"""simple docstring"""





import argparse
import os
import re

import packaging.version


lowerCAmelCase_     :   Any     =	'''examples/'''
lowerCAmelCase_     :   Optional[Any]     =	{
    '''examples''': (re.compile(R'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VERSION")\n'''),
    '''init''': (re.compile(R'''^__version__\s+=\s+"([^"]+)"\s*$''', re.MULTILINE), '''__version__ = "VERSION"\n'''),
    '''setup''': (re.compile(R'''^(\s*)version\s*=\s*"[^"]+",''', re.MULTILINE), R'''\1version="VERSION",'''),
    '''doc''': (re.compile(R'''^(\s*)release\s*=\s*"[^"]+"$''', re.MULTILINE), '''release = "VERSION"\n'''),
}
lowerCAmelCase_     :   Optional[int]     =	{
    '''init''': '''src/transformers/__init__.py''',
    '''setup''': '''setup.py''',
}
lowerCAmelCase_     :   List[Any]     =	'''README.md'''



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )->    List[Any]:
							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    f.read()
							_SCREAMING_SNAKE_CASE		:						Any			  =    REPLACE_PATTERNS[pattern]
							_SCREAMING_SNAKE_CASE		:						List[Any]			  =    replace.replace("""VERSION"""     ,	__SCREAMING_SNAKE_CASE  )
							_SCREAMING_SNAKE_CASE		:						Any			  =    re_pattern.sub(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							with open(__SCREAMING_SNAKE_CASE     ,	"""w"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														f.write(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE  )->    int:
							for folder, directories, fnames in os.walk(__SCREAMING_SNAKE_CASE  ):
														# Removing some of the folders with non-actively maintained examples from the walk
														if "research_projects" in directories:
																					directories.remove("""research_projects"""  )
														if "legacy" in directories:
																					directories.remove("""legacy"""  )
														for fname in fnames:
																					if fname.endswith(""".py"""  ):
																												update_version_in_file(os.path.join(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )     ,	__SCREAMING_SNAKE_CASE     ,	pattern="""examples"""  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE=False  )->    Optional[int]:
							for pattern, fname in REPLACE_FILES.items():
														update_version_in_file(__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE     ,	__SCREAMING_SNAKE_CASE  )
							if not patch:
														update_version_in_examples(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Optional[int]:
							_SCREAMING_SNAKE_CASE		:						Tuple			  =    """🤗 Transformers currently provides the following architectures"""
							_SCREAMING_SNAKE_CASE		:						Any			  =    """1. Want to contribute a new model?"""
							with open(__SCREAMING_SNAKE_CASE     ,	"""r"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Any			  =    f.readlines()

							# Find the start of the list.
							_SCREAMING_SNAKE_CASE		:						Dict			  =    0
							while not lines[start_index].startswith(_start_prompt  ):
														start_index += 1
							start_index += 1

							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    start_index
							# Update the lines in the model list.
							while not lines[index].startswith(_end_prompt  ):
														if lines[index].startswith("""1."""  ):
																					_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    lines[index].replace(
																					    """https://huggingface.co/docs/transformers/main/model_doc"""     ,	"""https://huggingface.co/docs/transformers/model_doc"""     ,	)
														index += 1

							with open(__SCREAMING_SNAKE_CASE     ,	"""w"""     ,	encoding="""utf-8"""     ,	newline="""\n"""  ) as f:
														f.writelines(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_()->    Any:
							with open(REPLACE_FILES["""init"""]     ,	"""r"""  ) as f:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    f.read()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    REPLACE_PATTERNS["""init"""][0].search(__SCREAMING_SNAKE_CASE  ).groups()[0]
							return packaging.version.parse(__SCREAMING_SNAKE_CASE  )



def      lowerCamelCase_(__SCREAMING_SNAKE_CASE=False  )->    Union[str, Any]:
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    get_version()
							if patch and default_version.is_devrelease:
														raise ValueError("""Can't create a patch version from the dev branch, checkout a released version!"""  )
							if default_version.is_devrelease:
														_SCREAMING_SNAKE_CASE		:						Any			  =    default_version.base_version
							elif patch:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    F"""{default_version.major}.{default_version.minor}.{default_version.micro + 1}"""
							else:
														_SCREAMING_SNAKE_CASE		:						Dict			  =    F"""{default_version.major}.{default_version.minor + 1}.0"""

							# Now let's ask nicely if that's the right one.
							_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    input(F"""Which version are you releasing? [{default_version}]"""  )
							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														_SCREAMING_SNAKE_CASE		:						Any			  =    default_version

							print(F"""Updating version to {version}."""  )
							global_version_update(__SCREAMING_SNAKE_CASE     ,	patch=__SCREAMING_SNAKE_CASE  )
							if not patch:
														print("""Cleaning main README, don't forget to run `make fix-copies`."""  )
														clean_main_ref_in_model_list()



def      lowerCamelCase_()->    Any:
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    get_version()
							_SCREAMING_SNAKE_CASE		:						Dict			  =    F"""{current_version.major}.{current_version.minor + 1}.0.dev0"""
							_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    current_version.base_version

							# Check with the user we got that right.
							_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    input(F"""Which version are we developing now? [{dev_version}]"""  )
							if len(__SCREAMING_SNAKE_CASE  ) == 0:
														_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    dev_version

							print(F"""Updating version to {version}."""  )
							global_version_update(__SCREAMING_SNAKE_CASE  )
							print("""Cleaning main README, don't forget to run `make fix-copies`."""  )
							clean_main_ref_in_model_list()


if __name__ == "__main__":
			lowerCAmelCase_     :   Optional[int]     =	argparse.ArgumentParser()
			parser.add_argument('''--post_release''', action='''store_true''', help='''Whether this is pre or post release.''')
			parser.add_argument('''--patch''', action='''store_true''', help='''Whether or not this is a patch release.''')
			lowerCAmelCase_     :   Optional[Any]     =	parser.parse_args()
			if not args.post_release:
						pre_release_work(patch=args.patch)
			elif args.patch:
						print('''Nothing to do after a patch :-)''')
			else:
						post_release_work()



"""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)


