



"""simple docstring"""




import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		Any							)    ->  int:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       XCLIPTextConfig()

     # derive patch size from model name
     _UpperCAmelCase     :      int														=       model_name.find("patch"							)
     _UpperCAmelCase     :      str														=       int(model_name[start_idx + len("patch"							) : start_idx + len("patch"							) + 2]							)
     _UpperCAmelCase     :      Union[str, Any]														=       XCLIPVisionConfig(patch_size=SCREAMING_SNAKE_CASE__		,    num_frames=SCREAMING_SNAKE_CASE__							)

     if "large" in model_name:
          _UpperCAmelCase     :      List[Any]														=       768
          _UpperCAmelCase     :      Tuple														=       3_072
          _UpperCAmelCase     :      int														=       12

          _UpperCAmelCase     :      Tuple														=       1_024
          _UpperCAmelCase     :      Any														=       4_096
          _UpperCAmelCase     :      int														=       16
          _UpperCAmelCase     :      Optional[int]														=       24
          _UpperCAmelCase     :      Dict														=       768
          _UpperCAmelCase     :      Optional[Any]														=       3_072

     if model_name == "xclip-large-patch14-16-frames":
          _UpperCAmelCase     :      str														=       336

     _UpperCAmelCase     :      Optional[Any]														=       XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     if "large" in model_name:
          _UpperCAmelCase     :      str														=       768

     return config

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  str:





     '''simple docstring'''

     if name == "token_embedding.weight":
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("token_embedding.weight"		,    "text_model.embeddings.token_embedding.weight"							)
     if name == "positional_embedding":
          _UpperCAmelCase     :      Optional[int]														=       name.replace("positional_embedding"		,    "text_model.embeddings.position_embedding.weight"							)
     if "ln_1" in name:
          _UpperCAmelCase     :      Optional[int]														=       name.replace("ln_1"		,    "layer_norm1"							)
     if "ln_2" in name:
          _UpperCAmelCase     :      Any														=       name.replace("ln_2"		,    "layer_norm2"							)
     if "c_fc" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("c_fc"		,    "fc1"							)
     if "c_proj" in name:
          _UpperCAmelCase     :      Dict														=       name.replace("c_proj"		,    "fc2"							)
     if name.startswith("transformer.resblocks"							):
          _UpperCAmelCase     :      Dict														=       name.replace("transformer.resblocks"		,    "text_model.encoder.layers"							)
     if "attn.out_proj" in name and "message" not in name:
          _UpperCAmelCase     :      Any														=       name.replace("attn.out_proj"		,    "self_attn.out_proj"							)
     if "ln_final" in name:
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("ln_final"		,    "text_model.final_layer_norm"							)
     # visual encoder
     if name == "visual.class_embedding":
          _UpperCAmelCase     :      Union[str, Any]														=       name.replace("visual.class_embedding"		,    "vision_model.embeddings.class_embedding"							)
     if name == "visual.positional_embedding":
          _UpperCAmelCase     :      Optional[Any]														=       name.replace("visual.positional_embedding"		,    "vision_model.embeddings.position_embedding.weight"							)
     if name.startswith("visual.transformer.resblocks"							):
          _UpperCAmelCase     :      Union[str, Any]														=       name.replace("visual.transformer.resblocks"		,    "vision_model.encoder.layers"							)
     if "visual.conv1" in name:
          _UpperCAmelCase     :      int														=       name.replace("visual.conv1"		,    "vision_model.embeddings.patch_embedding"							)
     if "visual.ln_pre" in name:
          _UpperCAmelCase     :      Tuple														=       name.replace("visual.ln_pre"		,    "vision_model.pre_layernorm"							)
     if "visual.ln_post" in name:
          _UpperCAmelCase     :      int														=       name.replace("visual.ln_post"		,    "vision_model.post_layernorm"							)
     if "visual.proj" in name:
          _UpperCAmelCase     :      List[Any]														=       name.replace("visual.proj"		,    "visual_projection.weight"							)
     if "text_projection" in name:
          _UpperCAmelCase     :      Tuple														=       name.replace("text_projection"		,    "text_projection.weight"							)
     # things on top
     if "prompts_visual_proj" in name:
          _UpperCAmelCase     :      List[str]														=       name.replace("prompts_visual_proj"		,    "prompts_visual_projection"							)
     if "prompts_visual_ln" in name:
          _UpperCAmelCase     :      Any														=       name.replace("prompts_visual_ln"		,    "prompts_visual_layernorm"							)
     # mit
     if name == "mit.positional_embedding":
          _UpperCAmelCase     :      int														=       name.replace("positional"		,    "position"							)
     if name.startswith("mit.resblocks"							):
          _UpperCAmelCase     :      Dict														=       name.replace("mit.resblocks"		,    "mit.encoder.layers"							)
     # prompts generator
     if name.startswith("prompts_generator.norm"							):
          _UpperCAmelCase     :      List[Any]														=       name.replace("prompts_generator.norm"		,    "prompts_generator.layernorm"							)

     return name

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str		,    SCREAMING_SNAKE_CASE__      :		List[Any]							)    ->  int:





     '''simple docstring'''

     for key in orig_state_dict.copy().keys():
          _UpperCAmelCase     :      List[str]														=       orig_state_dict.pop(SCREAMING_SNAKE_CASE__							)

          if "attn.in_proj" in key:
               _UpperCAmelCase     :      Tuple														=       key.split("."							)
               if key.startswith("visual"							):
                    _UpperCAmelCase     :      List[str]														=       key_split[3]
                    _UpperCAmelCase     :      str														=       config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              _UpperCAmelCase     :      int														=       val[
                                  :dim, :
                              ]
                              _UpperCAmelCase     :      Optional[Any]														=       val[
                                  dim : dim * 2, :
                              ]
                              _UpperCAmelCase     :      str														=       val[
                                  -dim:, :
                              ]
                         else:
                              _UpperCAmelCase     :      Optional[int]														=       val[
                                  :dim
                              ]
                              _UpperCAmelCase     :      Any														=       val[
                                  dim : dim * 2
                              ]
                              _UpperCAmelCase     :      Tuple														=       val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              _UpperCAmelCase     :      List[Any]														=       val[
                                  :dim, :
                              ]
                              _UpperCAmelCase     :      List[str]														=       val[
                                  dim : dim * 2, :
                              ]
                              _UpperCAmelCase     :      Optional[int]														=       val[
                                  -dim:, :
                              ]
                         else:
                              _UpperCAmelCase     :      Tuple														=       val[:dim]
                              _UpperCAmelCase     :      int														=       val[
                                  dim : dim * 2
                              ]
                              _UpperCAmelCase     :      int														=       val[-dim:]
               elif key.startswith("mit"							):
                    _UpperCAmelCase     :      int														=       key_split[2]
                    _UpperCAmelCase     :      Optional[int]														=       config.vision_config.mit_hidden_size
                    if "weight" in key:
                         _UpperCAmelCase     :      Any														=       val[:dim, :]
                         _UpperCAmelCase     :      List[Any]														=       val[dim : dim * 2, :]
                         _UpperCAmelCase     :      Dict														=       val[-dim:, :]
                    else:
                         _UpperCAmelCase     :      int														=       val[:dim]
                         _UpperCAmelCase     :      List[Any]														=       val[dim : dim * 2]
                         _UpperCAmelCase     :      str														=       val[-dim:]
               else:
                    _UpperCAmelCase     :      Dict														=       key_split[2]
                    _UpperCAmelCase     :      Optional[Any]														=       config.text_config.hidden_size
                    if "weight" in key:
                         _UpperCAmelCase     :      Tuple														=       val[:dim, :]
                         _UpperCAmelCase     :      str														=       val[
                             dim : dim * 2, :
                         ]
                         _UpperCAmelCase     :      Any														=       val[-dim:, :]
                    else:
                         _UpperCAmelCase     :      List[str]														=       val[:dim]
                         _UpperCAmelCase     :      List[Any]														=       val[
                             dim : dim * 2
                         ]
                         _UpperCAmelCase     :      List[str]														=       val[-dim:]
          else:
               _UpperCAmelCase     :      Tuple														=       rename_key(SCREAMING_SNAKE_CASE__							)
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    _UpperCAmelCase     :      Tuple														=       val.T
               _UpperCAmelCase     :      str														=       val

     return orig_state_dict

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple:





     '''simple docstring'''

     if num_frames == 8:
          _UpperCAmelCase     :      Any														=       "eating_spaghetti_8_frames.npy"
     elif num_frames == 16:
          _UpperCAmelCase     :      Any														=       "eating_spaghetti.npy"
     elif num_frames == 32:
          _UpperCAmelCase     :      str														=       "eating_spaghetti_32_frames.npy"
     _UpperCAmelCase     :      List[str]														=       hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"		,    filename=SCREAMING_SNAKE_CASE__		,    repo_type="dataset"		,    )
     _UpperCAmelCase     :      int														=       np.load(SCREAMING_SNAKE_CASE__							)
     return list(SCREAMING_SNAKE_CASE__							)

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[str]		,    SCREAMING_SNAKE_CASE__      :		Union[str, Any]=None		,    SCREAMING_SNAKE_CASE__      :		int=False							)    ->  Union[str, Any]:





     '''simple docstring'''

     _UpperCAmelCase     :      str														=       {
         # fully supervised kinetics-400 checkpoints
         "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
         "xclip-base-patch32-16-frames": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
         ),
         "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
         "xclip-base-patch16-16-frames": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
         ),
         "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
         "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
         # fully supervised kinetics-600 checkpoints
         "xclip-base-patch16-kinetics-600": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
         ),
         "xclip-base-patch16-kinetics-600-16-frames": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
         ),
         "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
         # few shot
         "xclip-base-patch16-hmdb-2-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
         ),
         "xclip-base-patch16-hmdb-4-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
         ),
         "xclip-base-patch16-hmdb-8-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
         ),
         "xclip-base-patch16-hmdb-16-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
         ),
         "xclip-base-patch16-ucf-2-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
         ),
         "xclip-base-patch16-ucf-4-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
         ),
         "xclip-base-patch16-ucf-8-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
         ),
         "xclip-base-patch16-ucf-16-shot": (
             "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
         ),
         # zero shot
         "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
     }

     _UpperCAmelCase     :      Union[str, Any]														=       model_to_url[model_name]
     _UpperCAmelCase     :      List[Any]														=       8
     if "16-frames" in model_name:
          _UpperCAmelCase     :      Optional[Any]														=       16
     elif "shot" in model_name:
          _UpperCAmelCase     :      List[Any]														=       32

     _UpperCAmelCase     :      Tuple														=       get_xclip_config(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[Any]														=       XCLIPModel(SCREAMING_SNAKE_CASE__							)
     model.eval()

     if "drive" in checkpoint_url:
          _UpperCAmelCase     :      Optional[int]														=       "pytorch_model.bin"
          gdown.cached_download(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    quiet=SCREAMING_SNAKE_CASE__							)
          _UpperCAmelCase     :      Optional[int]														=       torch.load(SCREAMING_SNAKE_CASE__		,    map_location="cpu"							)["model"]
     else:
          _UpperCAmelCase     :      int														=       torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE__							)["model"]

     _UpperCAmelCase     :      List[Any]														=       convert_state_dict(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       XCLIPModel(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase							,			_UpperCAmelCase     :      List[Any]														=       model.load_state_dict(SCREAMING_SNAKE_CASE__		,    strict=SCREAMING_SNAKE_CASE__							)
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     _UpperCAmelCase     :      Optional[int]														=       336 if model_name == "xclip-large-patch14-16-frames" else 224
     _UpperCAmelCase     :      List[str]														=       VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      Union[str, Any]														=       CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"							)
     _UpperCAmelCase     :      int														=       CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"							)
     _UpperCAmelCase     :      str														=       XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Union[str, Any]														=       prepare_video(SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      List[str]														=       processor(
         text=["playing sports", "eating spaghetti", "go shopping"]		,    videos=SCREAMING_SNAKE_CASE__		,    return_tensors="pt"		,    padding=SCREAMING_SNAKE_CASE__							)

     print("Shape of pixel values:"		,    inputs.pixel_values.shape							)

     with torch.no_grad():
          _UpperCAmelCase     :      Optional[Any]														=       model(**SCREAMING_SNAKE_CASE__							)

     # Verify outputs
     _UpperCAmelCase     :      Union[str, Any]														=       outputs.logits_per_video
     _UpperCAmelCase     :      str														=       logits_per_video.softmax(dim=1							)
     print("Probs:"		,    SCREAMING_SNAKE_CASE__							)
     # kinetics-400
     if model_name == "xclip-base-patch32":
          _UpperCAmelCase     :      Optional[Any]														=       torch.tensor([[0.0_019, 0.9_951, 0.0_030]]							)
     elif model_name == "xclip-base-patch32-16-frames":
          _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]							)
     elif model_name == "xclip-base-patch16":
          _UpperCAmelCase     :      Dict														=       torch.tensor([[0.0_083, 0.9_681, 0.0_236]]							)
     elif model_name == "xclip-base-patch16-16-frames":
          _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]							)
     elif model_name == "xclip-large-patch14":
          _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[0.0_062, 0.9_864, 0.0_075]]							)
     elif model_name == "xclip-large-patch14-16-frames":
          _UpperCAmelCase     :      Tuple														=       torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]							)
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[0.0_555, 0.8_914, 0.0_531]]							)
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          _UpperCAmelCase     :      Any														=       torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]							)
     elif model_name == "xclip-large-patch14-kinetics-600":
          _UpperCAmelCase     :      Optional[int]														=       torch.tensor([[0.0_036, 0.9_920, 0.0_045]]							)
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          _UpperCAmelCase     :      Tuple														=       torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          _UpperCAmelCase     :      Optional[Any]														=       torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          _UpperCAmelCase     :      Optional[Any]														=       torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]							)
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          _UpperCAmelCase     :      str														=       torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]							)
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          _UpperCAmelCase     :      List[Any]														=       torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          _UpperCAmelCase     :      Union[str, Any]														=       torch.tensor([[0.0_027, 0.9_904, 0.0_070]]							)
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          _UpperCAmelCase     :      str														=       torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]							)
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          _UpperCAmelCase     :      str														=       torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]							)
     else:
          raise ValueError(f'Model name {model_name} not supported'							)
     assert torch.allclose(SCREAMING_SNAKE_CASE__		,    SCREAMING_SNAKE_CASE__		,    atol=1E-3							)
     print("Looks ok!"							)

     if pytorch_dump_folder_path is not None:
          print(f'Saving model {model_name} to {pytorch_dump_folder_path}'							)
          model.save_pretrained(SCREAMING_SNAKE_CASE__							)

     if push_to_hub:
          print("Pushing model, processor and slow tokenizer files to the hub..."							)
          model.push_to_hub(SCREAMING_SNAKE_CASE__		,    organization="nielsr"							)
          processor.push_to_hub(SCREAMING_SNAKE_CASE__		,    organization="nielsr"							)
          slow_tokenizer.push_to_hub(SCREAMING_SNAKE_CASE__		,    organization="nielsr"							)


if __name__ == "__main__":
 _lowerCAmelCase							:   Optional[Any]				=				argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--model_name",
     default="xclip-base-patch32",
     type=str,
     help="Name of the model.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
 )
 parser.add_argument(
     "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
 )

 _lowerCAmelCase							:   Optional[int]				=				parser.parse_args()
 convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""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							:   Optional[Any]				=				logging.get_logger(__name__)


_lowerCAmelCase							:   List[str]				=				["model.decoder.embed_positions.weights"]

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		Optional[int]							)    ->  List[str]:





     '''simple docstring'''

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		OrderedDict		,    SCREAMING_SNAKE_CASE__      :		int							)    ->  Tuple[Dict, Dict]:





     '''simple docstring'''

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

def 		__snake_case		( SCREAMING_SNAKE_CASE__      :		str							)    ->  MusicgenDecoderConfig:





     '''simple docstring'''

     if checkpoint == "small":
          # default config values
          _UpperCAmelCase     :      Union[str, Any]														=       1_024
          _UpperCAmelCase     :      Tuple														=       24
          _UpperCAmelCase     :      List[str]														=       16
     elif checkpoint == "medium":
          _UpperCAmelCase     :      List[str]														=       1_536
          _UpperCAmelCase     :      List[str]														=       48
          _UpperCAmelCase     :      str														=       24
     elif checkpoint == "large":
          _UpperCAmelCase     :      Dict														=       2_048
          _UpperCAmelCase     :      Optional[int]														=       48
          _UpperCAmelCase     :      Tuple														=       32
     else:
          raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'							)
     _UpperCAmelCase     :      Union[str, 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 		__snake_case		( SCREAMING_SNAKE_CASE__      :		List[Any]		,    SCREAMING_SNAKE_CASE__      :		int=None		,    SCREAMING_SNAKE_CASE__      :		Tuple=None		,    SCREAMING_SNAKE_CASE__      :		Dict="cpu"							)    ->  Optional[int]:





     '''simple docstring'''

     _UpperCAmelCase     :      Tuple														=       MusicGen.get_pretrained(SCREAMING_SNAKE_CASE__		,    device=SCREAMING_SNAKE_CASE__							)
     _UpperCAmelCase     :      str														=       decoder_config_from_checkpoint(SCREAMING_SNAKE_CASE__							)

     _UpperCAmelCase     :      Optional[int]														=       fairseq_model.lm.state_dict()
     _UpperCAmelCase							,			_UpperCAmelCase     :      Union[str, Any]														=       rename_state_dict(
         SCREAMING_SNAKE_CASE__		,    hidden_size=decoder_config.hidden_size							)

     _UpperCAmelCase     :      Tuple														=       TaEncoderModel.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      List[str]														=       EncodecModel.from_pretrained("facebook/encodec_32khz"							)
     _UpperCAmelCase     :      Union[str, Any]														=       MusicgenForCausalLM(SCREAMING_SNAKE_CASE__							).eval()

     # load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
     _UpperCAmelCase							,			_UpperCAmelCase     :      Optional[Any]														=       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
     _UpperCAmelCase     :      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
     _UpperCAmelCase     :      int														=       torch.arange(0		,    8		,    dtype=torch.long							).reshape(2		,    -1							)
     _UpperCAmelCase     :      List[Any]														=       input_ids.reshape(2 * 4		,    -1							)

     with torch.no_grad():
          _UpperCAmelCase     :      List[str]														=       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
     _UpperCAmelCase     :      Optional[int]														=       AutoTokenizer.from_pretrained("t5-base"							)
     _UpperCAmelCase     :      str														=       AutoFeatureExtractor.from_pretrained("facebook/encodec_32khz"		,    padding_side="left"							)

     _UpperCAmelCase     :      Optional[Any]														=       MusicgenProcessor(feature_extractor=SCREAMING_SNAKE_CASE__		,    tokenizer=SCREAMING_SNAKE_CASE__							)

     # set the appropriate bos/pad token ids
     _UpperCAmelCase     :      Optional[int]														=       2_048
     _UpperCAmelCase     :      Union[str, Any]														=       2_048

     # set other default generation config params
     _UpperCAmelCase     :      Optional[int]														=       int(30 * audio_encoder.config.frame_rate							)
     _UpperCAmelCase     :      Union[str, Any]														=       True
     _UpperCAmelCase     :      Dict														=       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							:   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."
 )

 _lowerCAmelCase							:   List[str]				=				parser.parse_args()
 convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
