






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()
__snake_case    :    Any         =     logging.get_logger(__name__)

set_seed(7_70)


__snake_case    :    Union[str, Any]         =     {
    """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""",
}


__snake_case    :    int         =     {
    """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""",
    },
}

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







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					int=False		)  ->   Dict:

						"""simple docstring"""



						lowerCAmelCase__    						=						model_type
						if use_small:
												key += "_small"
						return os.path.join(UpperCamelCase_    ,       REMOTE_MODEL_PATHS[key]['file_name']		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Optional[int]		)  ->   Union[str, Any]:

						"""simple docstring"""



						os.makedirs(UpperCamelCase_    ,       exist_ok=UpperCamelCase_		)
						hf_hub_download(repo_id=UpperCamelCase_    ,       filename=UpperCamelCase_    ,       local_dir=UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					List[Any]    ,       UpperCamelCase_       :					Any=False    ,       UpperCamelCase_       :					Union[str, Any]="text"		)  ->   Optional[Any]:

						"""simple docstring"""



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

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

						lowerCAmelCase__    						=						ConfigClass(**checkpoint['model_args']		)
						lowerCAmelCase__    						=						ModelClass(config=UpperCamelCase_		)
						lowerCAmelCase__    						=						GenerationConfigClass()

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

																		lowerCAmelCase__    						=						state_dict.pop(UpperCamelCase_		)

						lowerCAmelCase__    						=						set(state_dict.keys()		) - set(model.state_dict().keys()		)
						lowerCAmelCase__    						=						{k for k in extra_keys if not k.endswith('.attn.bias'		)}
						lowerCAmelCase__    						=						set(model.state_dict().keys()		) - set(state_dict.keys()		)
						lowerCAmelCase__    						=						{k for k in missing_keys if not k.endswith('.attn.bias'		)}
						if len(UpperCamelCase_		) != 0:
												raise ValueError(F"extra keys found: {extra_keys}"		)
						if len(UpperCamelCase_		) != 0:
												raise ValueError(F"missing keys: {missing_keys}"		)
						model.load_state_dict(UpperCamelCase_    ,       strict=UpperCamelCase_		)
						lowerCAmelCase__    						=						model.num_parameters(exclude_embeddings=UpperCamelCase_		)
						lowerCAmelCase__    						=						checkpoint['best_val_loss'].item()
						logger.info(F"model loaded: {round(n_params/1e6    ,       1		)}M params, {round(UpperCamelCase_    ,       3		)} loss"		)
						model.eval()
						model.to(UpperCamelCase_		)
						del checkpoint, state_dict

						return model







def 	_UpperCamelCase   (    UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Any=False    ,       UpperCamelCase_       :					Optional[int]="text"		)  ->   Dict:

						"""simple docstring"""



						if model_type not in ("text", "coarse", "fine"):
												raise NotImplementedError()

						lowerCAmelCase__    						=						'cpu'  # do conversion on cpu

						lowerCAmelCase__    						=						_get_ckpt_path(UpperCamelCase_    ,       use_small=UpperCamelCase_		)
						lowerCAmelCase__    						=						_load_model(UpperCamelCase_    ,       UpperCamelCase_    ,       model_type=UpperCamelCase_    ,       use_small=UpperCamelCase_		)

						# load bark initial model
						lowerCAmelCase__    						=						_bark_load_model(UpperCamelCase_    ,       'cpu'    ,       model_type=UpperCamelCase_    ,       use_small=UpperCamelCase_		)

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

						if model.num_parameters(exclude_embeddings=UpperCamelCase_		) != 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
						lowerCAmelCase__    						=						5
						lowerCAmelCase__    						=						10

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

												lowerCAmelCase__    						=						model(UpperCamelCase_		)

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

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

												lowerCAmelCase__    						=						model(UpperCamelCase_    ,       UpperCamelCase_		)
												lowerCAmelCase__    						=						bark_model(UpperCamelCase_    ,       UpperCamelCase_		)

												lowerCAmelCase__    						=						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(UpperCamelCase_		).mkdir(exist_ok=UpperCamelCase_		)
						model.save_pretrained(UpperCamelCase_		)







def 	_UpperCamelCase   (    UpperCamelCase_       :					int    ,       UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					Union[str, Any]    ,       UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					Union[str, Any]    ,       )  ->   Optional[Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						os.path.join(UpperCamelCase_    ,       UpperCamelCase_		)

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

						lowerCAmelCase__    						=						BarkSemanticModel.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__    						=						BarkCoarseModel.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__    						=						BarkFineModel.from_pretrained(UpperCamelCase_		)
						lowerCAmelCase__    						=						EncodecModel.from_pretrained('facebook/encodec_24khz'		)

						lowerCAmelCase__    						=						BarkConfig.from_sub_model_configs(
						    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

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

						lowerCAmelCase__    						=						BarkModel(UpperCamelCase_		)

						lowerCAmelCase__    						=						semantic
						lowerCAmelCase__    						=						coarseAcoustic
						lowerCAmelCase__    						=						fineAcoustic
						lowerCAmelCase__    						=						codec

						lowerCAmelCase__    						=						bark_generation_config

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


if __name__ == "__main__":
					__snake_case    :    Union[str, Any]         =     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.""")

					__snake_case    :    str         =     parser.parse_args()

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













from typing import Dict, Optional

import numpy as np

import datasets


__snake_case    :    str         =     """
IoU is the area of overlap between the predicted segmentation and the ground truth divided by the area of union
between the predicted segmentation and the ground truth. For binary (two classes) or multi-class segmentation,
the mean IoU of the image is calculated by taking the IoU of each class and averaging them.
"""

__snake_case    :    Tuple         =     """
Args:
    predictions (`List[ndarray]`):
        List of predicted segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    references (`List[ndarray]`):
        List of ground truth segmentation maps, each of shape (height, width). Each segmentation map can be of a different size.
    num_labels (`int`):
        Number of classes (categories).
    ignore_index (`int`):
        Index that will be ignored during evaluation.
    nan_to_num (`int`, *optional*):
        If specified, NaN values will be replaced by the number defined by the user.
    label_map (`dict`, *optional*):
        If specified, dictionary mapping old label indices to new label indices.
    reduce_labels (`bool`, *optional*, defaults to `False`):
        Whether or not to reduce all label values of segmentation maps by 1. Usually used for datasets where 0 is used for background,
        and background itself is not included in all classes of a dataset (e.g. ADE20k). The background label will be replaced by 255.

Returns:
    `Dict[str, float | ndarray]` comprising various elements:
    - *mean_iou* (`float`):
        Mean Intersection-over-Union (IoU averaged over all categories).
    - *mean_accuracy* (`float`):
        Mean accuracy (averaged over all categories).
    - *overall_accuracy* (`float`):
        Overall accuracy on all images.
    - *per_category_accuracy* (`ndarray` of shape `(num_labels,)`):
        Per category accuracy.
    - *per_category_iou* (`ndarray` of shape `(num_labels,)`):
        Per category IoU.

Examples:

    >>> import numpy as np

    >>> mean_iou = datasets.load_metric(\"mean_iou\")

    >>> # suppose one has 3 different segmentation maps predicted
    >>> predicted_1 = np.array([[1, 2], [3, 4], [5, 255]])
    >>> actual_1 = np.array([[0, 3], [5, 4], [6, 255]])

    >>> predicted_2 = np.array([[2, 7], [9, 2], [3, 6]])
    >>> actual_2 = np.array([[1, 7], [9, 2], [3, 6]])

    >>> predicted_3 = np.array([[2, 2, 3], [8, 2, 4], [3, 255, 2]])
    >>> actual_3 = np.array([[1, 2, 2], [8, 2, 1], [3, 255, 1]])

    >>> predicted = [predicted_1, predicted_2, predicted_3]
    >>> ground_truth = [actual_1, actual_2, actual_3]

    >>> results = mean_iou.compute(predictions=predicted, references=ground_truth, num_labels=10, ignore_index=255, reduce_labels=False)
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {'mean_iou': 0.47750000000000004, 'mean_accuracy': 0.5916666666666666, 'overall_accuracy': 0.5263157894736842, 'per_category_iou': array([0.   , 0.   , 0.375, 0.4  , 0.5  , 0.   , 0.5  , 1.   , 1.   , 1.   ]), 'per_category_accuracy': array([0.        , 0.        , 0.75      , 0.66666667, 1.        , 0.        , 0.5       , 1.        , 1.        , 1.        ])}
"""

__snake_case    :    Any         =     """\
@software{MMSegmentation_Contributors_OpenMMLab_Semantic_Segmentation_2020,
author = {{MMSegmentation Contributors}},
license = {Apache-2.0},
month = {7},
title = {{OpenMMLab Semantic Segmentation Toolbox and Benchmark}},
url = {https://github.com/open-mmlab/mmsegmentation},
year = {2020}
}"""







def 	_UpperCamelCase   (    UpperCamelCase_       :					str    ,       UpperCamelCase_       :					Tuple    ,       UpperCamelCase_       :					Optional[Any]    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   List[Any]:

						"""simple docstring"""



						if label_map is not None:
												for old_id, new_id in label_map.items():
																		lowerCAmelCase__    						=						new_id

    # turn into Numpy arrays
						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)
						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)

						if reduce_labels:
												lowerCAmelCase__    						=						255
												lowerCAmelCase__    						=						label - 1
												lowerCAmelCase__    						=						255

						lowerCAmelCase__    						=						label != ignore_index
						lowerCAmelCase__    						=						np.not_equal(UpperCamelCase_    ,       UpperCamelCase_		)
						lowerCAmelCase__    						=						pred_label[mask]
						lowerCAmelCase__    						=						np.array(UpperCamelCase_		)[mask]

						lowerCAmelCase__    						=						pred_label[pred_label == label]

						lowerCAmelCase__    						=						np.histogram(UpperCamelCase_    ,       bins=UpperCamelCase_    ,       range=(0, num_labels - 1)		)[0]
						lowerCAmelCase__    						=						np.histogram(UpperCamelCase_    ,       bins=UpperCamelCase_    ,       range=(0, num_labels - 1)		)[0]
						lowerCAmelCase__    						=						np.histogram(UpperCamelCase_    ,       bins=UpperCamelCase_    ,       range=(0, num_labels - 1)		)[0]

						lowerCAmelCase__    						=						area_pred_label + area_label - area_intersect

						return area_intersect, area_union, area_pred_label, area_label







def 	_UpperCamelCase   (    UpperCamelCase_       :					Dict    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					int    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   Union[str, Any]:

						"""simple docstring"""



						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						lowerCAmelCase__    						=						np.zeros((num_labels,)    ,       dtype=np.floataa		)
						for result, gt_seg_map in zip(UpperCamelCase_    ,       UpperCamelCase_		):
												lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						intersect_and_union(
												    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)
												total_area_intersect += area_intersect
												total_area_union += area_union
												total_area_pred_label += area_pred_label
												total_area_label += area_label
						return total_area_intersect, total_area_union, total_area_pred_label, total_area_label







def 	_UpperCamelCase   (    UpperCamelCase_       :					List[str]    ,       UpperCamelCase_       :					Optional[int]    ,       UpperCamelCase_       :					str    ,       UpperCamelCase_       :					bool    ,       UpperCamelCase_       :					Optional[int] = None    ,       UpperCamelCase_       :					Optional[Dict[int, int]] = None    ,       UpperCamelCase_       :					bool = False    ,       )  ->   List[str]:

						"""simple docstring"""



						lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__						,       lowerCAmelCase__    						=						total_intersect_and_union(
						    UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_    ,       UpperCamelCase_		)

						# compute metrics
						lowerCAmelCase__    						=						{}

						lowerCAmelCase__    						=						total_area_intersect.sum() / total_area_label.sum()
						lowerCAmelCase__    						=						total_area_intersect / total_area_union
						lowerCAmelCase__    						=						total_area_intersect / total_area_label

						lowerCAmelCase__    						=						np.nanmean(UpperCamelCase_		)
						lowerCAmelCase__    						=						np.nanmean(UpperCamelCase_		)
						lowerCAmelCase__    						=						all_acc
						lowerCAmelCase__    						=						iou
						lowerCAmelCase__    						=						acc

						if nan_to_num is not None:
												lowerCAmelCase__    						=						{metric: np.nan_to_num(UpperCamelCase_    ,       nan=UpperCamelCase_		) for metric, metric_value in metrics.items()}

						return metrics


@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION		,					_KWARGS_DESCRIPTION)
class     __SCREAMING_SNAKE_CASE					(   datasets.Metric):






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return datasets.MetricInfo(
												    description=_DESCRIPTION     ,						citation=_CITATION     ,						inputs_description=_KWARGS_DESCRIPTION     ,						features=datasets.Features(
												        # 1st Seq - height dim, 2nd - width dim
												        {
												            'predictions': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'   )   )   ),
												            'references': datasets.Sequence(datasets.Sequence(datasets.Value('uint16'   )   )   ),
												        }   )     ,						reference_urls=[
												        'https://github.com/open-mmlab/mmsegmentation/blob/71c201b1813267d78764f306a297ca717827c4bf/mmseg/core/evaluation/metrics.py'
												    ]     ,						)







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = None     ,						_UpperCamelCase = None     ,						_UpperCamelCase = False     ,						):






												"""simple docstring"""
												lowerCAmelCase__    						=						mean_iou(
												    results=_UpperCamelCase     ,						gt_seg_maps=_UpperCamelCase     ,						num_labels=_UpperCamelCase     ,						ignore_index=_UpperCamelCase     ,						nan_to_num=_UpperCamelCase     ,						label_map=_UpperCamelCase     ,						reduce_labels=_UpperCamelCase     ,						)
												return iou_result





