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


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




def     lowercase__						(					lowerCAmelCase				:					Any ) ->      str:






				"""simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					List[Any] ,				lowerCAmelCase				:					Tuple ) ->      Tuple[Dict, Dict]:






				"""simple docstring"""




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




def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ) ->      MusicgenDecoderConfig:






				"""simple docstring"""




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




@torch.no_grad()
def     lowercase__						(					lowerCAmelCase				:					Optional[Any] ,				lowerCAmelCase				:					Dict=None ,				lowerCAmelCase				:					int=None ,				lowerCAmelCase				:					Tuple="cpu" ) ->      Tuple:






				"""simple docstring"""




				UpperCAmelCase									=					MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE ,				device=_SCREAMING_SNAKE_CASE )
				UpperCAmelCase									=					decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE )

				UpperCAmelCase									=					fairseq_model.lm.state_dict()
				UpperCAmelCase			,							UpperCAmelCase									=					rename_state_dict(
				    _SCREAMING_SNAKE_CASE ,				hidden_size=decoder_config.hidden_size )

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

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

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

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

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

				# init the composite model
				UpperCAmelCase									=					MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE ,				audio_encoder=_SCREAMING_SNAKE_CASE ,				decoder=_SCREAMING_SNAKE_CASE )

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

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

				with torch.no_grad():
								UpperCAmelCase									=					model(input_ids=_SCREAMING_SNAKE_CASE ,				decoder_input_ids=_SCREAMING_SNAKE_CASE ).logits

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

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

				UpperCAmelCase									=					MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE ,				tokenizer=_SCREAMING_SNAKE_CASE )

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

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

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

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


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

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





"""simple docstring"""

def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      int:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )
    if n == 0:
        return 0
    UpperCAmelCase									=					float('-inf' )
    for i in range(1 ,				n + 1 ):
        UpperCAmelCase									=					max(
            lowerCAmelCase ,				prices[i - 1] + naive_cut_rod_recursive(n - i ,				lowerCAmelCase ) )

    return max_revue




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Dict:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					[float('-inf' ) for _ in range(n + 1 )]
    return _top_down_cut_rod_recursive(lowerCAmelCase ,				lowerCAmelCase ,				lowerCAmelCase )




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ,				lowerCAmelCase				:					list ) ->      int:






    """simple docstring"""




    if max_rev[n] >= 0:
        return max_rev[n]
    elif n == 0:
        return 0
    else:
        UpperCAmelCase									=					float('-inf' )
        for i in range(1 ,				n + 1 ):
            UpperCAmelCase									=					max(
                lowerCAmelCase ,				prices[i - 1] + _top_down_cut_rod_recursive(n - i ,				lowerCAmelCase ,				lowerCAmelCase ) ,				)

        UpperCAmelCase									=					max_revenue

    return max_rev[n]




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Optional[int]:






    """simple docstring"""




    _enforce_args(lowerCAmelCase ,				lowerCAmelCase )

    # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
    # length 0.
    UpperCAmelCase									=					[float('-inf' ) for _ in range(n + 1 )]
    UpperCAmelCase									=					0

    for i in range(1 ,				n + 1 ):
        UpperCAmelCase									=					max_rev[i]
        for j in range(1 ,				i + 1 ):
            UpperCAmelCase									=					max(lowerCAmelCase ,				prices[j - 1] + max_rev[i - j] )

        UpperCAmelCase									=					max_revenue_i

    return max_rev[n]




def     lowercase__						(					lowerCAmelCase				:					int ,				lowerCAmelCase				:					list ) ->      Union[str, Any]:






    """simple docstring"""




    if n < 0:
        UpperCAmelCase									=					F"n must be greater than or equal to 0. Got n = {n}"
        raise ValueError(lowerCAmelCase )

    if n > len(lowerCAmelCase ):
        UpperCAmelCase									=					(
            'Each integral piece of rod must have a corresponding price. '
            F"Got n = {n} but length of prices = {len(lowerCAmelCase )}"
        )
        raise ValueError(lowerCAmelCase )




def     lowercase__						(					) ->      Optional[int]:






    """simple docstring"""




    UpperCAmelCase									=					[6, 10, 12, 15, 20, 23]
    UpperCAmelCase									=					len(lowerCAmelCase )

    # the best revenue comes from cutting the rod into 6 pieces, each
    # of length 1 resulting in a revenue of 6 * 6 = 36.
    UpperCAmelCase									=					36

    UpperCAmelCase									=					top_down_cut_rod(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					bottom_up_cut_rod(lowerCAmelCase ,				lowerCAmelCase )
    UpperCAmelCase									=					naive_cut_rod_recursive(lowerCAmelCase ,				lowerCAmelCase )

    assert expected_max_revenue == max_rev_top_down
    assert max_rev_top_down == max_rev_bottom_up
    assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
   main()



