


"""simple docstring"""
import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE							:      Optional[Any]						=      logging.get_logger(__name__)







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			str:
							snake_case_          =						UniSpeechSatForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE			,	config=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						downstream_dict["""projector.weight"""]
							snake_case_          =						downstream_dict["""projector.bias"""]
							snake_case_          =						downstream_dict["""model.post_net.linear.weight"""]
							snake_case_          =						downstream_dict["""model.post_net.linear.bias"""]
							return model







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Optional[Any]:
							snake_case_          =						UniSpeechSatForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE			,	config=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						downstream_dict["""model.linear.weight"""]
							snake_case_          =						downstream_dict["""model.linear.bias"""]
							return model







def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			Union[str, Any]:
							snake_case_          =						UniSpeechSatForXVector.from_pretrained(_SCREAMING_SNAKE_CASE			,	config=_SCREAMING_SNAKE_CASE			)
							snake_case_          =						downstream_dict["""connector.weight"""]
							snake_case_          =						downstream_dict["""connector.bias"""]
							for i, kernel_size in enumerate(hf_config.tdnn_kernel			):
														snake_case_          =						downstream_dict[
														    f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
														]
														snake_case_          =						downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

							snake_case_          =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
							snake_case_          =						downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
							snake_case_          =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
							snake_case_          =						downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
							snake_case_          =						downstream_dict["""objective.W"""]
							return model







@torch.no_grad()
def   _a			( _SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)      ->			int:
							snake_case_          =						torch.load(_SCREAMING_SNAKE_CASE			,	map_location="""cpu"""			)

							snake_case_          =						checkpoint["""Downstream"""]

							snake_case_          =						UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE			)
							snake_case_          =						WavaVecaFeatureExtractor.from_pretrained(
							    _SCREAMING_SNAKE_CASE			,	return_attention_mask=_SCREAMING_SNAKE_CASE			,	do_normalize=_SCREAMING_SNAKE_CASE			)

							snake_case_          =						hf_config.architectures[0]
							if arch.endswith("""ForSequenceClassification"""			):
														snake_case_          =						convert_classification(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							elif arch.endswith("""ForAudioFrameClassification"""			):
														snake_case_          =						convert_diarization(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							elif arch.endswith("""ForXVector"""			):
														snake_case_          =						convert_xvector(_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			,	_SCREAMING_SNAKE_CASE			)
							else:
														raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""			)

							if hf_config.use_weighted_layer_sum:
														snake_case_          =						checkpoint["""Featurizer"""]["""weights"""]

							hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE			)
							hf_model.save_pretrained(_SCREAMING_SNAKE_CASE			)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE							:      List[str]						=      argparse.ArgumentParser()
				parser.add_argument(
				    '--base_model_name', default=None, type=str, help='Name of the huggingface pretrained base model.'
				)
				parser.add_argument('--config_path', default=None, type=str, help='Path to the huggingface classifier config.')
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to the s3prl checkpoint.')
				parser.add_argument('--model_dump_path', default=None, type=str, help='Path to the final converted model.')
				__SCREAMING_SNAKE_CASE							:      str						=      parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)







"""simple docstring"""
import faiss  # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy  # noqa: F401 # Here to have a nice missing dependency error message early on
import requests  # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn  # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm  # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve  # From: mauve-text

import datasets


__SCREAMING_SNAKE_CASE							:      Dict						=      '\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n  title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n  author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n  booktitle = {NeurIPS},\n  year      = {2021}\n}\n\n'

__SCREAMING_SNAKE_CASE							:      Optional[int]						=      '\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n'

__SCREAMING_SNAKE_CASE							:      Tuple						=      '\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n    predictions: list of generated text to score. Each predictions\n        should be a string with tokens separated by spaces.\n    references: list of reference for each prediction. Each\n        reference should be a string with tokens separated by spaces.\nOptional Args:\n    num_buckets: the size of the histogram to quantize P and Q. Options: \'auto\' (default) or an integer\n    pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n    kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n    kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n    kmeans_max_iter: maximum number of k-means iterations. Default 500\n    featurize_model_name: name of the model from which features are obtained. Default \'gpt2-large\' Use one of [\'gpt2\', \'gpt2-medium\', \'gpt2-large\', \'gpt2-xl\'].\n    device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n    max_text_length: maximum number of tokens to consider. Default 1024\n    divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n    mauve_scaling_factor: "c" from the paper. Default 5.\n    verbose: If True (default), print running time updates\n    seed: random seed to initialize k-means cluster assignments.\nReturns:\n    mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n    frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n    divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n    p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n    q_hist: same as above, but with q_text.\nExamples:\n\n    >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n    >>> import datasets\n    >>> mauve = datasets.load_metric(\'mauve\')\n    >>> predictions = ["hello there", "general kenobi"]\n    >>> references = ["hello there", "general kenobi"]\n    >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n    >>> print(out.mauve) # doctest: +SKIP\n    1.0\n'





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





							'''simple docstring'''

							def 						lowerCAmelCase    (			self							: Tuple    )   ->Union[str, Any]:



														"""simple docstring"""



														return datasets.MetricInfo(
														    description=_DESCRIPTION						,    citation=_CITATION						,    homepage="""https://github.com/krishnap25/mauve"""						,    inputs_description=_KWARGS_DESCRIPTION						,    features=datasets.Features(
														        {
														            """predictions""": datasets.Value("""string"""						,    id="""sequence"""    ),
														            """references""": datasets.Value("""string"""						,    id="""sequence"""    ),
														        }    )						,    codebase_urls=["""https://github.com/krishnap25/mauve"""]						,    reference_urls=[
														        """https://arxiv.org/abs/2102.01454""",
														        """https://github.com/krishnap25/mauve""",
														    ]						,    )



							def 						lowerCAmelCase    (			self							: Optional[int]						,    UpperCAmelCase_							: Tuple						,    UpperCAmelCase_							: Any						,    UpperCAmelCase_							: Optional[Any]=None						,    UpperCAmelCase_							: Optional[Any]=None						,    UpperCAmelCase_							: List[Any]=None						,    UpperCAmelCase_							: Any=None						,    UpperCAmelCase_							: int="auto"						,    UpperCAmelCase_							: Dict=-1						,    UpperCAmelCase_							: Optional[Any]=0.9						,    UpperCAmelCase_							: Dict=5						,    UpperCAmelCase_							: Optional[int]=500						,    UpperCAmelCase_							: Any="gpt2-large"						,    UpperCAmelCase_							: Union[str, Any]=-1						,    UpperCAmelCase_							: Optional[Any]=1_024						,    UpperCAmelCase_							: Dict=25						,    UpperCAmelCase_							: Optional[Any]=5						,    UpperCAmelCase_							: int=True						,    UpperCAmelCase_							: Union[str, Any]=25						,    )   ->List[Any]:



														"""simple docstring"""



														snake_case_          =						compute_mauve(
														    p_text=UpperCAmelCase_						,    q_text=UpperCAmelCase_						,    p_features=UpperCAmelCase_						,    q_features=UpperCAmelCase_						,    p_tokens=UpperCAmelCase_						,    q_tokens=UpperCAmelCase_						,    num_buckets=UpperCAmelCase_						,    pca_max_data=UpperCAmelCase_						,    kmeans_explained_var=UpperCAmelCase_						,    kmeans_num_redo=UpperCAmelCase_						,    kmeans_max_iter=UpperCAmelCase_						,    featurize_model_name=UpperCAmelCase_						,    device_id=UpperCAmelCase_						,    max_text_length=UpperCAmelCase_						,    divergence_curve_discretization_size=UpperCAmelCase_						,    mauve_scaling_factor=UpperCAmelCase_						,    verbose=UpperCAmelCase_						,    seed=UpperCAmelCase_						,    )
														return out



