
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       =					logging.get_logger(__name__)


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






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






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






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	if checkpoint == "small":
		# default config values
		lowercase	    =       1024
		lowercase	    =       24
		lowercase	    =       16
	elif checkpoint == "medium":
		lowercase	    =       1536
		lowercase	    =       48
		lowercase	    =       24
	elif checkpoint == "large":
		lowercase	    =       2048
		lowercase	    =       48
		lowercase	    =       32
	else:
		raise ValueError(F'''Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'''				)
	lowercase	    =       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 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE=None					,				__SCREAMING_SNAKE_CASE="cpu"				):
	lowercase	    =       MusicGen.get_pretrained(__SCREAMING_SNAKE_CASE					,				device=__SCREAMING_SNAKE_CASE				)
	lowercase	    =       decoder_config_from_checkpoint(__SCREAMING_SNAKE_CASE				)

	lowercase	    =       fairseq_model.lm.state_dict()
	lowercase    ,   lowercase	    =       rename_state_dict(
	    __SCREAMING_SNAKE_CASE					,				hidden_size=decoder_config.hidden_size				)

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

	# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
	lowercase    ,   lowercase	    =       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
	lowercase	    =       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
	lowercase	    =       torch.arange(0					,				8					,				dtype=torch.long				).reshape(2					,				-1				)
	lowercase	    =       input_ids.reshape(2 * 4					,				-1				)

	with torch.no_grad():
		lowercase	    =       model(input_ids=__SCREAMING_SNAKE_CASE					,				decoder_input_ids=__SCREAMING_SNAKE_CASE				).logits

	if logits.shape != (8, 1, 2048):
		raise ValueError('Incorrect shape for logits'				)

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

	lowercase	    =       MusicgenProcessor(feature_extractor=__SCREAMING_SNAKE_CASE					,				tokenizer=__SCREAMING_SNAKE_CASE				)

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

	# set other default generation config params
	lowercase	    =       int(30 * audio_encoder.config.frame_rate				)
	lowercase	    =       True
	lowercase	    =       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__":
	UpperCAmelCase       =					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       =					parser.parse_args()
	convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)



'''simple docstring'''
import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


lowercase__											=False







class   a_ (      unittest.TestCase					):




			def 					lowerCAmelCase__					(       self					,   UpperCAmelCase=32	):
				set_seed(0	)
				a_      				=						UNetaDModel(sample_size=UpperCAmelCase					,   in_channels=3					,   out_channels=3	)
				a_      				=						torch.optim.SGD(model.parameters()					,   lr=0.00_01	)
				return model, optimizer






			@slow
			def 					lowerCAmelCase__					(       self	):
				a_      				=						"""cpu"""  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
				a_      				=						DDPMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )
				a_      				=						DDIMScheduler(
				    num_train_timesteps=10_00					,   beta_start=0.00_01					,   beta_end=0.02					,   beta_schedule="""linear"""					,   clip_sample=UpperCAmelCase					,   )

				assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

				# shared batches for DDPM and DDIM
				set_seed(0	)
				a_      				=						[torch.randn((4, 3, 32, 32)	).clip(-1					,   1	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randn((4, 3, 32, 32)	).to(UpperCAmelCase	) for _ in range(4	)]
				a_      				=						[torch.randint(0					,   10_00					,   (4,)	).long().to(UpperCAmelCase	) for _ in range(4	)]

				# train with a DDPM scheduler
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddpm_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				# recreate the model and optimizer, and retry with DDIM
				a_				,  a_      				=						self.get_model_optimizer(resolution=32	)
				model.train().to(UpperCAmelCase	)
				for i in range(4	):
					optimizer.zero_grad()
					a_      				=						ddim_scheduler.add_noise(clean_images[i]					,   noise[i]					,   timesteps[i]	)
					a_      				=						model(UpperCAmelCase					,   timesteps[i]	).sample
					a_      				=						torch.nn.functional.mse_loss(UpperCAmelCase					,   noise[i]	)
					loss.backward()
					optimizer.step()
				del model, optimizer

				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
				self.assertTrue(torch.allclose(UpperCAmelCase					,   UpperCAmelCase					,   atol=1e-5	)	)
