




from sklearn.metrics import fa_score

import datasets


UpperCAmelCase_		:				int									=     "\nThe F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:\nF1 = 2 * (precision * recall) / (precision + recall)\n"


UpperCAmelCase_		:				List[Any]									=     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n\n        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n\nExamples:\n\n    Example 1-A simple binary example\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n        >>> print(results)\n        {'f1': 0.5}\n\n    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n        >>> print(round(results['f1'], 2))\n        0.67\n\n    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n        >>> f1_metric = datasets.load_metric(\"f1\")\n        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n        >>> print(round(results['f1'], 2))\n        0.35\n\n    Example 4-A multiclass example, with different values for the `average` input.\n        >>> predictions = [0, 2, 1, 0, 0, 1]\n        >>> references = [0, 1, 2, 0, 1, 2]\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n        >>> print(round(results['f1'], 2))\n        0.33\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n        >>> print(round(results['f1'], 2))\n        0.27\n        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n        >>> print(results)\n        {'f1': array([0.8, 0. , 0. ])}\n"


UpperCAmelCase_		:				str									=     "\n@article{scikit-learn,\n    title={Scikit-learn: Machine Learning in {P}ython},\n    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n    journal={Journal of Machine Learning Research},\n    volume={12},\n    pages={2825--2830},\n    year={2011}\n}\n"




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

						def     A__						(		self   :Optional[Any] ):
										'''simple docstring'''

										return datasets.MetricInfo(
										    description=_DESCRIPTION    , citation=_CITATION    , inputs_description=_KWARGS_DESCRIPTION    , features=datasets.Features(
										        {
										            """predictions""": datasets.Sequence(datasets.Value("""int32""" ) ),
										            """references""": datasets.Sequence(datasets.Value("""int32""" ) ),
										        }
										        if self.config_name == """multilabel"""
										        else {
										            """predictions""": datasets.Value("""int32""" ),
										            """references""": datasets.Value("""int32""" ),
										        } )    , reference_urls=["""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html"""]    , )


						def     A__						(		self   :Tuple    , __snake_case   :Union[str, Any]    , __snake_case   :Optional[Any]    , __snake_case   :Optional[int]=None    , __snake_case   :Any=1    , __snake_case   :int="binary"    , __snake_case   :int=None ):
										'''simple docstring'''

										__magic_name__	:	List[str]								=fa_score(
										    __snake_case    , __snake_case    , labels=__snake_case    , pos_label=__snake_case    , average=__snake_case    , sample_weight=__snake_case )
										return {"f1": float(__snake_case ) if score.size == 1 else score}










import argparse
import re

import torch
from CLAP import create_model

from transformers import AutoFeatureExtractor, ClapConfig, ClapModel


UpperCAmelCase_		:				Optional[int]									=     {
    "text_branch": "text_model",
    "audio_branch": "audio_model.audio_encoder",
    "attn": "attention.self",
    "self.proj": "output.dense",
    "attention.self_mask": "attn_mask",
    "mlp.fc1": "intermediate.dense",
    "mlp.fc2": "output.dense",
    "norm1": "layernorm_before",
    "norm2": "layernorm_after",
    "bn0": "batch_norm",
}

UpperCAmelCase_		:				Optional[int]									=     AutoFeatureExtractor.from_pretrained("laion/clap-htsat-unfused", truncation="rand_trunc")




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Any								=create_model(
				    """HTSAT-tiny"""	,     """roberta"""	,     lowerCamelCase	,     precision="""fp32"""	,     device="""cuda:0""" if torch.cuda.is_available() else """cpu"""	,     enable_fusion=lowerCamelCase	,     fusion_type="""aff_2d""" if enable_fusion else None	,     )
				return model, model_cfg




def 		lowerCAmelCase_   (	lowerCamelCase   ):
				__magic_name__	:	Optional[int]								={}

				__magic_name__	:	Union[str, Any]								=R""".*sequential.(\d+).*"""
				__magic_name__	:	Union[str, Any]								=R""".*_projection.(\d+).*"""

				for key, value in state_dict.items():
								# check if any key needs to be modified
								for key_to_modify, new_key in KEYS_TO_MODIFY_MAPPING.items():
												if key_to_modify in key:
																__magic_name__	:	Union[str, Any]								=key.replace(lowerCamelCase	,     lowerCamelCase   )

								if re.match(lowerCamelCase	,     lowerCamelCase   ):
												# replace sequential layers with list
												__magic_name__	:	str								=re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )

												__magic_name__	:	Optional[Any]								=key.replace(F"sequential.{sequential_layer}."	,     F"layers.{int(lowerCamelCase   )//3}.linear."   )
								elif re.match(lowerCamelCase	,     lowerCamelCase   ):
												__magic_name__	:	Any								=int(re.match(lowerCamelCase	,     lowerCamelCase   ).group(1   )   )

												# Because in CLAP they use `nn.Sequential`...
												__magic_name__	:	int								=1 if projecton_layer == 0 else 2

												__magic_name__	:	List[Any]								=key.replace(F"_projection.{projecton_layer}."	,     F"_projection.linear{transformers_projection_layer}."   )

								if "audio" and "qkv" in key:
												# split qkv into query key and value
												__magic_name__	:	Dict								=value
												__magic_name__	:	List[str]								=mixed_qkv.size(0   ) // 3

												__magic_name__	:	Optional[Any]								=mixed_qkv[:qkv_dim]
												__magic_name__	:	Any								=mixed_qkv[qkv_dim : qkv_dim * 2]
												__magic_name__	:	Optional[Any]								=mixed_qkv[qkv_dim * 2 :]

												__magic_name__	:	str								=query_layer
												__magic_name__	:	int								=key_layer
												__magic_name__	:	str								=value_layer
								else:
												__magic_name__	:	int								=value

				return model_state_dict




def 		lowerCAmelCase_   (	lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase	,     lowerCamelCase=False   ):
				__magic_name__				, __magic_name__	:	Tuple								=init_clap(lowerCamelCase	,     enable_fusion=lowerCamelCase   )

				clap_model.eval()
				__magic_name__	:	int								=clap_model.state_dict()
				__magic_name__	:	Optional[Any]								=rename_state_dict(lowerCamelCase   )

				__magic_name__	:	Any								=ClapConfig()
				__magic_name__	:	Optional[Any]								=enable_fusion
				__magic_name__	:	Tuple								=ClapModel(lowerCamelCase   )

				# ignore the spectrogram embedding layer
				model.load_state_dict(lowerCamelCase	,     strict=lowerCamelCase   )

				model.save_pretrained(lowerCamelCase   )
				transformers_config.save_pretrained(lowerCamelCase   )


if __name__ == "__main__":
							UpperCAmelCase_		:				Optional[int]									=     argparse.ArgumentParser()
							parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
							parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
							parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
							parser.add_argument("--enable_fusion", action="store_true", help="Whether to enable fusion or not")
							UpperCAmelCase_		:				Dict									=     parser.parse_args()

							convert_clap_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.enable_fusion)




