




'''simple docstring'''







import argparse
import os
from pathlib import Path

import torch
from bark.generation import _load_model as _bark_load_model
from huggingface_hub import hf_hub_download

from transformers import EncodecConfig, EncodecModel, set_seed
from transformers.models.bark.configuration_bark import (
    BarkCoarseConfig,
    BarkConfig,
    BarkFineConfig,
    BarkSemanticConfig,
)
from transformers.models.bark.generation_configuration_bark import (
    BarkCoarseGenerationConfig,
    BarkFineGenerationConfig,
    BarkGenerationConfig,
    BarkSemanticGenerationConfig,
)
from transformers.models.bark.modeling_bark import BarkCoarseModel, BarkFineModel, BarkModel, BarkSemanticModel
from transformers.utils import logging


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

set_seed(7_70)


A									={
    """c_attn""": """att_proj""",
    """c_proj""": """out_proj""",
    """c_fc""": """in_proj""",
    """transformer.""": """""",
    """h.""": """layers.""",
    """ln_1""": """layernorm_1""",
    """ln_2""": """layernorm_2""",
    """ln_f""": """layernorm_final""",
    """wpe""": """position_embeds_layer""",
    """wte""": """input_embeds_layer""",
}


A									={
    """text_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text.pt""",
    },
    """coarse_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse.pt""",
    },
    """fine_small""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine.pt""",
    },
    """text""": {
        """repo_id""": """suno/bark""",
        """file_name""": """text_2.pt""",
    },
    """coarse""": {
        """repo_id""": """suno/bark""",
        """file_name""": """coarse_2.pt""",
    },
    """fine""": {
        """repo_id""": """suno/bark""",
        """file_name""": """fine_2.pt""",
    },
}

A									=os.path.dirname(os.path.abspath(__file__))
A									=os.path.join(os.path.expanduser('~'), '.cache')
A									=os.path.join(os.getenv('XDG_CACHE_HOME', default_cache_dir), 'suno', 'bark_v0')





def 		snake_case_							(_a			:							str					,     _a			:							int=False  ):
    UpperCAmelCase     =	model_type
    if use_small:
        key += "_small"
    return os.path.join(_lowerCamelCase					,     REMOTE_MODEL_PATHS[key]['''file_name''']  )





def 		snake_case_							(_a			:							Dict					,     _a			:							str  ):
    os.makedirs(_lowerCamelCase					,     exist_ok=_lowerCamelCase  )
    hf_hub_download(repo_id=_lowerCamelCase					,     filename=_lowerCamelCase					,     local_dir=_lowerCamelCase  )





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Dict					,     _a			:							Optional[Any]=False					,     _a			:							Union[str, Any]="text"  ):
    if model_type == "text":
        UpperCAmelCase     =	BarkSemanticModel
        UpperCAmelCase     =	BarkSemanticConfig
        UpperCAmelCase     =	BarkSemanticGenerationConfig
    elif model_type == "coarse":
        UpperCAmelCase     =	BarkCoarseModel
        UpperCAmelCase     =	BarkCoarseConfig
        UpperCAmelCase     =	BarkCoarseGenerationConfig
    elif model_type == "fine":
        UpperCAmelCase     =	BarkFineModel
        UpperCAmelCase     =	BarkFineConfig
        UpperCAmelCase     =	BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    UpperCAmelCase     =	F"{model_type}_small" if use_small else model_type
    UpperCAmelCase     =	REMOTE_MODEL_PATHS[model_key]
    if not os.path.exists(_lowerCamelCase  ):
        logger.info(F"{model_type} model not found, downloading into `{CACHE_DIR}`."  )
        _download(model_info['''repo_id''']					,     model_info['''file_name''']  )
    UpperCAmelCase     =	torch.load(_lowerCamelCase					,     map_location=_lowerCamelCase  )
    # this is a hack
    UpperCAmelCase     =	checkpoint['''model_args''']
    if "input_vocab_size" not in model_args:
        UpperCAmelCase     =	model_args['''vocab_size''']
        UpperCAmelCase     =	model_args['''vocab_size''']
        del model_args["vocab_size"]

    # convert Bark model arguments to HF Bark model arguments
    UpperCAmelCase     =	model_args.pop('''n_head'''  )
    UpperCAmelCase     =	model_args.pop('''n_embd'''  )
    UpperCAmelCase     =	model_args.pop('''n_layer'''  )

    UpperCAmelCase     =	ConfigClass(**checkpoint['''model_args''']  )
    UpperCAmelCase     =	ModelClass(config=_lowerCamelCase  )
    UpperCAmelCase     =	GenerationConfigClass()

    UpperCAmelCase     =	model_generation_config
    UpperCAmelCase     =	checkpoint['''model''']
    # fixup checkpoint
    UpperCAmelCase     =	'''_orig_mod.'''
    for k, v in list(state_dict.items()  ):
        if k.startswith(_lowerCamelCase  ):
            # replace part of the key with corresponding layer name in HF implementation
            UpperCAmelCase     =	k[len(_lowerCamelCase  ) :]
            for old_layer_name in new_layer_name_dict:
                UpperCAmelCase     =	new_k.replace(_lowerCamelCase					,     new_layer_name_dict[old_layer_name]  )

            UpperCAmelCase     =	state_dict.pop(_lowerCamelCase  )

    UpperCAmelCase     =	set(state_dict.keys()  ) - set(model.state_dict().keys()  )
    UpperCAmelCase     =	{k for k in extra_keys if not k.endswith('''.attn.bias'''  )}
    UpperCAmelCase     =	set(model.state_dict().keys()  ) - set(state_dict.keys()  )
    UpperCAmelCase     =	{k for k in missing_keys if not k.endswith('''.attn.bias'''  )}
    if len(_lowerCamelCase  ) != 0:
        raise ValueError(F"extra keys found: {extra_keys}"  )
    if len(_lowerCamelCase  ) != 0:
        raise ValueError(F"missing keys: {missing_keys}"  )
    model.load_state_dict(_lowerCamelCase					,     strict=_lowerCamelCase  )
    UpperCAmelCase     =	model.num_parameters(exclude_embeddings=_lowerCamelCase  )
    UpperCAmelCase     =	checkpoint['''best_val_loss'''].item()
    logger.info(F"model loaded: {round(n_params/1E6					,     1  )}M params, {round(_lowerCamelCase					,     3  )} loss"  )
    model.eval()
    model.to(_lowerCamelCase  )
    del checkpoint, state_dict

    return model





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							Optional[int]=False					,     _a			:							List[Any]="text"  ):
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    UpperCAmelCase     =	'''cpu'''  # do conversion on cpu

    UpperCAmelCase     =	_get_ckpt_path(_lowerCamelCase					,     use_small=_lowerCamelCase  )
    UpperCAmelCase     =	_load_model(_lowerCamelCase					,     _lowerCamelCase					,     model_type=_lowerCamelCase					,     use_small=_lowerCamelCase  )

    # load bark initial model
    UpperCAmelCase     =	_bark_load_model(_lowerCamelCase					,     '''cpu'''					,     model_type=_lowerCamelCase					,     use_small=_lowerCamelCase  )

    if model_type == "text":
        UpperCAmelCase     =	bark_model['''model''']

    if model.num_parameters(exclude_embeddings=_lowerCamelCase  ) != bark_model.get_num_params():
        raise ValueError('''initial and new models don\'t have the same number of parameters'''  )

    # check if same output as the bark model
    UpperCAmelCase     =	5
    UpperCAmelCase     =	1_0

    if model_type in ["text", "coarse"]:
        UpperCAmelCase     =	torch.randint(2_5_6					,     (batch_size, sequence_length)					,     dtype=torch.int  )
        UpperCAmelCase     =	bark_model(_lowerCamelCase  )[0]

        UpperCAmelCase     =	model(_lowerCamelCase  )

        # take last logits
        UpperCAmelCase     =	output_new_model_total.logits[:, [-1], :]

    else:
        UpperCAmelCase     =	3
        UpperCAmelCase     =	8
        UpperCAmelCase     =	torch.randint(2_5_6					,     (batch_size, sequence_length, n_codes_total)					,     dtype=torch.int  )

        UpperCAmelCase     =	model(_lowerCamelCase					,     _lowerCamelCase  )
        UpperCAmelCase     =	bark_model(_lowerCamelCase					,     _lowerCamelCase  )

        UpperCAmelCase     =	output_new_model_total.logits

    # output difference should come from the difference of self-attention implementation design
    if output_new_model.shape != output_old_model.shape:
        raise ValueError('''initial and new outputs don\'t have the same shape'''  )
    if (output_new_model - output_old_model).abs().max().item() > 1E-3:
        raise ValueError('''initial and new outputs are not equal'''  )

    Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
    model.save_pretrained(_lowerCamelCase  )





def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							List[str]					,     _a			:							Dict					,     _a			:							Dict					,     _a			:							Tuple					,     _a			:							str					,     ):
    UpperCAmelCase     =	os.path.join(_lowerCamelCase					,     _lowerCamelCase  )

    UpperCAmelCase     =	BarkSemanticConfig.from_pretrained(os.path.join(_lowerCamelCase					,     '''config.json'''  )  )
    UpperCAmelCase     =	BarkCoarseConfig.from_pretrained(os.path.join(_lowerCamelCase					,     '''config.json'''  )  )
    UpperCAmelCase     =	BarkFineConfig.from_pretrained(os.path.join(_lowerCamelCase					,     '''config.json'''  )  )
    UpperCAmelCase     =	EncodecConfig.from_pretrained('''facebook/encodec_24khz'''  )

    UpperCAmelCase     =	BarkSemanticModel.from_pretrained(_lowerCamelCase  )
    UpperCAmelCase     =	BarkCoarseModel.from_pretrained(_lowerCamelCase  )
    UpperCAmelCase     =	BarkFineModel.from_pretrained(_lowerCamelCase  )
    UpperCAmelCase     =	EncodecModel.from_pretrained('''facebook/encodec_24khz'''  )

    UpperCAmelCase     =	BarkConfig.from_sub_model_configs(
        _lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase					,     _lowerCamelCase  )

    UpperCAmelCase     =	BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config					,     coarseAcoustic.generation_config					,     fineAcoustic.generation_config  )

    UpperCAmelCase     =	BarkModel(_lowerCamelCase  )

    UpperCAmelCase     =	semantic
    UpperCAmelCase     =	coarseAcoustic
    UpperCAmelCase     =	fineAcoustic
    UpperCAmelCase     =	codec

    UpperCAmelCase     =	bark_generation_config

    Path(_lowerCamelCase  ).mkdir(exist_ok=_lowerCamelCase  )
    bark.save_pretrained(_lowerCamelCase					,     repo_id=_lowerCamelCase					,     push_to_hub=_lowerCamelCase  )


if __name__ == "__main__":
   A									=argparse.ArgumentParser()
   # Required parameters

   parser.add_argument('model_type', type=str, help='text, coarse or fine.')
   parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
   parser.add_argument('--is_small', action='store_true', help='convert the small version instead of the large.')

   A									=parser.parse_args()

   load_model(args.pytorch_dump_folder_path, model_type=args.model_type, use_small=args.is_small)







'''simple docstring'''







import argparse
import json
from pathlib import Path

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

from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


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





def 		snake_case_							(_a			:							Tuple					,     _a			:							Any=False					,     _a			:							str=False  ):
    UpperCAmelCase     =	'''backbone.''' if is_semantic else ''''''

    UpperCAmelCase     =	[]
    for i in range(config.num_hidden_layers  ):
        # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
        rename_keys.append((F"{prefix}blocks.{i}.norm1.weight", F"beit.encoder.layer.{i}.layernorm_before.weight")  )
        rename_keys.append((F"{prefix}blocks.{i}.norm1.bias", F"beit.encoder.layer.{i}.layernorm_before.bias")  )
        rename_keys.append(
            (F"{prefix}blocks.{i}.attn.proj.weight", F"beit.encoder.layer.{i}.attention.output.dense.weight")  )
        rename_keys.append(
            (F"{prefix}blocks.{i}.attn.proj.bias", F"beit.encoder.layer.{i}.attention.output.dense.bias")  )
        rename_keys.append((F"{prefix}blocks.{i}.norm2.weight", F"beit.encoder.layer.{i}.layernorm_after.weight")  )
        rename_keys.append((F"{prefix}blocks.{i}.norm2.bias", F"beit.encoder.layer.{i}.layernorm_after.bias")  )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.weight", F"beit.encoder.layer.{i}.intermediate.dense.weight")  )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc1.bias", F"beit.encoder.layer.{i}.intermediate.dense.bias")  )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.weight", F"beit.encoder.layer.{i}.output.dense.weight")  )
        rename_keys.append((F"{prefix}blocks.{i}.mlp.fc2.bias", F"beit.encoder.layer.{i}.output.dense.bias")  )

    # projection layer + position embeddings
    rename_keys.extend(
        [
            (F"{prefix}cls_token", '''beit.embeddings.cls_token'''),
            (F"{prefix}patch_embed.proj.weight", '''beit.embeddings.patch_embeddings.projection.weight'''),
            (F"{prefix}patch_embed.proj.bias", '''beit.embeddings.patch_embeddings.projection.bias'''),
            (F"{prefix}pos_embed", '''beit.embeddings.position_embeddings'''),
        ]  )

    if has_lm_head:
        # mask token + layernorm
        rename_keys.extend(
            [
                ('''mask_token''', '''beit.embeddings.mask_token'''),
                ('''norm.weight''', '''layernorm.weight'''),
                ('''norm.bias''', '''layernorm.bias'''),
            ]  )
    else:
        # layernorm + classification head
        rename_keys.extend(
            [
                ('''fc_norm.weight''', '''beit.pooler.layernorm.weight'''),
                ('''fc_norm.bias''', '''beit.pooler.layernorm.bias'''),
                ('''head.weight''', '''classifier.weight'''),
                ('''head.bias''', '''classifier.bias'''),
            ]  )

    return rename_keys





def 		snake_case_							(_a			:							str					,     _a			:							List[Any]					,     _a			:							List[Any]=False					,     _a			:							Union[str, Any]=False  ):
    for i in range(config.num_hidden_layers  ):
        UpperCAmelCase     =	'''backbone.''' if is_semantic else ''''''
        # queries, keys and values
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.attn.qkv.weight"  )
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.attn.q_bias"  )
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.attn.v_bias"  )

        UpperCAmelCase     =	in_proj_weight[
            : config.hidden_size, :
        ]
        UpperCAmelCase     =	q_bias
        UpperCAmelCase     =	in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCAmelCase     =	in_proj_weight[
            -config.hidden_size :, :
        ]
        UpperCAmelCase     =	v_bias

        # gamma_1 and gamma_2
        # we call them lambda because otherwise they are renamed when using .from_pretrained
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.gamma_1"  )
        UpperCAmelCase     =	state_dict.pop(F"{prefix}blocks.{i}.gamma_2"  )

        UpperCAmelCase     =	gamma_a
        UpperCAmelCase     =	gamma_a





def 		snake_case_							(_a			:							Union[str, Any]					,     _a			:							str					,     _a			:							Optional[int]  ):
    UpperCAmelCase     =	dct.pop(_a  )
    UpperCAmelCase     =	val





def 		snake_case_							():
    UpperCAmelCase     =	'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase     =	Image.open(requests.get(_a					,     stream=_a  ).raw  )
    return im





@torch.no_grad()
def 		snake_case_							(_a			:							Optional[Any]					,     _a			:							Optional[Any]					,     _a			:							Optional[Any]=False  ):
    UpperCAmelCase     =	False if '''rvlcdip''' in checkpoint_url else True
    UpperCAmelCase     =	BeitConfig(use_absolute_position_embeddings=_a					,     use_mask_token=_a  )

    # size of the architecture
    if "large" in checkpoint_url or "dit-l" in checkpoint_url:
        UpperCAmelCase     =	1_0_2_4
        UpperCAmelCase     =	4_0_9_6
        UpperCAmelCase     =	2_4
        UpperCAmelCase     =	1_6

    # labels
    if "rvlcdip" in checkpoint_url:
        UpperCAmelCase     =	1_6
        UpperCAmelCase     =	'''huggingface/label-files'''
        UpperCAmelCase     =	'''rvlcdip-id2label.json'''
        UpperCAmelCase     =	json.load(open(hf_hub_download(_a					,     _a					,     repo_type='''dataset'''  )					,     '''r'''  )  )
        UpperCAmelCase     =	{int(_a  ): v for k, v in idalabel.items()}
        UpperCAmelCase     =	idalabel
        UpperCAmelCase     =	{v: k for k, v in idalabel.items()}

    # load state_dict of original model, remove and rename some keys
    UpperCAmelCase     =	torch.hub.load_state_dict_from_url(_a					,     map_location='''cpu'''  )['''model''']

    UpperCAmelCase     =	create_rename_keys(_a					,     has_lm_head=_a  )
    for src, dest in rename_keys:
        rename_key(_a					,     _a					,     _a  )
    read_in_q_k_v(_a					,     _a					,     has_lm_head=_a  )

    # load HuggingFace model
    UpperCAmelCase     =	BeitForMaskedImageModeling(_a  ) if has_lm_head else BeitForImageClassification(_a  )
    model.eval()
    model.load_state_dict(_a  )

    # Check outputs on an image
    UpperCAmelCase     =	BeitImageProcessor(
        size=config.image_size					,     resample=PILImageResampling.BILINEAR					,     do_center_crop=_a  )
    UpperCAmelCase     =	prepare_img()

    UpperCAmelCase     =	image_processor(images=_a					,     return_tensors='''pt'''  )
    UpperCAmelCase     =	encoding['''pixel_values''']

    UpperCAmelCase     =	model(_a  )
    UpperCAmelCase     =	outputs.logits

    # verify logits
    UpperCAmelCase     =	[1, 1_6] if '''rvlcdip''' in checkpoint_url else [1, 1_9_6, 8_1_9_2]
    assert logits.shape == torch.Size(_a  ), "Shape of logits not as expected"

    Path(_a  ).mkdir(exist_ok=_a  )
    print(F"Saving model to {pytorch_dump_folder_path}"  )
    model.save_pretrained(_a  )
    print(F"Saving image processor to {pytorch_dump_folder_path}"  )
    image_processor.save_pretrained(_a  )

    if push_to_hub:
        if has_lm_head:
            UpperCAmelCase     =	'''dit-base''' if '''base''' in checkpoint_url else '''dit-large'''
        else:
            UpperCAmelCase     =	'''dit-base-finetuned-rvlcdip''' if '''dit-b''' in checkpoint_url else '''dit-large-finetuned-rvlcdip'''
        image_processor.push_to_hub(
            repo_path_or_name=Path(_a					,     _a  )					,     organization='''nielsr'''					,     commit_message='''Add image processor'''					,     use_temp_dir=_a					,     )
        model.push_to_hub(
            repo_path_or_name=Path(_a					,     _a  )					,     organization='''nielsr'''					,     commit_message='''Add model'''					,     use_temp_dir=_a					,     )


if __name__ == "__main__":
   A									=argparse.ArgumentParser()

   parser.add_argument(
       '--checkpoint_url',
       default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
       type=str,
       help='URL 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.'
   )
   parser.add_argument(
       '--push_to_hub',
       action='store_true',
   )
   A									=parser.parse_args()
   convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)




