





'''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_					:    int      	=			logging.get_logger(__name__)


lowerCAmelCase_					:    List[str]      	=			['model.decoder.embed_positions.weights']






def  _lowerCamelCase				( lowercase						:      Optional[int]						)	->	int:
				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  _lowerCamelCase				( lowercase						:      OrderedDict    ,							lowercase						:      int						)	->	Tuple[Dict, Dict]:
				_a								= list(state_dict.keys()						)
				_a								= {}
				for key in keys:
								_a								= state_dict.pop(lowercase						)
								_a								= rename_keys(lowercase						)
								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  _lowerCamelCase				( lowercase						:      str						)	->	MusicgenDecoderConfig:
				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=lowercase    ,							ffn_dim=hidden_size * 4    ,							num_hidden_layers=lowercase    ,							num_attention_heads=lowercase    ,							)
				return config






@torch.no_grad()
def  _lowerCamelCase				( lowercase						:      List[str]    ,							lowercase						:      int=None    ,							lowercase						:      Any=None    ,							lowercase						:      Dict="cpu"						)	->	Any:
				_a								= MusicGen.get_pretrained(lowercase    ,							device=lowercase						)
				_a								= decoder_config_from_checkpoint(lowercase						)

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

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

				# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
				_a	,					_a								= 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
				_a								= 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
				_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=lowercase    ,							decoder_input_ids=lowercase						).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=lowercase    ,							tokenizer=lowercase						)

				# 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(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__":
					lowerCAmelCase_					:    Dict      	=			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_					:    str      	=			parser.parse_args()
					convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)


from __future__ import annotations

from fractions import Fraction
from math import gcd, sqrt




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ):
 __a :  int						=  int(number**0.5  )
 return number == sq * sq




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ,   lowerCAmelCase__							:				int  ):
 __a :  int						=  x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
 __a :  int						=  x_den * y_den * z_den
 __a :  int						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
 top //= hcf
 bottom //= hcf
 return top, bottom




def    __UpperCamelCase     (				lowerCAmelCase__							:				int = 3_5  ):
 __a :  set						=  set()
 __a :  int
 __a :  Fraction						=  Fraction(0  )
 __a :  tuple[int, int]

 for x_num in range(1  ,   order + 1  ):
  for x_den in range(x_num + 1  ,   order + 1  ):
   for y_num in range(1  ,   order + 1  ):
    for y_den in range(y_num + 1  ,   order + 1  ):
     # n=1
     __a :  Any						=  x_num * y_den + x_den * y_num
     __a :  Dict						=  x_den * y_den
     __a :  int						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
     z_num //= hcf
     z_den //= hcf
     if 0 < z_num < z_den <= order:
      __a :  str						=  add_three(
          lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
      unique_s.add(lowerCAmelCase__  )

     # n=2
     __a :  Union[str, Any]						=  (
         x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
     )
     __a :  Optional[Any]						=  x_den * x_den * y_den * y_den
     if is_sq(lowerCAmelCase__  ) and is_sq(lowerCAmelCase__  ):
      __a :  Any						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  Optional[Any]						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  int						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
      z_num //= hcf
      z_den //= hcf
      if 0 < z_num < z_den <= order:
       __a :  str						=  add_three(
           lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
       unique_s.add(lowerCAmelCase__  )

                    # n=-1
     __a :  List[str]						=  x_num * y_num
     __a :  List[str]						=  x_den * y_num + x_num * y_den
     __a :  str						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
     z_num //= hcf
     z_den //= hcf
     if 0 < z_num < z_den <= order:
      __a :  Optional[int]						=  add_three(
          lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
      unique_s.add(lowerCAmelCase__  )

     # n=2
     __a :  List[str]						=  x_num * x_num * y_num * y_num
     __a :  Optional[int]						=  (
         x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
     )
     if is_sq(lowerCAmelCase__  ) and is_sq(lowerCAmelCase__  ):
      __a :  Union[str, Any]						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  List[str]						=  int(sqrt(lowerCAmelCase__  )  )
      __a :  Optional[int]						=  gcd(lowerCAmelCase__  ,   lowerCAmelCase__  )
      z_num //= hcf
      z_den //= hcf
      if 0 < z_num < z_den <= order:
       __a :  int						=  add_three(
           lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  ,   lowerCAmelCase__  )
       unique_s.add(lowerCAmelCase__  )

 for num, den in unique_s:
  total += Fraction(lowerCAmelCase__  ,   lowerCAmelCase__  )

 return total.denominator + total.numerator


if __name__ == "__main__":
      print(F"""{solution() = }""")
