
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()
__lowerCAmelCase					:			List[str]			      =		logging.get_logger(__name__)

set_seed(770)


__lowerCAmelCase					:			str			      =		{
    "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",
}


__lowerCAmelCase					:			str			      =		{
    "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",
    },
}

__lowerCAmelCase					:			List[Any]			      =		os.path.dirname(os.path.abspath(__file__))
__lowerCAmelCase					:			Union[str, Any]			      =		os.path.join(os.path.expanduser("~"), ".cache")
__lowerCAmelCase					:			int			      =		os.path.join(os.getenv("XDG_CACHE_HOME", default_cache_dir), "suno", "bark_v0")


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase=False	) ->						Optional[Any]:
    __lowercase		:			str	    =       model_type
    if use_small:
        key += "_small"
    return os.path.join(__lowerCAmelCase	,					REMOTE_MODEL_PATHS[key]['''file_name''']	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	) ->						int:
    os.makedirs(__lowerCAmelCase	,					exist_ok=__lowerCAmelCase	)
    hf_hub_download(repo_id=__lowerCAmelCase	,					filename=__lowerCAmelCase	,					local_dir=__lowerCAmelCase	)


def     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase=False	,					__lowerCAmelCase="text"	) ->						int:
    if model_type == "text":
        __lowercase		:			str	    =       BarkSemanticModel
        __lowercase		:			Any	    =       BarkSemanticConfig
        __lowercase		:			Any	    =       BarkSemanticGenerationConfig
    elif model_type == "coarse":
        __lowercase		:			int	    =       BarkCoarseModel
        __lowercase		:			Tuple	    =       BarkCoarseConfig
        __lowercase		:			int	    =       BarkCoarseGenerationConfig
    elif model_type == "fine":
        __lowercase		:			List[Any]	    =       BarkFineModel
        __lowercase		:			List[Any]	    =       BarkFineConfig
        __lowercase		:			Any	    =       BarkFineGenerationConfig
    else:
        raise NotImplementedError()
    __lowercase		:			Optional[int]	    =       F'{model_type}_small' if use_small else model_type
    __lowercase		:			List[str]	    =       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''']	)
    __lowercase		:			int	    =       torch.load(__lowerCAmelCase	,					map_location=__lowerCAmelCase	)
    # this is a hack
    __lowercase		:			str	    =       checkpoint['''model_args''']
    if "input_vocab_size" not in model_args:
        __lowercase		:			List[str]	    =       model_args['''vocab_size''']
        __lowercase		:			Any	    =       model_args['''vocab_size''']
        del model_args["vocab_size"]

    # convert Bark model arguments to HF Bark model arguments
    __lowercase		:			Dict	    =       model_args.pop('''n_head'''	)
    __lowercase		:			List[str]	    =       model_args.pop('''n_embd'''	)
    __lowercase		:			Dict	    =       model_args.pop('''n_layer'''	)

    __lowercase		:			Optional[Any]	    =       ConfigClass(**checkpoint['''model_args''']	)
    __lowercase		:			Any	    =       ModelClass(config=__lowerCAmelCase	)
    __lowercase		:			Union[str, Any]	    =       GenerationConfigClass()

    __lowercase		:			Tuple	    =       model_generation_config
    __lowercase		:			Any	    =       checkpoint['''model''']
    # fixup checkpoint
    __lowercase		:			Optional[int]	    =       '''_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
            __lowercase		:			List[str]	    =       k[len(__lowerCAmelCase	) :]
            for old_layer_name in new_layer_name_dict:
                __lowercase		:			List[Any]	    =       new_k.replace(__lowerCAmelCase	,					new_layer_name_dict[old_layer_name]	)

            __lowercase		:			Any	    =       state_dict.pop(__lowerCAmelCase	)

    __lowercase		:			str	    =       set(state_dict.keys()	) - set(model.state_dict().keys()	)
    __lowercase		:			Any	    =       {k for k in extra_keys if not k.endswith('''.attn.bias'''	)}
    __lowercase		:			int	    =       set(model.state_dict().keys()	) - set(state_dict.keys()	)
    __lowercase		:			Union[str, Any]	    =       {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	)
    __lowercase		:			List[Any]	    =       model.num_parameters(exclude_embeddings=__lowerCAmelCase	)
    __lowercase		:			List[str]	    =       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     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase=False	,					__lowerCAmelCase="text"	) ->						Tuple:
    if model_type not in ("text", "coarse", "fine"):
        raise NotImplementedError()

    __lowercase		:			List[str]	    =       '''cpu'''  # do conversion on cpu

    __lowercase		:			List[Any]	    =       _get_ckpt_path(__lowerCAmelCase	,					use_small=__lowerCAmelCase	)
    __lowercase		:			List[Any]	    =       _load_model(__lowerCAmelCase	,					__lowerCAmelCase	,					model_type=__lowerCAmelCase	,					use_small=__lowerCAmelCase	)

    # load bark initial model
    __lowercase		:			str	    =       _bark_load_model(__lowerCAmelCase	,					'''cpu'''	,					model_type=__lowerCAmelCase	,					use_small=__lowerCAmelCase	)

    if model_type == "text":
        __lowercase		:			List[Any]	    =       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
    __lowercase		:			str	    =       5
    __lowercase		:			Union[str, Any]	    =       10

    if model_type in ["text", "coarse"]:
        __lowercase		:			int	    =       torch.randint(256	,					(batch_size, sequence_length)	,					dtype=torch.int	)
        __lowercase		:			str	    =       bark_model(__lowerCAmelCase	)[0]

        __lowercase		:			int	    =       model(__lowerCAmelCase	)

        # take last logits
        __lowercase		:			Union[str, Any]	    =       output_new_model_total.logits[:, [-1], :]

    else:
        __lowercase		:			Dict	    =       3
        __lowercase		:			Optional[Any]	    =       8
        __lowercase		:			Optional[int]	    =       torch.randint(256	,					(batch_size, sequence_length, n_codes_total)	,					dtype=torch.int	)

        __lowercase		:			Dict	    =       model(__lowerCAmelCase	,					__lowerCAmelCase	)
        __lowercase		:			int	    =       bark_model(__lowerCAmelCase	,					__lowerCAmelCase	)

        __lowercase		:			List[Any]	    =       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     UpperCAmelCase_  (						__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					) ->						str:
    __lowercase		:			Optional[int]	    =       os.path.join(__lowerCAmelCase	,					__lowerCAmelCase	)

    __lowercase		:			List[str]	    =       BarkSemanticConfig.from_pretrained(os.path.join(__lowerCAmelCase	,					'''config.json'''	)	)
    __lowercase		:			str	    =       BarkCoarseConfig.from_pretrained(os.path.join(__lowerCAmelCase	,					'''config.json'''	)	)
    __lowercase		:			List[Any]	    =       BarkFineConfig.from_pretrained(os.path.join(__lowerCAmelCase	,					'''config.json'''	)	)
    __lowercase		:			int	    =       EncodecConfig.from_pretrained('''facebook/encodec_24khz'''	)

    __lowercase		:			Dict	    =       BarkSemanticModel.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       BarkCoarseModel.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Optional[Any]	    =       BarkFineModel.from_pretrained(__lowerCAmelCase	)
    __lowercase		:			Tuple	    =       EncodecModel.from_pretrained('''facebook/encodec_24khz'''	)

    __lowercase		:			Union[str, Any]	    =       BarkConfig.from_sub_model_configs(
        __lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	,					__lowerCAmelCase	)

    __lowercase		:			Tuple	    =       BarkGenerationConfig.from_sub_model_configs(
        semantic.generation_config	,					coarseAcoustic.generation_config	,					fineAcoustic.generation_config	)

    __lowercase		:			Dict	    =       BarkModel(__lowerCAmelCase	)

    __lowercase		:			Optional[int]	    =       semantic
    __lowercase		:			str	    =       coarseAcoustic
    __lowercase		:			Dict	    =       fineAcoustic
    __lowercase		:			Dict	    =       codec

    __lowercase		:			Optional[int]	    =       bark_generation_config

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


if __name__ == "__main__":
 __lowerCAmelCase					:			Tuple			      =		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.")

 __lowerCAmelCase					:			str			      =		parser.parse_args()

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




import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
 import onnxruntime as ort


@nightly
@require_onnxruntime
@require_torch_gpu
class 						__lowerCAmelCase	(	unittest.TestCase      ):



      """simple docstring"""







      @property
      def 		snake_case_			(      self		:       Union[str, Any]				):
          return (
              "CUDAExecutionProvider",
              {
                  "gpu_mem_limit": "15000000000",  # 15GB
                  "arena_extend_strategy": "kSameAsRequested",
              },
          )




      @property
      def 		snake_case_			(      self		:       Optional[Any]				):
          __lowercase		:			Tuple	    =       ort.SessionOptions()
          __lowercase		:			List[Any]	    =       False
          return options




      def 		snake_case_			(      self		:       Dict				):
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo.png'''				)
          __lowercase		:			int	    =       load_image(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/overture-creations-5sI6fQgYIuo_mask.png'''				)
          __lowercase		:			Optional[Any]	    =       load_numpy(
              '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
              '''/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy'''				)

          # using the PNDM scheduler by default
          __lowercase		:			Union[str, Any]	    =       OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
              '''CompVis/stable-diffusion-v1-4'''				,       revision='''onnx'''				,       safety_checker=_snake_case				,       feature_extractor=_snake_case				,       provider=self.gpu_provider				,       sess_options=self.gpu_options				,       )
          pipe.set_progress_bar_config(disable=_snake_case				)

          __lowercase		:			Tuple	    =       '''A red cat sitting on a park bench'''

          __lowercase		:			int	    =       np.random.RandomState(0				)
          __lowercase		:			int	    =       pipe(
              prompt=_snake_case				,       image=_snake_case				,       mask_image=_snake_case				,       strength=0.75				,       guidance_scale=7.5				,       num_inference_steps=15				,       generator=_snake_case				,       output_type='''np'''				,       )

          __lowercase		:			Dict	    =       output.images[0]

          assert image.shape == (512, 512, 3)
          assert np.abs(expected_image - image				).max() < 1E-2


