
"""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()
_lowerCamelCase	:    int          =      logging.get_logger(__name__)


_lowerCamelCase	:    Any          =      ['''model.decoder.embed_positions.weights''']






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






def 	a_		(	__lowercase       :     OrderedDict	, __lowercase       :     int			)			->  Tuple[Dict, Dict]:
			_snake_case          =      list(state_dict.keys()			)
			_snake_case          =      {}
			for key in keys:
						_snake_case          =      state_dict.pop(__lowercase			)
						_snake_case          =      rename_keys(__lowercase			)
						if "in_proj_weight" in key:
									# split fused qkv proj
									_snake_case          =      val[:hidden_size, :]
									_snake_case          =      val[hidden_size : 2 * hidden_size, :]
									_snake_case          =      val[-hidden_size:, :]
						elif "enc_to_dec_proj" in key:
									_snake_case          =      val
						else:
									_snake_case          =      val
			return state_dict, enc_dec_proj_state_dict






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






@torch.no_grad()
def 	a_		(	__lowercase       :     Union[str, Any]	, __lowercase       :     Union[str, Any]=None	, __lowercase       :     Union[str, Any]=None	, __lowercase       :     Any="cpu"			)			->  str:
			_snake_case          =      MusicGen.get_pretrained(__lowercase	, device=__lowercase			)
			_snake_case          =      decoder_config_from_checkpoint(__lowercase			)

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

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

			# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
			_snake_case     , _snake_case          =      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
			_snake_case          =      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
			_snake_case          =      torch.arange(0	, 8	, dtype=torch.long			).reshape(2	, -1			)
			_snake_case          =      input_ids.reshape(2 * 4	, -1			)

			with torch.no_grad():
						_snake_case          =      model(input_ids=__lowercase	, decoder_input_ids=__lowercase			).logits

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

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

			_snake_case          =      MusicgenProcessor(feature_extractor=__lowercase	, tokenizer=__lowercase			)

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

			# set other default generation config params
			_snake_case          =      int(30 * audio_encoder.config.frame_rate			)
			_snake_case          =      True
			_snake_case          =      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__":
			_lowerCamelCase	:    List[str]          =      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	:    str          =      parser.parse_args()
			convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)

def 	a_		(	__lowercase       :     list[int]	, __lowercase       :     list[int]			)			->  tuple[float, float]:

			# Check if the input is valid
			if not len(__lowercase			) == len(__lowercase			) == 3:
						raise ValueError('Please enter a valid equation.'			)
			if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
						raise ValueError('Both a & b of two equations can\'t be zero.'			)

			# Extract the coefficients
			_snake_case     , _snake_case     , _snake_case          =      equationa
			_snake_case     , _snake_case     , _snake_case          =      equationa

			# Calculate the determinants of the matrices
			_snake_case          =      aa * ba - aa * ba
			_snake_case          =      ca * ba - ca * ba
			_snake_case          =      aa * ca - aa * ca

			# Check if the system of linear equations has a solution (using Cramer's rule)
			if determinant == 0:
						if determinant_x == determinant_y == 0:
									raise ValueError('Infinite solutions. (Consistent system)'			)
						else:
									raise ValueError('No solution. (Inconsistent system)'			)
			else:
						if determinant_x == determinant_y == 0:
									# Trivial solution (Inconsistent system)
									return (0.0, 0.0)
						else:
									_snake_case          =      determinant_x / determinant
									_snake_case          =      determinant_y / determinant
									# Non-Trivial Solution (Consistent system)
									return (x, y)