






from dataclasses import dataclass
from typing import Tuple

import numpy as np
import torch







@dataclass
class 		_A						:
  lowercase_						:			torch.Tensor  # [batch_size x 3]
  lowercase_						:			torch.Tensor  # [batch_size x 3]
  lowercase_						:			torch.Tensor  # [batch_size x 3]
  lowercase_						:			torch.Tensor  # [batch_size x 3]
  lowercase_						:			int
  lowercase_						:			int
  lowercase_						:			float
  lowercase_						:			float
  lowercase_						:			Tuple[int]

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
     assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
     assert len(self.x.shape			) == len(self.y.shape			) == len(self.z.shape			) == len(self.origin.shape			) == 2

  def    a					(    self						:	Optional[Any]			):
     """simple docstring"""

     return torch.from_numpy(np.array([self.width, self.height]   ,				dtype=np.floataa			)			)

  def    a					(    self						:	str			):
     """simple docstring"""

     return torch.from_numpy(np.array([self.x_fov, self.y_fov]   ,				dtype=np.floataa			)			)

  def    a					(    self						:	str			):
     """simple docstring"""

     __UpperCamelCase	:			str					=					torch.arange(self.height * self.width			)
     __UpperCamelCase	:			str					=					torch.stack(
         [
             pixel_indices % self.width,
             torch.div(lowerCamelCase__   ,				self.width   ,				rounding_mode="""trunc"""			),
         ]   ,				axis=1   ,				)
     return coords

  @property
  def    a					(    self						:	Optional[int]			):
     """simple docstring"""

     __UpperCamelCase	,     *__UpperCamelCase	:			Dict					=					self.shape
     __UpperCamelCase	:			Optional[int]					=					int(np.prod(lowerCamelCase__			)			)

     __UpperCamelCase	:			Tuple					=					self.get_image_coords()
     __UpperCamelCase	:			Optional[Any]					=					torch.broadcast_to(coords.unsqueeze(0			)   ,				[batch_size * inner_batch_size, *coords.shape]			)
     __UpperCamelCase	:			Dict					=					self.get_camera_rays(lowerCamelCase__			)

     __UpperCamelCase	:			int					=					rays.view(lowerCamelCase__   ,				inner_batch_size * self.height * self.width   ,				2   ,				3			)

     return rays

  def    a					(    self						:	List[Any]   ,				lowerCamelCase__						:	torch.Tensor			):
     """simple docstring"""

     __UpperCamelCase	,     *__UpperCamelCase	,     __UpperCamelCase	:			List[str]					=					coords.shape
     assert n_coords == 2
     assert batch_size == self.origin.shape[0]

     __UpperCamelCase	:			Optional[Any]					=					coords.view(lowerCamelCase__   ,				-1   ,				2			)

     __UpperCamelCase	:			Optional[Any]					=					self.resolution()
     __UpperCamelCase	:			List[str]					=					self.fov()

     __UpperCamelCase	:			List[str]					=					(flat.float() / (res - 1)) * 2 - 1
     __UpperCamelCase	:			Dict					=					fracs * torch.tan(fov / 2			)

     __UpperCamelCase	:			Any					=					fracs.view(lowerCamelCase__   ,				-1   ,				2			)
     __UpperCamelCase	:			int					=					(
         self.z.view(lowerCamelCase__   ,				1   ,				3			)
         + self.x.view(lowerCamelCase__   ,				1   ,				3			) * fracs[:, :, :1]
         + self.y.view(lowerCamelCase__   ,				1   ,				3			) * fracs[:, :, 1:]
     )
     __UpperCamelCase	:			Optional[Any]					=					directions / directions.norm(dim=-1   ,				keepdim=lowerCamelCase__			)
     __UpperCamelCase	:			str					=					torch.stack(
         [
             torch.broadcast_to(self.origin.view(lowerCamelCase__   ,				1   ,				3			)   ,				[batch_size, directions.shape[1], 3]			),
             directions,
         ]   ,				dim=2   ,				)
     return rays.view(lowerCamelCase__   ,				*lowerCamelCase__   ,				2   ,				3			)

  def    a					(    self						:	Optional[Any]   ,				lowerCamelCase__						:	int   ,				lowerCamelCase__						:	int			):
     """simple docstring"""

     assert width * self.height == height * self.width, "The aspect ratio should not change."
     return DifferentiableProjectiveCamera(
         origin=self.origin   ,				x=self.x   ,				y=self.y   ,				z=self.z   ,				width=lowerCamelCase__   ,				height=lowerCamelCase__   ,				x_fov=self.x_fov   ,				y_fov=self.y_fov   ,				)







def 				__lowerCamelCase     (							__lowerCAmelCase							:					int							)     ->  DifferentiableProjectiveCamera:
   __UpperCamelCase	:			List[str]					=					[]
   __UpperCamelCase	:			Tuple					=					[]
   __UpperCamelCase	:			Optional[Any]					=					[]
   __UpperCamelCase	:			Any					=					[]
   for theta in np.linspace(0					,							2 * np.pi					,							num=20							):
      __UpperCamelCase	:			Optional[int]					=					np.array([np.sin(__lowerCAmelCase							), np.cos(__lowerCAmelCase							), -0.5]							)
      z /= np.sqrt(np.sum(z**2							)							)
      __UpperCamelCase	:			List[str]					=					-z * 4
      __UpperCamelCase	:			List[Any]					=					np.array([np.cos(__lowerCAmelCase							), -np.sin(__lowerCAmelCase							), 0.0]							)
      __UpperCamelCase	:			int					=					np.cross(__lowerCAmelCase					,							__lowerCAmelCase							)
      origins.append(__lowerCAmelCase							)
      xs.append(__lowerCAmelCase							)
      ys.append(__lowerCAmelCase							)
      zs.append(__lowerCAmelCase							)
   return DifferentiableProjectiveCamera(
       origin=torch.from_numpy(np.stack(__lowerCAmelCase					,							axis=0							)							).float()					,							x=torch.from_numpy(np.stack(__lowerCAmelCase					,							axis=0							)							).float()					,							y=torch.from_numpy(np.stack(__lowerCAmelCase					,							axis=0							)							).float()					,							z=torch.from_numpy(np.stack(__lowerCAmelCase					,							axis=0							)							).float()					,							width=__lowerCAmelCase					,							height=__lowerCAmelCase					,							x_fov=0.7					,							y_fov=0.7					,							shape=(1, len(__lowerCAmelCase							))					,							)











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()
UpperCamelCase    							=      logging.get_logger(__name__)


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


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


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


def 				__lowerCamelCase     (							__lowerCAmelCase							:					str							)     ->  MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      __UpperCamelCase	:			int					=					1024
      __UpperCamelCase	:			Union[str, Any]					=					24
      __UpperCamelCase	:			int					=					16
   elif checkpoint == "medium":
      __UpperCamelCase	:			List[Any]					=					1536
      __UpperCamelCase	:			Dict					=					48
      __UpperCamelCase	:			Dict					=					24
   elif checkpoint == "large":
      __UpperCamelCase	:			List[Any]					=					2048
      __UpperCamelCase	:			str					=					48
      __UpperCamelCase	:			Optional[int]					=					32
   else:
      raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
   __UpperCamelCase	:			Any					=					MusicgenDecoderConfig(
       hidden_size=__lowerCAmelCase					,							ffn_dim=hidden_size * 4					,							num_hidden_layers=__lowerCAmelCase					,							num_attention_heads=__lowerCAmelCase					,							)
   return config


@torch.no_grad()
def 				__lowerCamelCase     (							__lowerCAmelCase							:					List[str]					,							__lowerCAmelCase							:					Optional[Any]=None					,							__lowerCAmelCase							:					Tuple=None					,							__lowerCAmelCase							:					str="cpu"							)     ->  Optional[int]:
   __UpperCamelCase	:			str					=					MusicGen.get_pretrained(__lowerCAmelCase					,							device=__lowerCAmelCase							)
   __UpperCamelCase	:			int					=					decoder_config_from_checkpoint(__lowerCAmelCase							)

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

   __UpperCamelCase	:			List[Any]					=					TaEncoderModel.from_pretrained("""t5-base"""							)
   __UpperCamelCase	:			Dict					=					EncodecModel.from_pretrained("""facebook/encodec_32khz"""							)
   __UpperCamelCase	:			List[str]					=					MusicgenForCausalLM(__lowerCAmelCase							).eval()

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

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

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

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

   # init the composite model
   __UpperCamelCase	:			Tuple					=					MusicgenForConditionalGeneration(text_encoder=__lowerCAmelCase					,							audio_encoder=__lowerCAmelCase					,							decoder=__lowerCAmelCase							)

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

   # 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	:			int					=					model(input_ids=__lowerCAmelCase					,							decoder_input_ids=__lowerCAmelCase							).logits

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

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

   __UpperCamelCase	:			Any					=					MusicgenProcessor(feature_extractor=__lowerCAmelCase					,							tokenizer=__lowerCAmelCase							)

   # set the appropriate bos/pad token ids
   __UpperCamelCase	:			Tuple					=					2048
   __UpperCamelCase	:			int					=					2048

   # set other default generation config params
   __UpperCamelCase	:			str					=					int(30 * audio_encoder.config.frame_rate							)
   __UpperCamelCase	:			List[str]					=					True
   __UpperCamelCase	:			Tuple					=					3.0

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

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


if __name__ == "__main__":
       UpperCamelCase    							=      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.'
       )

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



