



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 A						(       __UpperCamelCase					)						->      int:
			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 A						(       __UpperCamelCase ,	__UpperCamelCase					)						->      Tuple[Dict, Dict]:
			A__       =    list(state_dict.keys()					)
			A__       =    {}
			for key in keys:
						A__       =    state_dict.pop(__UpperCamelCase					)
						A__       =    rename_keys(__UpperCamelCase					)
						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 A						(       __UpperCamelCase					)						->      MusicgenDecoderConfig:
			if checkpoint == "small":
						# default config values
						A__       =    1_024
						A__       =    24
						A__       =    16
			elif checkpoint == "medium":
						A__       =    1_536
						A__       =    48
						A__       =    24
			elif checkpoint == "large":
						A__       =    2_048
						A__       =    48
						A__       =    32
			else:
						raise ValueError(f'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''					)
			A__       =    MusicgenDecoderConfig(
			    hidden_size=__UpperCamelCase ,	ffn_dim=hidden_size * 4 ,	num_hidden_layers=__UpperCamelCase ,	num_attention_heads=__UpperCamelCase ,	)
			return config


@torch.no_grad()
def A						(       __UpperCamelCase ,	__UpperCamelCase=None ,	__UpperCamelCase=None ,	__UpperCamelCase="cpu"					)						->      Tuple:
			A__       =    MusicGen.get_pretrained(__UpperCamelCase ,	device=__UpperCamelCase					)
			A__       =    decoder_config_from_checkpoint(__UpperCamelCase					)

			A__       =    fairseq_model.lm.state_dict()
			A__     , A__       =    rename_state_dict(
			    __UpperCamelCase ,	hidden_size=decoder_config.hidden_size					)

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

			# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
			A__     , A__       =    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
			A__       =    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
			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=__UpperCamelCase ,	decoder_input_ids=__UpperCamelCase					).logits

			if logits.shape != (8, 1, 2_048):
						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=__UpperCamelCase ,	tokenizer=__UpperCamelCase					)

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

			# set other default generation config params
			A__       =    int(30 * audio_encoder.config.frame_rate					)
			A__       =    True
			A__       =    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__":
							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)









from typing import Dict

from .base import GenericTensor, Pipeline


class      __lowerCAmelCase     (		UpperCAmelCase_      ):
					"""simple docstring"""






					def     _a							(     self					:       Any				,					_snake_case					:       str=None				,					_snake_case					:       Dict=None				,					_snake_case					:       Any=None				,					**_snake_case					:       str					):






								"""simple docstring"""


								if tokenize_kwargs is None:
											A__       =    {}

								if truncation is not None:
											if "truncation" in tokenize_kwargs:
														raise ValueError(
														    'truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'					)
											A__       =    truncation

								A__       =    tokenize_kwargs

								A__       =    {}
								if return_tensors is not None:
											A__       =    return_tensors

								return preprocess_params, {}, postprocess_params



					def     _a							(     self					:       Any				,					_snake_case					:       Dict				,					**_snake_case					:       Optional[Any]					):






								"""simple docstring"""


								A__       =    self.framework
								A__       =    self.tokenizer(_snake_case				,					return_tensors=_snake_case				,					**_snake_case					)
								return model_inputs



					def     _a							(     self					:       List[Any]				,					_snake_case					:       Dict					):






								"""simple docstring"""


								A__       =    self.model(**_snake_case					)
								return model_outputs



					def     _a							(     self					:       Optional[Any]				,					_snake_case					:       List[Any]				,					_snake_case					:       str=False					):






								"""simple docstring"""


								if return_tensors:
											return model_outputs[0]
								if self.framework == "pt":
											return model_outputs[0].tolist()
								elif self.framework == "tf":
											return model_outputs[0].numpy().tolist()



					def __call__(     self					:       Dict				,					*_snake_case					:       int				,					**_snake_case					:       List[str]					):






								"""simple docstring"""


								return super().__call__(*_snake_case				,					**_snake_case					)




