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					:	List[Any]       =  logging.get_logger(__name__)


__lowerCamelCase					:	Dict       =  ['''model.decoder.embed_positions.weights''']




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		if "emb" in name:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    name.replace("emb"   , "model.decoder.embed_tokens"	)
		if "transformer" in name:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    name.replace("transformer"   , "model.decoder"	)
		if "cross_attention" in name:
				SCREAMING_SNAKE_CASE_  :		List[str]				      =    name.replace("cross_attention"   , "encoder_attn"	)
		if "linear1" in name:
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    name.replace("linear1"   , "fc1"	)
		if "linear2" in name:
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    name.replace("linear2"   , "fc2"	)
		if "norm1" in name:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    name.replace("norm1"   , "self_attn_layer_norm"	)
		if "norm_cross" in name:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    name.replace("norm_cross"   , "encoder_attn_layer_norm"	)
		if "norm2" in name:
				SCREAMING_SNAKE_CASE_  :		Dict				      =    name.replace("norm2"   , "final_layer_norm"	)
		if "out_norm" in name:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    name.replace("out_norm"   , "model.decoder.layer_norm"	)
		if "linears" in name:
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    name.replace("linears"   , "lm_heads"	)
		if "condition_provider.conditioners.description.output_proj" in name:
				SCREAMING_SNAKE_CASE_  :		int				      =    name.replace("condition_provider.conditioners.description.output_proj"   , "enc_to_dec_proj"	)
		return name




def       _snake_case	(   lowerCAmelCase							:   str   , lowerCAmelCase							:   Optional[Any]	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		int				      =    list(state_dict.keys()	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    {}
		for key in keys:
				SCREAMING_SNAKE_CASE_  :		Any				      =    state_dict.pop(lowerCAmelCase	)
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    rename_keys(lowerCAmelCase	)
				if "in_proj_weight" in key:
						# split fused qkv proj
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val[:hidden_size, :]
						SCREAMING_SNAKE_CASE_  :		Any				      =    val[hidden_size : 2 * hidden_size, :]
						SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    val[-hidden_size:, :]
				elif "enc_to_dec_proj" in key:
						SCREAMING_SNAKE_CASE_  :		str				      =    val
				else:
						SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    val
		return state_dict, enc_dec_proj_state_dict




def       _snake_case	(   lowerCAmelCase							:   str	):
		"""simple docstring"""







		if checkpoint == "small":
				# default config values
				SCREAMING_SNAKE_CASE_  :		int				      =    1_0_2_4
				SCREAMING_SNAKE_CASE_  :		int				      =    2_4
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    1_6
		elif checkpoint == "medium":
				SCREAMING_SNAKE_CASE_  :		Any				      =    1_5_3_6
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    4_8
				SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    2_4
		elif checkpoint == "large":
				SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    2_0_4_8
				SCREAMING_SNAKE_CASE_  :		Dict				      =    4_8
				SCREAMING_SNAKE_CASE_  :		List[Any]				      =    3_2
		else:
				raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'	)
		SCREAMING_SNAKE_CASE_  :		Any				      =    MusicgenDecoderConfig(
		    hidden_size=lowerCAmelCase   , ffn_dim=hidden_size * 4   , num_hidden_layers=lowerCAmelCase   , num_attention_heads=lowerCAmelCase   , )
		return config




@torch.no_grad()
def       _snake_case	(   lowerCAmelCase							:   List[str]   , lowerCAmelCase							:   List[str]=None   , lowerCAmelCase							:   Optional[int]=None   , lowerCAmelCase							:   Dict="cpu"	):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    MusicGen.get_pretrained(lowerCAmelCase   , device=lowerCAmelCase	)
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    decoder_config_from_checkpoint(lowerCAmelCase	)

		SCREAMING_SNAKE_CASE_  :		Any				      =    fairseq_model.lm.state_dict()
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    rename_state_dict(
		    lowerCAmelCase   , hidden_size=decoder_config.hidden_size	)

		SCREAMING_SNAKE_CASE_  :		str				      =    TaEncoderModel.from_pretrained("t5-base"	)
		SCREAMING_SNAKE_CASE_  :		Tuple				      =    EncodecModel.from_pretrained("facebook/encodec_32khz"	)
		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    MusicgenForCausalLM(lowerCAmelCase	).eval()

		# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    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
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    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
		SCREAMING_SNAKE_CASE_  :		Dict				      =    torch.arange(0   , 8   , dtype=torch.long	).reshape(2   , -1	)
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    input_ids.reshape(2 * 4   , -1	)

		with torch.no_grad():
				SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    model(input_ids=lowerCAmelCase   , decoder_input_ids=lowerCAmelCase	).logits

		if logits.shape != (8, 1, 2_0_4_8):
				raise ValueError("Incorrect shape for logits"	)

		# now construct the processor
		SCREAMING_SNAKE_CASE_  :		str				      =    AutoTokenizer.from_pretrained("t5-base"	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"   , padding_side="left"	)

		SCREAMING_SNAKE_CASE_  :		Optional[int]				      =    MusicgenProcessor(feature_extractor=lowerCAmelCase   , tokenizer=lowerCAmelCase	)

		# set the appropriate bos/pad token ids
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    2_0_4_8
		SCREAMING_SNAKE_CASE_  :		List[str]				      =    2_0_4_8

		# set other default generation config params
		SCREAMING_SNAKE_CASE_  :		Optional[Any]				      =    int(3_0 * audio_encoder.config.frame_rate	)
		SCREAMING_SNAKE_CASE_  :		Union[str, Any]				      =    True
		SCREAMING_SNAKE_CASE_  :		List[Any]				      =    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__":
		__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  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							return abs(UpperCamelCase      ) if a == 0 else greatest_common_divisor(b % a				,			UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       (UpperCamelCase				,			UpperCamelCase      )      ->       int:
							while y:  # --> when y=0 then loop will terminate and return x as final GCD.
														lowerCamelCase__  ,		lowerCamelCase__					:	Tuple         =       y, x % y
							return abs(UpperCamelCase      )





def  SCREAMING_SNAKE_CASE_       ()      ->       Tuple:
							try:
														lowerCamelCase__					:	Dict         =       input("""Enter two integers separated by comma (,): """      ).split(""","""      )
														lowerCamelCase__					:	Any         =       int(nums[0]      )
														lowerCamelCase__					:	Optional[Any]         =       int(nums[1]      )
														print(
														    f'''greatest_common_divisor({num_a}, {num_a}) = '''
														    f'''{greatest_common_divisor(UpperCamelCase				,			UpperCamelCase      )}'''      )
														print(f'''By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(UpperCamelCase				,			UpperCamelCase      )}'''      )
							except (IndexError, UnboundLocalError, ValueError):
														print("""Wrong input"""      )


if __name__ == "__main__":
			main()

