"""simple docstring"""





import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase_     :   Tuple     =	logging.get_logger(__name__)

lowerCAmelCase_     :   Dict     =	{
    '''BridgeTower/bridgetower-base''': '''https://huggingface.co/BridgeTower/bridgetower-base/blob/main/config.json''',
    '''BridgeTower/bridgetower-base-itm-mlm''': (
        '''https://huggingface.co/BridgeTower/bridgetower-base-itm-mlm/blob/main/config.json'''
    ),
}




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"bridgetower_vision_model"





				def __init__(      self							:							Union[str, Any]      ,    _A							:							Union[str, Any]=7_6_8      ,    _A							:							Tuple=1_2      ,    _A							:							List[Any]=3      ,    _A							:							Dict=1_6      ,    _A							:							str=2_8_8      ,    _A							:							Optional[Any]=1      ,    _A							:							Union[str, Any]=1e-05      ,    _A							:							Any=False      ,    _A							:							int=True      ,    _A							:							Optional[int]=False      ,    **_A							:							int      ,    ):



											"""simple docstring"""







											super().__init__(**_A)
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						int			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    num_channels
											_SCREAMING_SNAKE_CASE		:						str			  =    patch_size
											_SCREAMING_SNAKE_CASE		:						int			  =    image_size
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    initializer_factor
											_SCREAMING_SNAKE_CASE		:						str			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    stop_gradient
											_SCREAMING_SNAKE_CASE		:						Any			  =    share_layernorm
											_SCREAMING_SNAKE_CASE		:						int			  =    remove_last_layer





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							Optional[int]      ,    _A							:							Union[str, os.PathLike]      ,    **_A							:							Union[str, Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Any			  =    cls.get_config_dict(_A      ,    **_A)

											if config_dict.get("""model_type""") == "bridgetower":
																		_SCREAMING_SNAKE_CASE		:						List[str]			  =    config_dict["""text_config"""]

											if "model_type" in config_dict and hasattr(cls      ,    """model_type""") and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																		    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

											return cls.from_dict(_A      ,    **_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"bridgetower_text_model"





				def __init__(      self							:							Any      ,    _A							:							int=5_0_2_6_5      ,    _A							:							Union[str, Any]=7_6_8      ,    _A							:							int=1_2      ,    _A							:							Tuple=1_2      ,    _A							:							Any=1      ,    _A							:							List[Any]=3_0_7_2      ,    _A							:							str="gelu"      ,    _A							:							List[Any]=0.1      ,    _A							:							Union[str, Any]=0.1      ,    _A							:							List[str]=5_1_4      ,    _A							:							Union[str, Any]=1      ,    _A							:							str=1e-05      ,    _A							:							Dict=1      ,    _A							:							Union[str, Any]=0      ,    _A							:							Any=2      ,    _A							:							Dict="absolute"      ,    _A							:							Dict=True      ,    **_A							:							Any      ,    ):



											"""simple docstring"""







											super().__init__(**_A)

											_SCREAMING_SNAKE_CASE		:						int			  =    vocab_size
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    initializer_factor
											_SCREAMING_SNAKE_CASE		:						str			  =    intermediate_size
											_SCREAMING_SNAKE_CASE		:						Any			  =    hidden_dropout_prob
											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    attention_probs_dropout_prob
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    max_position_embeddings
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    type_vocab_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						Any			  =    position_embedding_type
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    use_cache
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    pad_token_id
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    bos_token_id
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    eos_token_id





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							int      ,    _A							:							Union[str, os.PathLike]      ,    **_A							:							Tuple):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						Tuple			  =    cls.get_config_dict(_A      ,    **_A)

											if config_dict.get("""model_type""") == "bridgetower":
																		_SCREAMING_SNAKE_CASE		:						Any			  =    config_dict["""text_config"""]

											if "model_type" in config_dict and hasattr(cls      ,    """model_type""") and config_dict["model_type"] != cls.model_type:
																		logger.warning(
																		    f"""You are using a model of type {config_dict["model_type"]} to instantiate a model of type """
																		    f"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""")

											return cls.from_dict(_A      ,    **_A)




class  _snake_case		(    __snake_case ):







				"""simple docstring"""






				a			=						"bridgetower"





				def __init__(      self							:							List[str]      ,    _A							:							str=True      ,    _A							:							Tuple="gelu"      ,    _A							:							Optional[Any]=7_6_8      ,    _A							:							Dict=1      ,    _A							:							Tuple=1e-05      ,    _A							:							Dict=False      ,    _A							:							Tuple="add"      ,    _A							:							Tuple=1_2      ,    _A							:							Any=6      ,    _A							:							Union[str, Any]=False      ,    _A							:							Dict=False      ,    _A							:							str=None      ,    _A							:							Optional[Any]=None      ,    **_A							:							Optional[int]      ,    ):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						str			  =    kwargs.pop("""text_config_dict"""      ,    _A)
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    kwargs.pop("""vision_config_dict"""      ,    _A)

											super().__init__(**_A)
											_SCREAMING_SNAKE_CASE		:						str			  =    share_cross_modal_transformer_layers
											_SCREAMING_SNAKE_CASE		:						int			  =    hidden_act
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    hidden_size
											_SCREAMING_SNAKE_CASE		:						Optional[int]			  =    initializer_factor
											_SCREAMING_SNAKE_CASE		:						str			  =    layer_norm_eps
											_SCREAMING_SNAKE_CASE		:						Dict			  =    share_link_tower_layers
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    link_tower_type
											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    num_attention_heads
											_SCREAMING_SNAKE_CASE		:						Dict			  =    num_hidden_layers
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    tie_word_embeddings
											_SCREAMING_SNAKE_CASE		:						Any			  =    init_layernorm_from_vision_encoder

											if text_config is None:
																		_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    {}
																		logger.info("""`text_config` is `None`. Initializing the `BridgeTowerTextConfig` with default values.""")

											if vision_config is None:
																		_SCREAMING_SNAKE_CASE		:						Tuple			  =    {}
																		logger.info("""`vision_config` is `None`. Initializing the `BridgeTowerVisionConfig` with default values.""")

											_SCREAMING_SNAKE_CASE		:						Optional[Any]			  =    BridgeTowerTextConfig(**_A)
											_SCREAMING_SNAKE_CASE		:						Union[str, Any]			  =    BridgeTowerVisionConfig(**_A)





				@classmethod
				def 		_lowerCAmelCase		(      cls							:							str      ,    _A							:							BridgeTowerTextConfig      ,    _A							:							BridgeTowerVisionConfig      ,    **_A							:							Union[str, Any]):



											"""simple docstring"""







											return cls(text_config=text_config.to_dict()      ,    vision_config=vision_config.to_dict()      ,    **_A)





				def 		_lowerCAmelCase		(      self							:							List[Any]):



											"""simple docstring"""







											_SCREAMING_SNAKE_CASE		:						List[Any]			  =    copy.deepcopy(self.__dict__)
											_SCREAMING_SNAKE_CASE		:						Tuple			  =    self.text_config.to_dict()
											_SCREAMING_SNAKE_CASE		:						Dict			  =    self.vision_config.to_dict()
											_SCREAMING_SNAKE_CASE		:						List[str]			  =    self.__class__.model_type
											return output



"""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)


