




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()
__UpperCamelCase					:       List[str]    					=      logging.get_logger(__name__)


__UpperCamelCase					:       Optional[int]    					=      ["model.decoder.embed_positions.weights"]





def 		__A		(	__lowerCamelCase    )      ->       Optional[Any]:
	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		(	__lowerCamelCase							,			__lowerCamelCase    )      ->       Tuple[Dict, Dict]:
	a										=							list(state_dict.keys()    )
	a										=							{}
	for key in keys:
		a										=							state_dict.pop(__lowerCamelCase    )
		a										=							rename_keys(__lowerCamelCase    )
		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		(	__lowerCamelCase    )      ->       MusicgenDecoderConfig:
	if checkpoint == "small":
		# default config values
		a										=							1024
		a										=							24
		a										=							16
	elif checkpoint == "medium":
		a										=							1536
		a										=							48
		a										=							24
	elif checkpoint == "large":
		a										=							2048
		a										=							48
		a										=							32
	else:
		raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'    )
	a										=							MusicgenDecoderConfig(
	    hidden_size=__lowerCamelCase							,			ffn_dim=hidden_size * 4							,			num_hidden_layers=__lowerCamelCase							,			num_attention_heads=__lowerCamelCase							,			)
	return config





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase="cpu"    )      ->       Dict:
	a										=							MusicGen.get_pretrained(__lowerCamelCase							,			device=__lowerCamelCase    )
	a										=							decoder_config_from_checkpoint(__lowerCamelCase    )

	a										=							fairseq_model.lm.state_dict()
	a				, a										=							rename_state_dict(
	    __lowerCamelCase							,			hidden_size=decoder_config.hidden_size    )

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

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

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

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

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

	# init the composite model
	a										=							MusicgenForConditionalGeneration(text_encoder=__lowerCamelCase							,			audio_encoder=__lowerCamelCase							,			decoder=__lowerCamelCase    )

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

	# 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=__lowerCamelCase							,			decoder_input_ids=__lowerCamelCase    ).logits

	if logits.shape != (8, 1, 2048):
		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=__lowerCamelCase							,			tokenizer=__lowerCamelCase    )

	# set the appropriate bos/pad token ids
	a										=							2048
	a										=							2048

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

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


if __name__ == "__main__":
		__UpperCamelCase					:       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."
		)

		__UpperCamelCase					:       Any    					=      parser.parse_args()
		convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


logging.set_verbosity_info()
__UpperCamelCase					:       Union[str, Any]    					=      logging.get_logger(__name__)

__UpperCamelCase					:       Any    					=      {
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
__UpperCamelCase					:       Dict    					=      [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]





def 		__A		(	__lowerCamelCase    )      ->       List[str]:
	a										=							{}
	with open(__lowerCamelCase							,			"""r"""    ) as file:
		for line_number, line in enumerate(__lowerCamelCase    ):
			a										=							line.strip()
			if line:
				a										=							line.split()
				a										=							line_number
				a										=							words[0]
				a										=							value
	return result





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Dict:
	for attribute in key.split("""."""    ):
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )

	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    ).shape
	elif weight_type is not None and weight_type == "param":
		a										=							hf_pointer
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		a										=							shape_pointer.shape

		# let's reduce dimension
		a										=							value[0]
	else:
		a										=							hf_pointer.shape

	if hf_shape != value.shape:
		raise ValueError(
		    f'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
		    f' {value.shape} for {full_name}'    )

	if weight_type == "weight":
		a										=							value
	elif weight_type == "weight_g":
		a										=							value
	elif weight_type == "weight_v":
		a										=							value
	elif weight_type == "bias":
		a										=							value
	elif weight_type == "param":
		for attribute in hf_param_name.split("""."""    ):
			a										=							getattr(__lowerCamelCase							,			__lowerCamelCase    )
		a										=							value
	else:
		a										=							value

	logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'    )





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Union[str, Any]:
	a										=							None
	for param_key in PARAM_MAPPING.keys():
		if full_name.endswith(__lowerCamelCase    ):
			a										=							PARAM_MAPPING[full_name.split("""."""    )[-1]]
			a										=							"""param"""

	if weight_type is not None and weight_type != "param":
		a										=							""".""".join([key, weight_type]    )
	elif weight_type is not None and weight_type == "param":
		a										=							""".""".join([key, hf_param_name]    )
	else:
		a										=							key

	a										=							value if """lm_head""" in full_key else value[0]


__UpperCamelCase					:       List[Any]    					=      {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None    )      ->       Optional[Any]:
	a										=							False
	for key, mapped_key in MAPPING.items():
		a										=							"""wav2vec2.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
		if key in name or key.split("""w2v_model."""    )[-1] == name.split("""."""    )[0]:
			a										=							True
			if "*" in mapped_key:
				a										=							name.split(__lowerCamelCase    )[0].split("""."""    )[-2]
				a										=							mapped_key.replace("""*"""							,			__lowerCamelCase    )
			if "weight_g" in name:
				a										=							"""weight_g"""
			elif "weight_v" in name:
				a										=							"""weight_v"""
			elif "bias" in name:
				a										=							"""bias"""
			elif "weight" in name:
				# TODO: don't match quantizer.weight_proj
				a										=							"""weight"""
			else:
				a										=							None
			if hf_dict is not None:
				rename_dict(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			else:
				set_recursively(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
			return is_used
	return is_used





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       List[Any]:
	a										=							[]
	a										=							fairseq_model.state_dict()

	a										=							hf_model.wavaveca.feature_extractor

	for name, value in fairseq_dict.items():
		a										=							False
		if "conv_layers" in name:
			load_conv_layer(
			    __lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			hf_model.config.feat_extract_norm == """group"""							,			)
			a										=							True
		else:
			a										=							load_wavaveca_layer(__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )
		if not is_used:
			unused_weights.append(__lowerCamelCase    )

	logger.warning(f'Unused weights: {unused_weights}'    )





def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase    )      ->       Any:
	a										=							full_name.split("""conv_layers."""    )[-1]
	a										=							name.split("""."""    )
	a										=							int(items[0]    )
	a										=							int(items[1]    )

	if type_id == 0:
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'    )
			a										=							value
			logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'    )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'    )
			a										=							value
			logger.info(f'Feat extract conv layer {layer_id} was initialized from {full_name}.'    )
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'    )
			a										=							value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
		elif "weight" in name:
			if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
				raise ValueError(
				    f'{full_name} has size {value.shape}, but'
				    f' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'    )
			a										=							value
			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
	else:
		unused_weights.append(__lowerCamelCase    )





@torch.no_grad()
def 		__A		(	__lowerCamelCase							,			__lowerCamelCase							,			__lowerCamelCase=None							,			__lowerCamelCase=None							,			__lowerCamelCase=True							,			__lowerCamelCase=False    )      ->       List[Any]:
	if config_path is not None:
		a										=							WavaVecaConfig.from_pretrained(__lowerCamelCase    )
	else:
		a										=							WavaVecaConfig()

	if is_seq_class:
		a										=							read_txt_into_dict(__lowerCamelCase    )
		a										=							idalabel
		a										=							WavaVecaForSequenceClassification(__lowerCamelCase    )
		a										=							WavaVecaFeatureExtractor(
		    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
		feature_extractor.save_pretrained(__lowerCamelCase    )

	elif is_finetuned:
		if dict_path:
			a										=							Dictionary.load(__lowerCamelCase    )

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			a										=							target_dict.pad_index
			a										=							target_dict.bos_index
			a										=							target_dict.eos_index
			a										=							len(target_dict.symbols    )
			a										=							os.path.join(__lowerCamelCase							,			"""vocab.json"""    )
			if not os.path.isdir(__lowerCamelCase    ):
				logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCamelCase    )    )
				return
			os.makedirs(__lowerCamelCase							,			exist_ok=__lowerCamelCase    )
			a										=							target_dict.indices

			# fairseq has the <pad> and <s> switched
			a										=							0
			a										=							1
			with open(__lowerCamelCase							,			"""w"""							,			encoding="""utf-8"""    ) as vocab_handle:
				json.dump(__lowerCamelCase							,			__lowerCamelCase    )
			a										=							WavaVecaCTCTokenizer(
			    __lowerCamelCase							,			unk_token=target_dict.unk_word							,			pad_token=target_dict.pad_word							,			bos_token=target_dict.bos_word							,			eos_token=target_dict.eos_word							,			word_delimiter_token="""|"""							,			do_lower_case=__lowerCamelCase							,			)
			a										=							True if config.feat_extract_norm == """layer""" else False
			a										=							WavaVecaFeatureExtractor(
			    feature_size=1							,			sampling_rate=1_6000							,			padding_value=0							,			do_normalize=__lowerCamelCase							,			return_attention_mask=__lowerCamelCase							,			)
			a										=							WavaVecaProcessor(feature_extractor=__lowerCamelCase							,			tokenizer=__lowerCamelCase    )
			processor.save_pretrained(__lowerCamelCase    )

		a										=							WavaVecaForCTC(__lowerCamelCase    )
	else:
		a										=							WavaVecaForPreTraining(__lowerCamelCase    )

	if is_finetuned or is_seq_class:
		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]							,			arg_overrides={"""data""": """/""".join(dict_path.split("""/"""    )[:-1]    )}    )
	else:
		a										=							argparse.Namespace(task="""audio_pretraining"""    )
		a										=							fairseq.tasks.setup_task(__lowerCamelCase    )

		a				, a				, a										=							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							,			task=__lowerCamelCase    )

	a										=							model[0].eval()

	recursively_load_weights(__lowerCamelCase							,			__lowerCamelCase							,			not is_finetuned    )

	hf_wavavec.save_pretrained(__lowerCamelCase    )


if __name__ == "__main__":
		__UpperCamelCase					:       int    					=      argparse.ArgumentParser()
		parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
		parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
		parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
		parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
		parser.add_argument(
		    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
		)
		parser.add_argument(
		    "--is_seq_class",
		    action="store_true",
		    help="Whether the model to convert is a fine-tuned sequence classification model or not",
		)
		__UpperCamelCase					:       Union[str, Any]    					=      parser.parse_args()

		__UpperCamelCase					:       Any    					=      not args.not_finetuned and not args.is_seq_class
		convert_wavaveca_checkpoint(
		    args.checkpoint_path,
		    args.pytorch_dump_folder_path,
		    args.config_path,
		    args.dict_path,
		    is_finetuned,
		    args.is_seq_class,
		)

