





'''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()
lowercase_					= logging.get_logger(__name__)

set_seed(770)


lowercase_					= {
    """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""",
}


lowercase_					= {
    """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""",
    },
}

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





def lowerCamelCase						(     __lowerCamelCase :    int					,       __lowerCamelCase :    Tuple=False    )						->Any:
    _SCREAMING_SNAKE_CASE									=			model_type
    if use_small:
        key += "_small"
    return os.path.join(__lowerCamelCase					,       REMOTE_MODEL_PATHS[key]["""file_name"""]    )





def lowerCamelCase						(     __lowerCamelCase :    Any					,       __lowerCamelCase :    int    )						->Any:
    os.makedirs(__lowerCamelCase					,       exist_ok=__lowerCamelCase    )
    hf_hub_download(repo_id=__lowerCamelCase					,       filename=__lowerCamelCase					,       local_dir=__lowerCamelCase    )





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

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

    _SCREAMING_SNAKE_CASE									=			ConfigClass(**checkpoint["""model_args"""]    )
    _SCREAMING_SNAKE_CASE									=			ModelClass(config=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			GenerationConfigClass()

    _SCREAMING_SNAKE_CASE									=			model_generation_config
    _SCREAMING_SNAKE_CASE									=			checkpoint["""model"""]
    # fixup checkpoint
    _SCREAMING_SNAKE_CASE									=			"""_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
            _SCREAMING_SNAKE_CASE									=			k[len(__lowerCamelCase    ) :]
            for old_layer_name in new_layer_name_dict:
                _SCREAMING_SNAKE_CASE									=			new_k.replace(__lowerCamelCase					,       new_layer_name_dict[old_layer_name]    )

            _SCREAMING_SNAKE_CASE									=			state_dict.pop(__lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			"""cpu"""  # do conversion on cpu

    _SCREAMING_SNAKE_CASE									=			_get_ckpt_path(__lowerCamelCase					,       use_small=__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			_load_model(__lowerCamelCase					,       __lowerCamelCase					,       model_type=__lowerCamelCase					,       use_small=__lowerCamelCase    )

    # load bark initial model
    _SCREAMING_SNAKE_CASE									=			_bark_load_model(__lowerCamelCase					,       """cpu"""					,       model_type=__lowerCamelCase					,       use_small=__lowerCamelCase    )

    if model_type == "text":
        _SCREAMING_SNAKE_CASE									=			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
    _SCREAMING_SNAKE_CASE									=			5
    _SCREAMING_SNAKE_CASE									=			10

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

        _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase    )

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

    else:
        _SCREAMING_SNAKE_CASE									=			3
        _SCREAMING_SNAKE_CASE									=			8
        _SCREAMING_SNAKE_CASE									=			torch.randint(256					,       (batch_size, sequence_length, n_codes_total)					,       dtype=torch.int    )

        _SCREAMING_SNAKE_CASE									=			model(__lowerCamelCase					,       __lowerCamelCase    )
        _SCREAMING_SNAKE_CASE									=			bark_model(__lowerCamelCase					,       __lowerCamelCase    )

        _SCREAMING_SNAKE_CASE									=			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 lowerCamelCase						(     __lowerCamelCase :    List[str]					,       __lowerCamelCase :    Any					,       __lowerCamelCase :    Dict					,       __lowerCamelCase :    Optional[Any]					,       __lowerCamelCase :    Optional[int]					,       __lowerCamelCase :    Union[str, Any]					,       )						->List[str]:
    _SCREAMING_SNAKE_CASE									=			os.path.join(__lowerCamelCase					,       __lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			BarkSemanticConfig.from_pretrained(os.path.join(__lowerCamelCase					,       """config.json"""    )    )
    _SCREAMING_SNAKE_CASE									=			BarkCoarseConfig.from_pretrained(os.path.join(__lowerCamelCase					,       """config.json"""    )    )
    _SCREAMING_SNAKE_CASE									=			BarkFineConfig.from_pretrained(os.path.join(__lowerCamelCase					,       """config.json"""    )    )
    _SCREAMING_SNAKE_CASE									=			EncodecConfig.from_pretrained("""facebook/encodec_24khz"""    )

    _SCREAMING_SNAKE_CASE									=			BarkSemanticModel.from_pretrained(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			BarkCoarseModel.from_pretrained(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			BarkFineModel.from_pretrained(__lowerCamelCase    )
    _SCREAMING_SNAKE_CASE									=			EncodecModel.from_pretrained("""facebook/encodec_24khz"""    )

    _SCREAMING_SNAKE_CASE									=			BarkConfig.from_sub_model_configs(
        __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase					,       __lowerCamelCase    )

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

    _SCREAMING_SNAKE_CASE									=			BarkModel(__lowerCamelCase    )

    _SCREAMING_SNAKE_CASE									=			semantic
    _SCREAMING_SNAKE_CASE									=			coarseAcoustic
    _SCREAMING_SNAKE_CASE									=			fineAcoustic
    _SCREAMING_SNAKE_CASE									=			codec

    _SCREAMING_SNAKE_CASE									=			bark_generation_config

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


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

      lowercase_					= parser.parse_args()

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





import hashlib
import unittest
from typing import Dict

import numpy as np

from transformers import (
    MODEL_FOR_MASK_GENERATION_MAPPING,
    TF_MODEL_FOR_MASK_GENERATION_MAPPING,
    is_vision_available,
    pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)


if is_vision_available():
				from PIL import Image
else:





				class       _snake_case					:







							@staticmethod
							def 			SCREAMING_SNAKE_CASE    (				*_a						,   **_a		):
								pass






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		str:







	'''simple docstring'''


	__magic_name__		:		Optional[int]	   =     hashlib.mda(image.tobytes()   )
	return m.hexdigest()[:10]






def   lowerCAmelCase_						(					_snake_case      :  Image   )				->		Dict:







	'''simple docstring'''


	__magic_name__		:		List[Any]	   =     np.array(_snake_case   )
	__magic_name__		:		Optional[int]	   =     npimg.shape
	return {"hash": hashimage(_snake_case   ), "shape": shape}





@is_pipeline_test
@require_vision
@require_torch
class       _snake_case					(     unittest.TestCase				):
			UpperCamelCase__          =    dict(
			    (list(MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if MODEL_FOR_MASK_GENERATION_MAPPING else [])				)
			UpperCamelCase__          =    dict(
			    (list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items()				) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else [])				)







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a						,   _a		):
				__magic_name__		:		Dict	   =     MaskGenerationPipeline(model=_a						,   image_processor=_a		)
				return image_segmenter, [
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				    "./tests/fixtures/tests_samples/COCO/000000039769.png",
				]







			def 			SCREAMING_SNAKE_CASE    (				self						,   _a						,   _a		):
				pass







			@require_tf
			@unittest.skip("Image segmentation not implemented in TF"		)
			def 			SCREAMING_SNAKE_CASE    (				self		):
				pass







			@slow
			@require_torch
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		Dict	   =     pipeline("mask-generation"						,   model="facebook/sam-vit-huge"		)

				__magic_name__		:		str	   =     image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg"						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Dict	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				# fmt: off
				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.0_21},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				        {"mask": {"hash": "e2d0b7a0b7", "shape": (480, 640)}, "scores": 0.99_67},
				        {"mask": {"hash": "453c7844bd", "shape": (480, 640)}, "scores": 0.9_93},
				        {"mask": {"hash": "3d44f2926d", "shape": (480, 640)}, "scores": 0.99_09},
				        {"mask": {"hash": "64033ddc3f", "shape": (480, 640)}, "scores": 0.98_79},
				        {"mask": {"hash": "801064ff79", "shape": (480, 640)}, "scores": 0.98_34},
				        {"mask": {"hash": "6172f276ef", "shape": (480, 640)}, "scores": 0.97_16},
				        {"mask": {"hash": "b49e60e084", "shape": (480, 640)}, "scores": 0.96_12},
				        {"mask": {"hash": "a811e775fd", "shape": (480, 640)}, "scores": 0.95_99},
				        {"mask": {"hash": "a6a8ebcf4b", "shape": (480, 640)}, "scores": 0.95_52},
				        {"mask": {"hash": "9d8257e080", "shape": (480, 640)}, "scores": 0.95_32},
				        {"mask": {"hash": "32de6454a8", "shape": (480, 640)}, "scores": 0.95_16},
				        {"mask": {"hash": "af3d4af2c8", "shape": (480, 640)}, "scores": 0.94_99},
				        {"mask": {"hash": "3c6db475fb", "shape": (480, 640)}, "scores": 0.94_83},
				        {"mask": {"hash": "c290813fb9", "shape": (480, 640)}, "scores": 0.94_64},
				        {"mask": {"hash": "b6f0b8f606", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "92ce16bfdf", "shape": (480, 640)}, "scores": 0.9_43},
				        {"mask": {"hash": "c749b25868", "shape": (480, 640)}, "scores": 0.94_08},
				        {"mask": {"hash": "efb6cab859", "shape": (480, 640)}, "scores": 0.93_35},
				        {"mask": {"hash": "1ff2eafb30", "shape": (480, 640)}, "scores": 0.93_26},
				        {"mask": {"hash": "788b798e24", "shape": (480, 640)}, "scores": 0.92_62},
				        {"mask": {"hash": "abea804f0e", "shape": (480, 640)}, "scores": 0.89_99},
				        {"mask": {"hash": "7b9e8ddb73", "shape": (480, 640)}, "scores": 0.89_86},
				        {"mask": {"hash": "cd24047c8a", "shape": (480, 640)}, "scores": 0.89_84},
				        {"mask": {"hash": "6943e6bcbd", "shape": (480, 640)}, "scores": 0.88_73},
				        {"mask": {"hash": "b5f47c9191", "shape": (480, 640)}, "scores": 0.88_71}
				    ]						,   )
				# fmt: on







			@require_torch
			@slow
			def 			SCREAMING_SNAKE_CASE    (				self		):
				__magic_name__		:		str	   =     "facebook/sam-vit-huge"
				__magic_name__		:		str	   =     pipeline("mask-generation"						,   model=_a		)

				__magic_name__		:		Tuple	   =     image_segmenter(
				    "http://images.cocodataset.org/val2017/000000039769.jpg"						,   pred_iou_thresh=1						,   points_per_batch=256		)

				# Shortening by hashing
				__magic_name__		:		Any	   =     []
				for i, o in enumerate(outputs["masks"]		):
					new_outupt += [{"mask": mask_to_test_readable(_a		), "scores": outputs["scores"][i]}]

				self.assertEqual(
				    nested_simplify(_a						,   decimals=4		)						,   [
				        {"mask": {"hash": "115ad19f5f", "shape": (480, 640)}, "scores": 1.04_44},
				        {"mask": {"hash": "6affa964c6", "shape": (480, 640)}, "scores": 1.02_10},
				        {"mask": {"hash": "dfe28a0388", "shape": (480, 640)}, "scores": 1.01_67},
				        {"mask": {"hash": "c0a5f4a318", "shape": (480, 640)}, "scores": 1.01_32},
				        {"mask": {"hash": "fe8065c197", "shape": (480, 640)}, "scores": 1.00_53},
				    ]						,   )
