



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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:List[str]				)  ->     List[Any]:
   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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:OrderedDict	,   _snake_case	:int				)  ->     Tuple[Dict, Dict]:
   _A      =       list(state_dict.keys()				)
   _A      =       {}
   for key in keys:
      _A      =       state_dict.pop(_snake_case				)
      _A      =       rename_keys(_snake_case				)
      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 	SCREAMING_SNAKE_CASE_							(	_snake_case	:str				)  ->     MusicgenDecoderConfig:
   if checkpoint == "small":
      # default config values
      _A      =       1_024
      _A      =       24
      _A      =       16
   elif checkpoint == "medium":
      _A      =       1_536
      _A      =       48
      _A      =       24
   elif checkpoint == "large":
      _A      =       2_048
      _A      =       48
      _A      =       32
   else:
      raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''				)
   _A      =       MusicgenDecoderConfig(
       hidden_size=_snake_case	,   ffn_dim=hidden_size * 4	,   num_hidden_layers=_snake_case	,   num_attention_heads=_snake_case	,   )
   return config

@torch.no_grad()
def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:Optional[int]	,   _snake_case	:int=None	,   _snake_case	:int=None	,   _snake_case	:Optional[int]="cpu"				)  ->     List[str]:
   _A      =       MusicGen.get_pretrained(_snake_case	,   device=_snake_case				)
   _A      =       decoder_config_from_checkpoint(_snake_case				)

   _A      =       fairseq_model.lm.state_dict()
   _A  ,    _A      =       rename_state_dict(
       _snake_case	,   hidden_size=decoder_config.hidden_size				)

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

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

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

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

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

   # init the composite model
   _A      =       MusicgenForConditionalGeneration(text_encoder=_snake_case	,   audio_encoder=_snake_case	,   decoder=_snake_case				)

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

   # 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=_snake_case	,   decoder_input_ids=_snake_case				).logits

   if logits.shape != (8, 1, 2_048):
      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=_snake_case	,   tokenizer=_snake_case				)

   # set the appropriate bos/pad token ids
   _A      =       2_048
   _A      =       2_048

   # 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(_snake_case				).mkdir(exist_ok=_snake_case				)
      logger.info(F'''Saving model {checkpoint} to {pytorch_dump_folder}'''				)
      model.save_pretrained(_snake_case				)
      processor.save_pretrained(_snake_case				)

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


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)








UpperCAmelCase_		 =					0  # The first color of the flag.
UpperCAmelCase_		 =					1  # The second color of the flag.
UpperCAmelCase_		 =					2  # The third color of the flag.
UpperCAmelCase_		 =					(red, white, blue)

def 	SCREAMING_SNAKE_CASE_							(	_snake_case	:list				)  ->     list:
   if not sequence:
      return []
   if len(_snake_case				) == 1:
      return list(_snake_case				)
   _A      =       0
   _A      =       len(_snake_case				) - 1
   _A      =       0
   while mid <= high:
      if sequence[mid] == colors[0]:
         _A  ,    _A      =       sequence[mid], sequence[low]
         low += 1
         mid += 1
      elif sequence[mid] == colors[1]:
         mid += 1
      elif sequence[mid] == colors[2]:
         _A  ,    _A      =       sequence[high], sequence[mid]
         high -= 1
      else:
         _A      =       F'''The elements inside the sequence must contains only {colors} values'''
         raise ValueError(_snake_case				)
   return sequence


if __name__ == "__main__":
   import doctest

   doctest.testmod()

   UpperCAmelCase_		 =					input("""Enter numbers separated by commas:\n""").strip()
   UpperCAmelCase_		 =					[int(item.strip()) for item in user_input.split(""",""")]
   print(f'{dutch_national_flag_sort(unsorted)}')



