

"""simple docstring"""



import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


SCREAMING_SNAKE_CASE_					:	Tuple							  =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_					:	int							  =						{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}



class 					a						(       _lowerCamelCase				):



				"""simple docstring"""



				UpperCAmelCase						 =					"instructblip_vision_model"

				def __init__(     self: int    ,						UpperCamelCase: Any=14_08    ,						UpperCamelCase: Optional[Any]=61_44    ,						UpperCamelCase: Optional[int]=39    ,						UpperCamelCase: Optional[int]=16    ,						UpperCamelCase: Tuple=2_24    ,						UpperCamelCase: List[Any]=14    ,						UpperCamelCase: Optional[int]="gelu"    ,						UpperCamelCase: Optional[int]=1e-6    ,						UpperCamelCase: int=0.0    ,						UpperCamelCase: Optional[Any]=1e-1_0    ,						UpperCamelCase: List[Any]=True    ,						**UpperCamelCase: Optional[Any]    ,						):






									"""simple docstring"""


									super().__init__(**UpperCamelCase       )

									A__										=  hidden_size
									A__										=  intermediate_size
									A__										=  num_hidden_layers
									A__										=  num_attention_heads
									A__										=  patch_size
									A__										=  image_size
									A__										=  initializer_range
									A__										=  attention_dropout
									A__										=  layer_norm_eps
									A__										=  hidden_act
									A__										=  qkv_bias





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






									"""simple docstring"""


									cls._set_token_in_kwargs(UpperCamelCase       )

									A__			,				A__										=  cls.get_config_dict(UpperCamelCase    ,						**UpperCamelCase       )

									# get the vision config dict if we are loading from InstructBlipConfig
									if config_dict.get("""model_type"""       ) == "instructblip":
														A__										=  config_dict["""vision_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(UpperCamelCase    ,						**UpperCamelCase       )



class 					a						(       _lowerCamelCase				):



				"""simple docstring"""



				UpperCAmelCase						 =					"instructblip_qformer"

				def __init__(     self: Optional[Any]    ,						UpperCamelCase: Any=3_05_22    ,						UpperCamelCase: Optional[int]=7_68    ,						UpperCamelCase: int=12    ,						UpperCamelCase: Optional[Any]=12    ,						UpperCamelCase: Optional[Any]=30_72    ,						UpperCamelCase: List[str]="gelu"    ,						UpperCamelCase: List[str]=0.1    ,						UpperCamelCase: List[Any]=0.1    ,						UpperCamelCase: Optional[Any]=5_12    ,						UpperCamelCase: Optional[Any]=0.02    ,						UpperCamelCase: List[Any]=1e-1_2    ,						UpperCamelCase: Dict=0    ,						UpperCamelCase: Tuple="absolute"    ,						UpperCamelCase: str=2    ,						UpperCamelCase: List[str]=14_08    ,						**UpperCamelCase: Tuple    ,						):






									"""simple docstring"""


									super().__init__(pad_token_id=UpperCamelCase    ,						**UpperCamelCase       )

									A__										=  vocab_size
									A__										=  hidden_size
									A__										=  num_hidden_layers
									A__										=  num_attention_heads
									A__										=  hidden_act
									A__										=  intermediate_size
									A__										=  hidden_dropout_prob
									A__										=  attention_probs_dropout_prob
									A__										=  max_position_embeddings
									A__										=  initializer_range
									A__										=  layer_norm_eps
									A__										=  position_embedding_type
									A__										=  cross_attention_frequency
									A__										=  encoder_hidden_size





				@classmethod
				def 				UpperCamelCase	(     cls: List[str]    ,						UpperCamelCase: Union[str, os.PathLike]    ,						**UpperCamelCase: List[Any]       ):






									"""simple docstring"""


									cls._set_token_in_kwargs(UpperCamelCase       )

									A__			,				A__										=  cls.get_config_dict(UpperCamelCase    ,						**UpperCamelCase       )

									# get the qformer config dict if we are loading from InstructBlipConfig
									if config_dict.get("""model_type"""       ) == "instructblip":
														A__										=  config_dict["""qformer_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(UpperCamelCase    ,						**UpperCamelCase       )



class 					a						(       _lowerCamelCase				):



				"""simple docstring"""



				UpperCAmelCase						 =					"instructblip"
				UpperCAmelCase						 =					True

				def __init__(     self: Tuple    ,						UpperCamelCase: int=None    ,						UpperCamelCase: Optional[int]=None    ,						UpperCamelCase: str=None    ,						UpperCamelCase: Any=32    ,						**UpperCamelCase: str       ):






									"""simple docstring"""


									super().__init__(**UpperCamelCase       )

									if vision_config is None:
														A__										=  {}
														logger.info("""vision_config is None. initializing the InstructBlipVisionConfig with default values."""       )

									if qformer_config is None:
														A__										=  {}
														logger.info("""qformer_config is None. Initializing the InstructBlipQFormerConfig with default values."""       )

									if text_config is None:
														A__										=  {}
														logger.info("""text_config is None. Initializing the text config with default values (`OPTConfig`)."""       )

									A__										=  InstructBlipVisionConfig(**UpperCamelCase       )
									A__										=  InstructBlipQFormerConfig(**UpperCamelCase       )
									A__										=  text_config["""model_type"""] if """model_type""" in text_config else """opt"""
									A__										=  CONFIG_MAPPING[text_model_type](**UpperCamelCase       )

									A__										=  self.text_config.tie_word_embeddings
									A__										=  self.text_config.is_encoder_decoder

									A__										=  num_query_tokens
									A__										=  self.vision_config.hidden_size
									A__										=  self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
									A__										=  1.0
									A__										=  0.02

				@classmethod
				def 				UpperCamelCase	(     cls: Union[str, Any]    ,						UpperCamelCase: InstructBlipVisionConfig    ,						UpperCamelCase: InstructBlipQFormerConfig    ,						UpperCamelCase: PretrainedConfig    ,						**UpperCamelCase: int    ,						):






									"""simple docstring"""


									return cls(
									    vision_config=vision_config.to_dict()    ,						qformer_config=qformer_config.to_dict()    ,						text_config=text_config.to_dict()    ,						**UpperCamelCase    ,						)





				def 				UpperCamelCase	(     self: List[str]       ):






									"""simple docstring"""


									A__										=  copy.deepcopy(self.__dict__       )
									A__										=  self.vision_config.to_dict()
									A__										=  self.qformer_config.to_dict()
									A__										=  self.text_config.to_dict()
									A__										=  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()
SCREAMING_SNAKE_CASE_					:	Union[str, Any]							  =						logging.get_logger(__name__)


SCREAMING_SNAKE_CASE_					:	Any							  =						['model.decoder.embed_positions.weights']





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





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





def  _snake_case     (    UpperCAmelCase_  :					str			):
     if checkpoint == "small":
          # default config values
          A__										=  1024
          A__										=  24
          A__										=  16
     elif checkpoint == "medium":
          A__										=  1536
          A__										=  48
          A__										=  24
     elif checkpoint == "large":
          A__										=  2048
          A__										=  48
          A__										=  32
     else:
          raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""			)
     A__										=  MusicgenDecoderConfig(
         hidden_size=UpperCAmelCase_					,   ffn_dim=hidden_size * 4					,   num_hidden_layers=UpperCAmelCase_					,   num_attention_heads=UpperCAmelCase_					,   )
     return config





@torch.no_grad()
def  _snake_case     (    UpperCAmelCase_  :					int					,   UpperCAmelCase_  :					List[Any]=None					,   UpperCAmelCase_  :					Tuple=None					,   UpperCAmelCase_  :					Any="cpu"			):
     A__										=  MusicGen.get_pretrained(UpperCAmelCase_					,   device=UpperCAmelCase_			)
     A__										=  decoder_config_from_checkpoint(UpperCAmelCase_			)

     A__										=  fairseq_model.lm.state_dict()
     A__			,				A__										=  rename_state_dict(
         UpperCAmelCase_					,   hidden_size=decoder_config.hidden_size			)

     A__										=  TaEncoderModel.from_pretrained("""t5-base"""			)
     A__										=  EncodecModel.from_pretrained("""facebook/encodec_32khz"""			)
     A__										=  MusicgenForCausalLM(UpperCAmelCase_			).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     A__			,				A__										=  decoder.load_state_dict(UpperCAmelCase_					,   strict=UpperCAmelCase_			)

     for key in missing_keys.copy():
          if key.startswith(("""text_encoder""", """audio_encoder""")			) or key in EXPECTED_MISSING_KEYS:
               missing_keys.remove(UpperCAmelCase_			)

     if len(UpperCAmelCase_			) > 0:
          raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}"""			)

     if len(UpperCAmelCase_			) > 0:
          raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}"""			)

     # init the composite model
     A__										=  MusicgenForConditionalGeneration(text_encoder=UpperCAmelCase_					,   audio_encoder=UpperCAmelCase_					,   decoder=UpperCAmelCase_			)

     # load the pre-trained enc-dec projection (from the decoder state dict)
     model.enc_to_dec_proj.load_state_dict(UpperCAmelCase_			)

     # check we can do a forward pass
     A__										=  torch.arange(0					,   8					,   dtype=torch.long			).reshape(2					,   -1			)
     A__										=  input_ids.reshape(2 * 4					,   -1			)

     with torch.no_grad():
          A__										=  model(input_ids=UpperCAmelCase_					,   decoder_input_ids=UpperCAmelCase_			).logits

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

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

     A__										=  MusicgenProcessor(feature_extractor=UpperCAmelCase_					,   tokenizer=UpperCAmelCase_			)

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

     # set other default generation config params
     A__										=  int(30 * audio_encoder.config.frame_rate			)
     A__										=  True
     A__										=  3.0

     if pytorch_dump_folder is not None:
          Path(UpperCAmelCase_			).mkdir(exist_ok=UpperCAmelCase_			)
          logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}"""			)
          model.save_pretrained(UpperCAmelCase_			)
          processor.save_pretrained(UpperCAmelCase_			)

     if repo_id:
          logger.info(F"""Pushing model {checkpoint} to {repo_id}"""			)
          model.push_to_hub(UpperCAmelCase_			)
          processor.push_to_hub(UpperCAmelCase_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE_					:	int							  =						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.'
 )

 SCREAMING_SNAKE_CASE_					:	Tuple							  =						parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






