




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class  _lowerCamelCase						(			SCREAMING_SNAKE_CASE__   ):

      """simple docstring"""

      lowerCAmelCase__			: Optional[Any]    =					42
      lowerCAmelCase__			: Any    =					None




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=0.999					,     lowercase_="cosine"					,     )       ->	Tuple:

    """simple docstring"""


    if alpha_transform_type == "cosine":

        def alpha_bar_fn(lowercase_      ):
            return math.cos((t + 0.008) / 1.008 * math.pi / 2      ) ** 2

    elif alpha_transform_type == "exp":

        def alpha_bar_fn(lowercase_      ):
            return math.exp(t * -12.0      )

    else:
        raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}"      )

    __UpperCamelCase  		=						[]
    for i in range(__UpperCamelCase      ):
        __UpperCamelCase  		=						i / num_diffusion_timesteps
        __UpperCamelCase  		=						(i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar_fn(__UpperCamelCase      ) / alpha_bar_fn(__UpperCamelCase      )					,     __UpperCamelCase      )      )
    return torch.tensor(__UpperCamelCase					,     dtype=torch.floataa      )


class  _lowerCamelCase						(			SCREAMING_SNAKE_CASE__							,			SCREAMING_SNAKE_CASE__   ):

      """simple docstring"""

      @register_to_config
      def __init__(     self					: List[str] ,       snake_case					: Any = 1000 ,       snake_case					: List[Any] = "fixed_small_log" ,       snake_case					: Union[str, Any] = True ,       snake_case					: Optional[int] = 1.0 ,       snake_case					: str = "epsilon" ,       snake_case					: List[Any] = "squaredcos_cap_v2" ,       ):

          if beta_schedule != "squaredcos_cap_v2":
              raise ValueError('''UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\''''				)

          __UpperCamelCase  		=						betas_for_alpha_bar(_lowercase				)

          __UpperCamelCase  		=						1.0 - self.betas
          __UpperCamelCase  		=						torch.cumprod(self.alphas ,       dim=0				)
          __UpperCamelCase  		=						torch.tensor(1.0				)

          # standard deviation of the initial noise distribution
          __UpperCamelCase  		=						1.0

          # setable values
          __UpperCamelCase  		=						None
          __UpperCamelCase  		=						torch.from_numpy(np.arange(0 ,       _lowercase				)[::-1].copy()				)

          __UpperCamelCase  		=						variance_type







      def       snake_case	(     self					: Optional[int] ,       snake_case					: Dict ,       snake_case					: Any = None				):
          return sample







      def       snake_case	(     self					: int ,       snake_case					: Optional[int] ,       snake_case					: int = None				):
          __UpperCamelCase  		=						num_inference_steps
          __UpperCamelCase  		=						(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
          __UpperCamelCase  		=						(np.arange(0 ,       _lowercase				) * step_ratio).round()[::-1].copy().astype(np.intaa				)
          __UpperCamelCase  		=						torch.from_numpy(_lowercase				).to(_lowercase				)







      def       snake_case	(     self					: List[str] ,       snake_case					: List[Any] ,       snake_case					: Tuple=None ,       snake_case					: str=None ,       snake_case					: Optional[Any]=None				):

          if prev_timestep is None:
              __UpperCamelCase  		=						t - 1

          __UpperCamelCase  		=						self.alphas_cumprod[t]
          __UpperCamelCase  		=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          __UpperCamelCase  		=						1 - alpha_prod_t
          __UpperCamelCase  		=						1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
              __UpperCamelCase  		=						self.betas[t]
          else:
              __UpperCamelCase  		=						1 - alpha_prod_t / alpha_prod_t_prev

          # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
          # and sample from it to get previous sample
          # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
          __UpperCamelCase  		=						beta_prod_t_prev / beta_prod_t * beta

          if variance_type is None:
              __UpperCamelCase  		=						self.config.variance_type

          # hacks - were probably added for training stability
          if variance_type == "fixed_small_log":
              __UpperCamelCase  		=						torch.log(torch.clamp(_lowercase ,       min=1E-20				)				)
              __UpperCamelCase  		=						torch.exp(0.5 * variance				)
          elif variance_type == "learned_range":
              # NOTE difference with DDPM scheduler
              __UpperCamelCase  		=						variance.log()
              __UpperCamelCase  		=						beta.log()

              __UpperCamelCase  		=						(predicted_variance + 1) / 2
              __UpperCamelCase  		=						frac * max_log + (1 - frac) * min_log

          return variance







      def       snake_case	(     self					: int ,       snake_case					: Optional[Any] ,       snake_case					: Union[str, Any] ,       snake_case					: List[str] ,       snake_case					: Dict = None ,       snake_case					: Tuple=None ,       snake_case					: Any = True ,       ):
          __UpperCamelCase  		=						timestep

          if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
              __UpperCamelCase  		=						torch.split(_lowercase ,       sample.shape[1] ,       dim=1				)
          else:
              __UpperCamelCase  		=						None

          # 1. compute alphas, betas
          if prev_timestep is None:
              __UpperCamelCase  		=						t - 1

          __UpperCamelCase  		=						self.alphas_cumprod[t]
          __UpperCamelCase  		=						self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
          __UpperCamelCase  		=						1 - alpha_prod_t
          __UpperCamelCase  		=						1 - alpha_prod_t_prev

          if prev_timestep == t - 1:
              __UpperCamelCase  		=						self.betas[t]
              __UpperCamelCase  		=						self.alphas[t]
          else:
              __UpperCamelCase  		=						1 - alpha_prod_t / alpha_prod_t_prev
              __UpperCamelCase  		=						1 - beta

          # 2. compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
          if self.config.prediction_type == "epsilon":
              __UpperCamelCase  		=						(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
          elif self.config.prediction_type == "sample":
              __UpperCamelCase  		=						model_output
          else:
              raise ValueError(
                  F"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
                  ''' for the UnCLIPScheduler.'''				)

          # 3. Clip "predicted x_0"
          if self.config.clip_sample:
              __UpperCamelCase  		=						torch.clamp(
                  _lowercase ,       -self.config.clip_sample_range ,       self.config.clip_sample_range				)

          # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          __UpperCamelCase  		=						(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
          __UpperCamelCase  		=						alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

          # 5. Compute predicted previous sample µ_t
          # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
          __UpperCamelCase  		=						pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

          # 6. Add noise
          __UpperCamelCase  		=						0
          if t > 0:
              __UpperCamelCase  		=						randn_tensor(
                  model_output.shape ,       dtype=model_output.dtype ,       generator=_lowercase ,       device=model_output.device				)

              __UpperCamelCase  		=						self._get_variance(
                  _lowercase ,       predicted_variance=_lowercase ,       prev_timestep=_lowercase ,       )

              if self.variance_type == "fixed_small_log":
                  __UpperCamelCase  		=						variance
              elif self.variance_type == "learned_range":
                  __UpperCamelCase  		=						(0.5 * variance).exp()
              else:
                  raise ValueError(
                      F"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
                      ''' for the UnCLIPScheduler.'''				)

              __UpperCamelCase  		=						variance * variance_noise

          __UpperCamelCase  		=						pred_prev_sample + variance

          if not return_dict:
              return (pred_prev_sample,)

          return UnCLIPSchedulerOutput(prev_sample=_lowercase ,       pred_original_sample=_lowercase				)







      def       snake_case	(     self					: Tuple ,       snake_case					: Tuple ,       snake_case					: Optional[int] ,       snake_case					: Union[str, Any] ,       ):
          __UpperCamelCase  		=						self.alphas_cumprod.to(device=original_samples.device ,       dtype=original_samples.dtype				)
          __UpperCamelCase  		=						timesteps.to(original_samples.device				)

          __UpperCamelCase  		=						alphas_cumprod[timesteps] ** 0.5
          __UpperCamelCase  		=						sqrt_alpha_prod.flatten()
          while len(sqrt_alpha_prod.shape				) < len(original_samples.shape				):
              __UpperCamelCase  		=						sqrt_alpha_prod.unsqueeze(-1				)

          __UpperCamelCase  		=						(1 - alphas_cumprod[timesteps]) ** 0.5
          __UpperCamelCase  		=						sqrt_one_minus_alpha_prod.flatten()
          while len(sqrt_one_minus_alpha_prod.shape				) < len(original_samples.shape				):
              __UpperCamelCase  		=						sqrt_one_minus_alpha_prod.unsqueeze(-1				)

          __UpperCamelCase  		=						sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
          return noisy_samples












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()
a_   		=     logging.get_logger(__name__)


a_   		=     ["model.decoder.embed_positions.weights"]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple[Dict, Dict]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	MusicgenDecoderConfig:

    """simple docstring"""


    if checkpoint == "small":
        # default config values
        __UpperCamelCase  		=						10_24
        __UpperCamelCase  		=						24
        __UpperCamelCase  		=						16
    elif checkpoint == "medium":
        __UpperCamelCase  		=						15_36
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						24
    elif checkpoint == "large":
        __UpperCamelCase  		=						20_48
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						32
    else:
        raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."      )
    __UpperCamelCase  		=						MusicgenDecoderConfig(
        hidden_size=lowercase_					,     ffn_dim=hidden_size * 4					,     num_hidden_layers=lowercase_					,     num_attention_heads=lowercase_					,     )
    return config




@torch.no_grad()
def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_=None					,     lowercase_=None					,     lowercase_="cpu"      )       ->	List[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						MusicGen.get_pretrained(lowercase_					,     device=lowercase_      )
    __UpperCamelCase  		=						decoder_config_from_checkpoint(lowercase_      )

    __UpperCamelCase  		=						fairseq_model.lm.state_dict()
    __UpperCamelCase		,      __UpperCamelCase  		=						rename_state_dict(
        lowercase_					,     hidden_size=decoder_config.hidden_size      )

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

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

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

    if len(lowercase_      ) > 0:
        raise ValueError(F"Missing key(s) in state_dict: {missing_keys}"      )

    if len(lowercase_      ) > 0:
        raise ValueError(F"Unexpected key(s) in state_dict: {unexpected_keys}"      )

    # init the composite model
    __UpperCamelCase  		=						MusicgenForConditionalGeneration(text_encoder=lowercase_					,     audio_encoder=lowercase_					,     decoder=lowercase_      )

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

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

    with torch.no_grad():
        __UpperCamelCase  		=						model(input_ids=lowercase_					,     decoder_input_ids=lowercase_      ).logits

    if logits.shape != (8, 1, 20_48):
        raise ValueError('''Incorrect shape for logits'''      )

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

    __UpperCamelCase  		=						MusicgenProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )

    # set the appropriate bos/pad token ids
    __UpperCamelCase  		=						20_48
    __UpperCamelCase  		=						20_48

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

    if pytorch_dump_folder is not None:
        Path(lowercase_      ).mkdir(exist_ok=lowercase_      )
        logger.info(F"Saving model {checkpoint} to {pytorch_dump_folder}"      )
        model.save_pretrained(lowercase_      )
        processor.save_pretrained(lowercase_      )

    if repo_id:
        logger.info(F"Pushing model {checkpoint} to {repo_id}"      )
        model.push_to_hub(lowercase_      )
        processor.push_to_hub(lowercase_      )


if __name__ == "__main__":
 a_   		=     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."
 )

 a_   		=     parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






