

"""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()
_UpperCamelCase		     =    logging.get_logger(__name__)


_UpperCamelCase		     =    ["""model.decoder.embed_positions.weights"""]



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						Tuple:
 if "emb" in name:
  lowerCAmelCase__   :				Union[str, Any] 			=				name.replace("emb"					,					"model.decoder.embed_tokens"   )
 if "transformer" in name:
  lowerCAmelCase__   :				List[str] 			=				name.replace("transformer"					,					"model.decoder"   )
 if "cross_attention" in name:
  lowerCAmelCase__   :				Tuple 			=				name.replace("cross_attention"					,					"encoder_attn"   )
 if "linear1" in name:
  lowerCAmelCase__   :				Optional[Any] 			=				name.replace("linear1"					,					"fc1"   )
 if "linear2" in name:
  lowerCAmelCase__   :				List[Any] 			=				name.replace("linear2"					,					"fc2"   )
 if "norm1" in name:
  lowerCAmelCase__   :				str 			=				name.replace("norm1"					,					"self_attn_layer_norm"   )
 if "norm_cross" in name:
  lowerCAmelCase__   :				int 			=				name.replace("norm_cross"					,					"encoder_attn_layer_norm"   )
 if "norm2" in name:
  lowerCAmelCase__   :				str 			=				name.replace("norm2"					,					"final_layer_norm"   )
 if "out_norm" in name:
  lowerCAmelCase__   :				Tuple 			=				name.replace("out_norm"					,					"model.decoder.layer_norm"   )
 if "linears" in name:
  lowerCAmelCase__   :				str 			=				name.replace("linears"					,					"lm_heads"   )
 if "condition_provider.conditioners.description.output_proj" in name:
  lowerCAmelCase__   :				Tuple 			=				name.replace("condition_provider.conditioners.description.output_proj"					,					"enc_to_dec_proj"   )
 return name



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



def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						MusicgenDecoderConfig:
 if checkpoint == "small":
  # default config values
  lowerCAmelCase__   :				Any 			=				1_0_2_4
  lowerCAmelCase__   :				int 			=				2_4
  lowerCAmelCase__   :				Optional[Any] 			=				1_6
 elif checkpoint == "medium":
  lowerCAmelCase__   :				List[Any] 			=				1_5_3_6
  lowerCAmelCase__   :				int 			=				4_8
  lowerCAmelCase__   :				List[str] 			=				2_4
 elif checkpoint == "large":
  lowerCAmelCase__   :				List[Any] 			=				2_0_4_8
  lowerCAmelCase__   :				Optional[int] 			=				4_8
  lowerCAmelCase__   :				int 			=				3_2
 else:
  raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."""   )
 lowerCAmelCase__   :				List[str] 			=				MusicgenDecoderConfig(
     hidden_size=lowercase__					,					ffn_dim=hidden_size * 4					,					num_hidden_layers=lowercase__					,					num_attention_heads=lowercase__					,					)
 return config



@torch.no_grad()
def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__=None					,					lowercase__=None					,					lowercase__="cpu"   )			->						List[str]:
 lowerCAmelCase__   :				Union[str, Any] 			=				MusicGen.get_pretrained(lowercase__					,					device=lowercase__   )
 lowerCAmelCase__   :				int 			=				decoder_config_from_checkpoint(lowercase__   )

 lowerCAmelCase__   :				str 			=				fairseq_model.lm.state_dict()
 lowerCAmelCase__			,						lowerCAmelCase__   :				Any 			=				rename_state_dict(
     lowercase__					,					hidden_size=decoder_config.hidden_size   )

 lowerCAmelCase__   :				Dict 			=				TaEncoderModel.from_pretrained("t5-base"   )
 lowerCAmelCase__   :				Optional[Any] 			=				EncodecModel.from_pretrained("facebook/encodec_32khz"   )
 lowerCAmelCase__   :				int 			=				MusicgenForCausalLM(lowercase__   ).eval()

 # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
 lowerCAmelCase__			,						lowerCAmelCase__   :				Union[str, Any] 			=				decoder.load_state_dict(lowercase__					,					strict=lowercase__   )

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

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

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

 # init the composite model
 lowerCAmelCase__   :				Dict 			=				MusicgenForConditionalGeneration(text_encoder=lowercase__					,					audio_encoder=lowercase__					,					decoder=lowercase__   )

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

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

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

 if logits.shape != (8, 1, 2_0_4_8):
  raise ValueError("Incorrect shape for logits"   )

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

 lowerCAmelCase__   :				str 			=				MusicgenProcessor(feature_extractor=lowercase__					,					tokenizer=lowercase__   )

 # set the appropriate bos/pad token ids
 lowerCAmelCase__   :				Optional[int] 			=				2_0_4_8
 lowerCAmelCase__   :				List[Any] 			=				2_0_4_8

 # set other default generation config params
 lowerCAmelCase__   :				Tuple 			=				int(3_0 * audio_encoder.config.frame_rate   )
 lowerCAmelCase__   :				Tuple 			=				True
 lowerCAmelCase__   :				int 			=				3.0

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

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


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"""

def       SCREAMING_SNAKE_CASE							( lowercase__   )			->						float:
 return 1_0 - x * x



def       SCREAMING_SNAKE_CASE							( lowercase__					,					lowercase__   )			->						float:
 # Bolzano theory in order to find if there is a root between a and b
 if equation(lowercase__   ) * equation(lowercase__   ) >= 0:
  raise ValueError("Wrong space!"   )

 lowerCAmelCase__   :				Union[str, Any] 			=				a
 while (b - a) >= 0.01:
  # Find middle point
  lowerCAmelCase__   :				int 			=				(a + b) / 2
  # Check if middle point is root
  if equation(lowercase__   ) == 0.0:
   break
  # Decide the side to repeat the steps
  if equation(lowercase__   ) * equation(lowercase__   ) < 0:
   lowerCAmelCase__   :				str 			=				c
  else:
   lowerCAmelCase__   :				List[str] 			=				c
 return c


if __name__ == "__main__":
    import doctest

    doctest.testmod()

    print(bisection(-2, 5))
    print(bisection(0, 6))






