



"""simple docstring"""





import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase       :					Optional[Any]      					=					logging.get_logger('''transformers.models.speecht5''')
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			Tuple					, __UpperCamelCase      :			str    ):


						'''simple docstring'''






						hf_model.apply_weight_norm()

						snake_case_   : int						=			checkpoint["input_conv.weight_g"]
						snake_case_   : Tuple						=			checkpoint["input_conv.weight_v"]
						snake_case_   : str						=			checkpoint["input_conv.bias"]

						for i in range(len(config.upsample_rates    )    ):
												snake_case_   : Optional[Any]						=			checkpoint[F'upsamples.{i}.1.weight_g']
												snake_case_   : List[str]						=			checkpoint[F'upsamples.{i}.1.weight_v']
												snake_case_   : int						=			checkpoint[F'upsamples.{i}.1.bias']

						for i in range(len(config.upsample_rates    ) * len(config.resblock_kernel_sizes    )    ):
												for j in range(len(config.resblock_dilation_sizes    )    ):
																		snake_case_   : Optional[Any]						=			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
																		snake_case_   : Optional[Any]						=			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
																		snake_case_   : Optional[Any]						=			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

																		snake_case_   : Union[str, Any]						=			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
																		snake_case_   : Any						=			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
																		snake_case_   : Tuple						=			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

						snake_case_   : List[Any]						=			checkpoint["output_conv.1.weight_g"]
						snake_case_   : Union[str, Any]						=			checkpoint["output_conv.1.weight_v"]
						snake_case_   : int						=			checkpoint["output_conv.1.bias"]

						hf_model.remove_weight_norm()
@torch.no_grad()
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			int					, __UpperCamelCase      :			Union[str, Any]					, __UpperCamelCase      :			Dict=None					, __UpperCamelCase      :			Dict=None					, ):


						'''simple docstring'''






						if config_path is not None:
												snake_case_   : Dict						=			SpeechTaHifiGanConfig.from_pretrained(lowerCamelCase__    )
						else:
												snake_case_   : Optional[int]						=			SpeechTaHifiGanConfig()

						snake_case_   : Optional[int]						=			SpeechTaHifiGan(lowerCamelCase__    )

						snake_case_   : int						=			torch.load(lowerCamelCase__    )
						load_weights(orig_checkpoint["""model"""]["""generator"""]					, lowerCamelCase__					, lowerCamelCase__    )

						snake_case_   : str						=			np.load(lowerCamelCase__    )
						snake_case_   : Optional[Any]						=			stats[0].reshape(-1    )
						snake_case_   : Union[str, Any]						=			stats[1].reshape(-1    )
						snake_case_   : Optional[int]						=			torch.from_numpy(lowerCamelCase__    ).float()
						snake_case_   : int						=			torch.from_numpy(lowerCamelCase__    ).float()

						model.save_pretrained(lowerCamelCase__    )

						if repo_id:
												print("""Pushing to the hub..."""    )
												model.push_to_hub(lowerCamelCase__    )


if __name__ == "__main__":
					__lowerCAmelCase       :					List[Any]      					=					argparse.ArgumentParser()
					parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to original checkpoint''')
					parser.add_argument('''--stats_path''', required=True, default=None, type=str, help='''Path to stats.npy file''')
					parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
					parser.add_argument(
					    '''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
					)
					parser.add_argument(
					    '''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
					)

					__lowerCAmelCase       :					str      					=					parser.parse_args()
					convert_hifigan_checkpoint(
					    args.checkpoint_path,
					    args.stats_path,
					    args.pytorch_dump_folder_path,
					    args.config_path,
					    args.push_to_hub,
					)








"""simple docstring"""





from sklearn.metrics import fa_score, matthews_corrcoef

import datasets

from .record_evaluation import evaluate as evaluate_record


__lowerCAmelCase       :					List[str]      					=					'''\
@article{wang2019superglue,
  title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},
  author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},
  journal={arXiv preprint arXiv:1905.00537},
  year={2019}
}
'''

__lowerCAmelCase       :					Optional[Any]      					=					'''\
SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after
GLUE with a new set of more difficult language understanding tasks, improved
resources, and a new public leaderboard.
'''

__lowerCAmelCase       :					str      					=					'''
Compute SuperGLUE evaluation metric associated to each SuperGLUE dataset.
Args:
    predictions: list of predictions to score. Depending on the SuperGlUE subset:
        - for \'record\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'prediction_text\': the predicted answer text
        - for \'multirc\': list of question-answer dictionaries with the following keys:
            - \'idx\': index of the question-answer pair as specified by the dataset
            - \'prediction\': the predicted answer label
        - otherwise: list of predicted labels
    references: list of reference labels. Depending on the SuperGLUE subset:
        - for \'record\': list of question-answers dictionaries with the following keys:
            - \'idx\': index of the question as specified by the dataset
            - \'answers\': list of possible answers
        - otherwise: list of reference labels
Returns: depending on the SuperGLUE subset:
    - for \'record\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1\': F1 score
    - for \'multirc\':
        - \'exact_match\': Exact match between answer and gold answer
        - \'f1_m\': Per-question macro-F1 score
        - \'f1_a\': Average F1 score over all answers
    - for \'axb\':
        \'matthews_correlation\': Matthew Correlation
    - for \'cb\':
        - \'accuracy\': Accuracy
        - \'f1\': F1 score
    - for all others:
        - \'accuracy\': Accuracy
Examples:

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'copa\')  # any of ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'cb\')
    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'record\')
    >>> predictions = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'prediction_text\': \'answer\'}]
    >>> references = [{\'idx\': {\'passage\': 0, \'query\': 0}, \'answers\': [\'answer\', \'another_answer\']}]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'multirc\')
    >>> predictions = [{\'idx\': {\'answer\': 0, \'paragraph\': 0, \'question\': 0}, \'prediction\': 0}, {\'idx\': {\'answer\': 1, \'paragraph\': 2, \'question\': 3}, \'prediction\': 1}]
    >>> references = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'exact_match\': 1.0, \'f1_m\': 1.0, \'f1_a\': 1.0}

    >>> super_glue_metric = datasets.load_metric(\'super_glue\', \'axb\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = super_glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''
def 	__lowerCAmelCase						(		__UpperCamelCase      :			List[str]					, __UpperCamelCase      :			Any    ):


						'''simple docstring'''






						return float((preds == labels).mean()    )
def 	__lowerCAmelCase						(		__UpperCamelCase      :			Any					, __UpperCamelCase      :			Optional[int]					, __UpperCamelCase      :			str="binary"    ):


						'''simple docstring'''






						snake_case_   : Optional[Any]						=			simple_accuracy(__UpperCamelCase					, __UpperCamelCase    )
						snake_case_   : Dict						=			float(fa_score(y_true=__UpperCamelCase					, y_pred=__UpperCamelCase					, average=__UpperCamelCase    )    )
						return {
						    "accuracy": acc,
						    "f1": fa,
						}
def 	__lowerCAmelCase						(		__UpperCamelCase      :			str					, __UpperCamelCase      :			int    ):


						'''simple docstring'''






						snake_case_   : List[Any]						=			{}
						for id_pred, label in zip(__UpperCamelCase					, __UpperCamelCase    ):
												snake_case_   : Optional[int]						=			F'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
												snake_case_   : Union[str, Any]						=			id_pred["""prediction"""]
												if question_id in question_map:
																		question_map[question_id].append((pred, label)    )
												else:
																		snake_case_   : str						=			[(pred, label)]
						snake_case_			,		snake_case_   : List[str]						=			[], []
						for question, preds_labels in question_map.items():
												snake_case_			,		snake_case_   : Optional[Any]						=			zip(*__UpperCamelCase    )
												snake_case_   : int						=			fa_score(y_true=__UpperCamelCase					, y_pred=__UpperCamelCase					, average="""macro"""    )
												fas.append(__UpperCamelCase    )
												snake_case_   : Dict						=			int(sum(pred == label for pred, label in preds_labels    ) == len(__UpperCamelCase    )    )
												ems.append(__UpperCamelCase    )
						snake_case_   : Optional[int]						=			float(sum(__UpperCamelCase    ) / len(__UpperCamelCase    )    )
						snake_case_   : Any						=			sum(__UpperCamelCase    ) / len(__UpperCamelCase    )
						snake_case_   : int						=			float(fa_score(y_true=__UpperCamelCase					, y_pred=[id_pred["""prediction"""] for id_pred in ids_preds]    )    )
						return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}






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





		"""simple docstring"""






		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								if self.config_name not in [
								    "boolq",
								    "cb",
								    "copa",
								    "multirc",
								    "record",
								    "rte",
								    "wic",
								    "wsc",
								    "wsc.fixed",
								    "axb",
								    "axg",
								]:
														raise KeyError(
														    """You should supply a configuration name selected in """
														    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""   )
								return datasets.MetricInfo(
								    description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(self._get_feature_types()   )							,    codebase_urls=[]							,    reference_urls=[]							,    format="""numpy""" if not self.config_name == """record""" and not self.config_name == """multirc""" else None							,    )





		def    UpperCAmelCase__							(					self   ) ->						Optional[int]:







								'''simple docstring'''

								if self.config_name == "record":
														return {
														    "predictions": {
														        "idx": {
														            "passage": datasets.Value("""int64"""   ),
														            "query": datasets.Value("""int64"""   ),
														        },
														        "prediction_text": datasets.Value("""string"""   ),
														    },
														    "references": {
														        "idx": {
														            "passage": datasets.Value("""int64"""   ),
														            "query": datasets.Value("""int64"""   ),
														        },
														        "answers": datasets.Sequence(datasets.Value("""string"""   )   ),
														    },
														}
								elif self.config_name == "multirc":
														return {
														    "predictions": {
														        "idx": {
														            "answer": datasets.Value("""int64"""   ),
														            "paragraph": datasets.Value("""int64"""   ),
														            "question": datasets.Value("""int64"""   ),
														        },
														        "prediction": datasets.Value("""int64"""   ),
														    },
														    "references": datasets.Value("""int64"""   ),
														}
								else:
														return {
														    "predictions": datasets.Value("""int64"""   ),
														    "references": datasets.Value("""int64"""   ),
														}





		def    UpperCAmelCase__							(					self							,    _lowercase							,    _lowercase   ) ->						List[str]:







								'''simple docstring'''

								if self.config_name == "axb":
														return {"matthews_correlation": matthews_corrcoef(_lowercase							,    _lowercase   )}
								elif self.config_name == "cb":
														return acc_and_fa(_lowercase							,    _lowercase							,    fa_avg="""macro"""   )
								elif self.config_name == "record":
														snake_case_   : Optional[Any]						=			[
														    {
														        """qas""": [
														            {"""id""": ref["""idx"""]["""query"""], """answers""": [{"""text""": ans} for ans in ref["""answers"""]]}
														            for ref in references
														        ]
														    }
														]
														snake_case_   : Dict						=			{pred["""idx"""]["""query"""]: pred["""prediction_text"""] for pred in predictions}
														return evaluate_record(_lowercase							,    _lowercase   )[0]
								elif self.config_name == "multirc":
														return evaluate_multirc(_lowercase							,    _lowercase   )
								elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
														return {"accuracy": simple_accuracy(_lowercase							,    _lowercase   )}
								else:
														raise KeyError(
														    """You should supply a configuration name selected in """
														    """[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]"""   )

