




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
from ..auto import CONFIG_MAPPING


a_   		=     logging.get_logger(__name__)

a_   		=     {
    """microsoft/table-transformer-detection""": (
        """https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
    ),
}

class  _lowerCamelCase						(			_snake_case   ):

      """simple docstring"""

      lowerCAmelCase__			: str    =					"""table-transformer"""
      lowerCAmelCase__			: Optional[Any]    =					["""past_key_values"""]
      lowerCAmelCase__			: Dict    =					{
          """hidden_size""": """d_model""",
          """num_attention_heads""": """encoder_attention_heads""",
      }







      def __init__(     self					: Optional[int] ,       snake_case					: Optional[int]=True ,       snake_case					: Any=None ,       snake_case					: str=3 ,       snake_case					: Optional[int]=100 ,       snake_case					: Optional[int]=6 ,       snake_case					: List[str]=2048 ,       snake_case					: Tuple=8 ,       snake_case					: Optional[int]=6 ,       snake_case					: Union[str, Any]=2048 ,       snake_case					: Tuple=8 ,       snake_case					: List[Any]=0.0 ,       snake_case					: List[str]=0.0 ,       snake_case					: Union[str, Any]=True ,       snake_case					: Optional[Any]="relu" ,       snake_case					: Optional[int]=256 ,       snake_case					: Optional[int]=0.1 ,       snake_case					: List[str]=0.0 ,       snake_case					: Dict=0.0 ,       snake_case					: str=0.02 ,       snake_case					: Union[str, Any]=1.0 ,       snake_case					: Optional[Any]=False ,       snake_case					: Tuple="sine" ,       snake_case					: Optional[int]="resnet50" ,       snake_case					: int=True ,       snake_case					: List[str]=False ,       snake_case					: Any=1 ,       snake_case					: Union[str, Any]=5 ,       snake_case					: Optional[int]=2 ,       snake_case					: Union[str, Any]=1 ,       snake_case					: List[str]=1 ,       snake_case					: Dict=5 ,       snake_case					: Tuple=2 ,       snake_case					: Tuple=0.1 ,       **snake_case					: List[str] ,       ):
          if backbone_config is not None and use_timm_backbone:
              raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.'''				)

          if not use_timm_backbone:
              if backbone_config is None:
                  logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''				)
                  __UpperCamelCase  		=						CONFIG_MAPPING["resnet"](out_features=['''stage4''']				)
              elif isinstance(snake_case_ ,       snake_case_				):
                  __UpperCamelCase  		=						backbone_config.get('''model_type'''				)
                  __UpperCamelCase  		=						CONFIG_MAPPING[backbone_model_type]
                  __UpperCamelCase  		=						config_class.from_dict(snake_case_				)
              # set timm attributes to None
              __UpperCamelCase  		=						None, None, None

          __UpperCamelCase  		=						use_timm_backbone
          __UpperCamelCase  		=						backbone_config
          __UpperCamelCase  		=						num_channels
          __UpperCamelCase  		=						num_queries
          __UpperCamelCase  		=						d_model
          __UpperCamelCase  		=						encoder_ffn_dim
          __UpperCamelCase  		=						encoder_layers
          __UpperCamelCase  		=						encoder_attention_heads
          __UpperCamelCase  		=						decoder_ffn_dim
          __UpperCamelCase  		=						decoder_layers
          __UpperCamelCase  		=						decoder_attention_heads
          __UpperCamelCase  		=						dropout
          __UpperCamelCase  		=						attention_dropout
          __UpperCamelCase  		=						activation_dropout
          __UpperCamelCase  		=						activation_function
          __UpperCamelCase  		=						init_std
          __UpperCamelCase  		=						init_xavier_std
          __UpperCamelCase  		=						encoder_layerdrop
          __UpperCamelCase  		=						decoder_layerdrop
          __UpperCamelCase  		=						encoder_layers
          __UpperCamelCase  		=						auxiliary_loss
          __UpperCamelCase  		=						position_embedding_type
          __UpperCamelCase  		=						backbone
          __UpperCamelCase  		=						use_pretrained_backbone
          __UpperCamelCase  		=						dilation
          # Hungarian matcher
          __UpperCamelCase  		=						class_cost
          __UpperCamelCase  		=						bbox_cost
          __UpperCamelCase  		=						giou_cost
          # Loss coefficients
          __UpperCamelCase  		=						mask_loss_coefficient
          __UpperCamelCase  		=						dice_loss_coefficient
          __UpperCamelCase  		=						bbox_loss_coefficient
          __UpperCamelCase  		=						giou_loss_coefficient
          __UpperCamelCase  		=						eos_coefficient
          super().__init__(is_encoder_decoder=snake_case_ ,       **snake_case_				)







      @property
      def       snake_case	(     self					: Union[str, Any]				):
          return self.encoder_attention_heads







      @property
      def       snake_case	(     self					: Any				):
          return self.d_model



class  _lowerCamelCase						(			_snake_case   ):

      """simple docstring"""

      lowerCAmelCase__			: List[str]    =					version.parse("1.11"   )







      @property
      def       snake_case	(     self					: List[Any]				):
          return OrderedDict(
              [
                  ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
                  ('''pixel_mask''', {0: '''batch'''}),
              ]				)







      @property
      def       snake_case	(     self					: Union[str, Any]				):
          return 1E-5







      @property
      def       snake_case	(     self					: Dict				):
          return 12












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()
a_   		=     logging.get_logger(__name__)


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Optional[Any]:

    """simple docstring"""


    if "emb" in name:
        __UpperCamelCase  		=						name.replace('''emb'''					,     '''model.decoder.embed_tokens'''      )
    if "transformer" in name:
        __UpperCamelCase  		=						name.replace('''transformer'''					,     '''model.decoder'''      )
    if "cross_attention" in name:
        __UpperCamelCase  		=						name.replace('''cross_attention'''					,     '''encoder_attn'''      )
    if "linear1" in name:
        __UpperCamelCase  		=						name.replace('''linear1'''					,     '''fc1'''      )
    if "linear2" in name:
        __UpperCamelCase  		=						name.replace('''linear2'''					,     '''fc2'''      )
    if "norm1" in name:
        __UpperCamelCase  		=						name.replace('''norm1'''					,     '''self_attn_layer_norm'''      )
    if "norm_cross" in name:
        __UpperCamelCase  		=						name.replace('''norm_cross'''					,     '''encoder_attn_layer_norm'''      )
    if "norm2" in name:
        __UpperCamelCase  		=						name.replace('''norm2'''					,     '''final_layer_norm'''      )
    if "out_norm" in name:
        __UpperCamelCase  		=						name.replace('''out_norm'''					,     '''model.decoder.layer_norm'''      )
    if "linears" in name:
        __UpperCamelCase  		=						name.replace('''linears'''					,     '''lm_heads'''      )
    if "condition_provider.conditioners.description.output_proj" in name:
        __UpperCamelCase  		=						name.replace('''condition_provider.conditioners.description.output_proj'''					,     '''enc_to_dec_proj'''      )
    return name




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Tuple[Dict, Dict]:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	MusicgenDecoderConfig:

    """simple docstring"""


    if checkpoint == "small":
        # default config values
        __UpperCamelCase  		=						10_24
        __UpperCamelCase  		=						24
        __UpperCamelCase  		=						16
    elif checkpoint == "medium":
        __UpperCamelCase  		=						15_36
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						24
    elif checkpoint == "large":
        __UpperCamelCase  		=						20_48
        __UpperCamelCase  		=						48
        __UpperCamelCase  		=						32
    else:
        raise ValueError(F"Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}."      )
    __UpperCamelCase  		=						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[Any]:

    """simple docstring"""


    __UpperCamelCase  		=						MusicGen.get_pretrained(lowercase_					,     device=lowercase_      )
    __UpperCamelCase  		=						decoder_config_from_checkpoint(lowercase_      )

    __UpperCamelCase  		=						fairseq_model.lm.state_dict()
    __UpperCamelCase		,      __UpperCamelCase  		=						rename_state_dict(
        lowercase_					,     hidden_size=decoder_config.hidden_size      )

    __UpperCamelCase  		=						TaEncoderModel.from_pretrained('''t5-base'''      )
    __UpperCamelCase  		=						EncodecModel.from_pretrained('''facebook/encodec_32khz'''      )
    __UpperCamelCase  		=						MusicgenForCausalLM(lowercase_      ).eval()

    # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
    __UpperCamelCase		,      __UpperCamelCase  		=						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
    __UpperCamelCase  		=						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
    __UpperCamelCase  		=						torch.arange(0					,     8					,     dtype=torch.long      ).reshape(2					,     -1      )
    __UpperCamelCase  		=						input_ids.reshape(2 * 4					,     -1      )

    with torch.no_grad():
        __UpperCamelCase  		=						model(input_ids=lowercase_					,     decoder_input_ids=lowercase_      ).logits

    if logits.shape != (8, 1, 20_48):
        raise ValueError('''Incorrect shape for logits'''      )

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

    __UpperCamelCase  		=						MusicgenProcessor(feature_extractor=lowercase_					,     tokenizer=lowercase_      )

    # set the appropriate bos/pad token ids
    __UpperCamelCase  		=						20_48
    __UpperCamelCase  		=						20_48

    # set other default generation config params
    __UpperCamelCase  		=						int(30 * audio_encoder.config.frame_rate      )
    __UpperCamelCase  		=						True
    __UpperCamelCase  		=						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__":
 a_   		=     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."
 )

 a_   		=     parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)






