




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	:						List[str]			=     logging.get_logger(__name__)


_UpperCAmelCase	:						Optional[Any]			=     ["model.decoder.embed_positions.weights"]



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



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



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



@torch.no_grad()
def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase=None,			lowerCamelCase=None,			lowerCamelCase="cpu"   ):
  lowercase    :Dict      	=						MusicGen.get_pretrained(lowerCamelCase,			device=lowerCamelCase   )
  lowercase    :Optional[Any]      	=						decoder_config_from_checkpoint(lowerCamelCase   )

  lowercase    :Union[str, Any]      	=						fairseq_model.lm.state_dict()
  lowercase	,    lowercase    :str      	=						rename_state_dict(
      lowerCamelCase,			hidden_size=decoder_config.hidden_size   )

  lowercase    :Optional[Any]      	=						TaEncoderModel.from_pretrained("t5-base"   )
  lowercase    :str      	=						EncodecModel.from_pretrained("facebook/encodec_32khz"   )
  lowercase    :Tuple      	=						MusicgenForCausalLM(lowerCamelCase   ).eval()

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

  with torch.no_grad():
    lowercase    :Union[str, Any]      	=						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
  lowercase    :str      	=						AutoTokenizer.from_pretrained("t5-base"   )
  lowercase    :str      	=						AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz",			padding_side="left"   )

  lowercase    :Union[str, Any]      	=						MusicgenProcessor(feature_extractor=lowerCamelCase,			tokenizer=lowerCamelCase   )

  # set the appropriate bos/pad token ids
  lowercase    :Tuple      	=						2048
  lowercase    :Optional[Any]      	=						2048

  # set other default generation config params
  lowercase    :Tuple      	=						int(30 * audio_encoder.config.frame_rate   )
  lowercase    :Optional[Any]      	=						True
  lowercase    :Dict      	=						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	:						Optional[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."
     )

     _UpperCAmelCase	:						Union[str, Any]			=     parser.parse_args()
     convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)









def       UpperCAmelCase__  (     lowerCamelCase,			lowerCamelCase = False   ):
  if n == 2:
    return True
  if not n % 2 or n < 2:
    return False
  if n > 5 and n % 10 not in (1, 3, 7, 9):  # can quickly check last digit
    return False
  if n > 3317044064679887385961981 and not allow_probable:
    raise ValueError(
        "Warning: upper bound of deterministic test is exceeded. "
        "Pass allow_probable=True to allow probabilistic test. "
        "A return value of True indicates a probable prime."   )
  # array bounds provided by analysis
  lowercase    :Optional[Any]      	=						[
      2047,
      1373653,
      25326001,
      3215031751,
      2152302898747,
      3474749660383,
      341550071728321,
      1,
      3825123056546413051,
      1,
      1,
      318665857834031151167461,
      3317044064679887385961981,
  ]

  lowercase    :List[Any]      	=						[2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41]
  for idx, _p in enumerate(lowerCamelCase,			1   ):
    if n < _p:
      # then we have our last prime to check
      lowercase    :Tuple      	=						primes[:idx]
      break
  lowercase	,    lowercase    :str      	=						n - 1, 0
  # break up n -1 into a power of 2 (s) and
  # remaining odd component
  # essentially, solve for d * 2 ** s == n - 1
  while d % 2 == 0:
    d //= 2
    s += 1
  for prime in plist:
    lowercase    :Tuple      	=						False
    for r in range(lowerCamelCase   ):
      lowercase    :Any      	=						pow(lowerCamelCase,			d * 2**r,			lowerCamelCase   )
      # see article for analysis explanation for m
      if (r == 0 and m == 1) or ((m + 1) % n == 0):
        lowercase    :Any      	=						True
        # this loop will not determine compositeness
        break
    if pr:
      continue
    # if pr is False, then the above loop never evaluated to true,
    # and the n MUST be composite
    return False
  return True



def       UpperCAmelCase__  (     ):
  assert not miller_rabin(561   )
  assert miller_rabin(563   )
  # 2047

  assert not miller_rabin(838201   )
  assert miller_rabin(838207   )
  # 1_373_653

  assert not miller_rabin(17316001   )
  assert miller_rabin(17316017   )
  # 25_326_001

  assert not miller_rabin(3078386641   )
  assert miller_rabin(3078386653   )
  # 3_215_031_751

  assert not miller_rabin(1713045574801   )
  assert miller_rabin(1713045574819   )
  # 2_152_302_898_747

  assert not miller_rabin(2779799728307   )
  assert miller_rabin(2779799728327   )
  # 3_474_749_660_383

  assert not miller_rabin(113850023909441   )
  assert miller_rabin(113850023909527   )
  # 341_550_071_728_321

  assert not miller_rabin(1275041018848804351   )
  assert miller_rabin(1275041018848804391   )
  # 3_825_123_056_546_413_051

  assert not miller_rabin(79666464458507787791867   )
  assert miller_rabin(79666464458507787791951   )
  # 318_665_857_834_031_151_167_461

  assert not miller_rabin(552840677446647897660333   )
  assert miller_rabin(552840677446647897660359   )
  # 3_317_044_064_679_887_385_961_981
  # upper limit for probabilistic test


if __name__ == "__main__":
     test_miller_rabin()



