




"""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()
a            =							logging.get_logger(__name__)


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





def 							lowercase    (snake_case__      :       List[Any]       )   ->     str:


				'''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    (snake_case__      :       List[Any]					,				snake_case__      :       List[str]       )   ->     Optional[int]:


				'''simple docstring'''






				lowerCAmelCase              = list(state_dict.keys()       )
				lowerCAmelCase              = {}
				for key in keys:
								lowerCAmelCase              = state_dict.pop(a_       )
								lowerCAmelCase              = rename_keys(a_       )
								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    (snake_case__      :       Any       )   ->     str:


				'''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=a_					,				ffn_dim=hidden_size * 4					,				num_hidden_layers=a_					,				num_attention_heads=a_					,				)
				return config





@torch.no_grad()
def 							lowercase    (snake_case__      :       Optional[Any]					,				snake_case__      :       Dict=None					,				snake_case__      :       Any=None					,				snake_case__      :       Tuple="cpu"       )   ->     Union[str, Any]:


				'''simple docstring'''






				lowerCAmelCase              = MusicGen.get_pretrained(a_					,				device=a_       )
				lowerCAmelCase              = decoder_config_from_checkpoint(a_       )

				lowerCAmelCase              = fairseq_model.lm.state_dict()
				lowerCAmelCase       ,  lowerCAmelCase              = rename_state_dict(
				    a_					,				hidden_size=decoder_config.hidden_size       )

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

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

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

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

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

				# init the composite model
				lowerCAmelCase              = MusicgenForConditionalGeneration(text_encoder=a_					,				audio_encoder=a_					,				decoder=a_       )

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

				# 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=a_					,				decoder_input_ids=a_       ).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=a_					,				tokenizer=a_       )

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

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


if __name__ == "__main__":
		a            =							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.'
		)

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













import numpy as np
from sklearn.datasets import fetch_california_housing
from sklearn.metrics import mean_absolute_error, mean_squared_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor





def 						__UpperCAmelCase			(     a_):
							return (data["data"], data["target"])





def 						__UpperCAmelCase			(     a_							,     a_							,     a_):
							snake_case_				=							XGBRegressor(verbosity=0							,     random_state=42)
							xgb.fit(a_							,     a_)
							# Predict target for test data
							snake_case_				=							xgb.predict(a_)
							snake_case_				=							predictions.reshape(len(a_)							,     1)
							return predictions





def 						__UpperCAmelCase			(     ):
							snake_case_				=							fetch_california_housing()
							snake_case_			,		snake_case_				=							data_handling(a_)
							snake_case_			,		snake_case_			,		snake_case_			,		snake_case_				=							train_test_split(
							    a_							,     a_							,     test_size=0.25							,     random_state=1)
							snake_case_				=							xgboost(a_							,     a_							,     a_)
							# Error printing
							print(f'''Mean Absolute Error : {mean_absolute_error(a_							,     a_)}''')
							print(f'''Mean Square Error  : {mean_squared_error(a_							,     a_)}''')


if __name__ == "__main__":
	import doctest

	doctest.testmod(verbose=True)
	main()





