"""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					: int        =logging.get_logger(__name__)


__lowerCAmelCase					: Dict        =["model.decoder.embed_positions.weights"]


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :OrderedDict      ,	lowerCAmelCase__  :int	)				->					Optional[Any]:
      '''simple docstring'''


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :str	)				->					Any:
      '''simple docstring'''


      if checkpoint == "small":
            # default config values
            lowercase														=			1_0_2_4
            lowercase														=			2_4
            lowercase														=			1_6
      elif checkpoint == "medium":
            lowercase														=			1_5_3_6
            lowercase														=			4_8
            lowercase														=			2_4
      elif checkpoint == "large":
            lowercase														=			2_0_4_8
            lowercase														=			4_8
            lowercase														=			3_2
      else:
            raise ValueError(f'Checkpoint should be one of `[\'small\', \'medium\', \'large\']`, got {checkpoint}.'	)
      lowercase														=			MusicgenDecoderConfig(
          hidden_size=_A      ,	ffn_dim=hidden_size * 4      ,	num_hidden_layers=_A      ,	num_attention_heads=_A      ,	)
      return config


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Any=None      ,	lowerCAmelCase__  :Dict=None      ,	lowerCAmelCase__  :str="cpu"	)				->					List[str]:
      '''simple docstring'''


      lowercase														=			MusicGen.get_pretrained(_A      ,	device=_A	)
      lowercase														=			decoder_config_from_checkpoint(_A	)

      lowercase														=			fairseq_model.lm.state_dict()
      lowercase														=			rename_state_dict(
          _A      ,	hidden_size=decoder_config.hidden_size	)

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

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

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

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

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

      # init the composite model
      lowercase														=			MusicgenForConditionalGeneration(text_encoder=_A      ,	audio_encoder=_A      ,	decoder=_A	)

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

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

      with torch.no_grad():
            lowercase														=			model(input_ids=_A      ,	decoder_input_ids=_A	).logits

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

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

      lowercase														=			MusicgenProcessor(feature_extractor=_A      ,	tokenizer=_A	)

      # set the appropriate bos/pad token ids
      lowercase														=			2_0_4_8
      lowercase														=			2_0_4_8

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

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

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


if __name__ == "__main__":
   __lowerCAmelCase					: List[Any]        =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					: Optional[Any]        =parser.parse_args()
   convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)




"""simple docstring"""






import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging


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


def     UpperCAmelCase__ (       lowerCAmelCase__  :Tuple      ,	lowerCAmelCase__  :Dict      ,	lowerCAmelCase__  :str      ,	lowerCAmelCase__  :Optional[int]	)				->					Any:
      '''simple docstring'''


      lowercase														=			original_name.split("""."""	)[0]
      lowercase														=			key.split("""."""	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 2]	)
      lowercase														=			int(key_list[key_list.index(lowerCAmelCase__	) - 1]	)
      lowercase														=			orig_block_num - offset

      lowercase														=			key.replace(f'{orig_block_num}.{layer_num}.{original_name}'      ,	f'block.{new_block_num}.{layer_num}.{new_name}'	)
      return key


def     UpperCAmelCase__ (       lowerCAmelCase__  :Optional[Any]	)				->					Union[str, Any]:
      '''simple docstring'''


      lowercase														=			OrderedDict()
      lowercase							,							lowercase														=			0, 0
      for key, value in state_dict.items():
            if key.startswith("""network"""	):
                  lowercase														=			key.replace("""network"""      ,	"""poolformer.encoder"""	)
            if "proj" in key:
                  # Works for the first embedding as well as the internal embedding layers
                  if key.endswith("""bias"""	) and "patch_embed" not in key:
                        patch_emb_offset += 1
                  lowercase														=			key[: key.find("""proj"""	)]
                  lowercase														=			key.replace(lowerCAmelCase__      ,	f'patch_embeddings.{total_embed_found}.'	)
                  lowercase														=			key.replace("""proj"""      ,	"""projection"""	)
                  if key.endswith("""bias"""	):
                        total_embed_found += 1
            if "patch_embeddings" in key:
                  lowercase														=			"""poolformer.encoder.""" + key
            if "mlp.fc1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc1"""      ,	"""output.conv1"""	)
            if "mlp.fc2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""mlp.fc2"""      ,	"""output.conv2"""	)
            if "norm1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm1"""      ,	"""before_norm"""	)
            if "norm2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""norm2"""      ,	"""after_norm"""	)
            if "layer_scale_1" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_1"""      ,	"""layer_scale_1"""	)
            if "layer_scale_2" in key:
                  lowercase														=			replace_key_with_offset(lowerCAmelCase__      ,	lowerCAmelCase__      ,	"""layer_scale_2"""      ,	"""layer_scale_2"""	)
            if "head" in key:
                  lowercase														=			key.replace("""head"""      ,	"""classifier"""	)
            lowercase														=			value
      return new_state_dict


def     UpperCAmelCase__ (       )				->					Any:
      '''simple docstring'''


      lowercase														=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowercase														=			Image.open(requests.get(lowerCAmelCase__      ,	stream=lowerCAmelCase__	).raw	)

      return image


@torch.no_grad()
def     UpperCAmelCase__ (       lowerCAmelCase__  :Any      ,	lowerCAmelCase__  :List[str]      ,	lowerCAmelCase__  :Tuple	)				->					Any:
      '''simple docstring'''


      lowercase														=			PoolFormerConfig()

      # set attributes based on model_name
      lowercase														=			"""huggingface/label-files"""
      lowercase														=			model_name[-3:]
      lowercase														=			1_0_0_0
      lowercase														=			"""imagenet-1k-id2label.json"""
      lowercase														=			(1, 1_0_0_0)

      # set config attributes
      lowercase														=			json.load(open(hf_hub_download(lowerCAmelCase__      ,	lowerCAmelCase__      ,	repo_type="""dataset"""	)      ,	"""r"""	)	)
      lowercase														=			{int(lowerCAmelCase__	): v for k, v in idalabel.items()}
      lowercase														=			idalabel
      lowercase														=			{v: k for k, v in idalabel.items()}
      if size == "s12":
            lowercase														=			[2, 2, 6, 2]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s24":
            lowercase														=			[4, 4, 1_2, 4]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			0.9
      elif size == "s36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[6_4, 1_2_8, 3_2_0, 5_1_2]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.9
      elif size == "m36":
            lowercase														=			[6, 6, 1_8, 6]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      elif size == "m48":
            lowercase														=			[8, 8, 2_4, 8]
            lowercase														=			[9_6, 1_9_2, 3_8_4, 7_6_8]
            lowercase														=			4.0
            lowercase														=			1e-6
            lowercase														=			0.95
      else:
            raise ValueError(f'Size {size} not supported'	)

      # load image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)

      # Prepare image
      lowercase														=			prepare_img()
      lowercase														=			image_processor(images=lowerCAmelCase__      ,	return_tensors="""pt"""	).pixel_values

      logger.info(f'Converting model {model_name}...'	)

      # load original state dict
      lowercase														=			torch.load(lowerCAmelCase__      ,	map_location=torch.device("""cpu"""	)	)

      # rename keys
      lowercase														=			rename_keys(lowerCAmelCase__	)

      # create HuggingFace model and load state dict
      lowercase														=			PoolFormerForImageClassification(lowerCAmelCase__	)
      model.load_state_dict(lowerCAmelCase__	)
      model.eval()

      # Define image processor
      lowercase														=			PoolFormerImageProcessor(crop_pct=lowerCAmelCase__	)
      lowercase														=			image_processor(images=prepare_img()      ,	return_tensors="""pt"""	).pixel_values

      # forward pass
      lowercase														=			model(lowerCAmelCase__	)
      lowercase														=			outputs.logits

      # define expected logit slices for different models
      if size == "s12":
            lowercase														=			torch.tensor([-0.3_045, -0.6_758, -0.4_869]	)
      elif size == "s24":
            lowercase														=			torch.tensor([0.4_402, -0.1_374, -0.8_045]	)
      elif size == "s36":
            lowercase														=			torch.tensor([-0.6_080, -0.5_133, -0.5_898]	)
      elif size == "m36":
            lowercase														=			torch.tensor([0.3_952, 0.2_263, -1.2_668]	)
      elif size == "m48":
            lowercase														=			torch.tensor([0.1_167, -0.0_656, -0.3_423]	)
      else:
            raise ValueError(f'Size {size} not supported'	)

      # verify logits
      assert logits.shape == expected_shape
      assert torch.allclose(logits[0, :3]      ,	lowerCAmelCase__      ,	atol=1e-2	)

      # finally, save model and image processor
      logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'	)
      Path(lowerCAmelCase__	).mkdir(exist_ok=lowerCAmelCase__	)
      model.save_pretrained(lowerCAmelCase__	)
      print(f'Saving image processor to {pytorch_dump_folder_path}'	)
      image_processor.save_pretrained(lowerCAmelCase__	)


if __name__ == "__main__":
       __lowerCAmelCase					: List[Any]        =argparse.ArgumentParser()

       parser.add_argument(
           """--model_name""",
           default="""poolformer_s12""",
           type=str,
           help="""Name of the model you'd like to convert.""",
       )
       parser.add_argument(
           """--checkpoint_path""", default=None, type=str, help="""Path to the original PyTorch checkpoint (.pth file)."""
       )
       parser.add_argument(
           """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
       )
       __lowerCAmelCase					: int        =parser.parse_args()
       convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)



