



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()
_lowercase    	=		logging.get_logger(__name__)


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



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



def 		lowerCAmelCase__     (       UpperCamelCase_					:							OrderedDict			,							UpperCamelCase_					:							int						)->	Tuple[Dict, Dict]:
    A__					=       list(state_dict.keys()						)
    A__					=       {}
    for key in keys:
        A__					=       state_dict.pop(UpperCamelCase_						)
        A__					=       rename_keys(UpperCamelCase_						)
        if "in_proj_weight" in key:
            # split fused qkv proj
            A__					=       val[:hidden_size, :]
            A__					=       val[hidden_size : 2 * hidden_size, :]
            A__					=       val[-hidden_size:, :]
        elif "enc_to_dec_proj" in key:
            A__					=       val
        else:
            A__					=       val
    return state_dict, enc_dec_proj_state_dict



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



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

    A__					=       fairseq_model.lm.state_dict()
    A__			,						A__					=       rename_state_dict(
        UpperCamelCase_			,							hidden_size=decoder_config.hidden_size						)

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

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

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

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

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

    # init the composite model
    A__					=       MusicgenForConditionalGeneration(text_encoder=UpperCamelCase_			,							audio_encoder=UpperCamelCase_			,							decoder=UpperCamelCase_						)

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

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

    with torch.no_grad():
        A__					=       model(input_ids=UpperCamelCase_			,							decoder_input_ids=UpperCamelCase_						).logits

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

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

    A__					=       MusicgenProcessor(feature_extractor=UpperCamelCase_			,							tokenizer=UpperCamelCase_						)

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

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

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

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


if __name__ == "__main__":
   _lowercase    	=		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."
   )

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





import argparse
import json
import re
from pathlib import Path

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

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowercase    	=		logging.get_logger(__name__)



def 		lowerCAmelCase__     (       UpperCamelCase_					:							Optional[Any]						)->	Union[str, Any]:
    A__					=       MobileNetVaConfig(layer_norm_eps=0.001						)

    if "_quant" in model_name:
        raise ValueError('''Quantized models are not supported.'''						)

    A__					=       re.match(r'''^mobilenet_v1_([^_]*)_([^_]*)$'''			,							UpperCamelCase_						)
    if matches:
        A__					=       float(matches[1]						)
        A__					=       int(matches[2]						)

    # The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
    # the usual 1000. The first class (index 0) is "background".
    A__					=       1_0_0_1
    A__					=       '''imagenet-1k-id2label.json'''
    A__					=       '''huggingface/label-files'''
    A__					=       json.load(open(hf_hub_download(UpperCamelCase_			,							UpperCamelCase_			,							repo_type='''dataset'''						)			,							'''r'''						)						)
    A__					=       {int(UpperCamelCase_						) + 1: v for k, v in idalabel.items()}
    A__					=       '''background'''
    A__					=       idalabel
    A__					=       {v: k for k, v in idalabel.items()}

    return config



def 		lowerCAmelCase__     (       )->	Tuple:
    A__					=       '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    A__					=       Image.open(requests.get(UpperCamelCase_			,							stream=UpperCamelCase_						).raw						)
    return im



@torch.no_grad()
def 		lowerCAmelCase__     (       UpperCamelCase_					:							Union[str, Any]			,							UpperCamelCase_					:							List[str]			,							UpperCamelCase_					:							int			,							UpperCamelCase_					:							List[str]=False						)->	Tuple:
    A__					=       get_mobilenet_va_config(UpperCamelCase_						)

    # Load 🤗 model
    A__					=       MobileNetVaForImageClassification(UpperCamelCase_						).eval()

    # Load weights from TensorFlow checkpoint
    load_tf_weights_in_mobilenet_va(UpperCamelCase_			,							UpperCamelCase_			,							UpperCamelCase_						)

    # Check outputs on an image, prepared by MobileNetV1ImageProcessor
    A__					=       MobileNetVaImageProcessor(
        crop_size={'''width''': config.image_size, '''height''': config.image_size}			,							size={'''shortest_edge''': config.image_size + 3_2}			,							)
    A__					=       image_processor(images=prepare_img()			,							return_tensors='''pt'''						)
    A__					=       model(**UpperCamelCase_						)
    A__					=       outputs.logits

    assert logits.shape == (1, 1_0_0_1)

    if model_name == "mobilenet_v1_1.0_224":
        A__					=       torch.tensor([-4.1739, -1.1233, 3.1205]						)
    elif model_name == "mobilenet_v1_0.75_192":
        A__					=       torch.tensor([-3.9440, -2.3141, -0.3333]						)
    else:
        A__					=       None

    if expected_logits is not None:
        assert torch.allclose(logits[0, :3]			,							UpperCamelCase_			,							atol=1E-4						)

    Path(UpperCamelCase_						).mkdir(exist_ok=UpperCamelCase_						)
    print(f"Saving model {model_name} to {pytorch_dump_folder_path}"						)
    model.save_pretrained(UpperCamelCase_						)
    print(f"Saving image processor to {pytorch_dump_folder_path}"						)
    image_processor.save_pretrained(UpperCamelCase_						)

    if push_to_hub:
        print('''Pushing to the hub...'''						)
        A__					=       '''google/''' + model_name
        image_processor.push_to_hub(UpperCamelCase_						)
        model.push_to_hub(UpperCamelCase_						)


if __name__ == "__main__":
   _lowercase    	=		argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--model_name",
       default="mobilenet_v1_1.0_224",
       type=str,
       help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
   )
   parser.add_argument(
       "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", required=True, 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."
   )

   _lowercase    	=		parser.parse_args()
   convert_movilevit_checkpoint(
       args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
   )
