


'''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        =     logging.get_logger(__name__)


__lowerCamelCase        =     ['''model.decoder.embed_positions.weights''']



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



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



def  _a			(						__UpperCamelCase ):
     if checkpoint == "small":
          # default config values
          a_    :     List[Any]										=    1_0_2_4
          a_    :     Optional[Any]										=    2_4
          a_    :     Optional[Any]										=    1_6
     elif checkpoint == "medium":
          a_    :     Any										=    1_5_3_6
          a_    :     int										=    4_8
          a_    :     int										=    2_4
     elif checkpoint == "large":
          a_    :     Any										=    2_0_4_8
          a_    :     List[str]										=    4_8
          a_    :     Union[str, Any]										=    3_2
     else:
          raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.''' )
     a_    :     List[Any]										=    MusicgenDecoderConfig(
         hidden_size=__UpperCamelCase  ,  ffn_dim=hidden_size * 4  ,  num_hidden_layers=__UpperCamelCase  ,  num_attention_heads=__UpperCamelCase  ,  )
     return config



@torch.no_grad()
def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=None  ,  __UpperCamelCase=None  ,  __UpperCamelCase="cpu" ):
     a_    :     Optional[Any]										=    MusicGen.get_pretrained(__UpperCamelCase  ,  device=__UpperCamelCase )
     a_    :     List[Any]										=    decoder_config_from_checkpoint(__UpperCamelCase )

     a_    :     List[Any]										=    fairseq_model.lm.state_dict()
     a_    :     Dict										=    rename_state_dict(
         __UpperCamelCase  ,  hidden_size=decoder_config.hidden_size )

     a_    :     Any										=    TaEncoderModel.from_pretrained("""t5-base""" )
     a_    :     Union[str, Any]										=    EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
     a_    :     str										=    MusicgenForCausalLM(__UpperCamelCase ).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     a_    :     Optional[Any]										=    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_    :     Tuple										=    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_    :     Dict										=    torch.arange(0  ,  8  ,  dtype=torch.long ).reshape(2  ,  -1 )
     a_    :     List[str]										=    input_ids.reshape(2 * 4  ,  -1 )

     with torch.no_grad():
          a_    :     Optional[Any]										=    model(input_ids=__UpperCamelCase  ,  decoder_input_ids=__UpperCamelCase ).logits

     if logits.shape != (8, 1, 2_0_4_8):
          raise ValueError("""Incorrect shape for logits""" )

     # now construct the processor
     a_    :     Dict										=    AutoTokenizer.from_pretrained("""t5-base""" )
     a_    :     Union[str, Any]										=    AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""  ,  padding_side="""left""" )

     a_    :     List[str]										=    MusicgenProcessor(feature_extractor=__UpperCamelCase  ,  tokenizer=__UpperCamelCase )

     # set the appropriate bos/pad token ids
     a_    :     Optional[Any]										=    2_0_4_8
     a_    :     List[Any]										=    2_0_4_8

     # set other default generation config params
     a_    :     List[str]										=    int(3_0 * audio_encoder.config.frame_rate )
     a_    :     str										=    True
     a_    :     Tuple										=    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__":
       __lowerCamelCase        =     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        =     parser.parse_args()
       convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







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   a__	(						lowerCAmelCase_    ):
       lowerCamelCase__:     torch.FloatTensor
       lowerCamelCase__:     Optional[torch.FloatTensor]							    =     None



def  _a			(						__UpperCamelCase  ,  __UpperCamelCase=0.9_99  ,  __UpperCamelCase="cosine"  ,  ):
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__UpperCamelCase ):
               return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2

     elif alpha_transform_type == "exp":

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

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

     a_    :     List[Any]										=    []
     for i in range(__UpperCamelCase ):
          a_    :     str										=    i / num_diffusion_timesteps
          a_    :     str										=    (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   a__	(						lowerCAmelCase_						,       lowerCAmelCase_    ):

       @register_to_config
       def __init__(   self      :     str      ,							lowerCamelCase_      :     int = 1_0_0_0      ,							lowerCamelCase_      :     str = "fixed_small_log"      ,							lowerCamelCase_      :     bool = True      ,							lowerCamelCase_      :     Optional[float] = 1.0      ,							lowerCamelCase_      :     str = "epsilon"      ,							lowerCamelCase_      :     str = "squaredcos_cap_v2"      ,							):
            if beta_schedule != "squaredcos_cap_v2":
                 raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""    )

            a_    :     Dict										=    betas_for_alpha_bar(lowerCamelCase_    )

            a_    :     List[Any]										=    1.0 - self.betas
            a_    :     List[str]										=    torch.cumprod(self.alphas      ,							dim=0    )
            a_    :     List[Any]										=    torch.tensor(1.0    )

            # standard deviation of the initial noise distribution
            a_    :     int										=    1.0

            # setable values
            a_    :     List[str]										=    None
            a_    :     Optional[int]										=    torch.from_numpy(np.arange(0      ,							lowerCamelCase_    )[::-1].copy()    )

            a_    :     List[Any]										=    variance_type

       def 						UpperCAmelCase(   self      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None    ):
            return sample

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     Union[str, torch.device] = None    ):
            a_    :     Tuple										=    num_inference_steps
            a_    :     Tuple										=    (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
            a_    :     Tuple										=    (np.arange(0      ,							lowerCamelCase_    ) * step_ratio).round()[::-1].copy().astype(np.intaa    )
            a_    :     Dict										=    torch.from_numpy(lowerCamelCase_    ).to(lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     Tuple      ,							lowerCamelCase_      :     List[Any]=None      ,							lowerCamelCase_      :     Tuple=None      ,							lowerCamelCase_      :     str=None    ):
            if prev_timestep is None:
                 a_    :     List[Any]										=    t - 1

            a_    :     Any										=    self.alphas_cumprod[t]
            a_    :     Union[str, Any]										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     List[Any]										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 a_    :     int										=    self.betas[t]
            else:
                 a_    :     Optional[int]										=    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
            a_    :     int										=    beta_prod_t_prev / beta_prod_t * beta

            if variance_type is None:
                 a_    :     Optional[Any]										=    self.config.variance_type

            # hacks - were probably added for training stability
            if variance_type == "fixed_small_log":
                 a_    :     Dict										=    torch.log(torch.clamp(lowerCamelCase_      ,							min=1E-20    )    )
                 a_    :     List[Any]										=    torch.exp(0.5 * variance    )
            elif variance_type == "learned_range":
                 # NOTE difference with DDPM scheduler
                 a_    :     Dict										=    variance.log()
                 a_    :     Optional[Any]										=    beta.log()

                 a_    :     str										=    (predicted_variance + 1) / 2
                 a_    :     Optional[int]										=    frac * max_log + (1 - frac) * min_log

            return variance

       def 						UpperCAmelCase(   self      :     Union[str, Any]      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     int      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     Optional[int] = None      ,							lowerCamelCase_      :     List[str]=None      ,							lowerCamelCase_      :     bool = True      ,							):
            a_    :     List[str]										=    timestep

            if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
                 a_			,			a_    :     List[str]										=    torch.split(lowerCamelCase_      ,							sample.shape[1]      ,							dim=1    )
            else:
                 a_    :     Any										=    None

            # 1. compute alphas, betas
            if prev_timestep is None:
                 a_    :     Optional[Any]										=    t - 1

            a_    :     Tuple										=    self.alphas_cumprod[t]
            a_    :     Any										=    self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
            a_    :     int										=    1 - alpha_prod_t
            a_    :     int										=    1 - alpha_prod_t_prev

            if prev_timestep == t - 1:
                 a_    :     Optional[int]										=    self.betas[t]
                 a_    :     int										=    self.alphas[t]
            else:
                 a_    :     int										=    1 - alpha_prod_t / alpha_prod_t_prev
                 a_    :     Dict										=    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":
                 a_    :     Optional[int]										=    (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
            elif self.config.prediction_type == "sample":
                 a_    :     Optional[Any]										=    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:
                 a_    :     Dict										=    torch.clamp(
                     lowerCamelCase_      ,							-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
            a_    :     Dict										=    (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
            a_    :     Any										=    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
            a_    :     Optional[Any]										=    pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

            # 6. Add noise
            a_    :     Optional[int]										=    0
            if t > 0:
                 a_    :     Any										=    randn_tensor(
                     model_output.shape      ,							dtype=model_output.dtype      ,							generator=lowerCamelCase_      ,							device=model_output.device    )

                 a_    :     Tuple										=    self._get_variance(
                     lowerCamelCase_      ,							predicted_variance=lowerCamelCase_      ,							prev_timestep=lowerCamelCase_      ,							)

                 if self.variance_type == "fixed_small_log":
                      a_    :     str										=    variance
                 elif self.variance_type == "learned_range":
                      a_    :     Optional[Any]										=    (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."""    )

                 a_    :     List[str]										=    variance * variance_noise

            a_    :     List[Any]										=    pred_prev_sample + variance

            if not return_dict:
                 return (pred_prev_sample,)

            return UnCLIPSchedulerOutput(prev_sample=lowerCamelCase_      ,							pred_original_sample=lowerCamelCase_    )

       def 						UpperCAmelCase(   self      :     Tuple      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.FloatTensor      ,							lowerCamelCase_      :     torch.IntTensor      ,							):
            # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
            a_    :     Tuple										=    self.alphas_cumprod.to(device=original_samples.device      ,							dtype=original_samples.dtype    )
            a_    :     Union[str, Any]										=    timesteps.to(original_samples.device    )

            a_    :     List[str]										=    alphas_cumprod[timesteps] ** 0.5
            a_    :     Tuple										=    sqrt_alpha_prod.flatten()
            while len(sqrt_alpha_prod.shape    ) < len(original_samples.shape    ):
                 a_    :     Dict										=    sqrt_alpha_prod.unsqueeze(-1    )

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

            a_    :     List[str]										=    sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
            return noisy_samples



