

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      				=     logging.get_logger(__name__)


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


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if "emb" in name:
										__lowerCAmelCase					=							name.replace('emb'		,					'model.decoder.embed_tokens'      )
					if "transformer" in name:
										__lowerCAmelCase					=							name.replace('transformer'		,					'model.decoder'      )
					if "cross_attention" in name:
										__lowerCAmelCase					=							name.replace('cross_attention'		,					'encoder_attn'      )
					if "linear1" in name:
										__lowerCAmelCase					=							name.replace('linear1'		,					'fc1'      )
					if "linear2" in name:
										__lowerCAmelCase					=							name.replace('linear2'		,					'fc2'      )
					if "norm1" in name:
										__lowerCAmelCase					=							name.replace('norm1'		,					'self_attn_layer_norm'      )
					if "norm_cross" in name:
										__lowerCAmelCase					=							name.replace('norm_cross'		,					'encoder_attn_layer_norm'      )
					if "norm2" in name:
										__lowerCAmelCase					=							name.replace('norm2'		,					'final_layer_norm'      )
					if "out_norm" in name:
										__lowerCAmelCase					=							name.replace('out_norm'		,					'model.decoder.layer_norm'      )
					if "linears" in name:
										__lowerCAmelCase					=							name.replace('linears'		,					'lm_heads'      )
					if "condition_provider.conditioners.description.output_proj" in name:
										__lowerCAmelCase					=							name.replace('condition_provider.conditioners.description.output_proj'		,					'enc_to_dec_proj'      )
					return name


def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__      ):

					"""simple docstring"""






					__lowerCAmelCase					=							list(state_dict.keys()      )
					__lowerCAmelCase					=							{}
					for key in keys:
										__lowerCAmelCase					=							state_dict.pop(UpperCAmelCase__      )
										__lowerCAmelCase					=							rename_keys(UpperCAmelCase__      )
										if "in_proj_weight" in key:
															# split fused qkv proj
															__lowerCAmelCase					=							val[:hidden_size, :]
															__lowerCAmelCase					=							val[hidden_size : 2 * hidden_size, :]
															__lowerCAmelCase					=							val[-hidden_size:, :]
										elif "enc_to_dec_proj" in key:
															__lowerCAmelCase					=							val
										else:
															__lowerCAmelCase					=							val
					return state_dict, enc_dec_proj_state_dict


def        __lowercase    (  UpperCAmelCase__      ):

					"""simple docstring"""






					if checkpoint == "small":
										# default config values
										__lowerCAmelCase					=							1_024
										__lowerCAmelCase					=							24
										__lowerCAmelCase					=							16
					elif checkpoint == "medium":
										__lowerCAmelCase					=							1_536
										__lowerCAmelCase					=							48
										__lowerCAmelCase					=							24
					elif checkpoint == "large":
										__lowerCAmelCase					=							2_048
										__lowerCAmelCase					=							48
										__lowerCAmelCase					=							32
					else:
										raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""      )
					__lowerCAmelCase					=							MusicgenDecoderConfig(
					    hidden_size=UpperCAmelCase__		,					ffn_dim=hidden_size * 4		,					num_hidden_layers=UpperCAmelCase__		,					num_attention_heads=UpperCAmelCase__		,					)
					return config


@torch.no_grad()
def        __lowercase    (  UpperCAmelCase__		,					UpperCAmelCase__=None		,					UpperCAmelCase__=None		,					UpperCAmelCase__="cpu"      ):

					"""simple docstring"""






					__lowerCAmelCase					=							MusicGen.get_pretrained(UpperCAmelCase__		,					device=UpperCAmelCase__      )
					__lowerCAmelCase					=							decoder_config_from_checkpoint(UpperCAmelCase__      )

					__lowerCAmelCase					=							fairseq_model.lm.state_dict()
					__lowerCAmelCase,	__lowerCAmelCase					=							rename_state_dict(
					    UpperCAmelCase__		,					hidden_size=decoder_config.hidden_size      )

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

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

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

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

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

					# init the composite model
					__lowerCAmelCase					=							MusicgenForConditionalGeneration(text_encoder=UpperCAmelCase__		,					audio_encoder=UpperCAmelCase__		,					decoder=UpperCAmelCase__      )

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

					# check we can do a forward pass
					__lowerCAmelCase					=							torch.arange(0		,					8		,					dtype=torch.long      ).reshape(2		,					-1      )
					__lowerCAmelCase					=							input_ids.reshape(2 * 4		,					-1      )

					with torch.no_grad():
										__lowerCAmelCase					=							model(input_ids=UpperCAmelCase__		,					decoder_input_ids=UpperCAmelCase__      ).logits

					if logits.shape != (8, 1, 2_048):
										raise ValueError('Incorrect shape for logits'      )

					# now construct the processor
					__lowerCAmelCase					=							AutoTokenizer.from_pretrained('t5-base'      )
					__lowerCAmelCase					=							AutoFeatureExtractor.from_pretrained('facebook/encodec_32khz'		,					padding_side='left'      )

					__lowerCAmelCase					=							MusicgenProcessor(feature_extractor=UpperCAmelCase__		,					tokenizer=UpperCAmelCase__      )

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

					# set other default generation config params
					__lowerCAmelCase					=							int(30 * audio_encoder.config.frame_rate      )
					__lowerCAmelCase					=							True
					__lowerCAmelCase					=							3.0

					if pytorch_dump_folder is not None:
										Path(UpperCAmelCase__      ).mkdir(exist_ok=UpperCAmelCase__      )
										logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}"""      )
										model.save_pretrained(UpperCAmelCase__      )
										processor.save_pretrained(UpperCAmelCase__      )

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


if __name__ == "__main__":
		lowerCamelCase      				=     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      				=     parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)









from graphs.minimum_spanning_tree_kruskal import kruskal


def        __lowercase    (  ):

					"""simple docstring"""






					__lowerCAmelCase					=							9
					__lowerCAmelCase					=							[
					    [0, 1, 4],
					    [0, 7, 8],
					    [1, 2, 8],
					    [7, 8, 7],
					    [7, 6, 1],
					    [2, 8, 2],
					    [8, 6, 6],
					    [2, 3, 7],
					    [2, 5, 4],
					    [6, 5, 2],
					    [3, 5, 14],
					    [3, 4, 9],
					    [5, 4, 10],
					    [1, 7, 11],
					]

					__lowerCAmelCase					=							kruskal(UpperCAmelCase__		,					UpperCAmelCase__      )

					__lowerCAmelCase					=							[
					    [7, 6, 1],
					    [2, 8, 2],
					    [6, 5, 2],
					    [0, 1, 4],
					    [2, 5, 4],
					    [2, 3, 7],
					    [0, 7, 8],
					    [3, 4, 9],
					]

					assert sorted(UpperCAmelCase__      ) == sorted(UpperCAmelCase__      )






