASE__    )
					__a						:  Optional[int]									=     do_rescale if do_rescale is not None else self.do_rescale
					__a						:  Optional[Any]									=     rescale_factor if rescale_factor is not None else self.rescale_factor
					__a						:  Optional[Any]									=     do_normalize if do_normalize is not None else self.do_normalize
					__a						:  Optional[Any]									=     image_mean if image_mean is not None else self.image_mean
					__a						:  List[Any]									=     image_std if image_std is not None else self.image_std

					__a						:  int									=     make_list_of_images(SCREAMING_SNAKE_CASE__    )

					if not valid_images(SCREAMING_SNAKE_CASE__    ):
								raise ValueError(
								    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
								    'torch.Tensor, tf.Tensor or jax.ndarray.'    )

					if do_resize and size is None:
								raise ValueError('Size must be specified if do_resize is True.'    )

					if do_center_crop and crop_size is None:
								raise ValueError('Crop size must be specified if do_center_crop is True.'    )

					if do_rescale and rescale_factor is None:
								raise ValueError('Rescale factor must be specified if do_rescale is True.'    )

					if do_normalize and (image_mean is None or image_std is None):
								raise ValueError('Image mean and std must be specified if do_normalize is True.'    )

					# All transformations expect numpy arrays.
					__a						:  Any									=     [to_numpy_array(SCREAMING_SNAKE_CASE__    ) for image in images]

					if do_resize:
								__a						:  Any									=     [self.resize(image=SCREAMING_SNAKE_CASE__    ,       size=SCREAMING_SNAKE_CASE__    ,       resample=SCREAMING_SNAKE_CASE__    ) for image in images]

					if do_center_crop:
								__a						:  str									=     [self.center_crop(image=SCREAMING_SNAKE_CASE__    ,       size=SCREAMING_SNAKE_CASE__    ) for image in images]

					if do_rescale:
								__a						:  List[Any]									=     [self.rescale(image=SCREAMING_SNAKE_CASE__    ,       scale=SCREAMING_SNAKE_CASE__    ) for image in images]

					if do_normalize:
								__a						:  Any									=     [self.normalize(image=SCREAMING_SNAKE_CASE__    ,       mean=SCREAMING_SNAKE_CASE__    ,       std=SCREAMING_SNAKE_CASE__    ) for image in images]

					__a						:  Tuple									=     [to_channel_dimension_format(SCREAMING_SNAKE_CASE__    ,       SCREAMING_SNAKE_CASE__    ) for image in images]

					__a						:  int									=     {'pixel_values': images}
					return BatchFeature(data=SCREAMING_SNAKE_CASE__    ,       tensor_type=SCREAMING_SNAKE_CASE__    )










"""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)
