import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
_lowerCAmelCase       =		logging.get_logger("transformers.models.speecht5")
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case						):
					hf_model.apply_weight_norm()

					_UpperCamelCase			     =     checkpoint['''input_conv.weight_g''']
					_UpperCamelCase			     =     checkpoint['''input_conv.weight_v''']
					_UpperCamelCase			     =     checkpoint['''input_conv.bias''']

					for i in range(len(config.upsample_rates						)						):
										_UpperCamelCase			     =     checkpoint[f"""upsamples.{i}.1.weight_g"""]
										_UpperCamelCase			     =     checkpoint[f"""upsamples.{i}.1.weight_v"""]
										_UpperCamelCase			     =     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						)						):
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_g"""]
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs1.{j}.1.weight_v"""]
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs1.{j}.1.bias"""]

															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_g"""]
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs2.{j}.1.weight_v"""]
															_UpperCamelCase			     =     checkpoint[f"""blocks.{i}.convs2.{j}.1.bias"""]

					_UpperCamelCase			     =     checkpoint['''output_conv.1.weight_g''']
					_UpperCamelCase			     =     checkpoint['''output_conv.1.weight_v''']
					_UpperCamelCase			     =     checkpoint['''output_conv.1.bias''']

					hf_model.remove_weight_norm()
@torch.no_grad()
def 					_snake_case	(  __snake_case  ,     __snake_case  ,     __snake_case  ,     __snake_case=None  ,     __snake_case=None  ,     ):
					if config_path is not None:
										_UpperCamelCase			     =     SpeechTaHifiGanConfig.from_pretrained(__snake_case						)
					else:
										_UpperCamelCase			     =     SpeechTaHifiGanConfig()

					_UpperCamelCase			     =     SpeechTaHifiGan(__snake_case						)

					_UpperCamelCase			     =     torch.load(__snake_case						)
					load_weights(orig_checkpoint['''model''']['''generator''']  ,     __snake_case  ,     __snake_case						)

					_UpperCamelCase			     =     np.load(__snake_case						)
					_UpperCamelCase			     =     stats[0].reshape(-1						)
					_UpperCamelCase			     =     stats[1].reshape(-1						)
					_UpperCamelCase			     =     torch.from_numpy(__snake_case						).float()
					_UpperCamelCase			     =     torch.from_numpy(__snake_case						).float()

					model.save_pretrained(__snake_case						)

					if repo_id:
										print('''Pushing to the hub...'''						)
										model.push_to_hub(__snake_case						)


if __name__ == "__main__":
					_lowerCAmelCase       =		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       =		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"""




import datasets

from .evaluate import evaluate


lowerCAmelCase__				=					'\\n@inproceedings{Rajpurkar2016SQuAD10,\n  title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n  author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n  booktitle={EMNLP},\n  year={2016}\n}\n'

lowerCAmelCase__				=					'\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'

lowerCAmelCase__				=					'\nComputes SQuAD scores (F1 and EM).\nArgs:\n    predictions: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair as given in the references (see below)\n        - \'prediction_text\': the text of the answer\n    references: List of question-answers dictionaries with the following key-values:\n        - \'id\': id of the question-answer pair (see above),\n        - \'answers\': a Dict in the SQuAD dataset format\n            {\n                \'text\': list of possible texts for the answer, as a list of strings\n                \'answer_start\': list of start positions for the answer, as a list of ints\n            }\n            Note that answer_start values are not taken into account to compute the metric.\nReturns:\n    \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n    \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n    >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n    >>> squad_metric = datasets.load_metric("squad")\n    >>> results = squad_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'exact_match\': 100.0, \'f1\': 100.0}\n'





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

							def _lowercase       (self     ):

									"""simple docstring"""







									return datasets.MetricInfo(
									    description=_DESCRIPTION							,							citation=_CITATION							,							inputs_description=_KWARGS_DESCRIPTION							,							features=datasets.Features(
									        {
									            '''predictions''': {'''id''': datasets.Value('''string'''     ), '''prediction_text''': datasets.Value('''string'''     )},
									            '''references''': {
									                '''id''': datasets.Value('''string'''     ),
									                '''answers''': datasets.features.Sequence(
									                    {
									                        '''text''': datasets.Value('''string'''     ),
									                        '''answer_start''': datasets.Value('''int32'''     ),
									                    }     ),
									            },
									        }     )							,							codebase_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							reference_urls=['''https://rajpurkar.github.io/SQuAD-explorer/''']							,							)







							def _lowercase       (self							,							SCREAMING_SNAKE_CASE_							,							SCREAMING_SNAKE_CASE_     ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_    			=			{prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
									SCREAMING_SNAKE_CASE_    			=			[
									    {
									        '''paragraphs''': [
									            {
									                '''qas''': [
									                    {
									                        '''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
									                        '''id''': ref['''id'''],
									                    }
									                    for ref in references
									                ]
									            }
									        ]
									    }
									]
									SCREAMING_SNAKE_CASE_    			=			evaluate(dataset=SCREAMING_SNAKE_CASE_							,							predictions=SCREAMING_SNAKE_CASE_     )
									return score