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


__lowerCAmelCase    :							str													=						['''model.decoder.embed_positions.weights''']







def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""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 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__     ):





		"""simple docstring"""




		lowerCAmelCase__        =   list(state_dict.keys()     )
		lowerCAmelCase__        =   {}
		for key in keys:
				lowerCAmelCase__        =   state_dict.pop(__UpperCamelCase     )
				lowerCAmelCase__        =   rename_keys(__UpperCamelCase     )
				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 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		if checkpoint == "small":
				# default config values
				lowerCAmelCase__        =   1024
				lowerCAmelCase__        =   24
				lowerCAmelCase__        =   16
		elif checkpoint == "medium":
				lowerCAmelCase__        =   1536
				lowerCAmelCase__        =   48
				lowerCAmelCase__        =   24
		elif checkpoint == "large":
				lowerCAmelCase__        =   2048
				lowerCAmelCase__        =   48
				lowerCAmelCase__        =   32
		else:
				raise ValueError(f"""Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}."""     )
		lowerCAmelCase__        =   MusicgenDecoderConfig(
		    hidden_size=__UpperCamelCase			,  ffn_dim=hidden_size * 4			,  num_hidden_layers=__UpperCamelCase			,  num_attention_heads=__UpperCamelCase			,  )
		return config







@torch.no_grad()
def 							_UpperCAmelCase		(       lowerCamelCase__			,  lowerCamelCase__=None			,  lowerCamelCase__=None			,  lowerCamelCase__="cpu"     ):





		"""simple docstring"""




		lowerCAmelCase__        =   MusicGen.get_pretrained(__UpperCamelCase			,  device=__UpperCamelCase     )
		lowerCAmelCase__        =   decoder_config_from_checkpoint(__UpperCamelCase     )

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

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

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

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

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

		# 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(__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__":
			__lowerCAmelCase    :							Dict													=						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    :							List[str]													=						parser.parse_args()
			convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




"""simple docstring"""




def 							_UpperCAmelCase		(       lowerCamelCase__     ):





		"""simple docstring"""




		assert isinstance(lowerCamelCase__			,  lowerCamelCase__     ), f"""The input value of [n={number}] is not an integer"""

		if number == 1:
				return 2
		elif number < 1:
				lowerCAmelCase__        =   f"""The input value of [n={number}] has to be > 0"""
				raise ValueError(lowerCamelCase__     )
		else:
				lowerCAmelCase__        =   sylvester(number - 1     )
				lowerCAmelCase__        =   num - 1
				lowerCAmelCase__        =   num
				return lower * upper + 1


if __name__ == "__main__":
			print(F"The 8th number in Sylvester's sequence: {sylvester(8)}")




