"""simple docstring"""





from manim import *




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				def 		_lowerCAmelCase		(      self							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Rectangle(height=0.5      ,    width=0.5)
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    Rectangle(height=0.46      ,    width=0.46).set_stroke(width=0)

											_SCREAMING_SNAKE_CASE		:						Tuple			  =    [mem.copy() for i in range(6)]
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    [mem.copy() for i in range(6)]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    VGroup(*_A).arrange(_A      ,    buff=0)
											_SCREAMING_SNAKE_CASE		:						str			  =    VGroup(*_A).arrange(_A      ,    buff=0)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    VGroup(_A      ,    _A).arrange(_A      ,    buff=0)
											_SCREAMING_SNAKE_CASE		:						Any			  =    Text("""CPU"""      ,    font_size=2_4)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    Group(_A      ,    _A).arrange(_A      ,    buff=0.5      ,    aligned_edge=_A)
											cpu.move_to([-2.5, -0.5, 0])
											self.add(_A)

											_SCREAMING_SNAKE_CASE		:						Dict			  =    [mem.copy() for i in range(4)]
											_SCREAMING_SNAKE_CASE		:						int			  =    VGroup(*_A).arrange(_A      ,    buff=0)
											_SCREAMING_SNAKE_CASE		:						str			  =    Text("""GPU"""      ,    font_size=2_4)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    Group(_A      ,    _A).arrange(_A      ,    buff=0.5      ,    aligned_edge=_A)
											gpu.move_to([-1, -1, 0])
											self.add(_A)

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    [mem.copy() for i in range(6)]
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    VGroup(*_A).arrange(_A      ,    buff=0)

											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Text("""Model"""      ,    font_size=2_4)
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Group(_A      ,    _A).arrange(_A      ,    buff=0.5      ,    aligned_edge=_A)
											model.move_to([3, -1.0, 0])
											self.add(_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    []
											for i, rect in enumerate(_A):
																		rect.set_stroke(_A)
																		# target = fill.copy().set_fill(YELLOW, opacity=0.7)
																		# target.move_to(rect)
																		# self.add(target)

																		_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    Rectangle(height=0.46 / 4      ,    width=0.46 / 3).set_stroke(width=0.0).set_fill(_A      ,    opacity=0.7)

																		if i == 0:
																									cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)      ,    buff=0.02      ,    direction=_A)
																									cpu_target.set_x(cpu_target.get_x() + 0.1)
																		elif i == 3:
																									cpu_target.next_to(cpu_targs[0]      ,    direction=_A      ,    buff=0.0)
																		else:
																									cpu_target.next_to(cpu_targs[i - 1]      ,    direction=_A      ,    buff=0.0)
																		self.add(_A)
																		cpu_targs.append(_A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    [mem.copy() for i in range(6)]
											_SCREAMING_SNAKE_CASE		:						int			  =    VGroup(*_A).arrange(_A      ,    buff=0)

											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    Text("""Loaded Checkpoint"""      ,    font_size=2_4)
											_SCREAMING_SNAKE_CASE		:						Dict			  =    Group(_A      ,    _A).arrange(_A      ,    aligned_edge=_A      ,    buff=0.4)
											checkpoint.move_to([3, 0.5, 0])

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    Square(side_length=2.2)
											key.move_to([-5, 2, 0])

											_SCREAMING_SNAKE_CASE		:						str			  =    MarkupText(
											    f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""      ,    font_size=1_8      ,    )

											key_text.move_to([-5, 2.4, 0])

											self.add(_A      ,    _A)

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    MarkupText(
											    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""      ,    font_size=1_8      ,    )

											blue_text.next_to(_A      ,    DOWN * 2.4      ,    aligned_edge=key_text.get_left())

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    MarkupText(
											    f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>."""      ,    font_size=2_4      ,    )
											step_a.move_to([2, 2, 0])
											self.play(Write(_A)      ,    Write(_A))

											self.play(Write(_A      ,    run_time=1)      ,    Create(_A      ,    run_time=1))

											_SCREAMING_SNAKE_CASE		:						List[str]			  =    []
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    []
											for i, rect in enumerate(_A):
																		_SCREAMING_SNAKE_CASE		:						str			  =    fill.copy().set_fill(_A      ,    opacity=0.7)
																		target.move_to(_A)
																		first_animations.append(GrowFromCenter(_A      ,    run_time=1))

																		_SCREAMING_SNAKE_CASE		:						List[Any]			  =    target.copy()
																		cpu_target.generate_target()
																		if i < 5:
																									cpu_target.target.move_to(cpu_left_col_base[i + 1])
																		else:
																									cpu_target.target.move_to(cpu_right_col_base[i - 5])
																		second_animations.append(MoveToTarget(_A      ,    run_time=1.5))

											self.play(*_A)
											self.play(*_A)
											self.wait()



"""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_     =	logging.get_logger(__name__)


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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    Optional[int]:
							if "emb" in name:
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    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		:						Union[str, Any]			  =    name.replace("""cross_attention"""     ,	"""encoder_attn"""  )
							if "linear1" in name:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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		:						int			  =    name.replace("""norm1"""     ,	"""self_attn_layer_norm"""  )
							if "norm_cross" in name:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    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		:						Tuple			  =    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		:						str			  =    name.replace("""condition_provider.conditioners.description.output_proj"""     ,	"""enc_to_dec_proj"""  )
							return name



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



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    MusicgenDecoderConfig:
							if checkpoint == "small":
														# default config values
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    1_024
														_SCREAMING_SNAKE_CASE		:						str			  =    24
														_SCREAMING_SNAKE_CASE		:						Any			  =    16
							elif checkpoint == "medium":
														_SCREAMING_SNAKE_CASE		:						Dict			  =    1_536
														_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    48
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    24
							elif checkpoint == "large":
														_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_048
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    48
														_SCREAMING_SNAKE_CASE		:						str			  =    32
							else:
														raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""  )
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    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      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE=None     ,	__SCREAMING_SNAKE_CASE="cpu"  )->    str:
							_SCREAMING_SNAKE_CASE		:						str			  =    MusicGen.get_pretrained(__SCREAMING_SNAKE_CASE     ,	device=__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    decoder_config_from_checkpoint(__SCREAMING_SNAKE_CASE  )

							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    fairseq_model.lm.state_dict()
							_SCREAMING_SNAKE_CASE  ,				_SCREAMING_SNAKE_CASE		:						List[str]			  =    rename_state_dict(
							    __SCREAMING_SNAKE_CASE     ,	hidden_size=decoder_config.hidden_size  )

							_SCREAMING_SNAKE_CASE		:						Tuple			  =    TaEncoderModel.from_pretrained("""t5-base"""  )
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    EncodecModel.from_pretrained("""facebook/encodec_32khz"""  )
							_SCREAMING_SNAKE_CASE		:						str			  =    MusicgenForCausalLM(__SCREAMING_SNAKE_CASE  ).eval()

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

							with torch.no_grad():
														_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    model(input_ids=__SCREAMING_SNAKE_CASE     ,	decoder_input_ids=__SCREAMING_SNAKE_CASE  ).logits

							if logits.shape != (8, 1, 2_048):
														raise ValueError("""Incorrect shape for logits"""  )

							# now construct the processor
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    AutoTokenizer.from_pretrained("""t5-base"""  )
							_SCREAMING_SNAKE_CASE		:						List[str]			  =    AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz"""     ,	padding_side="""left"""  )

							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    MusicgenProcessor(feature_extractor=__SCREAMING_SNAKE_CASE     ,	tokenizer=__SCREAMING_SNAKE_CASE  )

							# set the appropriate bos/pad token ids
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    2_048
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    2_048

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


