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






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






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






@torch.no_grad()
def        SCREAMING_SNAKE_CASE_      (		lowerCAmelCase_:			List[Any]			,						lowerCAmelCase_:			Tuple=None			,						lowerCAmelCase_:			List[Any]=None			,						lowerCAmelCase_:			Union[str, Any]="cpu"  ):
      snake_case_  :      str  	=							MusicGen.get_pretrained(lowerCAmelCase_			,						device=lowerCAmelCase_  )
      snake_case_  :      List[Any]  	=							decoder_config_from_checkpoint(lowerCAmelCase_  )

      snake_case_  :      int  	=							fairseq_model.lm.state_dict()
      snake_case_  :      Optional[int]  	=							rename_state_dict(
          lowerCAmelCase_			,						hidden_size=decoder_config.hidden_size  )

      snake_case_  :      List[Any]  	=							TaEncoderModel.from_pretrained("t5-base"  )
      snake_case_  :      List[Any]  	=							EncodecModel.from_pretrained("facebook/encodec_32khz"  )
      snake_case_  :      Any  	=							MusicgenForCausalLM(lowerCAmelCase_  ).eval()

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

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

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

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

      # init the composite model
      snake_case_  :      List[Any]  	=							MusicgenForConditionalGeneration(text_encoder=lowerCAmelCase_			,						audio_encoder=lowerCAmelCase_			,						decoder=lowerCAmelCase_  )

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

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

      with torch.no_grad():
            snake_case_  :      int  	=							model(input_ids=lowerCAmelCase_			,						decoder_input_ids=lowerCAmelCase_  ).logits

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

      # now construct the processor
      snake_case_  :      Dict  	=							AutoTokenizer.from_pretrained("t5-base"  )
      snake_case_  :      List[Any]  	=							AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"			,						padding_side="left"  )

      snake_case_  :      Optional[int]  	=							MusicgenProcessor(feature_extractor=lowerCAmelCase_			,						tokenizer=lowerCAmelCase_  )

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

      # set other default generation config params
      snake_case_  :      str  	=							int(3_0 * audio_encoder.config.frame_rate  )
      snake_case_  :      Union[str, Any]  	=							True
      snake_case_  :      Any  	=							3.0

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

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


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)









import warnings
from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


UpperCAmelCase_		:					Any          =  logging.get_logger(__name__)

UpperCAmelCase_		:					List[str]          =  {
    "nvidia/segformer-b0-finetuned-ade-512-512": (
        "https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json"
    ),
    # See all SegFormer models at https://huggingface.co/models?filter=segformer
}




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       """segformer"""

						def __init__(		self :List[str]     ,			__snake_case :str=3     ,			__snake_case :Optional[Any]=4     ,			__snake_case :List[Any]=[2, 2, 2, 2]     ,			__snake_case :Dict=[8, 4, 2, 1]     ,			__snake_case :Optional[int]=[32, 64, 1_60, 2_56]     ,			__snake_case :Union[str, Any]=[7, 3, 3, 3]     ,			__snake_case :Optional[Any]=[4, 2, 2, 2]     ,			__snake_case :Tuple=[1, 2, 5, 8]     ,			__snake_case :List[Any]=[4, 4, 4, 4]     ,			__snake_case :Optional[Any]="gelu"     ,			__snake_case :Tuple=0.0     ,			__snake_case :Dict=0.0     ,			__snake_case :Optional[int]=0.1     ,			__snake_case :Optional[int]=0.02     ,			__snake_case :Tuple=0.1     ,			__snake_case :Union[str, Any]=1E-6     ,			__snake_case :int=2_56     ,			__snake_case :Optional[int]=2_55     ,			**__snake_case :Dict     ,			):
										'''simple docstring'''

										super().__init__(**__snake_case			)

										if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
														warnings.warn(
														    """Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
														    """ removed, as the behaviour will default to that of reshape_last_stage = True."""     ,			__snake_case     ,			)

										__magic_name__						:						Dict										=num_channels
										__magic_name__						:						str										=num_encoder_blocks
										__magic_name__						:						List[Any]										=depths
										__magic_name__						:						Optional[Any]										=sr_ratios
										__magic_name__						:						List[str]										=hidden_sizes
										__magic_name__						:						List[str]										=patch_sizes
										__magic_name__						:						Any										=strides
										__magic_name__						:						Optional[Any]										=mlp_ratios
										__magic_name__						:						str										=num_attention_heads
										__magic_name__						:						int										=hidden_act
										__magic_name__						:						List[Any]										=hidden_dropout_prob
										__magic_name__						:						Optional[Any]										=attention_probs_dropout_prob
										__magic_name__						:						Optional[Any]										=classifier_dropout_prob
										__magic_name__						:						List[str]										=initializer_range
										__magic_name__						:						List[str]										=drop_path_rate
										__magic_name__						:						List[Any]										=layer_norm_eps
										__magic_name__						:						List[str]										=decoder_hidden_size
										__magic_name__						:						Union[str, Any]										=kwargs.get("""reshape_last_stage"""     ,			__snake_case			)
										__magic_name__						:						Dict										=semantic_loss_ignore_index




class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       version.parse("""1.11"""      )

						@property
						def      A__    (		self :List[str]			):
										'''simple docstring'''

										return OrderedDict(
										    [
										        ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
										    ]			)

						@property
						def      A__    (		self :Any			):
										'''simple docstring'''

										return 1E-4


						@property
						def      A__    (		self :int			):
										'''simple docstring'''

										return 12




