


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()
snake_case      =   logging.get_logger(__name__)


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






def 							__lowercase     (				__lowerCAmelCase							:       Union[str, 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 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       Union[str, Any]     ):
							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 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]     ):

							if checkpoint == "small":
														# default config values
														a__    =  1_0_2_4
														a__    =  2_4
														a__    =  1_6
							elif checkpoint == "medium":
														a__    =  1_5_3_6
														a__    =  4_8
														a__    =  2_4
							elif checkpoint == "large":
														a__    =  2_0_4_8
														a__    =  4_8
														a__    =  3_2
							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 							__lowercase     (				__lowerCAmelCase							:       Optional[Any]      ,					__lowerCAmelCase							:       List[Any]=None      ,					__lowerCAmelCase							:       Optional[Any]=None      ,					__lowerCAmelCase							:       Any="cpu"     ):
							a__    =  MusicGen.get_pretrained(lowercase_      ,					device=lowercase_     )
							a__    =  decoder_config_from_checkpoint(lowercase_     )

							a__    =  fairseq_model.lm.state_dict()
							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__    =  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, 2_0_4_8):
														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__    =  2_0_4_8
							a__    =  2_0_4_8

							# set other default generation config params
							a__    =  int(3_0 * 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__":
						snake_case      =   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.'''
						)

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








from math import ceil, sqrt






def 							__lowercase     (				__lowerCAmelCase							:       int = 1_0_0_0_0_0_0     ):
							a__    =  0

							for outer_width in range(3      ,					(limit // 4) + 2     ):
														if outer_width**2 > limit:
																					a__    =  max(ceil(sqrt(outer_width**2 - limit     )     )      ,					1     )
														else:
																					a__    =  1
														if (outer_width - hole_width_lower_bound) % 2:
																					hole_width_lower_bound += 1

														answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1

							return answer


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




